serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
19,401 | #include <cuda_runtime.h>
#define REGISTER_BLOCKING 4
#define BLOCK_SIZE 64
__global__ void matmult_gpu4Kernel(int m, int n, int k, double * d_A, double * d_B, double * d_C);
// REGISTER BLOCKING ALONG THE ROWS OF C
/*
extern "C" {
void matmult_gpu4(int m, int n, int k, double * A, double * B, double * C){
double * d_A, * d_B, * d_C;
cudaMalloc((void **)&d_A, m * k * sizeof(double *));
cudaMalloc((void **)&d_B, k * n * sizeof(double *));
cudaMalloc((void **)&d_C, m * n * sizeof(double *));
cudaMemcpy(d_A, A, m * k * sizeof(double *), cudaMemcpyHostToDevice);
cudaMemcpy(d_B, B, k * n * sizeof(double *), cudaMemcpyHostToDevice);
//kernel block and grid size
dim3 dimBlock(BLOCK_SIZE/REGISTER_BLOCKING,BLOCK_SIZE,1);
dim3 dimGrid((int)ceil(((double)n)/(BLOCKSIZE)), (int)ceil(((double)m)/(BLOCKSIZE)));
matmult_gpu4Kernel<<<dimGrid,dimBlock>>>(m, n, k, d_A, d_B, d_C);
cudaMemcpy(C, d_C, m * n * sizeof(double *), cudaMemcpyDeviceToHost);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
}
__global__ void matmult_gpu4Kernel(int m, int n, int k, double * d_A, double * d_B, double * d_C){
int i, j, l, e;
i = blockIdx.y * blockDim.y + threadIdx.y;
j = REGISTER_BLOCKING*(blockIdx.x * blockDim.x + threadIdx.x);
double C_reg[REGISTER_BLOCKING] = {0};
if(i < m && j < n){
for(l=0;l < k;l++){
C_reg[0] += d_A[i*k + l] * d_B[l*n + j];
for(e = 1; e < REGISTER_BLOCKING; e++){
if(j + e < n)
C_reg[e] += d_A[i*k + l] * d_B[l*n + j + e];
}
}
d_C[i*n + j] = C_reg[0];
for(e = 1; e < REGISTER_BLOCKING; e++){
if(j + e < n)
d_C[i*n + j + e] = C_reg[e];
}
}
}
*/
// REGISTER BLOCKING ALONG THE COLUMNS OF C
extern "C" {
void matmult_gpu4(int m, int n, int k, double * A, double * B, double * C){
double * d_A, * d_B, * d_C;
cudaMalloc((void **)&d_A, m * k * sizeof(double *));
cudaMalloc((void **)&d_B, k * n * sizeof(double *));
cudaMalloc((void **)&d_C, m * n * sizeof(double *));
cudaMemcpy(d_A, A, m * k * sizeof(double *), cudaMemcpyHostToDevice);
cudaMemcpy(d_B, B, k * n * sizeof(double *), cudaMemcpyHostToDevice);
//kernel block and grid size
dim3 dimBlock(BLOCK_SIZE,BLOCK_SIZE/REGISTER_BLOCKING,1);
dim3 dimGrid((int)ceil(((double)n)/(BLOCK_SIZE)), (int)ceil(((double)m)/(BLOCK_SIZE)));
matmult_gpu4Kernel<<<dimGrid,dimBlock>>>(m, n, k, d_A, d_B, d_C);
cudaMemcpy(C, d_C, m * n * sizeof(double *), cudaMemcpyDeviceToHost);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
}
__global__ void matmult_gpu4Kernel(int m, int n, int k, double * d_A, double * d_B, double * d_C){
int i, j, l, e;
i = REGISTER_BLOCKING*(blockIdx.y * blockDim.y + threadIdx.y);
j = (blockIdx.x * blockDim.x + threadIdx.x);
double C_reg[REGISTER_BLOCKING] = {0};
if(i < m && j < n){
for(l=0;l < k;l++){
C_reg[0] += d_A[i*k + l] * d_B[l*n + j];
for(e = 1; e < REGISTER_BLOCKING; e++){
if(i + e < m)
C_reg[e] += d_A[(i+e)*k + l] * d_B[l*n + j];
}
}
d_C[i*n + j] = C_reg[0];
for(e = 1; e < REGISTER_BLOCKING; e++){
if(i + e < m)
d_C[(i+e)*n + j] = C_reg[e];
}
}
}
|
19,402 | #include<iostream>
#include<ctime>
#define exp 20
#define Size 512
using namespace std;
struct AoS{
float x,y;
};
void __global__ AoS(AoS* data,unsigned int n) {
unsigned int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx < n) {
data[idx].x += 1.0f;
data[idx].y += 2.0f;
}
}
int main() {
int dev = 0;
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, dev);
cout << "device " << dev << ": " << deviceProp.name << endl;
int N = 1 << 18;
dim3 block(Size,1);
dim3 grid((Size + N - 1) / Size, 1);
struct AoS a[N],*a_dev;
cudaMalloc((void**)&a_dev, sizeof(struct AoS)*N);
cudaMemcpy(a_dev, a, sizeof(a), cudaMemcpyHostToDevice);
clock_t start ,end;
start = clock();
AoS<<<grid, block>>>(a_dev, N);
cudaDeviceSynchronize();
end = clock();
cout << "sum time in gpu compute:" << end - start << "ms" << endl;
cudaFree(a_dev);
cudaDeviceReset();
return 0;
}
|
19,403 | #include <iostream>
#include "cuda_runtime_api.h"
int main(int argc, char* argv[]) {
(void)argc;
(void)argv;
cudaSetDevice(0);
cudaEvent_t start;
cudaEvent_t end;
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaEventRecord(start);
cudaEventRecord(end);
cudaEventSynchronize(end);
float elapsed_time;
cudaEventElapsedTime(&elapsed_time, start, end);
std::cout << "elapsed_time: " << elapsed_time << std::endl;
} |
19,404 | #include "includes.h"
__global__ void MatrixMulKernel(float *d_M, float *d_N, float *d_P,int width){
int Row = blockIdx.y*blockDim.y + threadIdx.y;
int Col = blockIdx.x*blockDim.x + threadIdx.x;
if ((Row < width)&&(Col < width)){
float Pvalue = 0;
for (int i = 0; i < width; ++i){
Pvalue += d_M[Row*width+i]*d_N[i*width+Col];
}
d_P[Row*width + Col] = Pvalue;
}
} |
19,405 | #include "includes.h"
__device__ void recover_deleted_rows(short *deleted_rows, const int search_depth, const int total_dl_matrix_row_num) {
for (int i = threadIdx.x; i < total_dl_matrix_row_num; i = i + blockDim.x) {
if (abs(deleted_rows[i]) > search_depth ||
deleted_rows[i] == search_depth) {
deleted_rows[i] = 0;
}
}
}
__global__ void recover_deleted_rows(int *deleted_rows, const int search_depth, const int total_dl_matrix_row_num) {
for (int i = threadIdx.x; i < total_dl_matrix_row_num; i = i + blockDim.x) {
if (abs(deleted_rows[i]) > search_depth ||
deleted_rows[i] == search_depth) {
deleted_rows[i] = 0;
}
}
} |
19,406 | __global__ void test_input_args(int* buffer_arg, int x) {
int val = buffer_arg[1];
buffer_arg[2] = 42;
buffer_arg[x] = 42;
buffer_arg[x + 1] = 42;
x = 1;
buffer_arg[x] = 42;
} |
19,407 | #include "includes.h"
using namespace std;
#define ULL unsigned long long
const long MAXDIM = 10;
const double RMIN = 2.0;
const double RMAX = 7.0;
#define MAX_THREADS 1024
#define MAX_BLOCKS 65535
//Global kernel code that runs on the device
__global__ void count_in(ULL *dev_count, long dev_ntotal,long dev_ndim, long dev_halfb, double dev_rsquare, long dev_base){
//Calculate the position of this kernel in the data
ULL blockID = (blockIdx.y * gridDim.x) + blockIdx.x;
ULL pos = (blockID * blockDim.x) + threadIdx.x;
//If this threads position in the data is further than we need to calculate
//Then we return
if(pos >= dev_ntotal) return;
double rtestsq = 0;
long idx = 0;
long index[MAXDIM+1];
for (long i = 0; i < dev_ndim; ++i) index[i] = 0;
//Convert the decimal number into another base system
while (pos != 0) {
long rem = pos % dev_base;
pos = pos / dev_base;
index[idx] = rem;
++idx;
}
for(long k = 0; k < dev_ndim; ++k){
double xk = index[k] - dev_halfb;
rtestsq += xk * xk;
}
//If the value is inside the sphere
//Atomically add 1 to the count
if(rtestsq < dev_rsquare){
atomicAdd(dev_count, 1);
}
} |
19,408 | #include <iostream>
#include <cstdlib>
#include <ctime>
#include <string>
#include <cuda_runtime.h>
#include <chrono>
using namespace std;
using namespace chrono;
const int MAX_TRIES = 5;
#define WORD_SIZE 1048576
void init_zero(int* a, int n) {
for (int i = 0; i < n; i++)
a[i] = 0;
}
void init_null(char* a, int n) {
for (int i = 0; i < n; i++)
a[i] = '\0';
}
int letterFill(char, string, string&);
void reportTime(const char* msg, steady_clock::duration span) {
double nsecs = double(span.count()) *
steady_clock::period::num / steady_clock::period::den;
std::cout << std::fixed;
std::cout << msg << " - took - " <<
nsecs << " secs" << std::endl;
}
//gets matches, and edits strings..
__global__ void searchLetter(char* empty, char* word, char* guess, int* count, int* fcount, int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
//if guessed letter is the letter at word[i]
if (guess[0] == word[i]){
//Add to count
count[i] = 1;
//Edit empty to have letter filled in.
empty[i] = word[i];
}
else{
count[i] = 0;
}
__syncthreads();
for (int stride = 1; stride < n; stride *= 2) {
if (i % (2 * stride) == 0)
count[i] += count[i + stride];
__syncthreads();
}
if (threadIdx.x == 0)
fcount[blockIdx.x] = count[i];
}
__global__ void count_final(int* fcount, int n) {
int i = threadIdx.x;
for (int stride = 1; i + stride < n; stride *= 2) {
if (i % (2 * stride) == 0)
fcount[i] += fcount[i + stride];
__syncthreads();
}
}
int main()
{
string name;
char letter;
int num_of_wrong_guesses = 0;
string word;
//choose and copy a word from array of words randomly
srand(time(NULL));
int n = rand() % 10;
//test data to make load slow.
word = "";
int l;
int fail1 = rand() % 26;
int fail2 = rand() % 26;
int fail3 = rand() % 26;
int fail4 = rand() % 26;
int super = rand() % 26;
for (l = 0; l<WORD_SIZE; l++){
int ran = rand() % 26;
if (ran == fail1 || ran == fail2 || ran == fail3 || ran == fail4){
}
else if (ran == super){
char cch = 'a' + ran;
word += cch;
word += cch;
word += cch;
}
else{
char cch = 'a' + ran;
word += cch;
}
}
int d;
cudaDeviceProp prop;
cudaGetDevice(&d);
cudaGetDeviceProperties(&prop, d);
int ntpb_x = prop.maxThreadsDim[0];
cout << prop.major << prop.minor << endl;
int nblks = (WORD_SIZE + ntpb_x - 1) / ntpb_x; // number of blocks
// Initialize the secret word with the * character.
string unknown(word.length(), '*');
// welcome the user
cout << "\n\nWelcome to Letter Search...Guess a Letter!";
cout << "\n\nEach letter is represented by a star.";
cout << "\n\nYou have to type only one letter in one try";
cout << "\n\nYou have " << MAX_TRIES << " tries to try and guess the word.";
cout << "\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~";
// Loop until the guesses are used up
while (num_of_wrong_guesses < MAX_TRIES)
{
//Uncomment below for better game play.
//cout << "\n\n" << unknown;
cout << "\n\nGuess a letter: ";
cin >> letter;
// Fill secret word with letter if the guess is correct,
// otherwise increment the number of wrong guesses.
//TODO
steady_clock::time_point ts, te;
std::srand(std::time(nullptr));
ts = steady_clock::now();
int n = word.length();
//MEMaloc d_empty, d_word, d_guess, d_count
char* d_empty;
cudaMalloc((void**)&d_empty, nblks * ntpb_x*sizeof(char));
char*d_word;
cudaMalloc((void**)&d_word, nblks * ntpb_x*sizeof(char));
char* d_guess;
cudaMalloc((void**)&d_guess, sizeof(char));
int* d_count;
cudaMalloc((void**)&d_count, nblks * ntpb_x*sizeof(int));
int* d_fcount;
cudaMalloc((void**)&d_fcount, nblks*sizeof(int));
char* wordchar = new char[nblks * ntpb_x];
init_null( wordchar, nblks * ntpb_x);
char* emptychar = new char[nblks * ntpb_x];
init_null( emptychar, nblks * ntpb_x);
char* guesschar = new char[sizeof(char)];
int * h_count = new int[nblks * ntpb_x];
init_zero(h_count, nblks * ntpb_x);
int * h_fcount = new int[nblks];
//int lets = 0; not needed
//this should go to kernal.
//lets = letterFill(letter, word, unknown);
//End going to kernel.
//TODO
//MEMCPY all above to device
//first put stings into char array
memcpy(wordchar, word.c_str(), word.length() + 1);
memcpy(emptychar, unknown.c_str(), unknown.length() + 1);
guesschar = &letter;
//Copy char arrays into cuda.
cudaMemcpy(d_empty, emptychar, nblks * ntpb_x * sizeof(char), cudaMemcpyHostToDevice);
cudaMemcpy(d_word, wordchar, nblks * ntpb_x * sizeof(char), cudaMemcpyHostToDevice);
cudaMemcpy(d_guess, guesschar, sizeof(char), cudaMemcpyHostToDevice);
cudaMemcpy(d_count, h_count, nblks * ntpb_x * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_fcount, h_count, nblks * sizeof(int), cudaMemcpyHostToDevice);
searchLetter <<<nblks, ntpb_x>>>(d_empty, d_word, d_guess, d_count, d_fcount, ntpb_x);
count_final <<<1, nblks>>>(d_fcount, nblks);
//reverse above steps.
cudaMemcpy(emptychar, d_empty, n * sizeof(char), cudaMemcpyDeviceToHost);
cudaMemcpy(wordchar, d_word, n * sizeof(char), cudaMemcpyDeviceToHost);
cudaMemcpy(guesschar, d_guess, sizeof(char), cudaMemcpyDeviceToHost);
cudaMemcpy(h_count, d_fcount, nblks*sizeof(int), cudaMemcpyDeviceToHost);
//copied back to chars, now copy to strings
unknown = emptychar;
te = steady_clock::now();
reportTime("Search Time: ", te - ts);
int final_count = h_count[0];
if (final_count == 0)
{
cout << endl << "Whoops! That letter isn't in there!" << endl;
num_of_wrong_guesses++;
}
else
{
cout << endl << "You found " << final_count << " letters! Isn't that exciting!" << endl;
}
// Tell user how many guesses has left.
cout << "You have " << MAX_TRIES - num_of_wrong_guesses;
cout << " guesses left." << endl;
// Check if user guessed the word.
if (word == unknown)
{
cout << word << endl;
cout << "Yeah! You got it!";
break;
}
// TODO
// cuda free var's
cudaFree(d_empty);
cudaFree(d_word);
cudaFree(d_guess);
cudaFree(d_count);
}
if (num_of_wrong_guesses == MAX_TRIES)
{
cout << "\nSorry, you lose...you've been hanged." << endl;
cout << "The word was : " << word << endl;
}
cin.ignore();
cin.get();
return 0;
}
/* Take a one character guess and the secret word, and fill in the
unfinished guessword. Returns number of characters matched.
Also, returns zero if the character is already guessed. */
int letterFill(char guess, string secretword, string &guessword)
{
int i;
int matches = 0;
int len = secretword.length();
for (i = 0; i< len; i++)
{
// Did we already match this letter in a previous guess?
if (guess == guessword[i])
return 0;
// Is the guess in the secret word?
if (guess == secretword[i])
{
guessword[i] = guess;
matches++;
}
}
return matches;
}
|
19,409 | #include "includes.h"
__device__ __forceinline__ size_t gpu_scalar_index(unsigned int x, unsigned int y)
{
return NX*y+x;
}
__global__ void gpu_efield(double *fi, double *ex, double *ey){
unsigned int y = blockIdx.y;
unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
unsigned int xp1 = (x + 1) % NX;
unsigned int yp1 = (y + 1) % NY;
unsigned int xm1 = (NX + x - 1) % NX;
unsigned int ym1 = (NY + y - 1) % NY;
double phi = fi[gpu_scalar_index(x, y)];
double phiL = fi[gpu_scalar_index(xm1, y)];
double phiR = fi[gpu_scalar_index(xp1, y)];
double phiU = fi[gpu_scalar_index(x, yp1)];
double phiD = fi[gpu_scalar_index(x, ym1)];
ex[gpu_scalar_index(x, y)] = 0.5*(phiL - phiR) / dx;
ey[gpu_scalar_index(x, y)] = 0.5*(phiD - phiU) / dy;
} |
19,410 | #include <cuda_runtime.h> //uchar4
__global__
void split_channels(uchar4 *input_image, unsigned char *red, unsigned char *green, unsigned char *blue){
int row = threadIdx.x;
int col = blockIdx.x;
int idx = col + row*360;
red[idx] = input_image[idx].x;
green[idx] = input_image[idx].y;
blue[idx] = input_image[idx].z;
}
__global__
void blur_channel(unsigned char *channel_in, unsigned char *channel_out, float *filter, int filter_size){
int row = threadIdx.x;
int col = blockIdx.x;
int idx = col + row*360;
int sum = 0;
int cnt = 0;
if (row >= 0){ sum += channel_in[col + (row-1)*360] * filter[1]; cnt++; }
if (col >= 0){ sum += channel_in[idx-1] * filter[3]; cnt++; }
if (col <= 360){ sum += channel_in[idx+1] * filter[5]; cnt++; }
if (row <= 480){ sum += channel_in[col + (row+1)*360] * filter[7]; cnt++; }
channel_out[idx] = sum / cnt;
}
__global__
void combine_channels(uchar4 *output_image, unsigned char *red, unsigned char *green, unsigned char *blue){
int row = threadIdx.x;
int col = blockIdx.x;
int idx = col + row*360;
output_image[idx] = make_uchar4(red[idx], green[idx], blue[idx], 255);
}
void blur_image(uchar4 *input_image, uchar4 *output_image,
unsigned char *red_in, unsigned char *red_out,
unsigned char *green_in, unsigned char * green_out,
unsigned char *blue_in, unsigned char *blue_out,
float * filter, int filter_size){
dim3 blockSize(360,1,1);
dim3 threadSize(480,1,1);
split_channels<<<blockSize, threadSize>>>(input_image, red_in, green_in, blue_in);
blur_channel<<<blockSize, threadSize>>>(red_in, red_out, filter, filter_size);
blur_channel<<<blockSize, threadSize>>>(green_in, green_out, filter, filter_size);
blur_channel<<<blockSize, threadSize>>>(blue_in, blue_out, filter, filter_size);
combine_channels<<<blockSize, threadSize>>>(output_image, red_out, green_out, blue_out);
}
|
19,411 | #include "cuda_runtime.h"
void initialize_cuda_runtime(void)
{
cudaSetDevice(0);
cudaFree(0);
} |
19,412 | #include <iomanip>
#include <iostream>
#include <string>
const long int GLOBAL_SIZE = 1024;
const int TILE_DIM = 32;
const int BLOCK_ROWS = 8;
const int NUM_ITERS = 100;
__global__ void copy(float* A, float* B)
{
int row = blockIdx.y * blockDim.x + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = 0; i < blockDim.x; i += blockDim.y)
B[(row + i) * GLOBAL_SIZE + col] = A[(row + i) * GLOBAL_SIZE + col];
}
__global__ void transposeNaive(float* A, float* B)
{
int row = blockIdx.y * blockDim.x + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = 0; i < blockDim.x; i += blockDim.y)
B[col * GLOBAL_SIZE + row + i] = A[(row + i) * GLOBAL_SIZE + col];
}
__global__ void transposeCoalescedOutput(float* A, float* B)
{
int row = blockIdx.y * blockDim.x + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = 0; i < blockDim.x; i += blockDim.y)
B[(row + i) * GLOBAL_SIZE + col] = A[col * GLOBAL_SIZE + row + i];
}
__global__ void transposeCoalescedSHM(float* A, float* B)
{
int row = blockIdx.y * blockDim.x + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
__shared__ float tile[TILE_DIM][TILE_DIM];
for (int i = 0; i < blockDim.x; i += blockDim.y)
tile[threadIdx.y + i][threadIdx.x] = A[(row + i) * GLOBAL_SIZE + col];
__syncthreads();
row = blockIdx.x * blockDim.x + threadIdx.y;
col = blockIdx.y * blockDim.x + threadIdx.x;
for (int i = 0; i < blockDim.x; i += blockDim.y)
B[(row + i) * GLOBAL_SIZE + col] = tile[threadIdx.x][threadIdx.y + i];
}
__global__ void transposeCoalescedOptimal(float* A, float* B)
{
int row = blockIdx.y * blockDim.x + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
__shared__ float tile[TILE_DIM][TILE_DIM + 1];
for (int i = 0; i < blockDim.x; i += blockDim.y)
tile[threadIdx.y + i][threadIdx.x] = A[(row + i) * GLOBAL_SIZE + col];
__syncthreads();
row = blockIdx.x * blockDim.x + threadIdx.y;
col = blockIdx.y * blockDim.x + threadIdx.x;
for (int i = 0; i < blockDim.x; i += blockDim.y)
B[(row + i) * GLOBAL_SIZE + col] = tile[threadIdx.x][threadIdx.y + i];
}
void initArray(float* A)
{
for (int i = 0; i < GLOBAL_SIZE * GLOBAL_SIZE; i++)
A[i] = static_cast<float>(i);
}
void printtArray(float* A)
{
for (int i = 0; i < GLOBAL_SIZE; i++) {
for (int j = 0; j < GLOBAL_SIZE; j++)
std::cout << std::left << std::setw(10) << A[i * GLOBAL_SIZE + j];
std::cout << "\n";
}
}
void printSummary(std::string& s, cudaEvent_t& start, cudaEvent_t& stop)
{
float milliseconds = 0.f;
cudaEventElapsedTime(&milliseconds, start, stop);
std::cout << std::left << std::setw(30) << s;
std::cout << 2 * GLOBAL_SIZE * GLOBAL_SIZE * sizeof(float) * NUM_ITERS / (milliseconds * 1e6);
std::cout << "\n";
}
int main()
{
float *A, *A_d, *B, *B_d;
const int data_size = GLOBAL_SIZE * GLOBAL_SIZE * sizeof(float);
cudaMallocHost(&A, data_size);
cudaMallocHost(&B, data_size);
cudaMalloc(&A_d, data_size);
cudaMalloc(&B_d, data_size);
const int grid_size = GLOBAL_SIZE / TILE_DIM; // 1024 / 32 = 32
dim3 grid(grid_size, grid_size); // 32 * 32
dim3 block(TILE_DIM, BLOCK_ROWS); // 32 x 8 = 256
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
// matrix copy
initArray(A);
cudaMemcpy(A_d, A, data_size, cudaMemcpyHostToDevice);
cudaEventRecord(start);
for (int i = 0; i < NUM_ITERS; i++)
copy<<<grid, block>>>(A_d, B_d);
cudaEventRecord(stop);
cudaMemcpy(B, B_d, data_size, cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
std::string s = "copy";
printSummary(s, start, stop);
// naive transpose
initArray(A);
cudaMemcpy(A_d, A, data_size, cudaMemcpyHostToDevice);
cudaEventRecord(start);
for (int i = 0; i < NUM_ITERS; i++)
transposeNaive<<<grid, block>>>(A_d, B_d);
cudaEventRecord(stop);
cudaMemcpy(B, B_d, data_size, cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
s = "transposeNaive";
printSummary(s, start, stop);
// transpose with coalesced memory access for output only
initArray(A);
cudaMemcpy(A_d, A, data_size, cudaMemcpyHostToDevice);
cudaEventRecord(start);
for (int i = 0; i < NUM_ITERS; i++)
transposeCoalescedOutput<<<grid, block>>>(A_d, B_d);
cudaEventRecord(stop);
cudaMemcpy(B, B_d, data_size, cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
s = "transposeCoalescedOutput";
printSummary(s, start, stop);
// transpose with coalesced memory access for both input and output + shared
// memory
initArray(A);
cudaMemcpy(A_d, A, data_size, cudaMemcpyHostToDevice);
cudaEventRecord(start);
for (int i = 0; i < NUM_ITERS; i++)
transposeCoalescedSHM<<<grid, block>>>(A_d, B_d);
cudaEventRecord(stop);
cudaMemcpy(B, B_d, data_size, cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
s = "transposeCoalescedSHM";
printSummary(s, start, stop);
// transpose with optimal coalesced memory access (no bank conflict)
initArray(A);
cudaMemcpy(A_d, A, data_size, cudaMemcpyHostToDevice);
cudaEventRecord(start);
for (int i = 0; i < NUM_ITERS; i++)
transposeCoalescedOptimal<<<grid, block>>>(A_d, B_d);
cudaEventRecord(stop);
cudaMemcpy(B, B_d, data_size, cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
s = "transposeCoalescedOptimal";
printSummary(s, start, stop);
// Free
cudaFree(A_d);
cudaFree(B_d);
cudaFreeHost(A);
cudaFreeHost(B);
}
|
19,413 | #include "includes.h"
__global__ void cuArraysCopyToBatch_kernel(const float2 *imageIn, const int inNX, const int inNY, float2 *imageOut, const int outNX, const int outNY, const int nImagesX, const int nImagesY, const int strideX, const int strideY)
{
int idxImage = blockIdx.z;
int outx = threadIdx.x + blockDim.x*blockIdx.x;
int outy = threadIdx.y + blockDim.y*blockIdx.y;
if(idxImage >=nImagesX*nImagesY|| outx >= outNX || outy >= outNY) return;
int idxOut = idxImage*outNX*outNY + outx*outNY + outy;
int idxImageX = idxImage/nImagesY;
int idxImageY = idxImage%nImagesY;
int idxIn = (idxImageX*strideX+outx)*inNY + idxImageY*strideY+outy;
imageOut[idxOut] = imageIn[idxIn];
} |
19,414 | #include <stdio.h>
#include <cuda.h>
#include<sys/time.h>
#include <time.h>
#include<math.h>
// Kernel that executes on the CUDA device
__global__ void square_array(float *a, int N)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx<N) a[idx] = sqrtf(powf(2,a[idx]) + powf(a[idx],3));
}
// main routine that executes on the host
int main(void)
{
struct timeval iniciototal, finaltotal;
gettimeofday(&iniciototal, NULL);
int N, exec; // Number of elements in arrays
struct timeval inicio, final;
int tmili,tmilifim;
float media, soma ;
int tam;
for (N = 10; N <= 18; N++)
{
soma=0;
tam = pow(2,N);
for(exec =1; exec <= 10; exec++)
{
clock_t start, end;
double cpu_time_used;
start = clock();
gettimeofday(&inicio, NULL);
float *a_h, *a_d; // Pointer to host & device arrays
size_t size = tam * sizeof(float);
a_h = (float *)malloc(size); // Allocate array on host
cudaMalloc((void **) &a_d, size); // Allocate array on device
// Initialize host array and copy it to CUDA device
for (int i=0; i<tam; i++) a_h[i] = (float)i;
cudaMemcpy(a_d, a_h, size, cudaMemcpyHostToDevice);
// Do calculation on device:
int block_size = 32;
int n_blocks = tam/block_size + (tam%block_size == 0 ? 0:1);
square_array <<< n_blocks, block_size >>> (a_d, tam);
// Retrieve result from device and store it in host array
cudaMemcpy(a_h, a_d, sizeof(float)*tam, cudaMemcpyDeviceToHost);
//printf("%d\n", n_blocks);
// Print results
//for (int i=0; i<N; i++) printf("%d %f\n", i, a_h[i]);
// Cleanup
free(a_h);
cudaFree(a_d);
end = clock();
cpu_time_used = ((double) (end - start)) / CLOCKS_PER_SEC;
printf("\nTempo Clock: %f secs", cpu_time_used);
gettimeofday(&final, NULL);
tmili = (int) (1000 * (final.tv_sec - inicio.tv_sec) + (final.tv_usec - inicio.tv_usec) / 1000);
soma+=tmili;
}
media = soma/10;
printf("tamanho: %d \t tempo decorrido: %f\n", tam, media);
}
gettimeofday(&finaltotal, NULL);
tmilifim = (int) (1000 * (finaltotal.tv_sec - iniciototal.tv_sec) + (finaltotal.tv_usec - iniciototal.tv_usec) / 1000);
printf("tempo total decorrido: %d\n", tmilifim);
}
|
19,415 | #include "includes.h"
// ERROR CHECKING MACROS //////////////////////////////////////////////////////
__global__ void computeStateMinMax(int noControls, int noDims, int noPaths, int* dataPoints, float* xvals, float* xmins, float* xmaxes) {
for (int ii = 0; ii < noControls; ii++) {
float *xmin, *xmax;
xmin = (float*)malloc(noDims*sizeof(float));
xmax = (float*)malloc(noDims*sizeof(float));
if (ii == 0 || dataPoints[ii] > (noDims+1)) {
for (int jj = 0; jj < noDims; jj++) {
xmin[jj] = xvals[ii*noDims*noPaths + jj*noPaths];
xmax[jj] = xmin[jj];
}
for (int jj = 0; jj < noDims; jj++) {
for (int kk = 0; kk < dataPoints[ii]; kk++) {
float xtemp = xvals[ii*noDims*noPaths + jj*noPaths + kk];
if (xmin[jj] > xtemp) {
xmin[jj] = xtemp;
} else if (xmax[jj] < xtemp) {
xmax[jj] = xtemp;
}
}
}
// for (int jj = 0; jj < noDims; jj++) {
// xmin[jj] = xvals[ii*noDims*noPaths + jj];
// xmax[jj] = xmin[jj];
// }
// for (int jj = 0; jj < dataPoints[ii]; jj++) {
// for (int kk = 0; kk < noDims; kk ++) {
// float xtemp = xvals[ii*noDims*noPaths + jj*noDims + kk];
// if (xmin[kk] > xtemp) {
// xmin[kk] = xtemp;
// } else if (xmax[kk] < xtemp) {
// xmax[kk] = xtemp;
// }
// }
// }
for (int jj = 0; jj < noDims; jj++) {
xmins[ii*noDims + jj] = xmin[jj];
xmaxes[ii*noDims + jj] = xmax[jj];
// printf("Control %d: Xmin = %f Xmax = %f\n",ii,xmin[jj],xmax[jj]);
}
} else {
for (int jj = 0; jj < noDims; jj++) {
xmins[ii*noDims + jj] = xmins[jj];
xmaxes[ii*noDims + jj] = xmaxes[jj];
}
}
free(xmin);
free(xmax);
}
for (int ii = 0; ii < noDims; ii++) {
xmins[noControls*noDims + ii] = xmins[ii];
xmaxes[noControls*noDims + ii] = xmaxes[ii];
}
for (int ii = 1; ii < noControls; ii++) {
for (int jj = 0; jj < noDims; jj++) {
float xtemp = xmins[ii*noDims + jj];
if (xmins[noControls*noDims + jj] > xtemp) {
xmins[noControls*noDims + jj] = xtemp;
}
xtemp = xmaxes[ii*noDims + jj];
if (xmaxes[noControls*noDims + jj] < xtemp) {
xmaxes[noControls*noDims + jj] = xtemp;
}
}
}
} |
19,416 | #include<stdlib.h>
#include<stdio.h>
#include<unistd.h>
#include<sys/time.h>
#include<math.h>
#include<iostream>
struct Particle{
float px;
float py;
float pz;
float vx;
float vy;
float vz;
};
typedef struct timeval tval;
double get_elapsed(tval t0, tval t1);
void get_input_data(struct Particle **particles, int N, const int seed);
void kernel(struct Particle *particles, int N, int n_steps, const double delta_t, const int seed);
__global__
void kernel_cuda(struct Particle *particles, int N, int n_steps, const double delta_t, const int seed);
__host__ __device__
float rand_float(const int seed, int particle, int iter, int N);
#define HLINE "-------------------------------------------------------------\n"
int main(int argc, char *argv[])
{
int c;
int N=1000;
int n_threads=256;
int n_steps=100;
const int seed=2020;
const double delta_t=0.05;
tval t[2] = {0};
double elapsed[2] = {0};
while ((c = getopt(argc, argv, "n:p:s:h")) != -1)
switch (c)
{
case 'n':
N = atoi(optarg);
break;
case 'p':
n_threads = atoi(optarg);
break;
case 's':
n_steps = atoi(optarg);
break;
case 'h':
printf(
"Options:\n-n SIZE\t\tNum Particle\n-s ITERS\tNum Iters\n-p NTHREAD\tNumber of threads\n");
exit(1);
case '?':
break;
}
struct Particle* particles;
struct Particle* d_particles;
get_input_data(&particles, N, seed);
cudaMalloc(&d_particles, N*sizeof(struct Particle));
// For storing the final result copied from Device to Host
struct Particle* particles_=(struct Particle*)malloc(N*sizeof(struct Particle));
cudaMemcpy(d_particles, particles, N*sizeof(struct Particle), cudaMemcpyHostToDevice);
int n_blocks = (N + n_threads - 1) / n_threads;
printf(HLINE);
printf(" N\tNum steps\tNum Threads\tNum Blocks\n");
printf("%4d %8d %14d %16d\n", N, n_steps, n_threads, n_blocks);
printf(HLINE);
// Launch the CPU version
printf("Running particle simulation on the CPU...");
gettimeofday(&t[0], NULL);
kernel(particles, N, n_steps, delta_t, seed);
gettimeofday(&t[1], NULL);
elapsed[0] = get_elapsed(t[0], t[1]);
printf("Done!\n");
// Launch the GPU version
printf("Running particle simulation on the GPU...");
gettimeofday(&t[0], NULL);
for (int iter = 1; iter <= n_steps; iter++)
kernel_cuda<<<n_blocks, n_threads>>>(d_particles, N, iter, delta_t, seed);
cudaMemcpy(particles_, d_particles, N*sizeof(struct Particle), cudaMemcpyDeviceToHost);
gettimeofday(&t[1], NULL);
elapsed[1] = get_elapsed(t[0], t[1]);
printf("Done!\n");
printf("Comparing the output for each implementation...");
float error = 0.0f;
for (int i = 0; i < N; i++)
error = fmax(error, fabs(particles[i].px - particles_[i].px));
if (error < 1e-8){
printf("Correct!\n");
}
else{
printf("Not correct!\n");
}
printf("Elapsed CPU (ms): %f / Elapsed GPU (ms): %f\n", elapsed[0], elapsed[1]);
printf(HLINE);
free(particles);
free(particles_);
cudaFree(d_particles);
return 0;
}
__host__ __device__
float rand_float(const int seed, int particle, int iter, int N)
{
float result = (seed * particle + iter) % N;
return result;
}
__global__
void kernel_cuda(struct Particle *particles, int N, int iter, const double delta_t, const int seed)
{
int q = blockIdx.x * blockDim.x + threadIdx.x;
if(q < N){
// particles[q].vx = (rand() / (double)(RAND_MAX)) * 2 - 1;
// particles[q].vy = (rand() / (double)(RAND_MAX)) * 2 - 1;
// particles[q].vz = (rand() / (double)(RAND_MAX)) * 2 - 1;
particles[q].vx = rand_float(seed, q, iter, N) + 0.0f;
particles[q].vy = rand_float(seed, q, iter, N) + 0.1f;
particles[q].vz = rand_float(seed, q, iter, N) + 0.2f;
particles[q].px += delta_t * particles[q].vx;
particles[q].py += delta_t * particles[q].vy;
particles[q].pz += delta_t * particles[q].vz;
}
}
void kernel(struct Particle *particles, int N, int n_steps, const double delta_t, const int seed)
{
for (int iter = 1; iter <= n_steps; iter++)
{
for (int q = 0; q < N; q++)
{
// particles[q].vx = (rand() / (double)(RAND_MAX)) * 2 - 1;
// particles[q].vy = (rand() / (double)(RAND_MAX)) * 2 - 1;
// particles[q].vz = (rand() / (double)(RAND_MAX)) * 2 - 1;
particles[q].vx = rand_float(seed, q, iter, N) + 0.0f;
particles[q].vy = rand_float(seed, q, iter, N) + 0.1f;
particles[q].vz = rand_float(seed, q, iter, N) + 0.2f;
particles[q].px += delta_t * particles[q].vx;
particles[q].py += delta_t * particles[q].vy;
particles[q].pz += delta_t * particles[q].vz;
}
}
}
void get_input_data(struct Particle **particles, int N, const int seed )
{
struct Particle* particle_t = (struct Particle*)malloc(N*sizeof(struct Particle));
srand(seed);
for(int q=0; q < N; q++)
{
particle_t[q].px = (rand() / (double)(RAND_MAX)) * 2 - 1;
particle_t[q].py = (rand() / (double)(RAND_MAX)) * 2 - 1;
particle_t[q].pz = (rand() / (double)(RAND_MAX)) * 2 - 1;
particle_t[q].vx = (rand() / (double)(RAND_MAX)) * 2 - 1;
particle_t[q].vy = (rand() / (double)(RAND_MAX)) * 2 - 1;
particle_t[q].vz = (rand() / (double)(RAND_MAX)) * 2 - 1;
}
*particles = particle_t;
}
double get_elapsed(tval t0, tval t1)
{
return (double)(t1.tv_sec - t0.tv_sec) * 1000.0L + (double)(t1.tv_usec - t0.tv_usec) / 1000.0L;
} |
19,417 | #include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda.h>
#include <device_launch_parameters.h>
#define LIST_SIZE_GLOBAL 3000000
#define LIST_SIZE 10000
extern "C" __device__ unsigned long long load_store_index[LIST_SIZE];
extern "C" __device__ unsigned long long load_store_address[LIST_SIZE];
extern "C" __device__ unsigned long long load_store_check[LIST_SIZE];
extern "C" __device__ unsigned long long record_flag;
extern "C" __device__ unsigned long long call_count;
int memPro_kernel = 0;
void bambooLogRecordOff(){
long long local_record = 0;
cudaMemcpyToSymbol(record_flag, &local_record, sizeof(long long), 0, cudaMemcpyHostToDevice);
}
void bambooLogKernelBegin(long long i) {
cudaMemcpyToSymbol(call_count, &i, sizeof(long long), 0, cudaMemcpyHostToDevice);
i = 1;
cudaMemcpyToSymbol(record_flag, &i, sizeof(long long), 0, cudaMemcpyHostToDevice);
}
void bambooLogKernelEnd()
{
unsigned long long loadStoreIndex[LIST_SIZE] = {0};
unsigned long long loadStoreAddress[LIST_SIZE] = {0};
unsigned long long loadStoreCheck[LIST_SIZE] = {0};
FILE *profileFile = fopen("profile_mem_result.txt", "a");
for (int j=0; j < LIST_SIZE_GLOBAL; j+=LIST_SIZE)
{
cudaMemcpyFromSymbol(loadStoreIndex, load_store_index, LIST_SIZE * sizeof(long long), j*sizeof(long long), cudaMemcpyDeviceToHost);
cudaMemcpyFromSymbol(loadStoreAddress, load_store_address, LIST_SIZE * sizeof(long long), j*sizeof(long long), cudaMemcpyDeviceToHost);
cudaMemcpyFromSymbol(loadStoreCheck, load_store_check, LIST_SIZE * sizeof(long long), j*sizeof(long long), cudaMemcpyDeviceToHost);
for(long long i=0; i < LIST_SIZE; i++)
{
if(loadStoreIndex[i] != 0)
{
if (loadStoreCheck[i] == 0)
{
fprintf(profileFile, "L %lld %p\n", loadStoreIndex[i], (void*)loadStoreAddress[i]);
}
else
{
fprintf(profileFile, "S %lld %p\n", loadStoreIndex[i], (void*)loadStoreAddress[i]);
}
}
}
}
fclose(profileFile);
}
|
19,418 | #include <iostream>
using namespace std;
__global__ void SumaColMatrizKernel(int f,int c,float*Md,float*Nd){
float Pvalue=0;
for(int k=threadIdx.x;k<f*c;k+=c){
Pvalue=Pvalue+Md[k];
}
Nd[threadIdx.x]=Pvalue;
}
void SumaColMatrizHost(int f,int c,float*Mh){
float *P;
P=new float[c];
for (int j=0;j<c;j++){
P[j]=0;
for (int i=0;i<f;i++){
P[j]+=Mh[i*c+j];
}
}
cout<<"\nResultados HOST:"<<endl;
for (int j=0;j<c;j++) cout<<P[j]<<" ";
}
int main(){
int f=10,c=2;
cout<<"Filas: "<<f<<endl;
cout<<"Columnas: "<<c<<endl;
int size=f*c*sizeof(float);
int size2=c*sizeof(float);
//Guardando memoria en el host
float *Mh=(float*)malloc(size);
float *Nh=(float*)malloc(size2);
cout<<"Matriz: ";
for (int i=0;i<f*c;i++){
Mh[i]=i+1;
cout<<Mh[i]<<" ";
}
//Guardando memoria en el GPU
float *Md,*Nd;
cudaMalloc(&Md,size);
cudaMalloc(&Nd,size2);
cudaMemcpy(Md, Mh, size, cudaMemcpyHostToDevice);
cudaMemset(Nd, 0, size2);
//Suma columnas en GPU
int bloques=f/128+1;
dim3 tamGrid(bloques,1);
dim3 tamBlock(128,1,1);
SumaColMatrizKernel<<<tamGrid, tamBlock>>>(f,c,Md,Nd);
cudaMemcpy(Nh, Nd, size2, cudaMemcpyDeviceToHost);
//Suma columnas en HOST
SumaColMatrizHost(f,c,Mh);
cudaFree(Md); cudaFree(Nd);
cout<<"\nResultados GPU: "<<endl;
for(int i=0;i<c;i++){
cout<<Nh[i]<<" ";
}
} |
19,419 | // Jin Pyo Jeon
// Assign 7
#include <cuda.h>
#include <stdlib.h>
#include <time.h>
#include <stdio.h>
#include <math.h>
#include <assert.h>
#define T 1024 // Shared needs to be known at compile time??
#define B 65536
#define TB (B / T)
#define N (134217728)
// Times for Reduced and non-reduced dot product
// N Reduced Non-reduced Thread Count
// 2^27 8.95 8.91 1024
// 2^26 4.49 4.46 1024
// 2^20 0.072 0.072 1024
#define gpuErrCheck(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t e, const char *file, int line) {
if (e != cudaSuccess) {
fprintf(stderr, "GPU Assert: %s: %s %d\n", cudaGetErrorString(e), file, line);
}
}
__global__ void generate_partials(int* X, int* Y, int *S) {
__shared__ int XY[T];
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N) { XY[threadIdx.x] = X[i]; }
for (int stride = 1; stride <= threadIdx.x; stride *= 2) {
__syncthreads();
int in1 = XY[threadIdx.x - stride];
__syncthreads();
XY[threadIdx.x] += in1;
}
__syncthreads();
if (i < N) { Y[i] = XY[threadIdx.x]; }
if (threadIdx.x == blockDim.x - 1) { S[blockIdx.x] = XY[threadIdx.x]; }
}
__global__ void add_partials(int *S) {
__shared__ int PS[T];
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < B) { PS[threadIdx.x] = S[i]; }
for (int stride = 1; stride <= threadIdx.x; stride *= 2) {
__syncthreads();
int in1 = PS[threadIdx.x - stride];
__syncthreads();
PS[threadIdx.x] += in1;
}
__syncthreads();
if (i < B) { S[i] = PS[threadIdx.x]; }
}
__global__ void apply_partials(int *S, int *Y) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N) { Y[i] += S[blockIdx.x]; }
}
void random_ints(int * arr, size_t size){
int i = 0;
for (i = 0; i < size; i++) {
arr[i] = rand() % 2 ;
}
}
void printVec(int *vec, size_t width) {
for (int i = 0; i < width; i++) {
printf("%d ", vec[i]);
}
printf("\n");
}
void printVec(int *vec, long begin, long end) {
printf("[START: %ld] ", begin);
for (long i = begin; i < end; i++) {
printf("%d ", vec[i]);
}
printf("[END: %ld] \n", end);
}
void printVecElement(int *vec, long index) {
printf("%d ", vec[index]);
}
long addVec(int *vec, long begin, long end) {
long sum = 0;
for (long i = begin; i < end; i++) {
sum += vec[i];
}
return sum;
}
int main(int argc, char**argv) {
srand(time(NULL));
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
int *X, *Y, *S;
int *d_X, *d_Y, *d_S;
unsigned long long int size = N * sizeof(int);
gpuErrCheck( cudaMalloc((void**)&d_X, size) );
gpuErrCheck( cudaMalloc((void**)&d_Y, size) );
cudaMalloc((void**)&d_S, B * sizeof(int));
X = (int *)malloc(size);
Y = (int *)malloc(size);
S = (int *)malloc(sizeof(int) * B);
random_ints(X, N);
gpuErrCheck( cudaMemcpy(d_X, X, size, cudaMemcpyHostToDevice) );
dim3 threadDims(T, 1, 1);
dim3 blockDims(B, 1, 1);
generate_partials<<<blockDims, threadDims>>>(d_X, d_Y, d_S);
gpuErrCheck( cudaPeekAtLastError() );
gpuErrCheck( cudaDeviceSynchronize() );
// Test
gpuErrCheck( cudaMemcpy(Y, d_Y, size, cudaMemcpyDeviceToHost) );
gpuErrCheck( cudaMemcpy(S, d_S, sizeof(int) * B, cudaMemcpyDeviceToHost) );
for (long i = 0; i < 10; i++) {
printVecElement(S, i);
printVecElement(Y, ((i + 1) * T - 1));
printf("\n");
}
printVec(S, 20);
//
add_partials<<<TB, T>>>(d_S);
gpuErrCheck( cudaPeekAtLastError() );
gpuErrCheck( cudaDeviceSynchronize() );
// Test
gpuErrCheck( cudaMemcpy(S, d_S, sizeof(int) * B, cudaMemcpyDeviceToHost) );
printVec(S, 20);
for (long i = 0; i < 10; i++) {
printVec(Y, i * T, i * T + 10);
}
//
apply_partials<<<blockDims, threadDims>>>(d_S, d_Y);
gpuErrCheck( cudaPeekAtLastError() );
gpuErrCheck( cudaDeviceSynchronize() );
gpuErrCheck( cudaMemcpy(Y, d_Y, size, cudaMemcpyDeviceToHost) );
// Test
for (long i = 0; i < 10; i++) {
printVecElement(S, i);
printVec(Y, i * T, i * T + 10);
}
//
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
printf("The elapsed time of %f s\n", elapsedTime / 1000.0);
free(X); free(Y); free(S);
cudaFree(d_X);
cudaFree(d_Y);
cudaFree(d_S);
return 0;
}
|
19,420 | #include <math.h>
__device__ size_t calculateGlobalIndex() {
// Which block are we?
size_t const globalBlockIndex = blockIdx.x + blockIdx.y * gridDim.x;
// Which thread are we within the block?
size_t const localThreadIdx = threadIdx.x + blockDim.x * blockIdx.y;
// How big is each block?
size_t const threadsPerBlock = blockDim.x * blockDim.y;
// Which thread are we overall?
return localThreadIdx + globalBlockIndex*threadsPerBlock;
}
__global__ void closestPoint(double *corrBdy, double *dist,
double *bdy, double *mask,
int height, int width) {
// Calculate pixel's location
int x = (blockIdx.x * blockDim.x) + threadIdx.x;
int y = (blockIdx.y * blockDim.y) + threadIdx.y;
// Only execut valid pixels
if (x>=width || y>=height || mask[y*width+x]==0 || bdy[y*width+x]==1) {
return;
}
double min_dist = max(height, width);
double cur_dist = 0.0;
for (int i = 0; i < height; i++) {
for (int j = 0; j < width; j++) {
if (bdy[i*width+j] == 0)
continue;
cur_dist = sqrt(double((x-j)*(x-j))+double((y-i)*(y-i)));
if (cur_dist < min_dist) {
min_dist = cur_dist;
corrBdy[y*width+x] = j*height+i+1;
if (min_dist < 1.1)
break;
}
}
}
/*int bdyx = corrx[y*width+x];
int bdyy = corry[y*width+x];
if (x-bdyx != 0 || y-bdyy != 0) {
double angle = (double(x-bdyx) * gx[bdyy*width+bdyx] + double(y-bdyy) * gy[bdyy*width+bdyx])/sqrt(double((x-bdyx)*(x-bdyx))+double((y-bdyy)*(y-bdyy)));
if (angle < 0)
dist[y*width+x] = 100;
else
dist[y*width+x] = min_dist;
}*/
dist[y*width+x] = min_dist;
__syncthreads();
}
|
19,421 | //
// Created by Anikait Singh on 2019-08-01.
// for interpolation
//
//arr[i][j] to arr[j][i]
//r is rows
//c is columns
//static double transposeLookup2D(double *arr, int i, int j, int r, int c) {
// return arr[j * r + i];
//}
//arr[i][j][k][l] to arr[l][k][j][i]
//s1,s2,s3,s4 is size of i, j, k, l respetively
#ifdef __NVCC__
__device__
#endif
double transposeLookup4D(double *arr, int i, int j, int k, int l, int s1, int s2, int s3, int s4) {
//j, k, l are one based
j = j - 1;
k = k - 1;
l = l - 1;
return arr[l * s3 * s2 * s1 + k * s2 * s1 + j * s1 + i];
}
//ivec = 1
#ifdef __NVCC__
__global__
#endif
void r8herm3fcn(int ivec, int ivecd, double *fval, int i, int j, int k, double xp,
double yp, double zp, double hx, double hxi, double hy, double hyi,
double hz, double hzi, double *fin, int inf2, int inf3,
int nz) {
int iadr;
double xpi, xp2, xpi2, ax, axbar, bx, bxbar, ypi, yp2, ypi2, ay;
double aybar, by, bybar, zpi, zp2, zpi2, az, azbar, bz, bzbar, axp;
double axbarp, bxp, bxbarp, ayp, aybarp, byp, bybarp, azp, azbarp, bzp;
double bzbarp;
double sum = 0;
int ict[8] = {1,1,1,1,0,0,0,0};
//x
xpi = 1.0 - xp;
xp2 = xp * xp;
xpi2 = xpi * xpi;
ax = xp2 * (3.0 - 2.0 * xp);
axbar = 1.0 - ax;
bx = -xp2 * xpi;
bxbar = xpi2 * xp;
//y
ypi = 1.0 - yp;
yp2 = yp * yp;
ypi2 = ypi * ypi;
ay = yp2 * (3.0 - 2.0 * yp);
aybar = 1.0 - ay;
by = -yp2 * ypi;
bybar = ypi2 * yp;
//z
zpi = 1.0 - zp;
zp2 = zp * zp;
zpi2 = zpi * zpi;
az = zp2 * (3.0 - 2.0 * zp);
azbar = 1.0 - az;
bz = -zp2 * zpi;
bzbar = zpi2 * zp;
iadr = 0;
//derivatives
axp = 6.0 * xp * xpi;
axbarp = -axp;
bxp = xp * (3.0 * xp - 2.0);
bxbarp = xpi * (3.0 * xpi - 2.0);
ayp = 6.0 * yp * ypi;
aybarp = -ayp;
byp = yp * (3.0 * yp - 2.0);
bybarp = ypi * (3.0 * ypi - 2.0);
azp = 6.0 * zp * zpi;
azbarp = -azp;
bzp = zp * (3.0 * zp - 2.0);
bzbarp = zpi * (3.0 * zpi - 2.0);
int s1 = 8;
if (ict[0] == 1) {
// iadr = iadr + 1;
sum = azbar * (
axbar * (aybar * transposeLookup4D(fin, 0, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 0, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i + 1, j + 1, k, s1, inf2, inf3, nz))) +
az * (
axbar * (aybar * transposeLookup4D(fin, 0, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 0, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)));
sum = sum + hx * (
azbar * (
bxbar * (aybar * transposeLookup4D(fin, 1, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 1, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
bxbar * (aybar * transposeLookup4D(fin, 1, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 1, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hy * (
azbar * (
axbar * (bybar * transposeLookup4D(fin, 2, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 2, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
axbar * (bybar * transposeLookup4D(fin, 2, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 2, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hz * (
bzbar * (
axbar * (aybar * transposeLookup4D(fin, 3, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 3, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
axbar * (aybar * transposeLookup4D(fin, 3, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 3, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * hy * (
azbar * (
bxbar * (bybar * transposeLookup4D(fin, 4, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 4, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
bxbar * (bybar * transposeLookup4D(fin, 4, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 4, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * hz * (
bzbar * (
bxbar * (aybar * transposeLookup4D(fin, 5, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 5, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
bxbar * (aybar * transposeLookup4D(fin, 5, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 5, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hy * hz * (
bzbar * (
axbar * (bybar * transposeLookup4D(fin, 6, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 6, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
axbar * (bybar * transposeLookup4D(fin, 6, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 6, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * hy * hz * (
bzbar * (
bxbar * (bybar * transposeLookup4D(fin, 7, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 7, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
bxbar * (bybar * transposeLookup4D(fin, 7, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 7, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
//v is 0 so ignore
fval[iadr] = sum;
}
if (ict[1] == 1) {
iadr = iadr + 1;
sum = hxi * (
azbar * (
axbarp * (aybar * transposeLookup4D(fin, 0, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i, j + 1, k, s1, inf2, inf3, nz)) +
axp * (aybar * transposeLookup4D(fin, 0, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
axbarp * (aybar * transposeLookup4D(fin, 0, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
axp * (aybar * transposeLookup4D(fin, 0, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + (
azbar * (
bxbarp * (aybar * transposeLookup4D(fin, 1, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i, j + 1, k, s1, inf2, inf3, nz)) +
bxp * (aybar * transposeLookup4D(fin, 1, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
bxbarp * (aybar * transposeLookup4D(fin, 1, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bxp * (aybar * transposeLookup4D(fin, 1, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hxi * hy * (
azbar * (
axbarp * (bybar * transposeLookup4D(fin, 2, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i, j + 1, k, s1, inf2, inf3, nz)) +
axp * (bybar * transposeLookup4D(fin, 2, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
axbarp * (bybar * transposeLookup4D(fin, 2, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
axp * (bybar * transposeLookup4D(fin, 2, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hxi * hz * (
bzbar * (
axbarp * (aybar * transposeLookup4D(fin, 3, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i, j + 1, k, s1, inf2, inf3, nz)) +
axp * (aybar * transposeLookup4D(fin, 3, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
axbarp * (aybar * transposeLookup4D(fin, 3, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
axp * (aybar * transposeLookup4D(fin, 3, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hy * (
azbar * (
bxbarp * (bybar * transposeLookup4D(fin, 4, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i, j + 1, k, s1, inf2, inf3, nz)) +
bxp * (bybar * transposeLookup4D(fin, 4, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
bxbarp * (bybar * transposeLookup4D(fin, 4, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bxp * (bybar * transposeLookup4D(fin, 4, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hz * (
bzbar * (
bxbarp * (aybar * transposeLookup4D(fin, 5, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i, j + 1, k, s1, inf2, inf3, nz)) +
bxp * (aybar * transposeLookup4D(fin, 5, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
bxbarp * (aybar * transposeLookup4D(fin, 5, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bxp * (aybar * transposeLookup4D(fin, 5, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hxi * hy * hz * (
bzbar * (
axbarp * (bybar * transposeLookup4D(fin, 6, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i, j + 1, k, s1, inf2, inf3, nz)) +
axp * (bybar * transposeLookup4D(fin, 6, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
axbarp * (bybar * transposeLookup4D(fin, 6, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
axp * (bybar * transposeLookup4D(fin, 6, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hy * hz * (
bzbar * (
bxbarp * (bybar * transposeLookup4D(fin, 7, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i, j + 1, k, s1, inf2, inf3, nz)) +
bxp * (bybar * transposeLookup4D(fin, 7, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
bxbarp * (bybar * transposeLookup4D(fin, 7, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bxp * (bybar * transposeLookup4D(fin, 7, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
fval[iadr] = sum;
}
if (ict[2] == 1) {
iadr = iadr + 1;
sum = hyi * (
azbar * (
axbar * (aybarp * transposeLookup4D(fin, 0, i, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 0, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (aybarp * transposeLookup4D(fin, 0, i + 1, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 0, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
axbar * (aybarp * transposeLookup4D(fin, 0, i, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 0, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (aybarp * transposeLookup4D(fin, 0, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 0, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hyi * hx * (
azbar * (
bxbar * (aybarp * transposeLookup4D(fin, 1, i, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 1, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (aybarp * transposeLookup4D(fin, 1, i + 1, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 1, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
bxbar * (aybarp * transposeLookup4D(fin, 1, i, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 1, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (aybarp * transposeLookup4D(fin, 1, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 1, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + (
azbar * (
axbar * (bybarp * transposeLookup4D(fin, 2, i, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 2, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (bybarp * transposeLookup4D(fin, 2, i + 1, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 2, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
axbar * (bybarp * transposeLookup4D(fin, 2, i, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 2, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (bybarp * transposeLookup4D(fin, 2, i + 1, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 2, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hyi * hz * (
bzbar * (
axbar * (aybarp * transposeLookup4D(fin, 3, i, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 3, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (aybarp * transposeLookup4D(fin, 3, i + 1, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 3, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
axbar * (aybarp * transposeLookup4D(fin, 3, i, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 3, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (aybarp * transposeLookup4D(fin, 3, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 3, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * (
azbar * (
bxbar * (bybarp * transposeLookup4D(fin, 4, i, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 4, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (bybarp * transposeLookup4D(fin, 4, i + 1, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 4, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ az * (
bxbar * (bybarp * transposeLookup4D(fin, 4, i, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 4, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (bybarp * transposeLookup4D(fin, 4, i + 1, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 4, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * hyi * hz * (
bzbar * (
bxbar * (aybarp * transposeLookup4D(fin, 5, i, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 5, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (aybarp * transposeLookup4D(fin, 5, i + 1, j, k, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 5, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
bxbar * (aybarp * transposeLookup4D(fin, 5, i, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 5, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (aybarp * transposeLookup4D(fin, 5, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ayp * transposeLookup4D(fin, 5, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hz * (
bzbar * (
axbar * (bybarp * transposeLookup4D(fin, 6, i, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 6, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (bybarp * transposeLookup4D(fin, 6, i + 1, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 6, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
axbar * (bybarp * transposeLookup4D(fin, 6, i, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 6, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (bybarp * transposeLookup4D(fin, 6, i + 1, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 6, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * hz * (
bzbar * (
bxbar * (bybarp * transposeLookup4D(fin, 7, i, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 7, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (bybarp * transposeLookup4D(fin, 7, i + 1, j, k, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 7, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bz * (
bxbar * (bybarp * transposeLookup4D(fin, 7, i, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 7, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (bybarp * transposeLookup4D(fin, 7, i + 1, j, k + 1, s1, inf2, inf3, nz) +
byp * transposeLookup4D(fin, 7, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
fval[iadr] = sum;
}
if (ict[3] == 1) {
iadr = iadr + 1;
sum = hzi * (
azbarp * (
axbar * (aybar * transposeLookup4D(fin, 0, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 0, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ azp * (
axbar * (aybar * transposeLookup4D(fin, 0, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 0, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 0, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hzi * hx * (
azbarp * (
bxbar * (aybar * transposeLookup4D(fin, 1, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 1, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ azp * (
bxbar * (aybar * transposeLookup4D(fin, 1, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 1, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 1, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hzi * hy * (
azbarp * (
axbar * (bybar * transposeLookup4D(fin, 2, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 2, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ azp * (
axbar * (bybar * transposeLookup4D(fin, 2, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 2, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 2, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + (
bzbarp * (
axbar * (aybar * transposeLookup4D(fin, 3, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 3, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bzp * (
axbar * (aybar * transposeLookup4D(fin, 3, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (aybar * transposeLookup4D(fin, 3, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 3, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hzi * hx * hy * (
azbarp * (
bxbar * (bybar * transposeLookup4D(fin, 4, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 4, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ azp * (
bxbar * (bybar * transposeLookup4D(fin, 4, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 4, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 4, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * (
bzbarp * (
bxbar * (aybar * transposeLookup4D(fin, 5, i, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 5, i + 1, j, k, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bzp * (
bxbar * (aybar * transposeLookup4D(fin, 5, i, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (aybar * transposeLookup4D(fin, 5, i + 1, j, k + 1, s1, inf2, inf3, nz) +
ay * transposeLookup4D(fin, 5, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hy * (
bzbarp * (
axbar * (bybar * transposeLookup4D(fin, 6, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i, j + 1, k, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 6, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bzp * (
axbar * (bybar * transposeLookup4D(fin, 6, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
ax * (bybar * transposeLookup4D(fin, 6, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 6, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
sum = sum + hx * hy * (
bzbarp * (
bxbar * (bybar * transposeLookup4D(fin, 7, i, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i, j + 1, k, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 7, i + 1, j, k, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i + 1, j + 1, k, s1, inf2, inf3, nz)))
+ bzp * (
bxbar * (bybar * transposeLookup4D(fin, 7, i, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i, j + 1, k + 1, s1, inf2, inf3, nz)) +
bx * (bybar * transposeLookup4D(fin, 7, i + 1, j, k + 1, s1, inf2, inf3, nz) +
by * transposeLookup4D(fin, 7, i + 1, j + 1, k + 1, s1, inf2, inf3, nz)))
);
fval[iadr] = sum;
}
}
|
19,422 | #include "includes.h"
__global__ void transpose(int N, double *A)
{
int row,col,k;
double temp;
k = (blockIdx.y*gridDim.x+blockIdx.x)*(blockDim.x*blockDim.y)+(threadIdx.y*blockDim.x+threadIdx.x);
row = k/N;
col = k - row*N;
if(row<col){
temp = A[row*N+col];
A[row*N+col] = A[col*N+row];
A[col*N+row] = temp;
}
} |
19,423 | #include <stdio.h>
#include <stdlib.h>
#define N 10 // ( N )x( N ) matrix containing data
// The idea with an aligned array is that the GPU will perform better if you pad
// it's data array so that it can fit better in cache. CUDA accomplishes this
// with the cudaMallocPitch() call. pitch (of type size_t) is the number of bytes
// per row in the array on the device. This is equivilent to
// sizeof(arraytype)*(columns + paddingColumns) OR sizeof(arraytype)*numDeviceColumns
//
// This does mean that care must be taken when copying data to and from the device
// because the array is no longer completely linear (it has padding).
// CUDA offers a convenience function called cudaMemcpy2D() which allows you to specify
// the array rows/cols as well as the pitch for the source and the destination
__global__ void kernel(float *A, float *B, int devWidth)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
int o = i+devWidth*j;
B[o] = A[o];
//B[o] = o; // uncomment to print the indexes
}
void print(float *A, int nx, int ny)
{
for (int j = 0; j < ny; j++) {
for (int i = 0; i < nx; i++) {
printf("%4.0f ",A[i+nx*j]);
}
printf("\n");
}
printf("\n");
}
int main(int argc, char * argv[])
{
// Allocate on host
float *h_A, *h_B;
float *d_A, *d_B;
h_A = (float*)malloc(sizeof(float)*N*N);
h_B = (float*)malloc(sizeof(float)*N*N);
size_t h_pitch=sizeof(float)*N;
// Initialize Array h_A
for (int i = 0; i < N*N; i++) h_A[i]=i; //h_A[i]=0; <-- use to print the padded indexes
// Allocate pictched memory for padded arrays d_A and d_B
size_t d_pitch; // actual number of columns in the device array
cudaMallocPitch((void**)&d_A,&d_pitch,sizeof(float)*N,N);
cudaMallocPitch((void**)&d_B,&d_pitch,sizeof(float)*N,N);
int devWidth = d_pitch/sizeof(float);
// Copy memory from unpadded array A to padded array B
cudaMemcpy2D(d_A,d_pitch,h_A,h_pitch,sizeof(float)*N,N,cudaMemcpyHostToDevice);
dim3 threads = dim3(16,16);
dim3 blocks = dim3((N+16-1)/16,(N+16-1)/16);
kernel<<<blocks,threads>>>(d_A,d_B,devWidth);
// Copy memory from padded array B to unpadded array A
cudaMemcpy2D(h_B,h_pitch,d_B,d_pitch,sizeof(float)*N,N,cudaMemcpyDeviceToHost);
// Are they the same?
print(h_A,N,N);
print(h_B,N,N);
// Release Arrays
cudaFree(d_A);
cudaFree(d_B);
free(h_A);
free(h_B);
}
|
19,424 | #include <stdio.h>
#define N 10
__global__
void add(int *a, int *b) {
int i = blockIdx.x;
printf("Hello cuda from thread %d\n", i);
b[i] = 2*a[i];
}
int main() {
int ha[N], hb[N];
int *da, *db;
cudaMalloc((void **)&da, N*sizeof(int));
cudaMalloc((void **)&db, N*sizeof(int));
for (int i = 0; i<N; ++i) {
ha[i] = i;
}
cudaMemcpy(da, ha, N*sizeof(int), cudaMemcpyHostToDevice);
add<<<N, 1>>>(da, db);
cudaMemcpy(hb, db, N*sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i<N; ++i) {
printf("%d\n", hb[i]);
}
cudaFree(da);
cudaFree(db);
return 0;
}
|
19,425 | // file esempio sommavettore_gpu with herror handling
#include "stdio.h"
#define N 32 // 100
#define NumThPerBlock 32 //256
#define NumBlocks 1
static void HandleError( cudaError_t err, const char *file, int line) {
if (err != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString( err ), file, line);
exit(EXIT_FAILURE);
}
}
#define HANDLE_ERROR(err)(HandleError(err, __FILE__, __LINE__))
__global__ void add(int *d_a, int *d_b, int *d_c) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < N)
d_c[tid] = d_a[tid] + d_b[tid];
}
int main( void ) {
int a[N], b[N], c[N]; // host variables containing host pointers
int *dev_a, *dev_b, *dev_c; // host variables containing device pointers
// static allocation on device memory
HANDLE_ERROR(cudaMalloc((void**)&dev_a, N*sizeof(int)));
HANDLE_ERROR(cudaMalloc((void**)&dev_b, N*sizeof(int)));
HANDLE_ERROR(cudaMalloc((void**)&dev_c, N*sizeof(int)));
// host initializes arrays
for (int i=0; i<N; i++) {
a[i] = -i;
b[i] = i * i;
}
// copy arrays from host to device
HANDLE_ERROR(cudaMemcpy(dev_a, a, N*sizeof(int), cudaMemcpyHostToDevice));
HANDLE_ERROR(cudaMemcpy(dev_b, b, N*sizeof(int), cudaMemcpyHostToDevice));
add<<<NumBlocks, NumThPerBlock>>>(dev_a, dev_b, dev_c);
// TODO wait threads completion
//HANDLE_ERROR(cudaDeviceSynchronize());
//retrieve the result from device dev_c into c
HANDLE_ERROR(cudaMemcpy(c, dev_c, N*sizeof(int), cudaMemcpyDeviceToHost));
//show results
for (int i=0; i<N; i++) {
printf("%d + %d = %d\n", a[i], b[i], c[i]);
}
//free device memory
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
return 0;
}
|
19,426 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
__device__ float ed1D(float *x, float *y, int K){
int i;
float d=0;
for(i=0;i<K;i++)
d += pow((x[i] - y[i]), 2);
return sqrt(d);
}
__device__ float ed2D(float *x, float *y, int T, int K){
int i;
float d=0;
for (i =0; i < T; i++){
d += ed1D(&x[i*K], &y[i*K], K);
}
return d;
}
#define R 10
__device__ float DTW(float *x, float *y, int T, int K){
int i, j;
float dd[2*R+1][2]; // use this to walk through the dtw matrix one column at a time (only for values within constraint)
float n1, n2, n3, min; // dtw neighbours
// first column is just distances (between first R+1 of x-series and first frame of y)
dd[0][0] = ed1D(x, y, K);
for(j=1;j<R+1;j++){
dd[j][0] = ed1D(&x[j*K], y, K) + dd[j-1][0];
}
// now we step across the y-series. for each frame, we compute the next column of the dtw matrix, alternating its placement in the one of two columns on dd
int v = 1; // the next column of dtw-matrix is written into this column of dd
int w = 0;
for(i=1;i<T;i++){
// the diagonal of the dtw matrix in (i, j) is (i, 0). i+j takes values from R below to R above the diagonal (indexing the rows (frames of x-series) in DTW matrix). j+R indexes dd.
for(j= -R ;j< R+1 ;j++){
// bottom row is just the distancess (between first R+1 of y-series and first frame of x)
if (i+j==0){
dd[j+R][v] = ed1D(x, &y[i*K], K) + dd[0][w];
}
else if (i+j > 0 && i+j < T){
n1 = dd[j+R+1][w];
n2 = dd[j+R][w];
n3 = dd[j+R-1][v];
min = fminf(n1, n2);
min = fminf(min, n3);
dd[j][v] = ed1D(&x[(i+j)*K], &y[i*K], K) + min;
}
}
v = (v+1)%2;
w = (w+1)%2;
}
return dd[2*R][w];
}
/* KNN */
__global__ void knn(float *train, float *test, float *dist, size_t N_train, size_t T, size_t K, size_t d_mode)
{
/* each block processes an instance of test (load it into shared)
each thread loads a few elements of the test instance into shared memory
each thread computes the distance between the test instance and a train instance, until all train instances have been seen.
Assumptions:
- NUM_THREADS = N_TRAIN (so a single block can process the whole train set).
*/
extern __shared__ float Q[]; // query (test instance);
int NUM_THREADS = N_train;
int bx = blockIdx.x; // indexes the test instance (we have as many blocks as test instances)
int tx = threadIdx.x; // indexes the train instance
int M = T*K; // size of a test instance (to load into shared memory)
int S = (T + NUM_THREADS-1) / NUM_THREADS; //number of frames in time series loaded by each thread
int rr = S*K; // num elements each thread must load
float d;
int i;
// load test instance into shared (each thread loads R elements of test instance, corresponding to S rows of Q)
for (i=0; i < rr; i++){
int frame_num = tx*S + i/K;
if (frame_num < T)
Q[(tx*S + i/K)*K + (i%K)] = test[bx * M + tx * rr + i];
}
__syncthreads();
// compute distance between test instance (Q) and a train instance
if (d_mode == 0)
d = ed2D(Q, &train[tx*M], T, K);
else
d = DTW(Q, &train[tx*M], T, K);
dist[bx*N_train + tx] = d;
__syncthreads();
}
extern "C"{
void launch_kernel_knn(void *train, void *test, void *dist, size_t N_train, size_t N_test, size_t T, size_t K, size_t mode)
{
// for nuw, num_threads and n_train are the same (so we can use a single block to process a test case against the full training set). Relieve this limitation by looping over the kernel...
int M = T*K; // for shared memory
int NUM_THREADS = N_train;
knn<<<N_test, NUM_THREADS, M*sizeof(float)>>>((float*)train, (float*)test, (float*)dist, N_train, T, K, mode);
}}
__global__ void dtw_kern(float *Q, float *S, float *D, size_t T, size_t K){
*D = DTW(Q, S, T, K);
}
extern "C"{
void launch_kernel_dtw(void *Q, void *S, void *D, size_t T, size_t K)
{
dtw_kern<<<1, 1>>>((float*)Q, (float*)S, (float*)D,T, K);
}}
/* Vector Addition */
__global__ void add_0(float *a, float *b, float *c, size_t N){
int tid = blockIdx.x;
if (tid < N)
c[tid] = a[tid] + b[tid];
}
__global__ void add_1(float *a, float *b, float *c, size_t N){
int tid = threadIdx.x;
if (tid < N)
c[tid] = a[tid] + b[tid];
}
__global__ void add_2(float *a, float *b, float *c, size_t N){
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N)
c[tid] = a[tid] + b[tid];
}
extern "C"{
void launch_kernel_add(void *d_a, void *d_b, void *d_c, size_t N, int mode)
{
// launch kernel
if (mode == 0)
add_0<<<N, 1>>>((float*)d_a, (float*)d_b, (float *)d_c, N);
else if (mode == 1)
add_1<<<1, N>>>((float*)d_a, (float*)d_b, (float *)d_c, N);
else
add_2<<<(N + mode-1)/mode, mode>>>((float *)d_a, (float *)d_b, (float *)d_c, N);
}}
/* Vector Multiplication */
__global__ void multiply_0(float *a, float *b, float *c, size_t N){
extern __shared__ float temp[];
int tid = threadIdx.x;
if (tid < N)
temp[tid] = a[tid] * b[tid];
__syncthreads();
// thread 0 sums pairwise products
if ( tid == 0){
float sum = 0;
for (int i =0; i < N; i++)
sum += temp[i];
c[0] = sum;
}
}
__global__ void multiply_1(float *a, float *b, float *c, size_t N, size_t M){
c[0] = 0.0; // necessary to initialize since malloc doesn't clear memory
extern __shared__ float temp[];
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if (tid < N)
temp[threadIdx.x] = a[tid] * b[tid];
__syncthreads();
if (0 == threadIdx.x){
float sum = 0;
for (int i=0; i < M; i++)
sum += temp[i];
// now we add sum to c. but since different blocks are doing this potentially simultaneous (read-modify-write), one might read before another writes and hence over write eachother. so we need to use atomic operations - then read-modify-write is uninteruptable
atomicAdd(c, sum);
}
}
extern "C"{
void launch_kernel_multiply(void *d_a, void *d_b, void *d_c, size_t N, size_t M)
{
printf("%ld, %ld\n", N, M);
if (M==0)
multiply_0<<<1, N, N*sizeof(float)>>>((float*)d_a, (float*)d_b, (float *)d_c, N);
else
multiply_1<<<(N + M-1)/M, M, M*sizeof(float)>>>((float*)d_a, (float*)d_b, (float *)d_c, N, M);
printf("%s\n", cudaGetErrorString(cudaGetLastError()));
}}
/* Matrix Multiplication */
__global__ void matmult_0(float *a, float *b, float *c, size_t W1, size_t W2, size_t W3){
float cval = 0;
int col = threadIdx.x + blockIdx.x * blockDim.x;
int row = threadIdx.y + blockIdx.y * blockDim.y;
if (row < W1 && col < W3){
for (int i=0; i<W2; i++)
cval += a[row*W2 + i] * b[i*W3 + col];
c[row*W3 + col] = cval;
}
}
#define TILE_WIDTH 32
__global__ void matmult_1(float *a, float *b, float *c, size_t W1, size_t W2, size_t W3){
__shared__ float as[TILE_WIDTH][TILE_WIDTH];
__shared__ float bs[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x, by = blockIdx.y, tx = threadIdx.x, ty = threadIdx.y;
int row = by * TILE_WIDTH + ty;
int col = bx * TILE_WIDTH + tx;
float cval =0;
for (int i =0; i < (W2-1)/TILE_WIDTH+1; ++i){
if (row < W1 && i*TILE_WIDTH + tx < W2)
as[ty][tx] = a[row*W2 + i*TILE_WIDTH + tx];
else
as[ty][tx] = 0;
if (col < W3 && i*TILE_WIDTH+ty < W2)
bs[ty][tx] = b[(i*TILE_WIDTH+ty)*W3 + col];
else
bs[ty][tx] = 0;
__syncthreads();
for(int k=0; k <TILE_WIDTH; ++k)
cval += as[ty][k] * bs[k][tx];
__syncthreads();
}
if (row < W1 && col < W3)
c[row*W3 + col] = cval;
}
extern "C"{
void launch_kernel_matmul(void *da, void *db, void *dc, size_t W1, size_t W2, size_t W3, size_t mode){
if (mode == 0){
dim3 dimBlock(16, 16);
dim3 dimGrid((W3 + dimBlock.x - 1)/dimBlock.x, (W1 + dimBlock.y - 1)/dimBlock.y);
matmult_0<<<dimGrid, dimBlock>>>((float*)da, (float*)db, (float*)dc, W1, W2, W3);
}
else{
dim3 dimBlock(TILE_WIDTH, TILE_WIDTH);
dim3 dimGrid((W3-1)/TILE_WIDTH+1, (W1-1)/TILE_WIDTH+1);
matmult_1<<<dimGrid, dimBlock>>>((float*)da, (float*)db, (float*)dc, W1, W2, W3);
}
}}
/*
__global__ void stencil_1d(int *in, int *out, int RADIUS){
__shared__ int temp[BLOCK_SIZE + 2*RADIUS];
int gindex = threadIdx.x + blockIdx.x*blockDim.x;
int lindex = threadIdx.x + RADIUS;
// read input into shared
temp[lindex] = in[gindex];
if (threadIdx.x < RADIUS){
temp[lindex - RADIUS] = in[gindex - RADIUS];
temp[lindex + BLOCK_SIZE] = in[gindex + BLOCK_SIZE];
}
__syncthreads();
int result = 0;
for (int offset = -RADIUS ; offset <= RADIUS ; offset ++)
result += temp[lindex + offset];
out[gindex] = result;
}
*/
|
19,427 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
int main()
{
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, 0);
printf("Device name : %s\n", deviceProp.name);
printf("Total global memory : %d MB\n",deviceProp.totalGlobalMem / 1024 / 1024);
printf("Shared memory per block : %d\n",deviceProp.sharedMemPerBlock);
printf("Registers per block : %d\n",deviceProp.regsPerBlock);
printf("Warp size : %d\n", deviceProp.warpSize);
printf("Memory pitch : %d\n", deviceProp.memPitch);
printf("Max threads per block : %d\n",deviceProp.maxThreadsPerBlock);
printf("Max threads dimensions : x = %d, y = %d, z = % d\n", deviceProp.maxThreadsDim[0],deviceProp.maxThreadsDim[1],deviceProp.maxThreadsDim[2]);
printf("Max grid size: x = %d, y = %d, z = %d\n",deviceProp.maxGridSize[0], deviceProp.maxGridSize[1],deviceProp.maxGridSize[2]);
printf("Clock rate: %d\n", deviceProp.clockRate);
printf("Total constant memory: %d\n",deviceProp.totalConstMem);
printf("Compute capability: %d.%d\n",deviceProp.major, deviceProp.minor);
printf("Texture alignment: %d\n",deviceProp.textureAlignment);
printf("Device overlap: %d\n",deviceProp.deviceOverlap);
printf("Multiprocessor count: %d\n",deviceProp.multiProcessorCount);
printf("Kernel execution timeout enabled: %s\n", deviceProp.kernelExecTimeoutEnabled ? "true" :"false");
return 0;
}
|
19,428 | #include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
typedef unsigned int uint;
// O kernel processará o espaço do bloco, eliminando todos os múltiplos de k
__global__ void sieve(uint* d_array, uint k) {
int idx = threadIdx.x + (32 * blockIdx.x);
if ( (d_array[idx] % k) == 0) {
d_array[idx] = 0;
}
__syncthreads();
}
extern "C++" void onDevice(uint *ptrK, uint block_size, uint n, uint *h_array) {
uint k = *ptrK;
// Alocando os espaços na GPU para processamento.
uint* d_array;
// Allocando memória na GPU.
cudaMalloc((void**)&d_array, (block_size) * sizeof(uint));
// Copiando conteudo para a GPU.
cudaMemcpy(d_array, h_array, (block_size) * sizeof(uint), cudaMemcpyHostToDevice);
dim3 threadsPorBloco(32, 1, 1);
dim3 blocosPorGrid(ceil( (double)block_size/32), 1, 1);
// Uso da GPU para eliminar os múltiplos do bloco
// Invoka o kernel para operações de mod
sieve<<<blocosPorGrid, threadsPorBloco>>>(d_array, k);
// Detecção de erro caso o kernel apresente algum...
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess)
printf("Error: %s\n", cudaGetErrorString(err));
// Atualiza o array presente na CPU depois do resultado.
cudaMemcpy(h_array, d_array, block_size * sizeof(uint), cudaMemcpyDeviceToHost);
// Desaloca os espaços alocados.
cudaFree(d_array);
} |
19,429 | float h_A[]= {
0.9082878423605707, 0.689461402011782, 0.5463360405647449, 0.505607877073731, 0.7714555565241021, 0.7929819094507726, 0.6788033919030121, 0.5687240568421572, 0.8389623664839443, 0.634043583652253, 0.9457857509154202, 0.7519147147958488, 0.9843117506047065, 0.9676874072673204, 0.798158570093222, 0.9318072733451459, 0.7439244467681311, 0.9593856458178559, 0.5373705836989602, 0.6402254105908144, 0.7372196215076794, 0.6082568212678618, 0.6979426932104127, 0.7283842025429055, 0.9584905996605753, 0.5195155554982327, 0.975419587161416, 0.8096049813459658, 0.8383877189642344, 0.815163207043546, 0.9962296424815029, 0.8257100526244328, 0.7458081348791421, 0.8030882184553869, 0.5967364634067177, 0.9303342059438153, 0.7517915734760934, 0.8618913509260577, 0.6526211302888955, 0.8740412379001925, 0.9313006338700538, 0.9699971646917169, 0.8654325496479509, 0.6484988032489198, 0.9012295545964255, 0.8404307733332576, 0.5724524195941496, 0.7504545061224357, 0.9800762559127139, 0.6702715507227748, 0.9171044139131168, 0.7822184970721757, 0.8122396737793942, 0.7738198950889448, 0.7995687552098456, 0.998014937760773, 0.6557444679341392, 0.9737466545929512, 0.8573762861913232, 0.7235132768856634, 0.819484714941106, 0.5028266081567522, 0.6443833537471696, 0.7438185172172388, 0.6676246406348593, 0.5785496889988642, 0.9663132118586415, 0.9799734728777658, 0.7347309272468305, 0.5012577124571183, 0.7480357094701159, 0.83065157071153, 0.5495105239440944, 0.7742378113848907, 0.5297156277290647, 0.8405015409194474, 0.9870850031095237, 0.7183063739998541, 0.7003071457566671, 0.5878437627980221, 0.8382888149934464, 0.8263031847594484, 0.5501921078972174, 0.638697356392512, 0.6710087556650692, 0.9666158068402364, 0.566651830047104, 0.6899588664208733, 0.6460717393412477, 0.7291311807530819, 0.7224906851104436, 0.9844710627243523, 0.5086825869074691, 0.7090734870987312, 0.8297137648073487, 0.5500526905376764, 0.7123279180391103, 0.9305503256742669, 0.5488463249341886, 0.6092389446505548, 0.8643744864371559, 0.8549461149920099, 0.560488164313971, 0.6595018767619195, 0.6010501134011093, 0.9468472372814916, 0.9731044885171675, 0.8958118139219222, 0.9497959839954313, 0.5525867013495501, 0.5680263727795365, 0.6876072324079898, 0.7762832990785185, 0.8642705608498813, 0.5298976451365529, 0.605447857535641, 0.9948505555852337, 0.7781071765988862, 0.5203101401015189, 0.5471855442069545, 0.9303722262905725, 0.8487271068794108, 0.5089992288929608, 0.5699395327114334, 0.605145739718695, 0.6678086186557685, 0.6884732870439316, 0.9617180925598288, 0.7279632945162432, 0.6127210742184934, 0.7815049978335249, 0.7868464525148042, 0.8765095786499839, 0.664500504370235, 0.8577437865710569, 0.6532079349161648, 0.6534916056466487, 0.7171843587449118, 0.7621912793732784, 0.572076745492104, 0.7977747209929116, 0.7647082383559652, 0.9478102769183364, 0.9772570941244937, 0.8131091269744608, 0.6436331232327142, 0.7965748232282563, 0.6577843752933865, 0.7746790823670306, 0.9370317427408532, 0.7948643667715087, 0.7477002468282741, 0.8243730847671513, 0.5974622938077707, 0.8372200656105012, 0.6555293608299542, 0.8409164608668007, 0.9839838532225667, 0.7332559960252518, 0.9291392931291063, 0.9456571252968435, 0.7932413357679133, 0.693810632981536, 0.5198212506522079, 0.6076737470680571, 0.9259256031842424, 0.6968259351426311, 0.7224277792094516, 0.675160024153632, 0.5253118336028796, 0.7379302818186566, 0.9634115033012517, 0.6187461977387153, 0.5075698238411877, 0.8304608266281814, 0.9680937184172465, 0.9216584474114582, 0.577102163584859, 0.9444654772619816, 0.6542510822903571, 0.9420931373052908, 0.7649540465762221, 0.7391290580199563, 0.7798337612716988, 0.5577399167495917, 0.5733736445912001, 0.8672435721086805, 0.6550718957452499, 0.7343787791913701, 0.8286090363881562, 0.9874391614319419, 0.6317174927286415, 0.9991818265547765, 0.8882882165082715, 0.7865034733678209, 0.9754850091083322, 0.7883052334448991, 0.6053331540391218, 0.7381300336993806, 0.5614202557288888, 0.6055179557374848, 0.8551360852576411, 0.8664296368013171, 0.5669369122418195, 0.8612613902124622, 0.9308738987091685, 0.7082305956767609, 0.9059876316512645, 0.7141225708042331, 0.5983977032912194, 0.832108200624676, 0.7102909332116345, 0.8701113270819587, 0.6172341761198135, 0.5499460005149649, 0.8217479675970876, 0.980397487524319, 0.7339616077701528, 0.8944438943149617, 0.8824476610480294, 0.9448839365945957, 0.611293609696064, 0.6202127155281614, 0.8982133628146343, 0.9959880857911522, 0.7749987208553435, 0.8404221536619295, 0.5355160001825691, 0.8312896242843753, 0.6710963564461283, 0.6757778649882356, 0.9450591609527955, 0.7960770326073398, 0.7388337694250741, 0.5097990426205785, 0.617665105190204, 0.6491148352416893, 0.6680035091881055, 0.915146896528442, 0.8526611525364447, 0.8433234599750767, 0.779861781124638, 0.7587100807600625, 0.7084898047926786, 0.7109206823340974, 0.7914737203988049, 0.8327485312652257, 0.8994487364271566, 0.9435130477429098, 0.7943053647334768, 0.6493790432720916, 0.5780312623722645, 0.8959978348428679, 0.6784904510102145, 0.9396971164367234, 0.7492180506543771, 0.8131125420733959, 0.521781354983252, 0.9750428789018397, 0.7310365674974546, 0.5993537952006589, 0.8726455896068224, 0.7819331738029656, 0.6421887465079975, 0.6674999451602143, 0.5319971037910738, 0.852022593506808, 0.5446034241601883, 0.9504448229552447, 0.8207629928044318, 0.9422319176831089, 0.9158816268975133, 0.9259598936146547, 0.8107352797786818, 0.5128802465392901, 0.5569836938081356, 0.7710003412286411, 0.6807902564653832, 0.8074504373673679, 0.8605365138324785, 0.7205481352043726, 0.7071270841127113, 0.9789630898384751, 0.572000247690236, 0.9236598670633295, 0.7289016881238359, 0.6503267593640145, 0.7248816820644711, 0.9813099299336776, 0.5250299309234707, 0.5926874889658209, 0.848866338628683, 0.9537216699727843, 0.7506749287998197, 0.7870097033219563, 0.6370012865666241, 0.6911264063172331, 0.6461537466213743, 0.5472501634129935, 0.9706748703009256, 0.9750735726021467, 0.833838398315583, 0.7870068246408313, 0.5672666412997371, 0.7621597478957183, 0.6412523950240999, 0.9533626172329719, 0.9285077025186779, 0.9085561584524972, 0.9797735405031338, 0.6527782031717836, 0.7764494743905886, 0.7340597144688839, 0.6371944941542568, 0.9303288261846399, 0.7909196434669774, 0.6101772670989659, 0.8725237343461679, 0.7771588858663292, 0.8031843697809086, 0.7166049073677778, 0.8440435183372049, 0.6231208790809557, 0.6824566738446276, 0.6249253706778888, 0.6001319052590866, 0.8423979278754339, 0.9804912799166953, 0.533352790625, 0.9955194247841975, 0.8562691265351794, 0.7159805159468932, 0.848377286181451, 0.6874202044851473, 0.5296038989284669, 0.8881984978738485, 0.6128150491015979, 0.9118226540767179, 0.9264667046169273, 0.7007533887482096, 0.9456723551848253, 0.8842081250207321, 0.6188212346050516, 0.8632155132829304, 0.7960087145580457, 0.9376249517463054, 0.657711559847133, 0.8665043783490352, 0.8213535321636006, 0.8595132628598157, 0.5290955844346499, 0.7569463835834859, 0.8595107248093607, 0.7116230681634043, 0.8019263710483158, 0.5886840501554704, 0.5853636156105402, 0.8313542593139549, 0.9195630185044495, 0.8664108518571536, 0.8072808475125368, 0.7209969240077091, 0.9042605253119455, 0.6856525843280643, 0.5789925183558848, 0.5529231707683319, 0.6455244251596519, 0.6309951453562814, 0.8484892500188466, 0.5717063598026898, 0.543578485924304, 0.6482772987788462, 0.8808136933791948, 0.5500201867550136, 0.7162674501989618, 0.8446995144144044, 0.6258657921604354, 0.7889274548192431, 0.6926220623884864, 0.5869117708787261, 0.6049806958837627, 0.5544634417312186, 0.6803917209752559, 0.5329256947938479, 0.6251831028765815, 0.9686255487476989, 0.6693161272515193, 0.6018045002391936, 0.6136470647073029, 0.7006761698417143, 0.9560500202518736, 0.9082327996500732, 0.7149784609158061, 0.8535215404317731, 0.5762375704318814, 0.7295963980633104, 0.8164091337431321, 0.9298864868947626, 0.5719543554507007, 0.7245213540635813, 0.6397133863695404, 0.5113598148484136, 0.9796105495284875, 0.755777012760537, 0.6441523644223616, 0.5211088161337902, 0.7823077186881309, 0.6014077753033269, 0.5174382185526643, 0.7794087005265369, 0.8158853329183986, 0.8051218807143782, 0.8344496628081393, 0.7799038204848311, 0.7007343849256948, 0.5789651399775446, 0.9186939121254118, 0.5800649224972613, 0.997940792911789, 0.5076154260060124, 0.9114643288322005, 0.6168902101547552, 0.6879034339784261, 0.7885181098875279, 0.8478032667829125, 0.8471035604429518, 0.7830964701891708, 0.5834035994473403, 0.5912846408862826, 0.6126327910445984, 0.7113104378192491, 0.7623346088368388, 0.9544598356468286, 0.6368023913392699, 0.7839299049080267, 0.5157603849254793, 0.7707112601657498, 0.6200738627250632, 0.6860756384291924, 0.6971147194176204, 0.8992718498492973, 0.6200716542226198, 0.8235958992734276, 0.7105513978051694, 0.8308607940147007, 0.5009449724397756, 0.8404152443022201, 0.9271554334298377, 0.7615001160541264, 0.665795427599405, 0.7576434921462787, 0.677666528692124, 0.6153544761835656, 0.6599806758984768, 0.5549028258454989, 0.537087034381724, 0.9059423590846216, 0.6364919486632519, 0.8535692674336279, 0.8194822022406671, 0.732703036845648, 0.6408959353882342, 0.979134649001633, 0.8446012706391948, 0.727537959751966, 0.8992071757369342, 0.773033991078701, 0.6912035636680639, 0.6678239513620563, 0.579112166076426, 0.5246311471905317, 0.9247041272231906, 0.6219166314420218, 0.8966048768284001, 0.753449396196942, 0.825216094887963, 0.8966924548860679, 0.772027133798461, 0.6305688871119586, 0.6138557318036646, 0.8377193575618891, 0.575220792061786, 0.6811658955620439, 0.8063325675854576, 0.6024894409737116, 0.8321565863137921, 0.5853723971011682, 0.8211784462497813, 0.5621405057904378, 0.8060243555198678, 0.7615044568558158, 0.749418615771406, 0.9882220842117067, 0.5473214405997162, 0.9394105144036239, 0.9940938074739658, 0.844247121298296, 0.7104797988984898, 0.6194320807267331, 0.9744735968605656, 0.7159678852753074, 0.686481606445342, 0.7309094290988748, 0.7697582007653805, 0.9641044829530394, 0.6495325860132362, 0.8477250158522804, 0.8031072821919533, 0.733212344797883, 0.7040279272680836, 0.8333788219820861, 0.7106192911484892, 0.6054341860385409, 0.6645145929803637, 0.6749436215604394, 0.7342329016647018, 0.7487611514291992, 0.7341853173193882, 0.8319120463629286, 0.8471065816777889, 0.5450602752088898, 0.588963920357326, 0.9634946038397014, 0.5275860021549643, 0.6131238617233276, 0.9026521979716229, 0.8649774042461855, 0.6254856567851594, 0.587585401841259, 0.8702427455852668, 0.9734038850652624, 0.6907389234247403, 0.6742848406792503, 0.8892310557838489, 0.7005429100889178, 0.6323093396099213, 0.6629025660883385, 0.8097239561486289, 0.827971540355219, 0.9483600549325943, 0.7096273182420537, 0.6186179143139622, 0.869593638141843, 0.589465010335807, 0.8437662173288232, 0.6860581451873626, 0.6997440197245475, 0.7950864998630973, 0.6002173651070569, 0.5072396491933654, 0.5687157981012682, 0.8328051767954174, 0.6151009718360505, 0.5131032541978355, 0.9547384677445773, 0.8628382300004067, 0.8573698257063507, 0.5968707959804591, 0.9111539725677904, 0.6952103207359548, 0.9668626553945241, 0.6951090243475937, 0.7881437296558504, 0.6886910010927544, 0.9240602200264298, 0.5110176933860936, 0.6298130729260677, 0.7829937448417085, 0.6441845485354231, 0.8963213253725464, 0.9166886615283922, 0.6075291794609559, 0.8028977059410873, 0.9171693181925586, 0.6477863898916207, 0.5471484145923962, 0.6302032521165666, 0.6683021898564758, 0.7340124672781195, 0.5426796906112303, 0.8757562100828133, 0.8171621627299078, 0.8365153218732406, 0.8609893530223891, 0.9707393155196433, 0.7666186376531046, 0.9965525256324854, 0.5147487497819987, 0.9311713013883948, 0.7119007450065112, 0.9213851603626304, 0.5340260511161686, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 97, 99, 101, 103, 106, 108, 111, 113, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 142, 144, 146, 148, 150, 152, 158, 160, 163, 165, 168, 170, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 199, 201, 204, 206, 209, 211, 198, 116, 203, 198, 116, 198, 116, 203, 193, 215, 116, 156, 156, 193, 196, 196, 198, 215, 587, 589, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644, 646, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 680, 682, 684, 686, 688, 690, 692, 694, 696, 698, 700, 702, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 162, 157, 162, 157, 167, 141, 105, 141, 110, 213, 208, 162, 157, 162, 157, 162, 157, 167, 141, 110, 105, 777, 784, 203, 213, 208, 162, 157, 141, 110, 105, 213, 208, 213, 208, 787, 162, 157, 141, 771, 162, 157, 172, 777, 208, 162, 157, 141, 771, 162, 157, 172, 777, 213, 162, 157, 162, 157, 162, 157, 167, 141, 110, 105, 162, 157, 172, 777, 213, 208, 162, 157, 162, 157, 167, 141, 105, 141, 110, 213, 208, 162, 157, 162, 157, 167, 141, 748, 162, 157, 172, 777, 203, 213, 208, 162, 157, 162, 157, 162, 157, 167, 141, 110, 105, 162, 157, 172, 110, 105, 213, 208, 213, 208, 162, 157, 141, 110, 105, 213, 208, 213, 208, 162, 157, 162, 157, 162, 157, 167, 141, 110, 105, 162, 157, 172, 777, 213, 208, 162, 157, 162, 157, 162, 157, 167, 141, 771, 162, 157, 162, 157, 167, 172, 777, 203, 213, 208, 203, 213, 208, 798, 786, 798, 786, 798, 799, 798, 799, 798, 799, 798, 799, 798, 799, 798, 791, 799, 799, 799, 798, 799, 798, 7, 8, 9, 10, 11, 12, 13, 14, 15, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 799, 798, 799, 798, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 9, 10, 11, 12, 13, 14, 15, 1008, 1010, 1017, 1019, 1021, 1023, 1027, 1031, 1033, 1036, 1038, 1040, 1042, 1046, 1051, 1055, 1060, 1062, 1064, 1068, 1070, 1074, 1076, 1078, 1085, 1087, 1089, 1094, 1099, 1101, 1103, 1105, 1109, 1111, 1114, 1116, 1118, 1120, 1123, 1125, 1127, 1129, 1131, 1133, 1137, 1139, 1143, 1145, 1147, 1149, 1154, 1156, 1162, 1165, 1016, 1014, 1029, 1030, 1164, 1168, 1170, 1153, 1161, 1172, 1173, 1164, 1174, 1175, 1045, 1049, 1164, 801, 1054, 1058, 1164, 801, 1073, 1180, 1182, 1084, 1082, 1093, 1097, 1098, 1186, 1142, 1153, 1160, 1161, 1164, 10, 11, 12, 13, 14, 15, 1012, 796, 795, 1254, 1255, 797, 1025, 796, 795, 1206, 1256, 1257, 797, 1258, 1151, 796, 795, 1209, 792, 793, 1261, 1262, 1265, 1151, 796, 795, 1268, 1158, 796, 795, 1269, 1270, 1271, 1151, 796, 795, 1272, 1158, 796, 795, 1273, 1274, 1275, 1066, 796, 795, 1219, 1158, 796, 795, 1276, 797, 1080, 796, 795, 1279, 1280, 797, 1091, 796, 795, 1281, 1158, 796, 795, 1282, 1283, 797, 1107, 796, 795, 1232, 1158, 796, 795, 1234, 797, 801, 1151, 796, 795, 1238, 792, 793, 1135, 796, 795, 1244, 1158, 796, 795, 1285, 797, 1151, 796, 795, 1286, 1158, 796, 795, 1287, 1288, 797, 1289, 801, 9, 10, 11, 12, 13, 14, 15, 1296, 1297, 1298, 1299, 1301, 1302, 1303, 1304, 1305, 1308, 1310, 1311, 1312, 1313, 1314, 1315, 1317, 1318, 1319, 1320, 1321, 1323, 1324, 1325, 1327, 1329, 1330, 1331, 1333, 1334, 1335, 1337, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1347, 1348, 1349, 1350, 1351, 1353, 1354, 1355, 1356, 1358, 1359, 1360, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1388, 1389, 1390, 1391, 1393, 1394, 1395, 1397, 1398, 1399, 1400, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1408, 1413, 1418, 1424, 1425, 1426, 1429, 1432, 1433, 1436, 1439, 1440, 1444, 1448, 1453, 1456, 1460, 1464, 1470, 1476, 1480, 1484, 1487, 1490, 1492, 1412, 1493, 1417, 1423, 1422, 1493, 1447, 1452, 1459, 1469, 1468, 1475, 1474, 1493, 1483, 8, 9, 10, 11, 12, 13, 14, 15, 1504, 1505, 1506, 1509, 1510, 1512, 1513, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1529, 1530, 1531, 1532, 1533, 1475, 1474, 1328, 1338, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1475, 1474, 1542, 1543, 1493, 1491, 10, 11, 12, 13, 14, 15, 1396, 1411, 1306, 1416, 1572, 1396, 1421, 1574, 1396, 1316, 1576, 1577, 1326, 1322, 1578, 1336, 1332, 1579, 1346, 1443, 1580, 1396, 1451, 1361, 1357, 1467, 1463, 1584, 1586, 1396, 1473, 1588, 1589, 1387, 1479, 1590, 1396, 1392, 1592, 1593, 8, 9, 10, 11, 12, 13, 14, 15, 1600, 1601, 1602, 1603, 1605, 1606, 1608, 1609, 1610, 1612, 1613, 1615, 1616, 1618, 1619, 1621, 1622, 1623, 1624, 1625, 1626, 1629, 1630, 1631, 1633, 1634, 1636, 1637, 1638, 13, 14, 15, 1648, 1650, 1652, 1654, 1657, 1659, 1661, 1663, 1665, 1667, 1669, 1672, 1674, 13, 14, 15, 1628, 1676, 1583, 1617, 1620, 1571, 1604, 1656, 1582, 1607, 1614, 1627, 1635, 1671, 14, 15, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 14, 15, 1712, 1714, 1716, 1718, 1720, 1722, 1724, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1728, 1730, 1732, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1744, 1746, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1760, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 802, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1792, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
int h_C[]= {
1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 98, 100, 102, 104, 107, 109, 112, 114, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 143, 145, 147, 149, 151, 153, 159, 161, 164, 166, 169, 171, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 200, 202, 205, 207, 210, 212, 115, 115, 194, 115, 115, 115, 197, 195, 96, 96, 115, 154, 155, 214, 194, 195, 197, 214, 588, 590, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 641, 643, 645, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 681, 683, 685, 687, 689, 691, 693, 695, 697, 699, 701, 703, 238, 239, 250, 253, 254, 289, 334, 335, 343, 346, 363, 375, 376, 385, 386, 387, 388, 392, 723, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 765, 746, 773, 767, 768, 742, 736, 744, 745, 780, 779, 765, 764, 773, 766, 773, 767, 768, 760, 755, 737, 763, 785, 778, 780, 779, 765, 753, 760, 755, 754, 757, 756, 759, 758, 788, 765, 738, 769, 770, 773, 772, 775, 776, 782, 765, 739, 769, 770, 773, 772, 775, 776, 783, 765, 764, 773, 766, 773, 767, 768, 740, 762, 741, 773, 772, 775, 763, 780, 779, 765, 746, 773, 767, 768, 742, 743, 744, 745, 780, 779, 765, 746, 773, 767, 768, 747, 770, 773, 772, 775, 776, 778, 780, 749, 765, 764, 773, 766, 773, 767, 768, 760, 762, 750, 773, 772, 775, 752, 751, 780, 779, 783, 782, 765, 753, 760, 755, 754, 757, 756, 759, 758, 765, 764, 773, 766, 773, 767, 768, 760, 762, 761, 773, 772, 775, 763, 780, 779, 765, 764, 773, 766, 773, 767, 768, 769, 770, 773, 772, 773, 773, 774, 775, 776, 778, 780, 779, 781, 783, 782, 790, 790, 790, 790, 790, 790, 790, 790, 790, 789, 789, 789, 789, 790, 794, 790, 790, 794, 800, 800, 800, 800, 7, 8, 9, 10, 11, 12, 13, 14, 15, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 251, 252, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 336, 337, 338, 339, 340, 341, 342, 344, 345, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 377, 378, 379, 380, 381, 382, 383, 384, 389, 390, 391, 398, 412, 413, 415, 416, 851, 851, 851, 851, 434, 435, 446, 447, 457, 458, 460, 461, 467, 488, 490, 491, 507, 517, 518, 521, 522, 9, 10, 11, 12, 13, 14, 15, 1009, 1011, 1018, 1020, 1022, 1024, 1028, 1032, 1034, 1037, 1039, 1041, 1043, 1047, 1052, 1056, 1061, 1063, 1065, 1069, 1071, 1075, 1077, 1079, 1086, 1088, 1090, 1095, 1100, 1102, 1104, 1106, 1110, 1112, 1115, 1117, 1119, 1121, 1124, 1126, 1128, 1130, 1132, 1134, 1138, 1140, 1144, 1146, 1148, 1150, 1155, 1157, 1163, 1166, 1015, 1013, 1159, 838, 851, 1169, 1171, 1122, 851, 420, 421, 851, 423, 424, 1044, 1048, 790, 1050, 1053, 1057, 790, 1059, 1072, 1181, 1183, 1083, 1081, 1092, 1096, 790, 1187, 1141, 1152, 1159, 800, 800, 10, 11, 12, 13, 14, 15, 1201, 1248, 1200, 396, 397, 1202, 1205, 1204, 1203, 1026, 404, 405, 1207, 407, 1249, 1248, 1208, 1035, 1210, 1211, 418, 419, 422, 1249, 1248, 1212, 428, 1251, 1251, 1213, 432, 433, 436, 1249, 1248, 1214, 440, 1251, 1251, 1215, 444, 445, 448, 1218, 1217, 1216, 1067, 1251, 1251, 1220, 456, 1221, 1223, 1248, 1222, 465, 466, 1224, 1226, 1248, 1225, 472, 1251, 1251, 1227, 476, 477, 1228, 1231, 1230, 1229, 1108, 1251, 1251, 1233, 1113, 1235, 1236, 1249, 1248, 1237, 1122, 1239, 1240, 1243, 1242, 1241, 1136, 1251, 1251, 1245, 505, 1246, 1249, 1248, 1247, 511, 1251, 1251, 1250, 515, 516, 1252, 520, 1253, 9, 10, 11, 12, 13, 14, 15, 393, 394, 395, 1300, 399, 400, 401, 402, 403, 406, 408, 409, 410, 411, 414, 417, 1263, 1266, 425, 426, 427, 429, 430, 431, 1176, 437, 438, 439, 441, 442, 443, 1178, 449, 450, 451, 452, 453, 454, 455, 459, 462, 463, 464, 1352, 468, 469, 470, 471, 473, 474, 475, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 489, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 506, 508, 509, 510, 512, 513, 514, 1189, 519, 1191, 523, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1409, 1414, 1419, 1264, 1267, 1427, 1430, 1177, 1434, 1437, 1179, 1441, 1445, 1449, 1454, 1457, 1461, 1465, 1471, 1477, 1481, 1485, 1488, 1190, 1192, 1167, 1309, 1307, 1260, 1259, 1278, 1277, 1184, 1362, 1185, 1185, 1284, 1284, 1188, 1188, 8, 9, 10, 11, 12, 13, 14, 15, 1410, 1415, 1420, 1428, 1431, 1435, 1438, 1442, 1446, 1450, 1455, 1458, 1462, 1466, 1472, 1478, 1482, 1486, 1489, 526, 529, 530, 533, 534, 1508, 1507, 1511, 1514, 547, 548, 551, 554, 557, 558, 559, 560, 1528, 1527, 567, 568, 1528, 1527, 10, 11, 12, 13, 14, 15, 1570, 1552, 1570, 1553, 1573, 1570, 1554, 1575, 1570, 1566, 537, 538, 1556, 1555, 541, 1558, 1557, 544, 1560, 1559, 1581, 1570, 1561, 1563, 1562, 1565, 1564, 1585, 1587, 1570, 1566, 563, 564, 1568, 1567, 1591, 1570, 1569, 571, 572, 8, 9, 10, 11, 12, 13, 14, 15, 524, 525, 527, 528, 531, 532, 535, 536, 1611, 539, 540, 542, 543, 545, 546, 549, 550, 552, 553, 555, 556, 561, 562, 1632, 565, 566, 569, 570, 1639, 13, 14, 15, 1649, 1651, 1653, 1655, 1658, 1660, 1662, 1664, 1666, 1668, 1670, 1673, 1675, 13, 14, 15, 1690, 1692, 1688, 1685, 1686, 1680, 1681, 1683, 1687, 1682, 1684, 1689, 1691, 1690, 14, 15, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 14, 15, 1713, 1715, 1717, 1719, 1721, 1723, 1725, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1729, 1731, 1733, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1745, 1734, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1761, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1776, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 591, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
bool h_Op[]= {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
#define THREADS_PER_BLOCK 16
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 608
#define SIZE_OF_AC 1216
__device__ void
ac(float *A, const int *B, const int *C, const bool *Op, int n_iter) {
int i= blockDim.x * blockIdx.x + threadIdx.x;
__shared__ float R[114*THREADS_PER_BLOCK];
const int t= THREADS_PER_BLOCK;
__shared__ float final;
final=0;
R[i + 0*t] = A[i + 0*t];
R[i + 1*t] = A[i + 1*t];
R[i + 2*t] = A[i + 2*t];
R[i + 3*t] = A[i + 3*t];
R[i + 4*t] = A[i + 4*t];
R[i + 5*t] = A[i + 5*t];
R[i + 6*t] = A[i + 6*t];
R[i + 7*t] = A[i + 7*t];
R[i + 8*t] = A[i + 8*t];
R[i + 9*t] = A[i + 9*t];
R[i + 10*t] = A[i + 10*t];
R[i + 11*t] = A[i + 11*t];
R[i + 12*t] = A[i + 12*t];
R[i + 13*t] = A[i + 13*t];
R[i + 14*t] = A[i + 14*t];
R[i + 15*t] = A[i + 15*t];
R[i + 16*t] = A[i + 16*t];
R[i + 17*t] = A[i + 17*t];
R[i + 18*t] = A[i + 18*t];
R[i + 19*t] = A[i + 19*t];
R[i + 20*t] = A[i + 20*t];
R[i + 21*t] = A[i + 21*t];
R[i + 22*t] = A[i + 22*t];
R[i + 23*t] = A[i + 23*t];
R[i + 24*t] = A[i + 24*t];
R[i + 25*t] = A[i + 25*t];
R[i + 26*t] = A[i + 26*t];
R[i + 27*t] = A[i + 27*t];
R[i + 28*t] = A[i + 28*t];
R[i + 29*t] = A[i + 29*t];
R[i + 30*t] = A[i + 30*t];
R[i + 31*t] = A[i + 31*t];
R[i + 32*t] = A[i + 32*t];
R[i + 33*t] = A[i + 33*t];
R[i + 34*t] = A[i + 34*t];
R[i + 35*t] = A[i + 35*t];
R[i + 36*t] = A[i + 36*t];
R[i + 37*t] = A[i + 37*t];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 38*t] = Op[i + 0*t] ? R[B[i + 0*t]] * R[C[i + 0*t]] : R[B[i + 0*t]] + R[C[i + 0*t]];
R[i + 39*t] = Op[i + 1*t] ? R[B[i + 1*t]] * R[C[i + 1*t]] : R[B[i + 1*t]] + R[C[i + 1*t]];
R[i + 40*t] = Op[i + 2*t] ? R[B[i + 2*t]] * R[C[i + 2*t]] : R[B[i + 2*t]] + R[C[i + 2*t]];
R[i + 41*t] = Op[i + 3*t] ? R[B[i + 3*t]] * R[C[i + 3*t]] : R[B[i + 3*t]] + R[C[i + 3*t]];
R[i + 42*t] = Op[i + 4*t] ? R[B[i + 4*t]] * R[C[i + 4*t]] : R[B[i + 4*t]] + R[C[i + 4*t]];
R[i + 43*t] = Op[i + 5*t] ? R[B[i + 5*t]] * R[C[i + 5*t]] : R[B[i + 5*t]] + R[C[i + 5*t]];
R[i + 44*t] = Op[i + 6*t] ? R[B[i + 6*t]] * R[C[i + 6*t]] : R[B[i + 6*t]] + R[C[i + 6*t]];
R[i + 45*t] = Op[i + 7*t] ? R[B[i + 7*t]] * R[C[i + 7*t]] : R[B[i + 7*t]] + R[C[i + 7*t]];
__syncthreads();
R[i + 46*t] = Op[i + 8*t] ? R[B[i + 8*t]] * R[C[i + 8*t]] : R[B[i + 8*t]] + R[C[i + 8*t]];
R[i + 47*t] = Op[i + 9*t] ? R[B[i + 9*t]] * R[C[i + 9*t]] : R[B[i + 9*t]] + R[C[i + 9*t]];
R[i + 48*t] = Op[i + 10*t] ? R[B[i + 10*t]] * R[C[i + 10*t]] : R[B[i + 10*t]] + R[C[i + 10*t]];
R[i + 49*t] = Op[i + 11*t] ? R[B[i + 11*t]] * R[C[i + 11*t]] : R[B[i + 11*t]] + R[C[i + 11*t]];
R[i + 50*t] = Op[i + 12*t] ? R[B[i + 12*t]] * R[C[i + 12*t]] : R[B[i + 12*t]] + R[C[i + 12*t]];
__syncthreads();
R[i + 51*t] = Op[i + 13*t] ? R[B[i + 13*t]] * R[C[i + 13*t]] : R[B[i + 13*t]] + R[C[i + 13*t]];
R[i + 52*t] = Op[i + 14*t] ? R[B[i + 14*t]] * R[C[i + 14*t]] : R[B[i + 14*t]] + R[C[i + 14*t]];
R[i + 53*t] = Op[i + 15*t] ? R[B[i + 15*t]] * R[C[i + 15*t]] : R[B[i + 15*t]] + R[C[i + 15*t]];
R[i + 54*t] = Op[i + 16*t] ? R[B[i + 16*t]] * R[C[i + 16*t]] : R[B[i + 16*t]] + R[C[i + 16*t]];
R[i + 55*t] = Op[i + 17*t] ? R[B[i + 17*t]] * R[C[i + 17*t]] : R[B[i + 17*t]] + R[C[i + 17*t]];
R[i + 56*t] = Op[i + 18*t] ? R[B[i + 18*t]] * R[C[i + 18*t]] : R[B[i + 18*t]] + R[C[i + 18*t]];
R[i + 57*t] = Op[i + 19*t] ? R[B[i + 19*t]] * R[C[i + 19*t]] : R[B[i + 19*t]] + R[C[i + 19*t]];
R[i + 58*t] = Op[i + 20*t] ? R[B[i + 20*t]] * R[C[i + 20*t]] : R[B[i + 20*t]] + R[C[i + 20*t]];
R[i + 59*t] = Op[i + 21*t] ? R[B[i + 21*t]] * R[C[i + 21*t]] : R[B[i + 21*t]] + R[C[i + 21*t]];
R[i + 60*t] = Op[i + 22*t] ? R[B[i + 22*t]] * R[C[i + 22*t]] : R[B[i + 22*t]] + R[C[i + 22*t]];
R[i + 61*t] = Op[i + 23*t] ? R[B[i + 23*t]] * R[C[i + 23*t]] : R[B[i + 23*t]] + R[C[i + 23*t]];
R[i + 62*t] = Op[i + 24*t] ? R[B[i + 24*t]] * R[C[i + 24*t]] : R[B[i + 24*t]] + R[C[i + 24*t]];
__syncthreads();
R[i + 63*t] = Op[i + 25*t] ? R[B[i + 25*t]] * R[C[i + 25*t]] : R[B[i + 25*t]] + R[C[i + 25*t]];
R[i + 64*t] = Op[i + 26*t] ? R[B[i + 26*t]] * R[C[i + 26*t]] : R[B[i + 26*t]] + R[C[i + 26*t]];
R[i + 65*t] = Op[i + 27*t] ? R[B[i + 27*t]] * R[C[i + 27*t]] : R[B[i + 27*t]] + R[C[i + 27*t]];
R[i + 66*t] = Op[i + 28*t] ? R[B[i + 28*t]] * R[C[i + 28*t]] : R[B[i + 28*t]] + R[C[i + 28*t]];
R[i + 67*t] = Op[i + 29*t] ? R[B[i + 29*t]] * R[C[i + 29*t]] : R[B[i + 29*t]] + R[C[i + 29*t]];
R[i + 68*t] = Op[i + 30*t] ? R[B[i + 30*t]] * R[C[i + 30*t]] : R[B[i + 30*t]] + R[C[i + 30*t]];
R[i + 69*t] = Op[i + 31*t] ? R[B[i + 31*t]] * R[C[i + 31*t]] : R[B[i + 31*t]] + R[C[i + 31*t]];
R[i + 70*t] = Op[i + 32*t] ? R[B[i + 32*t]] * R[C[i + 32*t]] : R[B[i + 32*t]] + R[C[i + 32*t]];
R[i + 71*t] = Op[i + 33*t] ? R[B[i + 33*t]] * R[C[i + 33*t]] : R[B[i + 33*t]] + R[C[i + 33*t]];
R[i + 72*t] = Op[i + 34*t] ? R[B[i + 34*t]] * R[C[i + 34*t]] : R[B[i + 34*t]] + R[C[i + 34*t]];
R[i + 73*t] = Op[i + 35*t] ? R[B[i + 35*t]] * R[C[i + 35*t]] : R[B[i + 35*t]] + R[C[i + 35*t]];
R[i + 74*t] = Op[i + 36*t] ? R[B[i + 36*t]] * R[C[i + 36*t]] : R[B[i + 36*t]] + R[C[i + 36*t]];
__syncthreads();
R[i + 75*t] = Op[i + 37*t] ? R[B[i + 37*t]] * R[C[i + 37*t]] : R[B[i + 37*t]] + R[C[i + 37*t]];
R[i + 76*t] = Op[i + 38*t] ? R[B[i + 38*t]] * R[C[i + 38*t]] : R[B[i + 38*t]] + R[C[i + 38*t]];
R[i + 77*t] = Op[i + 39*t] ? R[B[i + 39*t]] * R[C[i + 39*t]] : R[B[i + 39*t]] + R[C[i + 39*t]];
R[i + 78*t] = Op[i + 40*t] ? R[B[i + 40*t]] * R[C[i + 40*t]] : R[B[i + 40*t]] + R[C[i + 40*t]];
R[i + 79*t] = Op[i + 41*t] ? R[B[i + 41*t]] * R[C[i + 41*t]] : R[B[i + 41*t]] + R[C[i + 41*t]];
R[i + 80*t] = Op[i + 42*t] ? R[B[i + 42*t]] * R[C[i + 42*t]] : R[B[i + 42*t]] + R[C[i + 42*t]];
__syncthreads();
R[i + 81*t] = Op[i + 43*t] ? R[B[i + 43*t]] * R[C[i + 43*t]] : R[B[i + 43*t]] + R[C[i + 43*t]];
R[i + 82*t] = Op[i + 44*t] ? R[B[i + 44*t]] * R[C[i + 44*t]] : R[B[i + 44*t]] + R[C[i + 44*t]];
R[i + 83*t] = Op[i + 45*t] ? R[B[i + 45*t]] * R[C[i + 45*t]] : R[B[i + 45*t]] + R[C[i + 45*t]];
R[i + 84*t] = Op[i + 46*t] ? R[B[i + 46*t]] * R[C[i + 46*t]] : R[B[i + 46*t]] + R[C[i + 46*t]];
R[i + 85*t] = Op[i + 47*t] ? R[B[i + 47*t]] * R[C[i + 47*t]] : R[B[i + 47*t]] + R[C[i + 47*t]];
R[i + 86*t] = Op[i + 48*t] ? R[B[i + 48*t]] * R[C[i + 48*t]] : R[B[i + 48*t]] + R[C[i + 48*t]];
R[i + 87*t] = Op[i + 49*t] ? R[B[i + 49*t]] * R[C[i + 49*t]] : R[B[i + 49*t]] + R[C[i + 49*t]];
__syncthreads();
R[i + 88*t] = Op[i + 50*t] ? R[B[i + 50*t]] * R[C[i + 50*t]] : R[B[i + 50*t]] + R[C[i + 50*t]];
R[i + 89*t] = Op[i + 51*t] ? R[B[i + 51*t]] * R[C[i + 51*t]] : R[B[i + 51*t]] + R[C[i + 51*t]];
R[i + 90*t] = Op[i + 52*t] ? R[B[i + 52*t]] * R[C[i + 52*t]] : R[B[i + 52*t]] + R[C[i + 52*t]];
R[i + 91*t] = Op[i + 53*t] ? R[B[i + 53*t]] * R[C[i + 53*t]] : R[B[i + 53*t]] + R[C[i + 53*t]];
R[i + 92*t] = Op[i + 54*t] ? R[B[i + 54*t]] * R[C[i + 54*t]] : R[B[i + 54*t]] + R[C[i + 54*t]];
R[i + 93*t] = Op[i + 55*t] ? R[B[i + 55*t]] * R[C[i + 55*t]] : R[B[i + 55*t]] + R[C[i + 55*t]];
__syncthreads();
R[i + 94*t] = Op[i + 56*t] ? R[B[i + 56*t]] * R[C[i + 56*t]] : R[B[i + 56*t]] + R[C[i + 56*t]];
R[i + 95*t] = Op[i + 57*t] ? R[B[i + 57*t]] * R[C[i + 57*t]] : R[B[i + 57*t]] + R[C[i + 57*t]];
R[i + 96*t] = Op[i + 58*t] ? R[B[i + 58*t]] * R[C[i + 58*t]] : R[B[i + 58*t]] + R[C[i + 58*t]];
__syncthreads();
R[i + 97*t] = Op[i + 59*t] ? R[B[i + 59*t]] * R[C[i + 59*t]] : R[B[i + 59*t]] + R[C[i + 59*t]];
R[i + 98*t] = Op[i + 60*t] ? R[B[i + 60*t]] * R[C[i + 60*t]] : R[B[i + 60*t]] + R[C[i + 60*t]];
R[i + 99*t] = Op[i + 61*t] ? R[B[i + 61*t]] * R[C[i + 61*t]] : R[B[i + 61*t]] + R[C[i + 61*t]];
__syncthreads();
R[i + 100*t] = Op[i + 62*t] ? R[B[i + 62*t]] * R[C[i + 62*t]] : R[B[i + 62*t]] + R[C[i + 62*t]];
R[i + 101*t] = Op[i + 63*t] ? R[B[i + 63*t]] * R[C[i + 63*t]] : R[B[i + 63*t]] + R[C[i + 63*t]];
R[i + 102*t] = Op[i + 64*t] ? R[B[i + 64*t]] * R[C[i + 64*t]] : R[B[i + 64*t]] + R[C[i + 64*t]];
__syncthreads();
R[i + 103*t] = Op[i + 65*t] ? R[B[i + 65*t]] * R[C[i + 65*t]] : R[B[i + 65*t]] + R[C[i + 65*t]];
R[i + 104*t] = Op[i + 66*t] ? R[B[i + 66*t]] * R[C[i + 66*t]] : R[B[i + 66*t]] + R[C[i + 66*t]];
__syncthreads();
R[i + 105*t] = Op[i + 67*t] ? R[B[i + 67*t]] * R[C[i + 67*t]] : R[B[i + 67*t]] + R[C[i + 67*t]];
__syncthreads();
R[i + 106*t] = Op[i + 68*t] ? R[B[i + 68*t]] * R[C[i + 68*t]] : R[B[i + 68*t]] + R[C[i + 68*t]];
__syncthreads();
R[i + 107*t] = Op[i + 69*t] ? R[B[i + 69*t]] * R[C[i + 69*t]] : R[B[i + 69*t]] + R[C[i + 69*t]];
__syncthreads();
R[i + 108*t] = Op[i + 70*t] ? R[B[i + 70*t]] * R[C[i + 70*t]] : R[B[i + 70*t]] + R[C[i + 70*t]];
__syncthreads();
R[i + 109*t] = Op[i + 71*t] ? R[B[i + 71*t]] * R[C[i + 71*t]] : R[B[i + 71*t]] + R[C[i + 71*t]];
__syncthreads();
R[i + 110*t] = Op[i + 72*t] ? R[B[i + 72*t]] * R[C[i + 72*t]] : R[B[i + 72*t]] + R[C[i + 72*t]];
__syncthreads();
R[i + 111*t] = Op[i + 73*t] ? R[B[i + 73*t]] * R[C[i + 73*t]] : R[B[i + 73*t]] + R[C[i + 73*t]];
__syncthreads();
R[i + 112*t] = Op[i + 74*t] ? R[B[i + 74*t]] * R[C[i + 74*t]] : R[B[i + 74*t]] + R[C[i + 74*t]];
__syncthreads();
R[i + 113*t] = Op[i + 75*t] ? R[B[i + 75*t]] * R[C[i + 75*t]] : R[B[i + 75*t]] + R[C[i + 75*t]];
if (i==0) { final += R[113*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
19,430 | #include "includes.h"
__global__ void set_with_value_util_kernel( float4 * __restrict buf, float v, int elem_count)
{
int elem_id = blockDim.x * blockIdx.x + threadIdx.x;
if (elem_id < elem_count)
{
float4 val;
val.x = v;
val.y = v;
val.z = v;
val.w = v;
buf[elem_id] = val;
}
} |
19,431 | //=====================================================================
// MAIN FUNCTION
//=====================================================================
void kernel_fin(float *initvalu, int initvalu_offset_ecc, int initvalu_offset_Dyad,
int initvalu_offset_SL, int initvalu_offset_Cyt, float *parameter, float *finavalu,
float JCaDyad, float JCaSL, float JCaCyt) {
//=====================================================================
// VARIABLES
//=====================================================================
// decoded input parameters
float BtotDyad; //
float CaMKIItotDyad; //
// compute variables
float Vmyo; // [L]
float Vdyad; // [L]
float VSL; // [L]
// float kDyadSL; // [L/msec]
float kSLmyo; // [L/msec]
float k0Boff; // [s^-1]
float k0Bon; // [uM^-1 s^-1] kon = koff/Kd
float k2Boff; // [s^-1]
float k2Bon; // [uM^-1 s^-1]
// float k4Boff; // [s^-1]
float k4Bon; // [uM^-1 s^-1]
float CaMtotDyad;
float Bdyad; // [uM dyad]
float J_cam_dyadSL; // [uM/msec dyad]
float J_ca2cam_dyadSL; // [uM/msec dyad]
float J_ca4cam_dyadSL; // [uM/msec dyad]
float J_cam_SLmyo; // [umol/msec]
float J_ca2cam_SLmyo; // [umol/msec]
float J_ca4cam_SLmyo; // [umol/msec]
//=====================================================================
// COMPUTATION
//=====================================================================
// decoded input parameters
BtotDyad = parameter[2]; //
CaMKIItotDyad = parameter[3]; //
// set variables
Vmyo = 2.1454e-11; // [L]
Vdyad = 1.7790e-14; // [L]
VSL = 6.6013e-13; // [L]
// kDyadSL = 3.6363e-16; // [L/msec]
kSLmyo = 8.587e-15; // [L/msec]
k0Boff = 0.0014; // [s^-1]
k0Bon = k0Boff / 0.2; // [uM^-1 s^-1] kon = koff/Kd
k2Boff = k0Boff / 100; // [s^-1]
k2Bon = k0Bon; // [uM^-1 s^-1]
// k4Boff = k2Boff; // [s^-1]
k4Bon = k0Bon; // [uM^-1 s^-1]
// ADJUST ECC incorporate Ca buffering from CaM, convert JCaCyt from uM/msec to mM/msec
finavalu[initvalu_offset_ecc + 35] = finavalu[initvalu_offset_ecc + 35] + 1e-3 * JCaDyad;
finavalu[initvalu_offset_ecc + 36] = finavalu[initvalu_offset_ecc + 36] + 1e-3 * JCaSL;
finavalu[initvalu_offset_ecc + 37] = finavalu[initvalu_offset_ecc + 37] + 1e-3 * JCaCyt;
// incorporate CaM diffusion between compartments
CaMtotDyad = initvalu[initvalu_offset_Dyad + 0] + initvalu[initvalu_offset_Dyad + 1]
+ initvalu[initvalu_offset_Dyad + 2] + initvalu[initvalu_offset_Dyad + 3]
+ initvalu[initvalu_offset_Dyad + 4] + initvalu[initvalu_offset_Dyad + 5]
+ CaMKIItotDyad
* (initvalu[initvalu_offset_Dyad + 6] + initvalu[initvalu_offset_Dyad + 7]
+ initvalu[initvalu_offset_Dyad + 8] + initvalu[initvalu_offset_Dyad + 9])
+ initvalu[initvalu_offset_Dyad + 12] + initvalu[initvalu_offset_Dyad + 13]
+ initvalu[initvalu_offset_Dyad + 14];
Bdyad = BtotDyad - CaMtotDyad; // [uM dyad]
J_cam_dyadSL = 1e-3
* (k0Boff * initvalu[initvalu_offset_Dyad + 0]
- k0Bon * Bdyad * initvalu[initvalu_offset_SL + 0]); // [uM/msec dyad]
J_ca2cam_dyadSL = 1e-3
* (k2Boff * initvalu[initvalu_offset_Dyad + 1]
- k2Bon * Bdyad * initvalu[initvalu_offset_SL + 1]); // [uM/msec dyad]
J_ca4cam_dyadSL = 1e-3
* (k2Boff * initvalu[initvalu_offset_Dyad + 2]
- k4Bon * Bdyad * initvalu[initvalu_offset_SL + 2]); // [uM/msec dyad]
J_cam_SLmyo = kSLmyo * (initvalu[initvalu_offset_SL + 0] - initvalu[initvalu_offset_Cyt + 0]);// [umol/msec]
J_ca2cam_SLmyo = kSLmyo * (initvalu[initvalu_offset_SL + 1] - initvalu[initvalu_offset_Cyt + 1]); // [umol/msec]
J_ca4cam_SLmyo = kSLmyo * (initvalu[initvalu_offset_SL + 2] - initvalu[initvalu_offset_Cyt + 2]); // [umol/msec]
// ADJUST CAM Dyad
finavalu[initvalu_offset_Dyad + 0] = finavalu[initvalu_offset_Dyad + 0] - J_cam_dyadSL;
finavalu[initvalu_offset_Dyad + 1] = finavalu[initvalu_offset_Dyad + 1] - J_ca2cam_dyadSL;
finavalu[initvalu_offset_Dyad + 2] = finavalu[initvalu_offset_Dyad + 2] - J_ca4cam_dyadSL;
// ADJUST CAM Sl
finavalu[initvalu_offset_SL + 0] = finavalu[initvalu_offset_SL + 0] + J_cam_dyadSL * Vdyad / VSL
- J_cam_SLmyo / VSL;
finavalu[initvalu_offset_SL + 1] = finavalu[initvalu_offset_SL + 1]
+ J_ca2cam_dyadSL * Vdyad / VSL - J_ca2cam_SLmyo / VSL;
finavalu[initvalu_offset_SL + 2] = finavalu[initvalu_offset_SL + 2]
+ J_ca4cam_dyadSL * Vdyad / VSL - J_ca4cam_SLmyo / VSL;
// ADJUST CAM Cyt
finavalu[initvalu_offset_Cyt + 0] = finavalu[initvalu_offset_Cyt + 0] + J_cam_SLmyo / Vmyo;
finavalu[initvalu_offset_Cyt + 1] = finavalu[initvalu_offset_Cyt + 1] + J_ca2cam_SLmyo / Vmyo;
finavalu[initvalu_offset_Cyt + 2] = finavalu[initvalu_offset_Cyt + 2] + J_ca4cam_SLmyo / Vmyo;
}
|
19,432 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
__global__ void addElement(int *a,int *b,int *t)
{
int v = threadIdx.y;
int n = v*blockDim.x+threadIdx.x;
t[n] = a[n]+b[n];
}
__global__ void addCol(int *a , int *b , int *t)
{
int lp =0;
int index = threadIdx.x;
for(lp = 0 ;lp<blockDim.x;lp++)
{
t[index] = a[index]+b[index];
index += blockDim.x;
}
}
__global__ void addRow(int *a , int *b , int *t)
{
int lp =0;
int index = threadIdx.x*blockDim.x;
for(lp = 0 ;lp<blockDim.x;lp++)
{
t[index] = a[index] + b[index];
index++;
}
}
int main(void)
{
int *a,*b,*t,n,i,j;
int *d_a,*d_b,*d_t;
printf("Enter the value of n: ");
scanf("%d",&n);
int size = sizeof(int)*n*n;
a = (int*)malloc(n*n*sizeof(int));
b = (int*)malloc(n*n*sizeof(int));
t = (int*)malloc(n*n*sizeof(int));
printf("Enter input matrix 1 : \n");
for(i = 0;i<n*n;i++)
scanf("%d",&a[i]);
printf("Enter input matrix 2 : \n");
for(i = 0;i<n*n;i++)
scanf("%d",&b[i]);
cudaMalloc((void**)&d_a,size);
cudaMalloc((void**)&d_b,size);
cudaMalloc((void**)&d_t,size);
cudaMemcpy(d_a,a,size,cudaMemcpyHostToDevice);
cudaMemcpy(d_b,b,size,cudaMemcpyHostToDevice);
printf("Enter 1 for Row \n 2 for Column \n 3 for Element \n");
int ch;
scanf("%d",&ch);
if(ch == 1)
{
dim3 block(n,1);
dim3 grid(1,1);
addRow<<<grid,block>>>(d_a,d_b,d_t);
}
if(ch == 2)
{
dim3 block(n,1);
dim3 grid(1,1);
addCol<<<grid,block>>>(d_a,d_b,d_t);
}
if(ch == 3)
{
dim3 block(n,n);
dim3 grid(1,1);
addElement<<<grid,block>>>(d_a,d_b,d_t);
}
cudaMemcpy(t,d_t,size,cudaMemcpyDeviceToHost);
printf("Result vector is :\n");
for(i = 0;i<n;i++)
{
for(j = 0;j<n;j++)
printf("%d ",t[i*n+j]);
printf("\n");
}
getchar();
cudaFree(d_a);
cudaFree(d_t);
return 0;
} |
19,433 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define BLOCKS 1024
#define THREADS 1024
#define SIZE BLOCKS*THREADS*16
void print(int *vec){
for(int i = 0; i < SIZE; i++)
printf("%d ", vec[i]);
printf("\n");
}
int *get_vector(int n){
int *res = (int *) malloc(sizeof(int) * n);
for(int i = 0; i < n; i++)
res[i] = 1;
return res;
}
__global__
void add(int *vec1, int *vec2, int times){
const int beg = threadIdx.x + blockIdx.x * blockDim.x;
int i, j;
for(i = beg; i < SIZE; i += blockDim.x * gridDim.x)
for(j = 0; j < times; j++)
vec2[i] = vec2[i] + vec1[i];
//__syncthreads();
}
int main(int argc, char *argv[]){
if(SIZE > 100 * 1024 * 1024); // If over 100 megabytes
printf("Size: %lf Mb\n", SIZE / (double) 1024 / (double) 1024);
int *vec1 = get_vector(SIZE);
int *vec2 = get_vector(SIZE);
void *gpuVec1, *gpuVec2;
cudaMalloc( &gpuVec1, sizeof(int) * SIZE );
cudaMalloc( &gpuVec2, sizeof(int) * SIZE );
cudaMemcpy( gpuVec1, vec1, sizeof(int) * SIZE, cudaMemcpyHostToDevice);
cudaMemcpy( gpuVec2, vec2, sizeof(int) * SIZE, cudaMemcpyHostToDevice);
printf("First element: %d\n", vec2[0]);
add<<<BLOCKS, THREADS>>>((int*) gpuVec1, (int*) gpuVec2, 10000);
cudaMemcpy( vec2, gpuVec2, sizeof(int) * SIZE, cudaMemcpyDeviceToHost);
//print(vec2);
printf("First element: %d\n", vec2[0]);
cudaFree(gpuVec1);
cudaFree(gpuVec2);
free(vec1);
free(vec2);
return 0;
}
|
19,434 | /******************************************************************************
*
* XXXII Heidelberg Physics Graduate Days - GPU Computing
*
* Gruppe : TODO
*
* File : main.cu
*
* Purpose : n-Body Computation
*
******************************************************************************/
#include <cmath>
#include <ctime>
#include <iostream>
#include <cstdlib>
#include <cstdio>
#include <iomanip>
const static int DEFAULT_NUM_ELEMENTS = 1024;
const static int DEFAULT_NUM_ITERATIONS = 5;
const static int DEFAULT_BLOCK_DIM = 128;
const static float TIMESTEP = 1e-6; // s
const static float GAMMA = 6.673e-11; // (Nm^2)/(kg^2)
//
// Structures
//
// Use a SOA (Structure of Arrays)
//
struct Body_t {
float4* posMass; /* x = x */
/* y = y */
/* z = z */
/* w = Mass */
float3* velocity; /* x = v_x*/
/* y = v_y */
/* z= v_z */
Body_t(): posMass(NULL), velocity(NULL) {}
};
//
// Function Prototypes
//
void printHelp(char *);
void printElement(Body_t, int, int);
//
// Device Functions
//
//
// Calculate the Distance of two points
//
__device__ float
getDistance(float4 a, float4 b)
{
// TODO: Calculate distance of two particles
}
//
// Calculate the forces between two bodies
//
__device__ void
bodyBodyInteraction(float4 bodyA, float4 bodyB, float3& force)
{
float distance = getDistance(bodyA, bodyB);
if (distance==0)
return;
// TODO: Calc Force
}
//
// Calculate the new velocity of one particle
//
__device__ void
calculateSpeed(float mass, float3& currentSpeed, float3 force)
{
// TODO: Calculate the new velocity of a particle
}
//
// n-Body Kernel for the speed calculation
//
__global__ void
simpleNbody_Kernel(int numElements, float4* bodyPos, float3* bodySpeed)
{
int elementId = blockIdx.x * blockDim.x + threadIdx.x;
float4 elementPosMass;
float3 elementForce;
float3 elementSpeed;
if (elementId < numElements) {
elementPosMass = bodyPos[elementId];
elementSpeed = bodySpeed[elementId];
elementForce = make_float3(0,0,0);
for (int i = 0; i < numElements; i++) {
if (i != elementId) {
bodyBodyInteraction(elementPosMass, bodyPos[i], elementForce);
}
}
calculateSpeed(elementPosMass.w, elementSpeed, elementForce);
bodySpeed[elementId] = elementSpeed;
}
}
__global__ void
sharedNbody_Kernel(int numElements, float4* bodyPos, float3* bodySpeed)
{
// Use the packed values and SOA to optimize load and store operations
/*TODO Kernel Code*/
}
//
// n-Body Kernel to update the position
// Neended to prevent write-after-read-hazards
//
__global__ void
updatePosition_Kernel(int numElements, float4* bodyPos, float3* bodySpeed)
{
int elementId = blockIdx.x * blockDim.x + threadIdx.x;
float4 elementPosMass;
float3 elementSpeed;
if (elementId < numElements) {
// TODO: Update position of a particle
}
}
//
// Main
//
//int
//main(int argc, char * argv[])
//{
// bool showHelp = chCommandLineGetBool("h", argc, argv);
// if (!showHelp) {
// showHelp = chCommandLineGetBool("help", argc, argv);
// }
//
// if (showHelp) {
// printHelp(argv[0]);
// exit(0);
// }
//
// std::cout << "***" << std::endl
// << "*** Starting ..." << std::endl
// << "***" << std::endl;
//
// ChTimer memCpyH2DTimer, memCpyD2HTimer;
// ChTimer kernelTimer;
//
// //
// // Allocate Memory
// //
// int numElements = 0;
// chCommandLineGet<int>(&numElements, "s", argc, argv);
// chCommandLineGet<int>(&numElements, "size", argc, argv);
// numElements = numElements != 0 ?
// numElements : DEFAULT_NUM_ELEMENTS;
// //
// // Host Memory
// //
// bool pinnedMemory = chCommandLineGetBool("p", argc, argv);
// if (!pinnedMemory) {
// pinnedMemory = chCommandLineGetBool("pinned-memory",argc,argv);
// }
//
// Body_t h_particles;
// if (!pinnedMemory) {
// // Pageable
// h_particles.posMass = static_cast<float4*>
// (malloc(static_cast<size_t>
// (numElements * sizeof(*(h_particles.posMass)))));
// h_particles.velocity = static_cast<float3*>
// (malloc(static_cast<size_t>
// (numElements * sizeof(*(h_particles.velocity)))));
// } else {
// // Pinned
// cudaMallocHost(&(h_particles.posMass),
// static_cast<size_t>
// (numElements * sizeof(*(h_particles.posMass))));
// cudaMallocHost(&(h_particles.velocity),
// static_cast<size_t>
// (numElements * sizeof(*(h_particles.velocity))));
// }
//
// // Init Particles
//// srand(static_cast<unsigned>(time(0)));
// srand(0); // Always the same random numbers
// for (int i = 0; i < numElements; i++) {
// h_particles.posMass[i].x = 1e-8*static_cast<float>(rand()); // Modify the random values to
// h_particles.posMass[i].y = 1e-8*static_cast<float>(rand()); // increase the position changes
// h_particles.posMass[i].z = 1e-8*static_cast<float>(rand()); // and the velocity
// h_particles.posMass[i].w = 1e4*static_cast<float>(rand());
// h_particles.velocity[i].x = 0.0f;
// h_particles.velocity[i].y = 0.0f;
// h_particles.velocity[i].z = 0.0f;
// }
//
// printElement(h_particles, 0, 0);
//
// // Device Memory
// Body_t d_particles;
// cudaMalloc(&(d_particles.posMass),
// static_cast<size_t>(numElements * sizeof(*(d_particles.posMass))));
// cudaMalloc(&(d_particles.velocity),
// static_cast<size_t>(numElements * sizeof(*(d_particles.velocity))));
//
// if (h_particles.posMass == NULL || h_particles.velocity == NULL ||
// d_particles.posMass == NULL || d_particles.velocity == NULL) {
// std::cout << "\033[31m***" << std::endl
// << "*** Error - Memory allocation failed" << std::endl
// << "***\033[0m" << std::endl;
//
// exit(-1);
// }
//
// //
// // Copy Data to the Device
// //
// memCpyH2DTimer.start();
//
// cudaMemcpy(d_particles.posMass, h_particles.posMass,
// static_cast<size_t>(numElements * sizeof(float4)),
// cudaMemcpyHostToDevice);
// cudaMemcpy(d_particles.velocity, h_particles.velocity,
// static_cast<size_t>(numElements * sizeof(float3)),
// cudaMemcpyHostToDevice);
//
// memCpyH2DTimer.stop();
//
// //
// // Get Kernel Launch Parameters
// //
// int blockSize = 0,
// gridSize = 0,
// numIterations = 0;
//
// // Number of Iterations
// chCommandLineGet<int>(&numIterations,"i", argc, argv);
// chCommandLineGet<int>(&numIterations,"num-iterations", argc, argv);
// numIterations = numIterations != 0 ? numIterations : DEFAULT_NUM_ITERATIONS;
//
// // Block Dimension / Threads per Block
// chCommandLineGet<int>(&blockSize,"t", argc, argv);
// chCommandLineGet<int>(&blockSize,"threads-per-block", argc, argv);
// blockSize = blockSize != 0 ? blockSize : DEFAULT_BLOCK_DIM;
//
// if (blockSize > 1024) {
// std::cout << "\033[31m***" << std::endl
// << "*** Error - The number of threads per block is too big" << std::endl
// << "***\033[0m" << std::endl;
//
// exit(-1);
// }
//
// gridSize = ceil(static_cast<float>(numElements) / static_cast<float>(blockSize));
//
// dim3 grid_dim = dim3(gridSize);
// dim3 block_dim = dim3(blockSize);
//
// std::cout << "***" << std::endl;
// std::cout << "*** Grid: " << gridSize << std::endl;
// std::cout << "*** Block: " << blockSize << std::endl;
// std::cout << "***" << std::endl;
//
// kernelTimer.start();
//
// for (int i = 0; i < numIterations; i ++) {
// simpleNbody_Kernel<<<grid_dim, block_dim>>>(numElements, d_particles.posMass,
// d_particles.velocity);
// updatePosition_Kernel<<<grid_dim, block_dim>>>(numElements, d_particles.posMass,
// d_particles.velocity);
//
// cudaMemcpy(h_particles.posMass, d_particles.posMass, sizeof(float4), cudaMemcpyDeviceToHost);
// cudaMemcpy(h_particles.velocity, d_particles.velocity, sizeof(float3), cudaMemcpyDeviceToHost);
// printElement(h_particles, 0, i+1);
// }
//
// // Synchronize
// cudaDeviceSynchronize();
//
// // Check for Errors
// cudaError_t cudaError = cudaGetLastError();
// if ( cudaError != cudaSuccess ) {
// std::cout << "\033[31m***" << std::endl
// << "***ERROR*** " << cudaError << " - " << cudaGetErrorString(cudaError)
// << std::endl
// << "***\033[0m" << std::endl;
//
// return -1;
// }
//
// kernelTimer.stop();
//
// //
// // Copy Back Data
// //
// memCpyD2HTimer.start();
//
// cudaMemcpy(h_particles.posMass, d_particles.posMass,
// static_cast<size_t>(numElements * sizeof(*(h_particles.posMass))),
// cudaMemcpyDeviceToHost);
// cudaMemcpy(h_particles.velocity, d_particles.velocity,
// static_cast<size_t>(numElements * sizeof(*(h_particles.velocity))),
// cudaMemcpyDeviceToHost);
//
// memCpyD2HTimer.stop();
//
// // Free Memory
// if (!pinnedMemory) {
// free(h_particles.posMass);
// free(h_particles.velocity);
// } else {
// cudaFreeHost(h_particles.posMass);
// cudaFreeHost(h_particles.velocity);
// }
//
// cudaFree(d_particles.posMass);
// cudaFree(d_particles.velocity);
//
// // Print Meassurement Results
// std::cout << "***" << std::endl
// << "*** Results:" << std::endl
// << "*** Num Elements: " << numElements << std::endl
// << "*** Time to Copy to Device: " << 1e3 * memCpyH2DTimer.getTime()
// << " ms" << std::endl
// << "*** Copy Bandwidth: "
// << 1e-9 * memCpyH2DTimer.getBandwidth(numElements * sizeof(h_particles))
// << " GB/s" << std::endl
// << "*** Time to Copy from Device: " << 1e3 * memCpyD2HTimer.getTime()
// << " ms" << std::endl
// << "*** Copy Bandwidth: "
// << 1e-9 * memCpyD2HTimer.getBandwidth(numElements * sizeof(h_particles))
// << " GB/s" << std::endl
// << "*** Time for n-Body Computation: " << 1e3 * kernelTimer.getTime()
// << " ms" << std::endl
// << "***" << std::endl;
//
// return 0;
//}
void
printHelp(char * argv)
{
std::cout << "Help:" << std::endl
<< " Usage: " << std::endl
<< " " << argv << " [-p] [-s <num-elements>] [-t <threads_per_block>]"
<< std::endl
<< "" << std::endl
<< " -p|--pinned-memory" << std::endl
<< " Use pinned Memory instead of pageable memory" << std::endl
<< "" << std::endl
<< " -s <num-elements>|--size <num-elements>" << std::endl
<< " Number of elements (particles)" << std::endl
<< "" << std::endl
<< " -t <threads_per_block>|--threads-per-block <threads_per_block>"
<< std::endl
<< " The number of threads per block" << std::endl
<< "" << std::endl;
}
//
// Print one element
//
void
printElement(Body_t particles, int elementId, int iteration)
{
float4 posMass = particles.posMass[elementId];
float3 velocity = particles.velocity[elementId];
std::cout << "***" << std::endl
<< "*** Printing Element " << elementId << " in iteration " << iteration << std::endl
<< "***" << std::endl
<< "*** Position: <"
<< std::setw(11) << std::setprecision(9) << posMass.x << "|"
<< std::setw(11) << std::setprecision(9) << posMass.y << "|"
<< std::setw(11) << std::setprecision(9) << posMass.z << "> [m]" << std::endl
<< "*** velocity: <"
<< std::setw(11) << std::setprecision(9) << velocity.x << "|"
<< std::setw(11) << std::setprecision(9) << velocity.y << "|"
<< std::setw(11) << std::setprecision(9) << velocity.z << "> [m/s]" << std::endl
<< "*** Mass: "
<< std::setw(11) << std::setprecision(9) << posMass.w << " kg"<< std::endl
<< "***" << std::endl;
}
|
19,435 | #include <algorithm>
#include <cassert>
#include <cstdlib>
#include <functional>
#include <iostream>
#include <vector>
#include <chrono>
using namespace std;
const int M = 1 << 3; //8
const int N = 1 << 3;
const int K = 1 << 3;
const int SHMEM_SIZE = 1 << 3; //4
__global__ void matrixMul(const int *a, const int *b, int *c);
void verify_result(vector<int> &a, vector<int> &b, vector<int> &c);
void afficheMatrix(vector<int>& m,int line, int colone);
auto get_time() { return chrono::high_resolution_clock::now(); }
int main()
{
size_t bytes_a = M * K * sizeof(int);
size_t bytes_b = K * N * sizeof(int);
size_t bytes_c = M * N * sizeof(int);
// CPU
vector<int> h_a(M * K);
vector<int> h_b(K * N);
vector<int> h_c(M * N);
generate(h_a.begin(), h_a.end(), []() { return rand() % 100; });
generate(h_b.begin(), h_b.end(), []() { return rand() % 100; });
// GPU
int *d_a, *d_b, *d_c;
cudaMalloc(&d_a, bytes_a);
cudaMalloc(&d_b, bytes_b);
cudaMalloc(&d_c, bytes_c);
// CPU ---> GPU
cudaMemcpy(d_a, h_a.data(), bytes_a, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, h_b.data(), bytes_b, cudaMemcpyHostToDevice);
int THREADS = 1 << 1; //2^1 =2
int BLOCKS_X = N / THREADS; //4
int BLOCKS_Y = M / THREADS; //4
dim3 threads(THREADS, THREADS);
dim3 blocks(BLOCKS_X, BLOCKS_Y);
auto start = get_time();
matrixMul<<<blocks, threads>>>(d_a, d_b, d_c); // <<< (2,2),(4,4) >>>
cudaMemcpy(h_c.data(), d_c, bytes_c, cudaMemcpyDeviceToHost);
auto finish = get_time();
auto duration =
chrono::duration_cast<chrono::milliseconds>(finish - start);
cout << "temps écoulé en kernel = " << duration.count() << " ms\n";
afficheMatrix(h_a,M,N);
afficheMatrix(h_b,M,N);
afficheMatrix(h_c,M,N);
verify_result(h_a, h_b, h_c);
cout << "terminé avec succès"<<endl;
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
return 0;
}
__global__ void matrixMul(const int *a, const int *b, int *c)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
__shared__ int s_a[SHMEM_SIZE];
__shared__ int s_b[SHMEM_SIZE];
int tmp = 0;
for (int i = 0; i < K; i += blockDim.x)
{
s_a[threadIdx.y * blockDim.x + threadIdx.x] = a[row * K + i + threadIdx.x];
s_b[threadIdx.y * blockDim.x + threadIdx.x] = b[i * N + threadIdx.y * N + col];
__syncthreads();
for (int j = 0; j < blockDim.x; j++)
tmp += s_a[threadIdx.y * blockDim.x + j] * s_b[j * blockDim.x + threadIdx.x];
__syncthreads();
}
c[row * N + col] = tmp;
}
void verify_result(vector<int> &a, vector<int> &b, vector<int> &c)
{
for (int row = 0; row < M; row++)
for (int col = 0; col < N; col++)
{
int tmp = 0;
for (int i = 0; i < K; i++)
tmp += a[row * K + i] * b[i * N + col];
assert(tmp == c[row * N + col]);
}
}
void afficheMatrix(vector<int>& m,int line, int colone)
{
for (int i = 0; i <line; i++)
{
for (int j = 0; j < colone; j++)
{
cout<<m[i]<<" ";
}
cout<<endl;
}
cout<<"\n_______________________________________"<<endl;
}
|
19,436 | // Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2022 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: joydeepb@cs.utexas.edu (Joydeep Biswas)
#include "cuda_runtime.h"
namespace ceres::internal {
// As the CUDA Toolkit documentation says, "although arbitrary in this case, is
// a common choice". This is determined by the warp size, max block size, and
// multiprocessor sizes of recent GPUs. For complex kernels with significant
// register usage and unusual memory patterns, the occupancy calculator API
// might provide better performance. See "Occupancy Calculator" under the CUDA
// toolkit documentation.
constexpr int kCudaBlockSize = 256;
template<typename SrcType, typename DstType>
__global__ void TypeConversionKernel(const SrcType* __restrict__ input,
DstType* __restrict__ output,
const int size) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < size) {
output[i] = static_cast<DstType>(input[i]);
}
}
void CudaFP64ToFP32(const double* input,
float* output,
const int size,
cudaStream_t stream) {
const int num_blocks = (size + kCudaBlockSize - 1) / kCudaBlockSize;
TypeConversionKernel<double, float>
<<<num_blocks, kCudaBlockSize, 0, stream>>>(input, output, size);
}
void CudaFP32ToFP64(const float* input,
double* output,
const int size,
cudaStream_t stream) {
const int num_blocks = (size + kCudaBlockSize - 1) / kCudaBlockSize;
TypeConversionKernel<float, double>
<<<num_blocks, kCudaBlockSize, 0, stream>>>(input, output, size);
}
template<typename T>
__global__ void SetZeroKernel(T* __restrict__ output, const int size) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < size) {
output[i] = T(0.0);
}
}
void CudaSetZeroFP32(float* output, const int size, cudaStream_t stream) {
const int num_blocks = (size + kCudaBlockSize - 1) / kCudaBlockSize;
SetZeroKernel<float><<<num_blocks, kCudaBlockSize, 0, stream>>>(output, size);
}
void CudaSetZeroFP64(double* output, const int size, cudaStream_t stream) {
const int num_blocks = (size + kCudaBlockSize - 1) / kCudaBlockSize;
SetZeroKernel<double><<<num_blocks, kCudaBlockSize, 0, stream>>>(
output, size);
}
template <typename SrcType, typename DstType>
__global__ void XPlusEqualsYKernel(DstType* __restrict__ x,
const SrcType* __restrict__ y,
const int size) {
const int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < size) {
x[i] = x[i] + DstType(y[i]);
}
}
void CudaDsxpy(double* x,
float* y,
const int size,
cudaStream_t stream) {
const int num_blocks = (size + kCudaBlockSize - 1) / kCudaBlockSize;
XPlusEqualsYKernel<float, double>
<<<num_blocks, kCudaBlockSize, 0, stream>>>(x, y, size);
}
} // namespace ceres_cuda_kernels |
19,437 | /*
compile
$ nvcc -o matrix_elementwise matrix_elementwise.cu
elementwise multiplication and subtraction
numpy version
import numpy as np
m1 = np.array(((0, 1, 2), (3, 4, 5), (6, 7, 8)))
m2 = np.array(((8, 7, 6), (5, 4, 3), (2, 1, 0)))
m1*m2 # or np.multiply(m1, m2)
m1-m2
*/
#include <stdio.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
// kernel of elementwise multiplication of 2 arrays
__global__
void kMatrixByMatrixElementwise(const int nThreads, const float *m1, const float *m2, float *output) {
/* Computes the product of two arrays (elementwise multiplication).
Inputs:
m1: array
m2: array
output: array,the results of the multiplication are to be stored here
*/
for (int i = blockIdx.x * blockDim.x + threadIdx.x;
i < nThreads;
i += blockDim.x * gridDim.x)
{
output[i] = m1[i] * m2[i];
}
}
// elementwise multiplication of 2 arrays
float* dMatrixByMatrixElementwise(const float *m1, const float *m2, float *output, const int width, const int height){
kMatrixByMatrixElementwise <<< width, height >>> ( width * height, m1, m2, output );
cudaDeviceSynchronize();
return output;
}
// kernel elementwise difference of 2 arrays
__global__
void kMatrixSubstractMatrix(const int nThreads, const float *m1, const float *m2, float *output) {
/* Computes the (elementwise) difference between two arrays
Inputs:
m1: array
m2: array
output: array,the results of the computation are to be stored here
*/
for (int i = blockIdx.x * blockDim.x + threadIdx.x;
i < nThreads;
i += blockDim.x * gridDim.x)
{
output[i] = m1[i] - m2[i];
}
}
// elementwise difference of 2 arrays
float* dMatrixSubstractMatrix(const float *m1, const float *m2, float *output, const int width, const int height){
kMatrixSubstractMatrix <<< width, height >>> ( width * height, m1, m2, output );
cudaDeviceSynchronize();
return output;
}
int main(void)
{
// host initialization
const int M_SIZE = 9; // 3x3 matrix
const int M_BYTES = M_SIZE * sizeof(float);
float h_m1[M_SIZE], h_m2[M_SIZE], h_out[M_SIZE];
for (int i = 0; i < M_SIZE; i++)
{
h_m1[i] = float(i); // 0, 1, .. 8
h_m2[i] = float(M_SIZE - 1 - i);
}
// GPU memory allocation and initialization
float *d_m1, *d_m2, *d_out;
cudaMalloc((void**) &d_m1, M_BYTES);
cudaMalloc((void**) &d_m2, M_BYTES);
cudaMalloc((void**) &d_out, M_BYTES);
cudaMemcpy(d_m1, h_m1, M_BYTES, cudaMemcpyHostToDevice);
cudaMemcpy(d_m2, h_m2, M_BYTES, cudaMemcpyHostToDevice);
// elementwise subtraction
dMatrixSubstractMatrix(d_m1, d_m2, d_out, 3, 3);
cudaMemcpy(h_out, d_out, M_BYTES, cudaMemcpyDeviceToHost);
// print result
printf("elementwise subtraction\n");
for (int i = 0; i < M_SIZE; i++)
{
printf("h_out[%d] = %f\n", i, h_out[i]);
}
// elementwise multiplication
dMatrixByMatrixElementwise(d_m1, d_m2, d_out, 3, 3);
cudaMemcpy(h_out, d_out, M_BYTES, cudaMemcpyDeviceToHost);
// print result
printf("elementwise multiplication\n");
for (int i = 0; i < M_SIZE; i++)
{
printf("h_out[%d] = %f\n", i, h_out[i]);
}
// free memory
cudaFree(d_m1);
cudaFree(d_m2);
cudaFree(d_out);
// free(h_m1);
// free(h_m2);
// free(h_out);
} |
19,438 | /*
Mnozenie macierzy CUDA,
Jakub Ciechowski GPU 2012
*/
#include <cuda.h>
#include <stdio.h>
#include <stdlib.h>
#define TILE_WIDTH 2
__global__ void sharedMul(int *M, int *N, int *P, int width) {
__shared__ int Ms[TILE_WIDTH][TILE_WIDTH];
__shared__ int Ns[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * TILE_WIDTH + ty;
int col = bx * TILE_WIDTH + tx;
int sum = 0;
for(int m = 0; m < width/TILE_WIDTH; m++) {
Ms[ty][tx] = M[row*width + (m*TILE_WIDTH + tx)];
Ns[ty][tx] = N[(m*TILE_WIDTH + ty)*width + col];
__syncthreads();
for(int k = 0; k < TILE_WIDTH; k++) {
sum += Ms[m+ty][k] * Ns[k][m+tx];
}
__syncthreads();
}
P[row*width+col] = sum;
}
// __global__ void naiveMul(int* M, int* N, int* P, int width) {
// // TODO
// // mnozenie macierzy powyzej 512 elementow
// int tx = threadIdx.x;
// int ty = threadIdx.y;
// int sum = 0;
// sum += M[ty*width+k] * N[k*width+tx];
// P[ty*width+tx] = sum;
// }
void printMat(int *a, int width) {
int i,j;
for(i=0;i<width;i++)
for(j=0;j<width;j++)
printf("%d%c",a[i*width+j],(j == (width-1))?'\n':'\t');
printf("\n");
}
int* genMatrix(int width) {
int *a = (int*)calloc(width*width, sizeof(int));
int i,j;
for(i=0;i<width;i++)
for(j=0;j<width;j++)
a[i*width+j] = rand()%10;
return a;
}
int *matMul(int *hostA, int *hostB, int width) {
int SIZE = width*width;
int *hostC = (int*)calloc(SIZE,sizeof(int));
int *devA, *devB, *devC;
cudaMalloc((void**) &devA, SIZE*sizeof(int));
cudaMalloc((void**) &devB, SIZE*sizeof(int));
cudaMalloc((void**) &devC, SIZE*sizeof(int));
dim3 gridDim(width/TILE_WIDTH, width/TILE_WIDTH);
dim3 blockDim(TILE_WIDTH,TILE_WIDTH);
// dim3 blockDim(width,width);
// dim3 gridDim(1,1);
cudaMemcpy(devA, hostA, SIZE*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(devB, hostB, SIZE*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(devC, hostC, SIZE*sizeof(int), cudaMemcpyHostToDevice);
sharedMul<<<gridDim,blockDim>>>(devA, devB, devC, width);
// naiveMul<<<gridDim,blockDim>>>(devA, devB, devC, width);
cudaMemcpy(hostC, devC, SIZE*sizeof(int), cudaMemcpyDeviceToHost);
return hostC;
}
int main(int argc, char** argv) {
int m = 4;
int *A;
int *B;
int *C;
if(argc > 1) {
m = atoi(argv[1]);
}
A = genMatrix(m);
printMat(A,m);
B = genMatrix(m);
printMat(B,m);
C = matMul(A,B,m);
printf("TILE = %d\n",TILE_WIDTH);
printMat(C,m);
return 0;
}
|
19,439 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <inttypes.h>
#define ErrorCheck(ans) { CheckFun((ans), __FILE__, __LINE__); }
inline void CheckFun(cudaError_t code, const char *file, int line) {
if (code != cudaSuccess) {
fprintf(stderr, "ERROR: %s %s %d\n", cudaGetErrorString(code), file, line);
exit(0);
}
}
__constant__ double3 centerClusters[32];
__device__ inline double calculateDistance(uchar4 &A, double3 &B) {
return sqrt((double)(A.x - B.x)*(A.x - B.x) + (A.y - B.y)*(A.y - B.y) + (A.z - B.z)*(A.z - B.z));
}
__global__ void KMeans(uchar4 * __restrict__ img, const uint32_t w, const uint32_t h, const uint32_t nc) {
uint32_t idx = threadIdx.x + blockIdx.x * blockDim.x;
uint32_t idy = threadIdx.y + blockIdx.y * blockDim.y;
uint32_t offsetx = blockDim.x * gridDim.x;
uint32_t offsety = blockDim.y * gridDim.y;
for (uint32_t i = idx; i < w; i += offsetx) {
for (uint32_t j = idy; j < h; j += offsety) {
double distanceMin = calculateDistance(img[j * w + i], centerClusters[0]);
uint32_t clusterNumber = 0;
for (uint32_t k = 1; k < nc; ++k) {
double distanceTmp = calculateDistance(img[j * w + i], centerClusters[k]);
if (distanceTmp < distanceMin) {
distanceMin = distanceTmp;
clusterNumber = k;
}
}
img[j * w + i].w = clusterNumber;
}
}
}
__host__ bool updateClusters(uchar4 * __restrict__ img, uchar4 * __restrict__ imgNew,
double3 * __restrict__ centerClustersHost, const uint32_t w, const uint32_t h, const uint32_t nc) {
uint64_t countElementOnCluster[32] = { 0 };
ulonglong3 sumElementOnCluster[32] = { make_ulonglong3(0, 0, 0) };
bool notEqual = false;
for (uint32_t i = 0; i < w*h; ++i) {
if (imgNew[i].w != img[i].w) notEqual = true;
countElementOnCluster[imgNew[i].w]++;
sumElementOnCluster[imgNew[i].w].x += imgNew[i].x;
sumElementOnCluster[imgNew[i].w].y += imgNew[i].y;
sumElementOnCluster[imgNew[i].w].z += imgNew[i].z;
}
for (uint32_t i = 0; i < nc; ++i) {
centerClustersHost[i].x = (double)sumElementOnCluster[i].x / (double)countElementOnCluster[i];
centerClustersHost[i].y = (double)sumElementOnCluster[i].y / (double)countElementOnCluster[i];
centerClustersHost[i].z = (double)sumElementOnCluster[i].z / (double)countElementOnCluster[i];
}
return notEqual;
}
__host__ void KMeans(uchar4 * __restrict__ img, uchar4 * __restrict__ imgNew,
double3 * __restrict__ centerClustersHost, const uint32_t w, const uint32_t h, const uint32_t nc) {
uchar4 *imgDev;
ErrorCheck(cudaMalloc(&imgDev, sizeof(uchar4) * w * h));
ErrorCheck(cudaMemcpy(imgDev, img, sizeof(uchar4) * w * h, cudaMemcpyHostToDevice));
bool flag = true;
while (flag) {
ErrorCheck(cudaMemcpyToSymbol(centerClusters, centerClustersHost, sizeof(double3) * 32));
KMeans <<<dim3(32,32), dim3(32, 32)>>> (imgDev, w, h, nc);
ErrorCheck(cudaGetLastError());
ErrorCheck(cudaMemcpy(imgNew, imgDev, sizeof(uchar4) * w * h, cudaMemcpyDeviceToHost));
flag = updateClusters(img, imgNew, centerClustersHost, w, h, nc);
uchar4 * imgTmp = imgNew;
imgNew = img;
img = imgTmp;
}
ErrorCheck(cudaFree(imgDev));
}
int main() {
char inputFileName[256], outFileName[256];
uint32_t w, h, nc;
double3 centerClustersHost[32];
scanf("%s", inputFileName);
scanf("%s", outFileName);
FILE *hFile = fopen(inputFileName, "rb");
fread(&w, sizeof(uint32_t), 1, hFile);
fread(&h, sizeof(uint32_t), 1, hFile);
uchar4 *img = (uchar4*)malloc(sizeof(uchar4) * h * w);
uchar4 *imgNew = (uchar4*)malloc(sizeof(uchar4) * w * h);
fread(img, sizeof(uchar4), h * w, hFile);
fclose(hFile);
scanf("%" SCNu32, &nc);
for (uint32_t i = 0; i < nc; ++i) {
int x, y;
scanf("%" SCNu32 "%" SCNu32, &x, &y);
centerClustersHost[i].x = img[w * y + x].x;
centerClustersHost[i].y = img[w * y + x].y;
centerClustersHost[i].z = img[w * y + x].z;
}
KMeans(img,imgNew, centerClustersHost, w, h, nc);
hFile = fopen(outFileName, "wb");
fwrite(&w, sizeof(uint32_t), 1, hFile);
fwrite(&h, sizeof(uint32_t), 1, hFile);
fwrite(img, sizeof(uchar4), w * h, hFile);
fclose(hFile);
free(imgNew);
free(img);
return 0;
}
|
19,440 | extern "C"
{
__global__ void binaryentropy_32(const int lengthX, const float *x, const float *y, float *z)
{
int i = threadIdx.x + blockIdx.x * blockDim.x;
if (i<lengthX)
{
z[i] = x[i]*log(x[i]/y[i])+ (1.0-x[i])*log((1.0-x[i])/(1.0-y[i]));
}
}
} |
19,441 | #include <cuda.h>
#include <math.h>
#include <iostream>
using namespace std;
__global__
void convStandard(uint32_t* d_MATDIM, uint32_t* d_KERDIM, double* mat, double* ker, double* res) {
uint32_t MATDIM = d_MATDIM[0];
uint32_t KERDIM = d_KERDIM[0];
uint32_t threadID = blockIdx.x * blockDim.x + threadIdx.x;
if (threadID < (MATDIM-KERDIM+1)*(MATDIM-KERDIM+1)) {
uint32_t index = KERDIM/2 + (KERDIM/2) * MATDIM + (threadID / (MATDIM-KERDIM+1)) * MATDIM + threadID % (MATDIM-KERDIM+1);
double sum = 0.0;
for(int i = -((int32_t) KERDIM)/2; i < ((int32_t) KERDIM)/2+1; i++){
for(int j = -((int32_t) KERDIM)/2; j < ((int32_t) KERDIM)/2+1; j++){
sum += mat[index + i*MATDIM + j] * ker[(i+KERDIM/2) * KERDIM + (j+KERDIM/2)];
}
}
res[threadID] = sum;
}
}
__global__
void convBinW(uint32_t* d_MATDIM, uint32_t* d_KERDIM, double* mat, unsigned char* ker, double* res) {
uint32_t MATDIM = d_MATDIM[0];
uint32_t KERDIM = d_KERDIM[0];
uint32_t threadID = blockIdx.x * blockDim.x + threadIdx.x;
if (threadID < (MATDIM-KERDIM+1)*(MATDIM-KERDIM+1)) {
uint32_t index_mat = KERDIM/2 + (KERDIM/2) * MATDIM + (threadID / (MATDIM-KERDIM+1)) * MATDIM + threadID % (MATDIM-KERDIM+1);
double sum = 0.0;
for(int i = -((int32_t) KERDIM)/2; i < ((int32_t) KERDIM)/2+1; i++){
for(int j = -((int32_t) KERDIM)/2; j < ((int32_t) KERDIM)/2+1; j++){
uint32_t index_ker = (i+KERDIM/2) * KERDIM + (j+KERDIM/2);
if ((unsigned char)((unsigned char)(ker[index_ker/8] << (index_ker % 8)) >> 7) == 1)
sum += mat[index_mat + i*MATDIM + j];
else
sum -= mat[index_mat + i*MATDIM + j];
}
}
res[threadID] = sum;
}
}
__global__
void convBinWBinI(uint32_t* d_MATDIM, uint32_t* d_KERDIM, unsigned char* mat, unsigned char* ker, unsigned char* res) {
uint32_t MATDIM = d_MATDIM[0];
uint32_t KERDIM = d_KERDIM[0];
uint32_t xnor_number = 0;
unsigned char bit_counter = 0;
unsigned char pop_count = 0;
uint32_t threadID = blockIdx.x * blockDim.x + threadIdx.x;
if (threadID < (MATDIM-KERDIM+1)*(MATDIM-KERDIM+1)) {
uint32_t index_mat = KERDIM/2 + (KERDIM/2) * MATDIM + (threadID / (MATDIM-KERDIM+1)) * MATDIM + threadID % (MATDIM-KERDIM+1);
for(int i = -((int32_t) KERDIM)/2; i < ((int32_t) KERDIM)/2+1; i++){
for(int j = -((int32_t) KERDIM)/2; j < ((int32_t) KERDIM)/2+1; j++){
uint32_t index_ker = (i+(KERDIM >> 1)) * KERDIM + (j+(KERDIM >> 1));
uint32_t index_mat_bin = index_mat + i*MATDIM + j;
if (bit_counter == 32) {
pop_count += (unsigned char) __popc((unsigned int) xnor_number);
bit_counter = 0;
xnor_number = 0;
} else {
if (((ker[index_ker >> 3] << (index_ker % 8)) >> 7) == ((mat[index_mat_bin >> 3] << (index_mat_bin % 8)) >> 7))
xnor_number |= 1 << bit_counter;
bit_counter++;
}
}
}
pop_count += (unsigned char) __popc((unsigned int) xnor_number);
res[threadID] = pop_count;
}
}
void initMat(uint32_t dim, double* mat) {
for (uint32_t i = 0; i < dim; i++) {
for (uint32_t j = 0; j < dim; j++) {
mat[i*dim+j] = (double) rand() / RAND_MAX * 2.0 - 1.0;
}
}
}
void convertToBinary(uint32_t dim, double* mat, uint32_t size_bin, unsigned char* mat_bin) {
uint32_t pos_bit = 0;
uint32_t pos_byte = 0;
unsigned char sum = 0;
for (uint32_t i = 0; i < dim; i++) {
for (uint32_t j = 0; j < dim; j++) {
if (mat[i*dim+j] >= 0) {
sum += pow(2, 7-pos_bit);
}
if (pos_bit == 7) {
mat_bin[pos_byte] = sum;
pos_byte++;
sum = 0;
pos_bit = 0;
} else {
pos_bit++;
}
}
}
if (dim*dim % 8 != 0) {
mat_bin[pos_byte] = sum;
}
}
void printMatrix(uint32_t dim, double* mat) {
cout << "dim: " << dim << "x" << dim << "\n{\n";
for (uint32_t i = 0; i < dim; i++) {
for (uint32_t j = 0; j < dim; j++) {
cout << mat[i*dim+j] << ", ";
}
cout << '\n';
}
cout << "}\n";
}
void printBinary(uint32_t dim, uint32_t size_bin, unsigned char* mat) {
unsigned char rest;
for (uint32_t i = 0; i < size_bin; i++) {
rest = mat[i];
for (uint32_t j = 0; j < 8; j++) {
if (i * 8 + j == dim*dim) {
cout << "\n";
break;
}
if(rest - pow(2,7-j) >= 0) {
rest = rest - pow(2,7-j);
cout << "1 ";
} else {
cout << "0 ";
}
if((i * 8 + j + 1) % dim == 0) {
cout << "\n";
}
}
}
}
int main(int argc, char* argv[]) {
if (argc != 4) {
cout << "Usage: srun out <int: dimension of input matrix> <int: dimension of kernel> <blocksize>\n";
return 0;
}
uint32_t MATDIM = strtol(argv[1], NULL, 10);
uint32_t KERDIM = strtol(argv[2], NULL, 10);
uint32_t N = strtol(argv[3], NULL, 10);
uint32_t h_MATDIM[1];
h_MATDIM[0] = MATDIM;
uint32_t h_KERDIM[1];
h_KERDIM[0] = KERDIM;
struct timespec tstart={0,0}, tend={0,0};
double elapsed;
// Matrix (double)
double h_mat[MATDIM*MATDIM];
// Kernel (double)
double h_ker[KERDIM*KERDIM];
// Matrix (bits)
unsigned char h_mat_bin[(uint32_t) ceil(MATDIM*MATDIM/8.0)];
// Kernel (bits)
unsigned char h_ker_bin[(uint32_t) ceil(KERDIM*KERDIM/8.0)];
// Result of standard convolution
double h_res_standard[(MATDIM-KERDIM+1)*(MATDIM-KERDIM+1)];
// Result of convolution with binary weights
double h_res_binW[(MATDIM-KERDIM+1)*(MATDIM-KERDIM+1)];
// Result of convolution with binary weights and binary inputs
unsigned char h_res_binWbinI[(MATDIM-KERDIM+1)*(MATDIM-KERDIM+1)];
uint32_t mat_size = MATDIM*MATDIM * sizeof(double);
uint32_t ker_size = KERDIM*KERDIM * sizeof(double);
uint32_t mat_bin_size = (uint32_t) ceil(MATDIM*MATDIM/8.0) * sizeof(unsigned char);
uint32_t ker_bin_size = (uint32_t) ceil(KERDIM*KERDIM/8.0) * sizeof(unsigned char);
uint32_t res_standard_size = (MATDIM-KERDIM+1)*(MATDIM-KERDIM+1) * sizeof(double);
uint32_t res_binW_size = (MATDIM-KERDIM+1)*(MATDIM-KERDIM+1) * sizeof(double);
uint32_t res_binWbinI_size = (MATDIM-KERDIM+1)*(MATDIM-KERDIM+1) * sizeof(unsigned char);
// Pointers for allocation on device
uint32_t *d_MATDIM, *d_KERDIM;
double *d_mat, *d_ker, *d_res_standard, *d_res_binW;
unsigned char *d_mat_bin, *d_ker_bin, *d_res_binWbinI;
// Allocate all matrices on device (cudaFree later!)
cudaMalloc((void**) &d_mat, mat_size);
cudaMalloc((void**) &d_ker, ker_size);
cudaMalloc((void**) &d_mat_bin, mat_bin_size);
cudaMalloc((void**) &d_ker_bin, ker_bin_size);
cudaMalloc((void**) &d_res_standard, res_standard_size);
cudaMalloc((void**) &d_res_binW, res_binW_size);
cudaMalloc((void**) &d_res_binWbinI, res_binWbinI_size);
cudaMalloc((void**) &d_MATDIM, sizeof(uint32_t));
cudaMalloc((void**) &d_KERDIM, sizeof(uint32_t));
// Seed for random number generation
srand(time(NULL));
// Randomize the values of the double matrix with values -1.0 ... 1.0
initMat(MATDIM, h_mat);
// Convert the double matrix into binary (0 = -1, 1 = 1)
convertToBinary(MATDIM, h_mat, (uint32_t) ceil(MATDIM*MATDIM/8.0), h_mat_bin);
// TODO DEBUG: Print the double matrix.
printMatrix(MATDIM, h_mat);
// TODO DEBUG: Print the binary matrix.
printBinary(MATDIM, (uint32_t) ceil(MATDIM*MATDIM/8.0), h_mat_bin);
initMat(KERDIM, h_ker);
// Convert the double matrix into binary
convertToBinary(KERDIM, h_ker, (uint32_t) ceil(KERDIM*KERDIM/8.0), h_ker_bin);
// TODO DEBUG: Print the double matrix.
printMatrix(KERDIM, h_ker);
// TODO DEBUG: Print the binary matrix.
printBinary(KERDIM, (uint32_t) ceil(KERDIM*KERDIM/8.0), h_ker_bin);
// Copy all the matrices to the device (except the result matrices)
cudaMemcpy(d_mat, h_mat, mat_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_ker, h_ker, ker_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_mat_bin, h_mat_bin, mat_bin_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_ker_bin, h_ker_bin, ker_bin_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_MATDIM, h_MATDIM, sizeof(uint32_t), cudaMemcpyHostToDevice);
cudaMemcpy(d_KERDIM, h_KERDIM, sizeof(uint32_t), cudaMemcpyHostToDevice);
uint32_t grid_size = ceil((MATDIM-KERDIM+1) * (MATDIM-KERDIM+1) / ((double) N));
// Compute the different modes of convolution
clock_gettime(CLOCK_MONOTONIC, &tstart);
convStandard<<<grid_size, N>>>(d_MATDIM, d_KERDIM, d_mat, d_ker, d_res_standard);
clock_gettime(CLOCK_MONOTONIC, &tend);
elapsed = ((double)tend.tv_sec + 1.0e-9*tend.tv_nsec) - ((double)tstart.tv_sec + 1.0e-9*tstart.tv_nsec);
cout << "Standard convolution took " << elapsed << " seconds.\n";
clock_gettime(CLOCK_MONOTONIC, &tstart);
convBinW<<<grid_size, N>>>(d_MATDIM, d_KERDIM, d_mat, d_ker_bin, d_res_binW);
clock_gettime(CLOCK_MONOTONIC, &tend);
elapsed = ((double)tend.tv_sec + 1.0e-9*tend.tv_nsec) - ((double)tstart.tv_sec + 1.0e-9*tstart.tv_nsec);
cout << "Binary weights took " << elapsed << " nanoseconds.\n";
clock_gettime(CLOCK_MONOTONIC, &tstart);
convBinWBinI<<<grid_size, N>>>(d_MATDIM, d_KERDIM, d_mat_bin, d_ker_bin, d_res_binWbinI);
clock_gettime(CLOCK_MONOTONIC, &tend);
elapsed = ((double)tend.tv_sec + 1.0e-9*tend.tv_nsec) - ((double)tstart.tv_sec + 1.0e-9*tstart.tv_nsec);
cout << "Binary inputs and binary weights took " << elapsed << " nanoseconds.\n";
cout << elapsed << "\n";
// Fetch the results from device
cudaMemcpy(h_res_standard, d_res_standard, res_standard_size, cudaMemcpyDeviceToHost);
cudaMemcpy(h_res_binW, d_res_binW, res_binW_size, cudaMemcpyDeviceToHost);
cudaMemcpy(h_res_binWbinI, d_res_binWbinI, res_binWbinI_size, cudaMemcpyDeviceToHost);
// TODO DEBUG: Print the results
cout << "Standard convolution DOUBLExDOUBLE\n";
printMatrix(MATDIM-KERDIM+1, h_res_standard);
cout << "Binary weight convolution DOUBLExBITS\n";
printMatrix(MATDIM-KERDIM+1, h_res_binW);
cout << "Binary weights and binary inputs BITSxBITS\n";
printMatrix(MATDIM-KERDIM+1, (double*) h_res_binWbinI);
cout << "dim: " << MATDIM-KERDIM+1 << "x" << MATDIM-KERDIM+1 << "\n{\n";
for (uint32_t i = 0; i < MATDIM-KERDIM+1; i++) {
for (uint32_t j = 0; j < MATDIM-KERDIM+1; j++) {
cout << (uint32_t) h_res_binWbinI[i*(MATDIM-KERDIM+1)+j] << ", ";
}
cout << '\n';
}
cout << "}\n";
cudaFree(d_mat);
cudaFree(d_ker);
cudaFree(d_mat_bin);
cudaFree(d_ker_bin);
cudaFree(d_res_standard);
cudaFree(d_res_binW);
cudaFree(d_res_binWbinI);
cudaFree(d_MATDIM);
cudaFree(d_KERDIM);
return 0;
}
|
19,442 | #include<stdio.h>
#include<stdlib.h>
#include<sys/time.h>
#define NUM 32
#define CUDA_ERROR_EXIT(str) do{\
cudaError err = cudaGetLastError();\
if( err != cudaSuccess){\
printf("Cuda Error: '%s' for %s\n", cudaGetErrorString(err), str);\
exit(-1);\
}\
}while(0);
__global__ void D_Mul(int *dA, int *dB, int *dC)
{
int i = threadIdx.x;
// int i = blockIdx.x * blockDim.x + threadIdx.x;
dC[i] = dA[i] * dB[i];
}
int main(int argc, char **argv)
{
int ctr;
int *hA, *hB, *hC;
int *dA, *dB, *dC;
int size = NUM * sizeof(int);
/*Allocate memory on the host (CPU) */
hA = (int *) malloc(size);
if(!hA){
perror("malloc");
exit(-1);
}
hB = (int *) malloc(size);
if(!hB){
perror("malloc");
exit(-1);
}
hC = (int *) malloc(size);
if(!hC){
perror("malloc");
exit(-1);
}
/*Initialize hA and hB*/
for(ctr=0; ctr < NUM; ++ctr)
hA[ctr] = hB[ctr] = ctr+1;
/*Allocate memory on the device (GPU) */
cudaMalloc(&dA, size);
CUDA_ERROR_EXIT("cudaMalloc");
cudaMalloc(&dB, size);
CUDA_ERROR_EXIT("cudaMalloc");
cudaMalloc(&dC, size);
CUDA_ERROR_EXIT("cudaMalloc");
/*Copy hA --> dA and hB --> dB */
cudaMemcpy(dA, hA, size, cudaMemcpyHostToDevice);
CUDA_ERROR_EXIT("memcpy1");
cudaMemcpy(dB, hB, size, cudaMemcpyHostToDevice);
CUDA_ERROR_EXIT("memcpy1");
/*Invoke the kernel*/
D_Mul<<<1, NUM>>>(dA, dB, dC);
//int blocks = (NUM + 1023) >> 10;
//D_Mul<<<blocks, 1024>>>(dA, dB, dC);
CUDA_ERROR_EXIT("kernel invocation");
printf("kernel successful\n");
/*Copy back results*/
cudaMemcpy(hC, dC, size, cudaMemcpyDeviceToHost);
CUDA_ERROR_EXIT("memcpy");
for(ctr=0; ctr < NUM; ++ctr)
printf("%d %d %d\n", hA[ctr], hB[ctr], hC[ctr]);
free(hA);
free(hB);
free(hC);
cudaFree(dA);
cudaFree(dB);
cudaFree(dC);
}
|
19,443 | #include <device_launch_parameters.h>
#include <cstdio>
extern "C" {
// PUT YOUR KERNEL FUNCTION HERE
__global__ void bfs_visit_next(
int* adjacencyList,
int* edgesOffset,
int* edgesSize,
int* distance,
int* parent,
int* currentQueue,
int* nextQueue,
int queueSize,
int* nextQueueSize,
int* degrees,
int* incrDegrees
)
{
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < queueSize)
{
int v = currentQueue[tid];
for (int i = edgesOffset[v]; i < edgesOffset[v] + edgesSize[v]; i++)
{
int u = adjacencyList[i];
if (parent[u] == -1) // Not visited
{ // Visit
parent[u] = v;
distance[u] = distance[v] + 1;
int pos = atomicAdd(nextQueueSize, 1);
nextQueue[pos] = u;
}
}
}
}
}
|
19,444 | #include <iostream>
#include "../include/gpu_queue.h"
#include <thrust/device_vector.h>
#define def_dvec(t) thrust::device_vector<t>
#define to_ptr(x) thrust::raw_pointer_cast(&x[0])
using namespace std;
const int MAX_QUEUE_SIZE = 50;
__global__ void test(float *output){
gpu_queue<float, MAX_QUEUE_SIZE> que;
for(int i=1;i<=MAX_QUEUE_SIZE;++i){
que.push(1.7*i);
}
int idx = 0, k = 0;
while(!que.empty()){
que.pop_k(k);
if(que.empty()) return;
output[idx] = que.front();
idx += 1;
output[idx] = que.back();
k += 1;
idx += 1;
}
}
int main(){
def_dvec(float) dev_out(40, 0);
test<<<1, 1>>>(to_ptr(dev_out));
for(auto k:dev_out) cout<<k<<' ';
cout<<endl;
return 0;
}
|
19,445 | #include <iostream>
#include <chrono>
void Run(int particlesCount, int blockSize, float3 *hostPositions, float deltaTime, float softeningRate, int iterationsCount, bool validate = false);
float3 *GenerateParticles(int particlesCount) {
auto *positions = new float3[particlesCount];
for (int i = 0; i < particlesCount; i++) {
positions[i].x = static_cast <float> (rand()) / static_cast <float> (RAND_MAX) * 0.5f + 0.25f;
positions[i].y = static_cast <float> (rand()) / static_cast <float> (RAND_MAX) * 0.5f + 0.25f;
positions[i].z = 1.0f;
}
return positions;
}
void Benchmark(int particlesCount, int blockSize, int iterationsCount) {
float deltaTime = 0.000001;
float softeningRate = 0.00001;
float3 *positions = GenerateParticles(particlesCount);
auto start = std::chrono::high_resolution_clock::now();
Run(particlesCount, blockSize, positions, deltaTime, softeningRate, iterationsCount);
auto finish = std::chrono::high_resolution_clock::now();
std::chrono::duration<double> elapsed = finish - start;
std::cout << "Time: " << elapsed.count() << std::endl;
delete[] positions;
}
int main(int argc, char **argv) {
srand(static_cast <unsigned> (time(nullptr)));
int particlesCount = 8192;
int blockSize = 1024;
int iterationsCount = 200000;
if (argc > 1) {
particlesCount = std::stoi(argv[1]);
}
if (argc > 2) {
blockSize = std::stoi(argv[2]);
}
if (argc > 3) {
iterationsCount = std::stoi(argv[3]);
}
if (particlesCount % blockSize != 0) {
std::cout << "Number of particles must be divisible by block size" << std::endl;
return -1;
}
if (blockSize % 4 != 0) {
std::cout << "Block size must be divisible by 4" << std::endl;
return -1;
}
Benchmark(particlesCount, blockSize, iterationsCount);
return 0;
}
|
19,446 | #include "includes.h"
extern "C" {
#ifndef REAL
#define REAL float
#endif
}
__global__ void ge_set (const int sd, const int fd, const REAL val, REAL* a, const int offset_a, const int ld_a) {
const int gid_0 = blockIdx.x * blockDim.x + threadIdx.x;
const int gid_1 = blockIdx.y * blockDim.y + threadIdx.y;
const bool valid = (gid_0 < sd) && (gid_1 < fd);
if (valid) {
a[offset_a + gid_0 + gid_1 * ld_a] = val;
}
} |
19,447 | #include<stdio.h>
#include<cuda.h>
__global__ void hwkernal(){
printf("hello world\n");
}
int main(){
hwkernal<<<1,5>>>();
cudaThreadSynchronize();;
}
|
19,448 | #include "includes.h"
__global__ void kernelF(const float *d_xAx, const float *d_bx, const float *d_c, float *d_y)
{
*d_y = *d_xAx + *d_bx + *d_c;
} |
19,449 | #include<iostream>
#include<cstdlib>
using namespace std;
__global__ void vectorAdd(int *a, int *b, int *result, int n) {
int tid = blockIdx.x*blockDim.x + threadIdx.x;
if(tid <= n) {
result[tid] = a[tid] + b[tid];
}
}
void print_array(int *a, int N) {
for(int i=0; i<N; i++) {
cout<<" "<<a[i];
}
cout<<endl;
}
void init_array(int *a, int N) {
for(int i=0; i<N; i++) {
a[i] = rand()%10 + 1;
}
}
int main() {
int *a, *b, *c;
int *a_dev, *b_dev, *c_dev;
int n = 8; //24
a = (int*)malloc(n * sizeof(n));
b = (int*)malloc(n * sizeof(n));
c = (int*)malloc(n * sizeof(n));
int size = n * sizeof(int);
cudaMalloc(&a_dev, size);
cudaMalloc(&b_dev, size);
cudaMalloc(&c_dev, size);
init_array(a, n);
init_array(b, n);
print_array(a, n);
print_array(b, n);
//cudaEvent_t start, end;
//cudaEventCreate(&start);
//cudaEventCreate(&end);
cudaMemcpy(a_dev, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(b_dev, b, size, cudaMemcpyHostToDevice);
//int threads = 1024;
//int blocks = (n+threads-1)/threads;
//cudaEventRecord(start);
//vectorAdd<<<blocks,threads>>>(a_dev, b_dev, c_dev, n);
vectorAdd<<<1,1024>>>(a_dev, b_dev, c_dev, n);
//cudaEventRecord(end);
//cudaDeviceSynchronize();
//float time = 0.0;
//cudaEventElapsedTime(&time, start, end);
cudaMemcpy(c, c_dev, size, cudaMemcpyDeviceToHost);
cout<<"Results : "<<endl;
print_array(c, n);
//cout<<"Time elapsed : "<<time<<endl;
cudaFree(a_dev);
cudaFree(b_dev);
cudaFree(c_dev);
return 0;
}
|
19,450 | __device__ float giveFloat(){ return 3.2;}
|
19,451 | #include <stdio.h>
#include "kernelMedianFilter.cu"
#define BNX 16
#define BNY 16
#if defined Zero
#define KER "kernelFilterZero"
#elif defined Shrink
#define KER "kernelFilterShrink"
#elif defined Extend
#define KER "kernelFilterExtend"
#else
#define KER "kernelFilterDiscard"
#endif
#ifdef Bubble
#define MDN "medianBubble"
#else
#define MDN "medianNoSort"
#endif
#define cuCheck(arg) getCudaError(__FILE__, __LINE__, arg)
inline void getCudaError(const char *file, const int line, cudaError_t cuerr)
{
if(cuerr != cudaSuccess)
{
printf("cudaError at %s(%d): %s\n", file, line, cudaGetErrorString(cuerr));
cudaDeviceReset();
exit(2);
}
}
int main(int argc, char *argv[])
{
const int nInX = 600;
const int nInY = 600;
size_t sizeXY = nInX*nInY*sizeof(float);
float *hostInput, *hostOutput;
hostInput = new float[nInX*nInY] ();
hostOutput = new float[nInX*nInY] ();
float *deviceInput, *deviceOutput;
cudaSetDevice(0);
cudaMalloc((float**) &deviceInput , sizeXY);
cudaMalloc((float**) &deviceOutput, sizeXY);
FILE* fp = fopen("example/data_orig.bin", "rb");
if( ! fp)
{
printf("Fatal error at %s(%d): fail to open file example/data_orig.bin",
__FILE__, __LINE__);
exit(1);
}
fread(hostInput, sizeXY, 1, fp);
fclose(fp);
cuCheck( cudaMemcpy(deviceInput, hostInput, sizeXY, cudaMemcpyHostToDevice) );
dim3 Block(BNX, BNY, 1);
dim3 Grid(ceil(1.0F*nInX/BNX), ceil(1.0F*nInY/BNY), 1);
float elapsedTime;
cudaEvent_t startEvent, stopEvent;
cudaEventCreate(&startEvent);
cudaEventCreate(&stopEvent);
// Warmup Launching
cudaEventRecord(startEvent, 0);
warmUp <<< Grid, Block >>> (nInX, nInY, deviceInput, deviceOutput);
cuCheck( cudaGetLastError() );
cudaEventRecord(stopEvent, 0);
cudaEventSynchronize(stopEvent);
cudaEventElapsedTime(&elapsedTime, startEvent, stopEvent);
// Kernel Launching
cudaEventRecord(startEvent, 0);
#if defined Zero
kernelFilterZero <<< Grid, Block >>> (nInX, nInY, deviceInput, deviceOutput);
#elif defined Shrink
kernelFilterShrink <<< Grid, Block >>> (nInX, nInY, deviceInput, deviceOutput);
#elif defined Extend
kernelFilterExtend <<< Grid, Block >>> (nInX, nInY, deviceInput, deviceOutput);
#else
kernelFilterDiscard <<< Grid, Block >>> (nInX, nInY, deviceInput, deviceOutput);
#endif
cuCheck( cudaGetLastError() );
cudaEventRecord(stopEvent, 0);
cudaEventSynchronize(stopEvent);
cudaEventElapsedTime(&elapsedTime, startEvent, stopEvent);
printf("Elapsed time of %s with %s @%dx%d is: %5.3f ms.\n", KER, MDN,
LSM, LSM, elapsedTime);
cuCheck( cudaMemcpy(hostOutput, deviceOutput, sizeXY, cudaMemcpyDeviceToHost) );
fp = fopen("example/data_filt.bin", "wb");
fwrite(hostOutput, sizeXY, 1, fp);
fclose(fp);
delete [] hostInput;
delete [] hostOutput;
cudaFree(deviceInput);
cudaFree(deviceOutput);
cudaDeviceReset();
return 0;
}
|
19,452 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define CLOCKS_PAR_SEC 1000000l
#define N 256
/************************************************************************/
/* Example */
/************************************************************************/
__global__ void matVec(float *a, float *b, float *c)
{
int x = threadIdx.x + blockIdx.x * blockDim.x;
int index = x * N;
float tmp = 0;
for (int i=0; i<N; i++)
{
tmp += a[index + i] * b[i];
}
c[x] = tmp;
}
/************************************************************************/
/* Main */
/************************************************************************/
int main(int argc, char* argv[])
{
float *host_a, *host_b, *host_c;
float *dev_a, *dev_b, *dev_c;
const int size = N * sizeof(float);
host_a = (float*)malloc( size * N);
host_b = (float*)malloc( size);
host_c = (float*)malloc( size);
// cudaHostAlloc(&dev_a, size*N, cudaHostAllocDefault);
cudaMalloc( (void**)&dev_a, size * N);
cudaMalloc( (void**)&dev_b, size );
cudaMalloc( (void**)&dev_c, size );
for (int i = 0; i < N*N; ++i)
{
host_a[i] = 3.0;
}
for (int i = 0; i < N; ++i )
{
host_b[i] = 2.0;
}
/* mesure du temps d'execution */
cudaEvent_t start, stop;
float time;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
/* Copie des données vers le GPU */
cudaMemcpy(dev_a, host_a, size * N, cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, host_b, size, cudaMemcpyHostToDevice);
/* execution de l'opération sur GPU */
dim3 ThreadPerBlock ( 128 , 1 );
dim3 BlockPerGrid ( N/128 , 1 );
matVec<<<BlockPerGrid, ThreadPerBlock>>>(dev_a, dev_b, dev_c);
cudaMemcpy( host_c, dev_c, size, cudaMemcpyDeviceToHost );
/* Fin de la mesure du temps d'execution du programme */
cudaEventRecord(stop, 0);
cudaEventSynchronize( stop );
cudaEventElapsedTime(&time, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
cudaFree( dev_c );
/* vérification des résultats */
for (int i=0; i<N; i++)
{
if (host_c[i] != 6*N)
{
// printf("erreur à l'adresse %d \n", i);
printf("c[%3d] = %5.1f \n", i, host_c[i] );
}
}
/* affichage du temps d'execution */
printf("temps écoule sur GPU : %f ms \n", time);
/**********************************************
execution de la même opération sur CPU
**********************************************/
int j=0;
clock_t t1, t2;
double tempsCPU;
t1 = clock();
/* execution de l'opération sur CPU */
for (j=0; j<1000; j++)
{
for (int i=0; i<N; i++)
host_c[i] = host_a[i] + host_b[i];
}
t2 = clock();
tempsCPU = (double)difftime(t2, t1)/(double)CLOCKS_PAR_SEC;
/* affichage du temps d'execution */
printf("temps écoule sur CPU: %f ms \n", tempsCPU * 1000.0 / j);
free(host_a);
free(host_b);
free(host_c);
return EXIT_SUCCESS;
}
|
19,453 |
#include <stdio.h>
#include <time.h>
#include <unistd.h>
#include <stdlib.h>
#include <math.h>
__global__ void Mat_hist(int x[], int z[], int n) {
int thread_id = threadIdx.x + blockIdx.x * blockDim.x;
__shared__ int hist[256];
if(threadIdx.x < 256)
hist[threadIdx.x]=0;
__syncthreads();
if(thread_id<n)
{
atomicAdd(&hist[x[thread_id]],1);
}
__syncthreads();
if(threadIdx.x < 256)
atomicAdd(&z[threadIdx.x],hist[threadIdx.x]);
}
/*
*argumentos
*1 - n_elementos
*2 - threads por bloco
*/
int main(int argc, char* argv[]) {
int n, th_p_blk;
int *h_x, *h_z, *h_z_res;
int *d_x, *d_z;
size_t size,size_hist;
th_p_blk = 1024;
n = 32;
if(argc > 1)
n = atoi(argv[1]);
if(argc > 2)
th_p_blk = atoi(argv[2]);
int blks = ceil((float)(n)/(float)th_p_blk);
/* Define vector length */
size = n*sizeof(int);
size_hist = 256*sizeof(int);
// Allocate memory for the vectors on host memory.
h_x = (int*) malloc(size);
h_z = (int*) malloc(size_hist);
h_z_res = (int*) malloc(size_hist);
for (int i = 0; i < 256; i++)
{
h_z_res[i] = 0;
h_z[i] = 0;
}
for (int i = 0; i < n; i++) {
h_x[i] = (int)rand()%256;
h_z_res[h_x[i]]++;
}
/* Allocate vectors in device memory */
cudaMalloc(&d_x, size);
cudaMalloc(&d_z, size_hist);
/* Copy vectors from host memory to device memory */
cudaMemcpy(d_x, h_x, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_z, h_z, size_hist, cudaMemcpyHostToDevice);
cudaEvent_t start, stop;
cudaEventCreate (&start);
cudaEventCreate (&stop);
cudaEventRecord (start, 0); // 0 is the stream number
// do Work…
/* Kernel Call */
Mat_hist<<<blks,th_p_blk>>>(d_x, d_z, n);
cudaThreadSynchronize();
cudaEventRecord (stop, 0);
cudaEventSynchronize (stop);
float elapsedTime;
cudaEventElapsedTime (&elapsedTime, start, stop);
printf ("[%d,%.5f],\n", n,elapsedTime);
cudaEventDestroy(start);
cudaEventDestroy(stop);
cudaMemcpy(h_z, d_z, size_hist, cudaMemcpyDeviceToHost);
bool certo=true;
//printf("threads/blk %d -- blocks %d\n",th_p_blk,blks);
for (int i = 0; i < 256; i++){
//printf("%d - %d\n",h_z_res[i],h_z[i]);
if(h_z_res[i] != h_z[i])
certo=false;
}
if(!certo)
printf("\n*****\n certo = %s\n*****\n", certo ? "true" : "false");
/* Free device memory */
cudaFree(d_x);
cudaFree(d_z);
/* Free host memory */
free(h_x);
free(h_z);
free(h_z_res);
return 0;
} /* main */
|
19,454 | #include <stdio.h>
#include <assert.h>
#include <pthread.h>
#define THREADS 4
int intervalsT=100000000;
double partialStore[]={0.0, 0.0, 0.0, 0.0};
// -:YOUR CODE HERE:-
void *threadRoutine(void *param) {
// -:YOUR CODE HERE:-
return 0;
}
void calculatePIHostMultiple(){
// -:YOUR CODE HERE:-
}
void calculatePIHostSingle(){
int i;
double height,x;
double store,base;
int intervals=100000000;
base=(double)(1.0/intervals);
for (i=0, store=0.0, x=0.0; i<intervals; i++) {
x = i * base;
height = 4/(1 + x * x);
store += base * height;
}
printf("PI (single th) =%f \n",store);
}
int main(){
calculatePIHostSingle();
calculatePIHostMultiple();
}
|
19,455 | #include <iostream>
#include <stdio.h>
#include "clahe.cuh"
#define BIN_SIZE 101
__global__ void clahe(float* L, int width, int height, int threshold, float* dCdf)
{
__shared__ int bins[BIN_SIZE];
computeHistogram(L, width, height, bins);
clipHistogram(bins, threshold);
generateCdf(bins, dCdf);
}
__global__ void transformRgbToLab(unsigned char* pixels, int width, int height, float* L, float* A, float* B){
// pixels: length = width * height * 3
// L: length = width * height
// A: length = width * height
// B: length = width * height
//https://stackoverflow.com/questions/49150250/convert-bgr-to-lab-without-opencv
//http://www.easyrgb.com/en/math.php
int i = threadIdx.x + blockDim.x * blockIdx.x;
if (i < width * height)
{
float r = (float)pixels[3*i]/255;
float g = (float)pixels[3*i+1]/255;
float b = (float)pixels[3*i+2]/255;
r = (r > 0.04045) ? std::pow((r + 0.055) / 1.055, 2.4) : r / 12.92; r *= 100;
g = (g > 0.04045) ? std::pow((g + 0.055) / 1.055, 2.4) : g / 12.92; g *= 100;
b = (b > 0.04045) ? std::pow((b + 0.055) / 1.055, 2.4) : b / 12.92; b *= 100;
// reference standard sRGB
float x = (r * 0.412453 + g * 0.357580 + b * 0.180423) / 95.047;
float y = (r * 0.212671 + g * 0.715160 + b * 0.072169) / 100.;
float z = (r * 0.019334 + g * 0.119193 + b * 0.950227) / 108.883;
x = (x > 0.008856)? std::pow(x, 0.3333) : (7.787 * x) + 0.137931;
y = (y > 0.008856)? std::pow(y, 0.3333) : (7.787 * y) + 0.137931;
z = (z > 0.008856)? std::pow(z, 0.3333) : (7.787 * z) + 0.137931;
L[i] = (116.0 * y) - 16.0;
A[i] = 500.0 * (x - y);
B[i] = 200.0 * (y - z);
}
}
__global__ void transformLabToRgb(unsigned char* pixels, int width, int height, float* L, float* A, float* B)
{
// Reference: https://stackoverflow.com/questions/7880264/convert-lab-color-to-rgb
// pixels: length = width * height * 3
// L: length = width * height
// A: length = width * height
// B: length = width * height
int i = (threadIdx.x + blockDim.x * blockIdx.x);
if (i < width * height)
{
float y = (L[i] + 16. ) / 116.;
float x = A[i] / 500. + y;
float z = y - B[i] / 200.;
z = (z > 0.0) ? z : 0.0;
y = (pow(y, 3) > 0.008856) ? pow(y, 3) : (y - 16. / 116.) / 7.787;
x = (pow(x, 3) > 0.008856) ? pow(x, 3) : (x - 16. / 116.) / 7.787;
z = (pow(z,3) > 0.008856) ? pow(z,3) : (z - 16. / 116.) / 7.787;
x = 95.047 * x / 100.;
y = 100.000 * y / 100.;
z = 108.883 * z / 100.;
float r = x * 3.2406 + y * -1.5372 + z * -0.4986;
float g = x * -0.9689 + y * 1.8758 + z * 0.0415;
float b = x * 0.0557 + y * -0.2040 + z * 1.0570;
r = (r > 0.0031308) ? 1.055 * pow(r , (1 / 2.4)) - 0.055 : 12.92 * r;
g = (g > 0.0031308) ? 1.055 * pow(g , (1 / 2.4)) - 0.055 : 12.92 * g;
b = (b > 0.0031308) ? 1.055 * pow( b , (1 / 2.4)) - 0.055 : 12.92 * b;
r = (r > 1) ? 1 : r; r = (r > 0) ? r : 0;
g = (g > 1) ? 1 : g; g = (g > 0) ? g : 0;
b = (b > 1) ? 1 : b; b = (b > 0) ? b : 0;
pixels[3 * i] = (unsigned char)(r * 255.);
pixels[3 * i + 1] = (unsigned char)(g * 255.);
pixels[3 * i + 2] = (unsigned char)(b * 255.);
}
}
// called by kernel<<<dimGrid, dimBlock>>>()
__device__ void computeHistogram(float* L, int width, int height, int* bins)
{
// L: length = width * height
// bins: length = BIN_SIZE
int row = threadIdx.y + blockIdx.y * blockDim.y;
int col = threadIdx.x + blockIdx.x * blockDim.x;
int i = row * width + col;
if (i < width * height)
{
int c = (int)L[i];
atomicAdd(&bins[c], 1);
}
}
// called by kernel<<<dimGrid, dimBlock>>>()
__device__ void clipHistogram(int* bins, int threshold)
{
__shared__ int count_overlimit;
int i = threadIdx.x + threadIdx.y * blockDim.x;
if(i == 0) count_overlimit=0;
__syncthreads();
if(i < BIN_SIZE)
{
if(bins[i] > threshold)
{
atomicAdd(&count_overlimit, bins[i] - threshold);
bins[i] = threshold;
}
}
__syncthreads();
if(i < BIN_SIZE)
bins[i] = bins[i] + count_overlimit/BIN_SIZE + (i < count_overlimit%BIN_SIZE);
__syncthreads();
}
// called by kernel<<<dimGrid, dimBlock>>>()
__device__ void generateCdf(int* bins, float* dCdf)
{
int i = threadIdx.x + threadIdx.y * blockDim.x;
// small array so here use sequential scan
if (i == 0)
{
for(int j = 1; j < BIN_SIZE; j++)
bins[j]= bins[j]+bins[j-1];
}
__syncthreads();
if ( i < BIN_SIZE )
{
dCdf[(blockIdx.x + blockIdx.y * gridDim.x) * BIN_SIZE + i] = (float)bins[i]/(float)bins[BIN_SIZE - 1];
}
__syncthreads();
}
__global__ void pixelInterpolate(float* L, int width, int height, float* dCdf)
{
__shared__ float topLeft[BIN_SIZE];
__shared__ float topRight[BIN_SIZE];
__shared__ float bottomLeft[BIN_SIZE];
__shared__ float bottomRight[BIN_SIZE];
int row = threadIdx.y + blockIdx.y * blockDim.y;
int col = threadIdx.x + blockIdx.x * blockDim.x;
int i = row * width + col;
int blockId = blockIdx.x + blockIdx.y * gridDim.x;
int blockCdf = blockId * BIN_SIZE;
int threadI = threadIdx.x + threadIdx.y * blockDim.x;
if (i < width * height)
{
int index = (int)L[i];
float temp = dCdf[index + blockCdf] * 100;
if (threadI < BIN_SIZE)
{
topLeft[threadI] = dCdf[blockCdf + threadI];
}
if (blockIdx.x != gridDim.x - 1 && blockIdx.y != gridDim.y - 1)
{
if (threadI < BIN_SIZE)
{
topRight[threadI] = dCdf[blockCdf + BIN_SIZE + threadI];
bottomLeft[threadI] = dCdf[blockCdf + gridDim.x * BIN_SIZE + threadI];
bottomRight[threadI] = dCdf[blockCdf + gridDim.x * BIN_SIZE + BIN_SIZE + threadI];
}
__syncthreads();
temp = (blockDim.x - threadIdx.x) * (blockDim.y - threadIdx.y) * topLeft[index]
+ threadIdx.x * (blockDim.y - threadIdx.y) * topRight[index]
+ (blockDim.x - threadIdx.x) * threadIdx.y * bottomLeft[index]
+ threadIdx.x * threadIdx.y * bottomRight[index];
L[i] = (temp / (blockDim.x * blockDim.y)) * 100;
}
else if (blockIdx.x == gridDim.x - 1 && blockIdx.y != gridDim.y - 1)
{
if (threadI < BIN_SIZE)
{
bottomLeft[threadI] = dCdf[blockCdf + gridDim.x * BIN_SIZE + threadI];
}
__syncthreads();
temp = (blockDim.y - threadIdx.y) * topLeft[index]
+ threadIdx.y * bottomLeft[index];
L[i] = (temp / blockDim.y) * 100;
}
else if (blockIdx.x != gridDim.x - 1 && blockIdx.y == gridDim.y - 1)
{
if (threadI < BIN_SIZE)
{
topRight[threadI] = dCdf[blockCdf + BIN_SIZE + threadI];
}
__syncthreads();
temp = (blockDim.x - threadIdx.x) * topLeft[index]
+ threadIdx.x * topRight[index];
L[i] = (temp / blockDim.x) * 100;
}
else
{
__syncthreads();
temp = topLeft[index];
L[i] = temp * 100;
}
}
__syncthreads();
} |
19,456 | #include "includes.h"
__global__ void intArrayIdentity(int size, int *input, int *output, int length) {
const int ix = threadIdx.x + blockIdx.x * (long)blockDim.x;
if (ix < size) {
// copy int array
const int *inArrayBody = &input[ix * length];
int *outArrayBody = &output[ix * length];
for (long i = 0; i < length; i++) {
outArrayBody[i] = inArrayBody[i];
}
}
} |
19,457 | #include <stdio.h>
int main() {
int nDevices;
cudaGetDeviceCount(&nDevices);
for (int i = 0; i < nDevices; i++) {
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, i);
printf("Device Number: %d\n", i);
printf(" Device name: %s\n", prop.name);
printf(" Memory Clock Rate (KHz): %d\n",
prop.memoryClockRate);
printf(" Global Memory (bytes): %ld\n",
prop.totalGlobalMem);
printf(" Memory Bus Width (bits): %d\n",
prop.memoryBusWidth);
printf(" Peak Memory Bandwidth (GB/s): %f\n",
2.0*prop.memoryClockRate*(prop.memoryBusWidth/8)/1.0e6);
printf(" Max Thread per block : %d \n",prop.maxThreadsPerBlock);
printf(" Multiproc count : %d \n",prop.multiProcessorCount);
printf(" Max Grid size : %d %d %d \n",prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
printf(" Max thread dim : %d %d %d \n",prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
printf(" Registres per block : %d \n",prop.regsPerBlock);
printf("\n");
}
}
|
19,458 | #include <stdlib.h>
#include <stdio.h>
#include<math.h>
#include<time.h>
// row-wise nonzero counting
__global__ void
count_nonzero(int n, float *A, int *nz){
int row = blockIdx.x*blockDim.x + threadIdx.x;
int count = 0;
for (int i=0; i<n; i++)
if (A[row*n+i] != 0)
count += 1;
nz[row] = count;
}
// partial sum calculation
__global__ void
partial_sum(int n, int *nz, int *x){
int row = blockIdx.x*blockDim.x + threadIdx.x;
int sum = 0;
for (int i=0; i<row+1; i++){
sum += nz[i];
}
x[row+1] = sum;
}
// collection of nonzero value and its column index
__global__ void
value_colidx(int n, int *nz, float *A, int *psum, int *x, float *y){
int row = blockIdx.x*blockDim.x + threadIdx.x;
int count = 0;
int thread_start = psum[row];
for (int i=0; i<n; i++){
// stop if all nz are found
if (count <nz[row]){
if (A[row*n+i] != 0){
x[thread_start+count] = i;
y[thread_start+count] = A[row*n+i];
count += 1;
}
}
}
}
int main(int argc, char **argv) {
//int n=2000;
int n = atoi(argv[1]);
int memSize = n*sizeof(int);
cudaEvent_t start, stop, start_all, stop_all;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventCreate(&start_all);
cudaEventCreate(&stop_all);
int *nz;
int*d_nz;
nz = (int*) malloc (n*sizeof(*nz));
cudaMalloc( (void**) &d_nz, memSize);
float *A, *d_A;
A = (float*) malloc (n*n*sizeof(*A));
cudaMalloc( (void**) &d_A, n*n*sizeof(float));
// initialize matrix
for(int j=0; j<n; j++){
nz[j] = 0;
for(int k=0; k<n; k++){
A[j*n+k] = 0;
if ((j+k) %2 != 0) A[j*n+k] = (float) j+k;
}
}
cudaMemcpy( d_A, A, n*memSize, cudaMemcpyHostToDevice);
cudaMemcpy( d_nz, nz, n*sizeof(int), cudaMemcpyHostToDevice);
dim3 block(1);
dim3 grid(n);
// counting number of nonzero elements from each row
cudaEventRecord(start);
cudaEventRecord(start_all);
count_nonzero<<<grid,block>>>(n, d_A, d_nz);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("runtime nz[s]: %f\n", milliseconds/1000.0);
// calculating partial sum
int *psum, *d_psum;
psum = (int*) malloc ((n+1)*sizeof(*psum));
cudaMalloc( (void**) &d_psum, n*memSize);
// no need nz in CPU in fact
//cudaMemcpy( d_nz, nz, memSize, cudaMemcpyHostToDevice);
cudaEventRecord(start);
partial_sum<<<grid,block>>>(n, d_nz, d_psum);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("runtime psum[s]: %f\n", milliseconds/1000.0);
// copy psum (rowptr) from GPU to CPU
cudaMemcpy( psum, d_psum, (n+1)*sizeof(*psum), cudaMemcpyDeviceToHost);
psum[0] = 0;
// size of CSR
int m = psum[n];
int *colidx, *d_colidx;
colidx = (int*) calloc(m, sizeof(int));
cudaMalloc( (void**) &d_colidx, m*sizeof(int));
float *value, *d_value;
value = (float*) malloc (m*sizeof(float));
cudaMalloc( (void**) &d_value, m*sizeof(float));
cudaMemcpy( d_colidx, colidx, m*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy( d_value, value, m*sizeof(float), cudaMemcpyHostToDevice);
// collect value and column index based on partial sum
cudaEventRecord(start);
value_colidx<<<grid,block>>>(n, d_nz, d_A, d_psum, d_colidx, d_value);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventRecord(stop_all);
milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("runtime CSR[s]: %f\n", milliseconds/1000.0);
cudaEventElapsedTime(&milliseconds, start_all, stop_all);
printf("runtime ALL[s]: %f\n", milliseconds/1000.0);
// copy colidx, value from GPU to CPU
cudaMemcpy( colidx, d_colidx, m*sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy( value, d_value, m*sizeof(float), cudaMemcpyDeviceToHost);
}
|
19,459 |
#include<stdio.h>
#include<stdlib.h>
__global__ void mul(int *a, int *b, int *c, int n)
{
int row = blockIdx.y*blockDim.y + threadIdx.y;
int col = blockIdx.x*blockDim.x + threadIdx.x;
int sum = 0;
int i;
if(row < n && col < n)
{
for(i =0; i<n; i++)
{
sum += a[row*n+i]*b[i*n+col];
}
c[row*n+col] = sum;
}
}
int main(int argc, char **argv)
{
int N;
int *a, *b, *c, *d, *da, *db, *dc;
int i,j,k;
scanf("%d",&N);
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
a = (int *)calloc(N*N,sizeof(int));
b = (int *)calloc(N*N,sizeof(int));
c = (int *)calloc(N*N,sizeof(int));
d = (int *)calloc(N*N,sizeof(int));
for(i = 0; i < N*N; i++)
{
a[i] = rand()%48;
b[i] = rand()%50;
}
int size = N*N*sizeof(int);
cudaMalloc(&da,size);
cudaMalloc(&db,size);
cudaMalloc(&dc,size);
cudaMemcpy(da,a,size,cudaMemcpyHostToDevice);
cudaMemcpy(db,b,size,cudaMemcpyHostToDevice);
dim3 grid((N+15)/16,(N+15)/16);
dim3 block(16,16);
cudaEventRecord(start);
mul<<<grid,block>>>(da,db,dc,N);
cudaEventRecord(stop);
cudaMemcpy(c,dc,size,cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
float ms;
cudaEventElapsedTime(&ms,start,stop);
for(i = 0; i<N; i++)
for(j = 0; j<N; j++)
{
d[i*N+j] = 0.0;
for(k = 0; k<N; k++)
{
d[i*N+j] += a[i*N+k]*b[k*N+j];
}
if(d[i*N+j] != c[i*N+j])
{
printf("Inmcoreect\n");
exit(-2);
}
}
printf("%lf\n",ms);
} |
19,460 | #include<iostream>
#include <sys/time.h>
using namespace std;
const int threadsPerBlock = 256;
const int N = (1 <<20) -3;
const int blocksPerGrid = (N + threadsPerBlock * 2 - 1)/ (threadsPerBlock * 2); // 维持block数量不变
const int iters = 100;
__global__ void kernel4(float* arr, float* out, int N){
__shared__ float s_data[threadsPerBlock];
unsigned int tid = threadIdx.x;
unsigned int i = threadIdx.x + blockIdx.x * (blockDim.x * 2); // 3的第一轮迭代，有一半的线程是idle的，现在把一个block的大小缩小一半
if(i < N){
s_data[tid] = arr[i] + arr[i + blockDim.x]; // 单独执行原来的第一轮迭代，后面代码不用变
}
__syncthreads();
for(int s = blockDim.x/2; s > 0; s>>=1){
if(tid < s && i + s < N){
s_data[tid] += s_data[tid + s];
}
__syncthreads();
}
if(tid == 0){
out[blockIdx.x] = s_data[0];
}
}
__device__ void warpRecude(volatile float* s_data, int tid){ // volatile 关键字很重要，保证s_data从相应的内存单元取出，这里应该指gpu内存
s_data[tid] += s_data[tid + 32];
s_data[tid] += s_data[tid + 16];
s_data[tid] += s_data[tid + 8];
s_data[tid] += s_data[tid + 4];
s_data[tid] += s_data[tid + 2];
s_data[tid] += s_data[tid + 1];
}
__global__ void kernel5(float* arr, float* out, int N){
__shared__ float s_data[threadsPerBlock];
unsigned int tid = threadIdx.x;
unsigned int i = threadIdx.x + blockIdx.x * (blockDim.x * 2); // 3的第一轮迭代，有一半的线程是idle的，现在把一个block的大小缩小一半
if(i < N){
s_data[tid] = arr[i] + arr[i + blockDim.x]; // 单独执行原来的第一轮迭代，后面代码不用变
}else{
s_data[tid] = 0;
}
__syncthreads();
for(int s = blockDim.x/2; s > 32; s>>=1){
if(tid < s && i + s < N){
s_data[tid] += s_data[tid + s];
}
__syncthreads();
}
if(tid < 32){
warpRecude(s_data, tid);
}
if(tid == 0){
out[blockIdx.x] = s_data[0];
}
}
template<unsigned int blockSize>
__device__ void warpRecude2(volatile float* s_data, int tid){ // volatile 关键字很重要，保证s_data从相应的内存单元取出，这里应该指gpu内存
if(blockSize >= 64) s_data[tid] += s_data[tid + 32]; // if 是防止blockSize小于64，比如blockSize为16，那么会直接到下面
if(blockSize >= 32) s_data[tid] += s_data[tid + 16];
if(blockSize >= 16) s_data[tid] += s_data[tid + 8];
if(blockSize >= 8) s_data[tid] += s_data[tid + 4];
if(blockSize >= 4) s_data[tid] += s_data[tid + 2];
if(blockSize >= 2) s_data[tid] += s_data[tid + 1];
}
template<unsigned int blockSize>
__global__ void reduce(float* arr, float* out, int N){
__shared__ float s_data[threadsPerBlock];
unsigned int tid = threadIdx.x;
unsigned int i = threadIdx.x + blockIdx.x * (blockDim.x * 2); // 3的第一轮迭代，有一半的线程是idle的，现在把一个block的大小缩小一半
if(i < N){
s_data[tid] = arr[i] + arr[i + blockDim.x]; // 单独执行原来的第一轮迭代，后面代码不用变
}else{
s_data[tid] = 0;
}
__syncthreads();
if(blockSize >= 1024){
if(tid < 512){
s_data[tid] += s_data[tid+512];
}
__syncthreads();
}
if(blockSize >= 512){
if(tid < 256){
s_data[tid] += s_data[tid+256];
}
__syncthreads();
}
if(blockSize >= 256){
if(tid < 128){
s_data[tid] += s_data[tid+128];
}
__syncthreads();
}
if(blockSize >= 128){
if(tid < 64){
s_data[tid] += s_data[tid+64];
}
__syncthreads();
}
if(tid < 32){
warpRecude2<blockSize>(s_data, tid);
}
if(tid == 0){
out[blockIdx.x] = s_data[0];
}
}
void kernel6(float* arr, float* out, int N, cudaStream_t &stream){ // 展开所有的循环，去除循环
switch(threadsPerBlock){
case 1024:
reduce<1024><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 512:
reduce<512><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 256:
reduce<256><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 128:
reduce<128><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 64:
reduce<64><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 32:
reduce<32><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 16:
reduce<16><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 8:
reduce<8><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 4:
reduce<4><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 2:
reduce<2><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
case 1:
reduce<1><<<blocksPerGrid, threadsPerBlock, 0, stream>>>(arr, out, N);break;
}
}
void varifyOutput(float* predict, float* arr, int N){
float pred = 0.0;
for(int i=0;i<blocksPerGrid;i++){
pred += predict[i];
}
float result = 0.0;
struct timeval s;
struct timeval e;
gettimeofday(&s,NULL);
for(int t=0;t<iters;t++){
result = 0.0;
for(int i=0;i<N;i++){
result += arr[i];
}
}
gettimeofday(&e,NULL);
cout << "CPU Elapse time: " << ((e.tv_sec-s.tv_sec)*1000000+(e.tv_usec-s.tv_usec)) / iters / 1000.0 << " ms" << endl;
cout << "predict: " << pred << endl << "result: " << result << endl;
}
int main(){
float* a_host, *r_host;
float* a_device, *r_device;
cudaMallocHost(&a_host, N * sizeof(float));
cudaMallocHost(&r_host, blocksPerGrid * sizeof(float));
cudaMalloc(&a_device, N * sizeof(float));
cudaMalloc(&r_device, blocksPerGrid * sizeof(float));
for(int i=0;i<N;i++){
a_host[i] = 1;
}
for(int i=0;i<blocksPerGrid;i++){
r_host[i] = 0.0;
}
cudaStream_t stream;
cudaStreamCreate(&stream);
cudaMemcpyAsync(a_device, a_host, N * sizeof(float), cudaMemcpyHostToDevice, stream);
cudaMemcpyAsync(r_device, r_host, blocksPerGrid * sizeof(float), cudaMemcpyHostToDevice, stream);
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
for(int i=0;i<iters;i++){
// kernel5<<<blocksPerGrid, threadsPerBlock, 0, stream>>>(a_device, r_device, N);
kernel6(a_device, r_device, N, stream);
}
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
cout << "GPU Elapse time: " << elapsedTime / iters << " ms" << endl;
cudaEventDestroy(start);
cudaEventDestroy(stop);
cudaMemcpy(r_host, r_device, blocksPerGrid * sizeof(float), cudaMemcpyDeviceToHost);
varifyOutput(r_host, a_host, N);
cudaFree(r_device);
cudaFree(a_device);
cudaFreeHost(r_host);
cudaFreeHost(a_host);
return 0;
} |
19,461 | #include <iostream>
//#include "gpu.hpp"
/*__global__ void
filter(unsigned int *input, unsigned int *od, int w, int h, int r)
{
}
extern "C"
double boxFilterRGBA(unsigned int *d_src, unsigned int *d_temp, unsigned int *d_dest, int width, int height,
int radius, int iterations, int nthreads, StopWatchInterface *timer)
{
checkCudaErrors(cudaBindTextureToArray(rgbaTex, d_array));
// var for kernel computation timing
double dKernelTime;
for (int i=0; i<iterations; i++)
{
// sync host and start kernel computation timer_kernel
dKernelTime = 0.0;
checkCudaErrors(cudaDeviceSynchronize());
sdkResetTimer(&timer);
// use texture for horizontal pass
d_boxfilter_rgba_x<<< height / nthreads, nthreads, 0 >>>(d_temp, width, height, radius);
d_boxfilter_rgba_y<<< width / nthreads, nthreads, 0 >>>(d_temp, d_dest, width, height, radius);
// sync host and stop computation timer_kernel
checkCudaErrors(cudaDeviceSynchronize());
dKernelTime += sdkGetTimerValue(&timer);
if (iterations > 1)
{
// copy result back from global memory to array
checkCudaErrors(cudaMemcpyToArray(d_tempArray, 0, 0, d_dest, width * height * sizeof(float), cudaMemcpyDeviceToDevice));
checkCudaErrors(cudaBindTextureToArray(rgbaTex, d_tempArray));
}
}
return ((dKernelTime/1000.)/(double)iterations);
}*/
|
19,462 | // Tests CUDA compilation pipeline construction in Driver.
// REQUIRES: clang-driver
// REQUIRES: x86-registered-target
// REQUIRES: nvptx-registered-target
// Simple compilation case. Compile device-side to PTX assembly and make sure
// we use it on the host side.
// RUN: %clang -### -target x86_64-linux-gnu -c %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-NOSAVE \
// RUN: -check-prefix HOST -check-prefix INCLUDES-DEVICE \
// RUN: -check-prefix NOLINK %s
// Typical compilation + link case.
// RUN: %clang -### -target x86_64-linux-gnu %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-NOSAVE \
// RUN: -check-prefix HOST -check-prefix INCLUDES-DEVICE \
// RUN: -check-prefix LINK %s
// Verify that --cuda-host-only disables device-side compilation, but doesn't
// disable host-side compilation/linking.
// RUN: %clang -### -target x86_64-linux-gnu --cuda-host-only %s 2>&1 \
// RUN: | FileCheck -check-prefix NODEVICE -check-prefix HOST \
// RUN: -check-prefix NOINCLUDES-DEVICE -check-prefix LINK %s
// Same test as above, but with preceeding --cuda-device-only to make sure only
// the last option has an effect.
// RUN: %clang -### -target x86_64-linux-gnu --cuda-device-only --cuda-host-only %s 2>&1 \
// RUN: | FileCheck -check-prefix NODEVICE -check-prefix HOST \
// RUN: -check-prefix NOINCLUDES-DEVICE -check-prefix LINK %s
// Verify that --cuda-device-only disables host-side compilation and linking.
// RUN: %clang -### -target x86_64-linux-gnu --cuda-device-only %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-NOSAVE \
// RUN: -check-prefix NOHOST -check-prefix NOLINK %s
// Same test as above, but with preceeding --cuda-host-only to make sure only
// the last option has an effect.
// RUN: %clang -### -target x86_64-linux-gnu --cuda-host-only --cuda-device-only %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-NOSAVE \
// RUN: -check-prefix NOHOST -check-prefix NOLINK %s
// Verify that --cuda-gpu-arch option passes the correct GPU archtecture to
// device compilation.
// RUN: %clang -### -target x86_64-linux-gnu --cuda-gpu-arch=sm_35 -c %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-NOSAVE \
// RUN: -check-prefix DEVICE-SM35 -check-prefix HOST \
// RUN: -check-prefix INCLUDES-DEVICE -check-prefix NOLINK %s
// Verify that there is one device-side compilation per --cuda-gpu-arch args
// and that all results are included on the host side.
// RUN: %clang -### -target x86_64-linux-gnu \
// RUN: --cuda-gpu-arch=sm_35 --cuda-gpu-arch=sm_30 -c %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-NOSAVE \
// RUN: -check-prefix DEVICE2 -check-prefix DEVICE-SM35 \
// RUN: -check-prefix DEVICE2-SM30 -check-prefix HOST \
// RUN: -check-prefix HOST-NOSAVE -check-prefix INCLUDES-DEVICE \
// RUN: -check-prefix NOLINK %s
// Verify that device-side results are passed to the correct tool when
// -save-temps is used.
// RUN: %clang -### -target x86_64-linux-gnu -save-temps -c %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-SAVE \
// RUN: -check-prefix HOST -check-prefix HOST-SAVE -check-prefix NOLINK %s
// Verify that device-side results are passed to the correct tool when
// -fno-integrated-as is used.
// RUN: %clang -### -target x86_64-linux-gnu -fno-integrated-as -c %s 2>&1 \
// RUN: | FileCheck -check-prefix DEVICE -check-prefix DEVICE-NOSAVE \
// RUN: -check-prefix HOST -check-prefix HOST-NOSAVE \
// RUN: -check-prefix HOST-AS -check-prefix NOLINK %s
// Match device-side preprocessor and compiler phases with -save-temps.
// DEVICE-SAVE: "-cc1" "-triple" "nvptx64-nvidia-cuda"
// DEVICE-SAVE-SAME: "-aux-triple" "x86_64--linux-gnu"
// DEVICE-SAVE-SAME: "-fcuda-is-device"
// DEVICE-SAVE-SAME: "-x" "cuda"
// DEVICE-SAVE: "-cc1" "-triple" "nvptx64-nvidia-cuda"
// DEVICE-SAVE-SAME: "-aux-triple" "x86_64--linux-gnu"
// DEVICE-SAVE-SAME: "-fcuda-is-device"
// DEVICE-SAVE-SAME: "-x" "cuda-cpp-output"
// Match the job that produces PTX assembly.
// DEVICE: "-cc1" "-triple" "nvptx64-nvidia-cuda"
// DEVICE-NOSAVE-SAME: "-aux-triple" "x86_64--linux-gnu"
// DEVICE-SAME: "-fcuda-is-device"
// DEVICE-SM35-SAME: "-target-cpu" "sm_35"
// DEVICE-SAME: "-o" "[[PTXFILE:[^"]*]]"
// DEVICE-NOSAVE-SAME: "-x" "cuda"
// DEVICE-SAVE-SAME: "-x" "ir"
// Match the call to ptxas (which assembles PTX to SASS).
// DEVICE:ptxas
// DEVICE-SM35-DAG: "--gpu-name" "sm_35"
// DEVICE-DAG: "--output-file" "[[CUBINFILE:[^"]*]]"
// DEVICE-DAG: "[[PTXFILE]]"
// Match another device-side compilation.
// DEVICE2: "-cc1" "-triple" "nvptx64-nvidia-cuda"
// DEVICE2-SAME: "-aux-triple" "x86_64--linux-gnu"
// DEVICE2-SAME: "-fcuda-is-device"
// DEVICE2-SM30-SAME: "-target-cpu" "sm_30"
// DEVICE2-SAME: "-o" "[[GPUBINARY2:[^"]*]]"
// DEVICE2-SAME: "-x" "cuda"
// Match no device-side compilation.
// NODEVICE-NOT: "-cc1" "-triple" "nvptx64-nvidia-cuda"
// NODEVICE-NOT: "-fcuda-is-device"
// INCLUDES-DEVICE:fatbinary
// INCLUDES-DEVICE-DAG: "--create" "[[FATBINARY:[^"]*]]"
// INCLUDES-DEVICE-DAG: "--image=profile=sm_{{[0-9]+}},file=[[CUBINFILE]]"
// INCLUDES-DEVICE-DAG: "--image=profile=compute_{{[0-9]+}},file=[[PTXFILE]]"
// Match host-side preprocessor job with -save-temps.
// HOST-SAVE: "-cc1" "-triple" "x86_64--linux-gnu"
// HOST-SAVE-SAME: "-aux-triple" "nvptx64-nvidia-cuda"
// HOST-SAVE-NOT: "-fcuda-is-device"
// HOST-SAVE-SAME: "-x" "cuda"
// Match host-side compilation.
// HOST: "-cc1" "-triple" "x86_64--linux-gnu"
// HOST-SAME: "-aux-triple" "nvptx64-nvidia-cuda"
// HOST-NOT: "-fcuda-is-device"
// HOST-SAME: "-o" "[[HOSTOUTPUT:[^"]*]]"
// HOST-NOSAVE-SAME: "-x" "cuda"
// HOST-SAVE-SAME: "-x" "cuda-cpp-output"
// INCLUDES-DEVICE-SAME: "-fcuda-include-gpubinary" "[[FATBINARY]]"
// Match external assembler that uses compilation output.
// HOST-AS: "-o" "{{.*}}.o" "[[HOSTOUTPUT]]"
// Match no GPU code inclusion.
// NOINCLUDES-DEVICE-NOT: "-fcuda-include-gpubinary"
// Match no host compilation.
// NOHOST-NOT: "-cc1" "-triple"
// NOHOST-NOT: "-x" "cuda"
// Match linker.
// LINK: "{{.*}}{{ld|link}}{{(.exe)?}}"
// LINK-SAME: "[[HOSTOUTPUT]]"
// Match no linker.
// NOLINK-NOT: "{{.*}}{{ld|link}}{{(.exe)?}}"
|
19,463 | struct node
{
int items[10];
int parent[10];
};
__global__ void generate_fp_tree(unsigned int* input, node *output)
{
int tx = threadIdx.x;
int gtx = blockIdx.x * blockDim.x + threadIdx.x;
if(input[gtx]!=0)
atomicAdd(&output[tx].items[input[gtx]-65],1);
//output[tx].items[input[gtx]-65]+=1;
//output[tx].items[0]+=input[gtx];
}
|
19,464 | // ##########################################################
// By Eugene Ch'ng | www.complexity.io
// Email: genechng@gmail.com
// ----------------------------------------------------------
// The ERC 'Lost Frontiers' Project
// Development for the Parallelisation of ABM Simulation
// ----------------------------------------------------------
// A Basic CUDA Application for ABM Development
//
// Filling arrays with block generated IDs
// identify a specific block ID and make changes for that kernel
// previously we used fillArray<<1,N>>, 1 block and N threads
// we now use <<N,1>> for N blocks and 1 thread
//
// LIMITS OF THREADS AND BLOCKS (use 01.DeviceInfo to check your GPU)
// The particular GPU used here has 1024 threads per block
// This presents a limit, but we can also use blocks per grid
// Each block (for this old AlienWare GPU) has 65535 blocks per grid
// Blocks and Threads have 3 dimensions (type dim3)
// We will explore how to combine both blocks and threads to create
// arbitrarily long numbers
// ----------------------------------------------------------
// How to compile:
// nvcc <filename>.cu -o <outputfile>
// ##########################################################
#include <stdio.h>
#include <iostream>
using namespace std;
// blockIdx is limited at 65535
#define N 10
// --------------------- CUDA KERNELS
// Fill arrays with device thread IDs
__global__ void fillArray(int *dev_arr)
{
// note that we no longer use the for loop here
// blockIdx.x is a device variable - it's the ID of the block
// fillArray kernel is called for each block and has its own ID
int bid = blockIdx.x;
// assign the dev_array element with threadIDx.x
dev_arr[bid] = bid;
// identifying a threads
if(bid == 5)
{
printf("**blockIdx.x 5 is called!!\n");
dev_arr[bid] = bid + 100;
}
}
// the main is a host code
int main(int argc, const char * argv[])
{
cout << "------------ initialising device and host arrays" << endl;
int arr[N]; // host variable
int *dev_arr; // device variable
for(int i=0; i<N; i++)
{
arr[i] = 0;
printf("host arr[%d] = %d\n", i, arr[i]);
}
cout << "------------ allocate device memory dev_arr" << endl;
// allocating a device array to copy to
// note the N * sizeof(int)
cudaMalloc( (void**)&dev_arr, N * sizeof(int) );
cout << "------------ copy arr to dev_arr" << endl;
// copying host array to device
// note the N * sizeof(int)
cudaMemcpy(dev_arr, arr, N * sizeof(int), cudaMemcpyHostToDevice);
cout << "------------ calling kernel fillArray" << endl;
// N block, and 1 thread
// previously we used fillArray<<1,N>>, 1 block and N threads
// we now use <<N,1>> for N blocks and 1 thread
fillArray<<<N,1>>>(dev_arr);
cout << "------------ copy dev_arr to arr" << endl;
// note the N * sizeof(int)
cudaMemcpy(arr, dev_arr, N * sizeof(int), cudaMemcpyDeviceToHost);
cout << "------------ printing changed host array" << endl;
for(int i=0; i<N; i++)
{
printf("** changed host arr[%d] = %d\n", i, arr[i]);
}
// ---- FREE ALLOCATED KERNEL MEMORY
cudaFree( dev_arr );
return 0;
}
|
19,465 | #include<bits/stdc++.h>
using namespace std;
#define BLOCK_SIZE 256
__global__ void pegasos_per_thread(int num_samples, int num_features, double * W, double * X, double * Y, double lambda, int num_iters, double * random_arr, int k) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int n_samples_per_thread = num_samples / k;
for(int sample=0; sample<n_samples_per_thread; sample++)
{
double lr = 1.0 / (lambda * (sample+1));
double pred_output = 0;
for (int i=0; i<num_features; i++)
pred_output += W[index * num_features + i] * X[(n_samples_per_thread * index + sample) * num_features + i];
if (Y[n_samples_per_thread * index + sample] * pred_output >= 1.0) {
for (int i=0; i<num_features; i++)
W[index * num_features + i] = (1.0 - lr * lambda) * W[index * num_features + i];
} else {
for (int i=0; i<num_features; i++)
W[index * num_features + i] = (1.0 - lr * lambda) * W[index * num_features + i] + (lr * Y[n_samples_per_thread * index + sample]) * X[(n_samples_per_thread * index + sample) * num_features + i];
}
}
}
int main() {
srand(time(NULL));
ifstream trainfile ("train.txt");
ifstream labelfile ("labels.txt");
int n_samples=20000;
int n_features=500;
int k = 100;
int numBlocks = (k + BLOCK_SIZE - 1) / BLOCK_SIZE;
int num_iters = 10;
double lambda = 1.0;
int n_samples_per_thread = n_samples / k;
double *W, *X, *Y, *final_W, *random_arr;
double *d_W, *d_X, *d_Y, *d_random_arr;
W = (double *) malloc(k * n_features * sizeof(double));
final_W = (double *) malloc(n_features * sizeof(double));
X = (double *) malloc(n_samples * n_features * sizeof(double));
Y = (double *) malloc(n_samples * sizeof(double));
random_arr = (double *) malloc(num_iters * sizeof(double));
cudaMalloc(&d_W, k * n_features * sizeof(double));
cudaMalloc(&d_X, n_samples * n_features * sizeof(double));
cudaMalloc(&d_Y, n_samples * sizeof(double));
cudaMalloc(&d_random_arr, num_iters * sizeof(double));
for (int i=0;i<n_samples;i++) {
for (int j=0;j<n_features;j++)
trainfile >> X[i*n_features + j];
}
for (int i=0;i<n_samples;i++) {
labelfile >> Y[i];
if (Y[i] == 0) {
Y[i] = -1;
}
}
for (int i=0;i<k;i++) {
for (int j=0;j<n_features;j++) W[i * n_features + j] = 0;
}
for (int i=0;i<n_features;i++) final_W[i] = 0;
for (int i=0;i<num_iters;i++) random_arr[i] = rand() % n_samples_per_thread;
cudaMemcpy(d_X, X, n_samples * n_features * sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_W, W, k * n_features * sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_Y, Y, n_samples * sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_random_arr, random_arr, num_iters * sizeof(double), cudaMemcpyHostToDevice);
pegasos_per_thread<<<numBlocks, BLOCK_SIZE>>>(n_samples, n_features, d_W, d_X, d_Y, lambda, num_iters, d_random_arr, k);
cudaMemcpy(W, d_W, k * n_features * sizeof(double), cudaMemcpyDeviceToHost);
// for (int i=0;i<k;i++)
// for (int j=0;j<n_features;j++)
// cout << W[i * n_features + j] << " ";
for (int i=0;i<k;i++) {
for (int j=0;j<n_features;j++) final_W[j] += (W[i * n_features + j]);
}
for (int i=0;i<n_features;i++) final_W[i] /= k;
// cout << "\nFinalW\n";
// for (int i=0;i<n_features;i++) {cout << final_W[i] << " ";}
double correct = 0.0;
for (int i=0;i<n_samples;i++) {
double val = 0.0;
for (int j=0;j<n_features;j++)
val += final_W[j] * X[i * n_features + j];
if (val * Y[i] >= 0)
correct += 1;
}
cout << "Correct " << correct << endl;
printf("Accuracy %lf\n", correct / n_samples);
return 0;
}
|
19,466 | #include "includes.h"
__global__ void scale_values(float *num, size_t size, float abs_max)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if(idx < size)
num[idx] = (abs_max + abs_max) * num[idx] - abs_max;
} |
19,467 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdlib.h>
#include <stdio.h>
int *IntArray(int length, int first = 0, int step = 0)
{
int *av = (int *)malloc(sizeof(int) * length);
for (int i = 0; i < length; i++)
{
av[i] = first + step * i;
}
return av;
}
bool CompIntArrays(int *a, int *b, int length)
{
for (int i = 0; i < length; i++)
{
if (a[i] != b[i]) return false;
}
return true;
}
bool CompFloatArrays(float *a, float *b, int length)
{
for (int i = 0; i < length; i++)
{
if (a[i] != b[i]) return false;
}
return true;
}
void PrintFloatArray(float *aa, int width, int length)
{
for (int i = 0; i < length; i++) {
printf("%3.3f ", aa[i]);
if ((i>0) && ((i + 1) % width == 0)) printf("\n");
}
printf("\n");
}
void PrintIntArray(int *aa, int width, int length)
{
for (int i = 0; i < length; i++) {
printf("%d ", aa[i]);
if ((i>0) && ((i + 1) % width == 0)) printf("\n");
}
printf("\n");
}
float *RndFloat0to1(int arraySize)
{
float *temp = (float*)malloc(arraySize * sizeof(float));
for (int i = 0; i<arraySize; i++) {
temp[i] = (float)rand() / (float)(RAND_MAX);
}
return temp;
}
|
19,468 | #include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#define BLOCKS 12
#define BLOCKSIZE 1024
//#define BSize 32
//#define QSize (BLOCKS*BLOCKSIZE)/BSize/32
#define BSize 24
#define QSize 16
struct kernel_para{
volatile int *A, *B, *C;
volatile int size;
volatile int block;
volatile int thread;
volatile int warp;
volatile int req;
volatile int funcId;
volatile int taskId;
volatile int doneHost;
int doneGPU;
};
struct kernel_para_GPU{
int warpId;
int baseId;
int queueId;
int locId;
int taskId;
int funcId;
};
typedef struct {
int contents[BSize][QSize]; // body of queue
int last[BSize]; // position of last element
}queue;
__device__ void init_queue(queue *q){
int tid = blockDim.x*blockIdx.x+threadIdx.x;
if(tid < BSize){
// start form 1, since 1st warp is used in scheduling
if(tid == 0){
q->last[tid] = 1;
}else{
q->last[tid] = 0;
}
for (int i = 0; i < QSize; i++){
q->contents[tid][i] = 0;
}
}
}
__device__ void MatMul_kernel(int *A, int *B, int *C, int M_height, int M_width, int N_width, int baseTid){
#if 1
int row = baseTid + (threadIdx.x & 0x1f);
if(row < M_height) {
for (int j = 0; j < N_width; j++){
int sum = 0;
for (int k = 0; k < M_width; k++){
int a = A[row * M_width + k];
int b = B[k * N_width + j];
sum += a * b;
}
C[row * N_width + j] = sum;
}
}
#endif
}
__global__ void deviceRT(volatile int *done, volatile int *totalExecTasks, volatile kernel_para_GPU *warpPool, volatile struct kernel_para *taskBuffer, struct kernel_para *taskArgs, queue *warpQ){
int tid = blockIdx.x*blockDim.x + threadIdx.x;
int warp;
int j;
if(tid < 32){
if(tid < BSize)
init_queue(warpQ);
warp = 0;
j = 0;
while(!(*done)){
if(*done) continue;
if(tid < BSize){
if(taskBuffer[tid].req == 1 && !(*done)){
warp = taskBuffer[tid].warp;
while(warp > 0){
if(warpQ->contents[tid][warpQ->last[tid]] == 0){
// printf("Scheduling:%d, %d\n", taskBuffer[tid].taskId, tid);
warpPool[tid*QSize + warpQ->last[tid]].queueId = tid;
warpPool[tid*QSize + warpQ->last[tid]].locId = warpQ->last[tid];
warpQ->contents[tid][warpQ->last[tid]] = 1;
warpPool[tid*QSize + warpQ->last[tid]].baseId = j*32;
warpPool[tid*QSize + warpQ->last[tid]].taskId = taskBuffer[tid].taskId;
warpPool[tid*QSize + warpQ->last[tid]].warpId = 1;
__threadfence();
warp--;
j++;
}
warpQ->last[tid]++;
if(warpQ->last[tid] == QSize){
if(tid == 0){
warpQ->last[tid] = 1;
}else{
warpQ->last[tid] = 0;
}
}
}
taskBuffer[tid].req = 0;
j = 0;
}
}
}
}else{
#if 1
int warpIdx = (blockIdx.x*blockDim.x + threadIdx.x)/32;
while(!(*done)){
while(warpPool[warpIdx].warpId == 0 && !(*done));
if(*done) return;
switch(taskArgs[warpPool[warpIdx].taskId].funcId){
case 1:
// if((threadIdx.x & 0x1f) == 0) printf("Before:%d, %d\n", warpPool[warpIdx].taskId, warpPool[warpIdx].baseId);
MatMul_kernel((int*)taskArgs[warpPool[warpIdx].taskId].A, (int*)taskArgs[warpPool[warpIdx].taskId].B, (int*)taskArgs[warpPool[warpIdx].taskId].C, taskArgs[warpPool[warpIdx].taskId].size, taskArgs[warpPool[warpIdx].taskId].size, taskArgs[warpPool[warpIdx].taskId].size, warpPool[warpIdx].baseId);
// if((threadIdx.x & 0x1f) == 0) printf("After:%p\n", taskArgs[warpPool[warpIdx].taskId].C);
break;
default: {
printf("kernel Type not found\n");
return;
}
}
#if 1
if((threadIdx.x & 0x1f) == 0){
if((atomicSub((int*)&taskArgs[warpPool[warpIdx].taskId].doneGPU,1)) ==1){
taskArgs[warpPool[warpIdx].taskId].doneHost = 0;
atomicAdd((int*)&totalExecTasks[0],1);
// printf("Kernel:%d\n", *totalExecTasks);
}
warpPool[warpIdx].warpId = 0;
warpQ->contents[warpPool[warpIdx].queueId][warpPool[warpIdx].locId] = 0;
__threadfence();
}
#endif
}
#endif
}
// }
}
|
19,469 | float h_A[]= {
0.6077304871894453, 0.8936961933315026, 0.7270382344822287, 0.8023482238859894, 0.6075262879503021, 0.6641276886564242, 0.6947977310727611, 0.7349836135509735, 0.9990753103838406, 0.8299995402483878, 0.7080981018287671, 0.6435698793550537, 0.5146250303212558, 0.9750651429625462, 0.7535471719286995, 0.7954907088643621, 0.5846605661119106, 0.8845511671988633, 0.8830953680301334, 0.9036883695233936, 0.7057442437551014, 0.565638704789128, 0.6650820231593992, 0.5351678685545055, 0.7225087716489379, 0.542079419224737, 0.5983154959345847, 0.5874137534100938, 0.8970822605996767, 0.988692141652958, 0.5531329424080711, 0.5746130926146933, 0.8562990410819367, 0.9958150695040241, 0.5084585076185779, 0.6929359816673681, 0.5011937999117615, 0.6262002673547813, 0.7172258979969032, 0.5384534465042494, 0.5453944073771485, 0.6334415790691448, 0.8528945650133986, 0.6484316256095004, 0.9653763238758419, 0.7115017864084678, 0.5396201432575601, 0.7314336056272668, 0.6010133901932092, 0.5546585965126516, 0.7336216444156685, 0.8594596462128925, 0.7331163390730948, 0.6804430710246299, 0.7226532403623585, 0.946262367118587, 0.8479437345681851, 0.9141256408072248, 0.93819115941898, 0.86506375015223, 0.8904104862143157, 0.7353930867562937, 0.7375363410987348, 0.5018078060009985, 0.508928556156468, 0.5030260050139146, 0.8402655670431577, 0.5015533686454323, 0.6614434965927423, 0.6551402007621985, 0.6219640392897853, 0.5246860477114734, 0.856900367576762, 0.8976724278923811, 0.7669115237418173, 0.6617684070750576, 0.7529933888713992, 0.8144209613997686, 0.541948337342274, 0.5016836700262621, 0.9904506262850501, 0.6815748475982562, 0.7326246186442684, 0.5020574384437908, 0.7872405126435797, 0.5710496422953526, 0.7548471293355141, 0.7341493124392846, 0.8772776849796255, 0.9855542906838674, 0.6984637313244942, 0.669977749083825, 0.5616871132407188, 0.7946420588754661, 0.9374900220085963, 0.9784591960699991, 0.7221325462792958, 0.7126245840259444, 0.6555488352919077, 0.7387040493774408, 0.9072064981730342, 0.5431666077070556, 0.9706588630090915, 0.5060319746036444, 0.6268872170029198, 0.5976879664135204, 0.5286812480980114, 0.8270825601034868, 0.9245840700735047, 0.8453451829768848, 0.61146716334915, 0.8072513476113714, 0.643188200140935, 0.8735995284177094, 0.5971103522106631, 0.8468741623714933, 0.9164903387492379, 0.6825003697962047, 0.9448091514164093, 0.5685283299282942, 0.7510165569715631, 0.6647751172115199, 0.671722431189447, 0.9211545810966385, 0.5067123047280512, 0.8750915076668405, 0.572982306303386, 0.8199966737644409, 0.6806268384924339, 0.8980159392737107, 0.6355438036772195, 0.8996526810150938, 0.5751380284751533, 0.7942772113956376, 0.8485144091211125, 0.9834095492546817, 0.5736684263696986, 0.9651703029465525, 0.9875424302175217, 0.6593742245842557, 0.7741512611582702, 0.558602822446158, 0.9459895739046723, 0.7843223657041642, 0.9363835667896402, 0.7610876942817715, 0.9232949959434247, 0.8078673574154356, 0.6031138388622471, 0.7475877700143339, 0.6406018518373049, 0.8057647488173008, 0.9209022625387628, 0.5716286497071721, 0.9558810461400031, 0.8733571885450973, 0.9699698786808644, 0.9790774953795842, 0.5293114293600089, 0.5372732352356515, 0.9083853698012753, 0.7806166504046719, 0.8751527996846902, 0.9776739085347852, 0.9742429724334001, 0.8953595734250159, 0.9638946624965519, 0.5160799683084616, 0.642118317037299, 0.6504606594549737, 0.5994471124532459, 0.7945800668649758, 0.8131661144748448, 0.7448749520163217, 0.8886323947614919, 0.985732770940324, 0.8406482703212617, 0.7085333511249454, 0.8494947752175452, 0.5088782239993301, 0.8140550222755774, 0.7119832091266642, 0.8480681622973415, 0.7024864070658166, 0.6295761475833481, 0.8211061739516443, 0.565210783319341, 0.8745499787189424, 0.6532366630587098, 0.5077480235092282, 0.7535464109434827, 0.889855447538198, 0.7912346638569813, 0.9482006159100561, 0.7480216917576786, 0.6573164427690329, 0.6429155126205939, 0.819800249157046, 0.8542565162385122, 0.810152498575017, 0.9862246071870013, 0.9814756609935732, 0.8562498947580977, 0.5493366239080402, 0.63963333817088, 0.5629892968993895, 0.9914838378805743, 0.7155992727455711, 0.594881620329265, 0.8135730966837744, 0.9613833912170304, 0.7624653338936869, 0.946217016135658, 0.8302563622832633, 0.6514999216942823, 0.5753257484779142, 0.6531751041832075, 0.9554001965423797, 0.935325382110522, 0.5500103981914408, 0.5115491989118183, 0.6015843278950539, 0.8087744465270137, 0.5221647212211163, 0.5707460617775546, 0.8277732823239767, 0.9457425939387083, 0.7784680075982333, 0.523683498923483, 0.6416813857762795, 0.7375578837013332, 0.6939476970164926, 0.657013285794285, 0.8253333528968283, 0.7972448800298588, 0.5608119848270192, 0.6741377341902426, 0.7412637870399628, 0.6292224715062635, 0.5868162304486556, 0.7272108408818942, 0.7774466654316261, 0.8969112537246564, 0.8617751166691456, 0.6949341874376577, 0.7964965270820268, 0.9645357994224799, 0.856787975358551, 0.8598297828901271, 0.7089456872709253, 0.548960879697256, 0.6056837791916061, 0.6940979701081309, 0.9125197937500813, 0.9869499537826305, 0.8092144672719253, 0.6538850717949727, 0.7974325349550477, 0.6709115216298163, 0.8134229584773732, 0.8131218594164379, 0.9546159183473839, 0.5894396168081038, 0.6365068967077319, 0.6677599887500725, 0.637568108740104, 0.6771524763413612, 0.6796445976040641, 0.6506570763186188, 0.5114209697856398, 0.964790503081095, 0.6530651660232027, 0.6747708354600443, 0.6915065126806282, 0.5371087819634859, 0.7199754396519129, 0.6673428501379022, 0.5049948371184059, 0.7848861457514458, 0.5935434860099471, 0.6191284310847527, 0.6943060739435422, 0.6371485343344283, 0.6106902442908768, 0.888043151821145, 0.5802116097571137, 0.694607198171155, 0.6972040507674084, 0.7218531732626228, 0.8534479905004304, 0.7888859052532426, 0.7185154364502468, 0.572572832268724, 0.8694186728110163, 0.967265770219241, 0.5726786048376513, 0.9892057768210392, 0.8370288443829308, 0.8877118305217699, 0.6914969771340058, 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0.5680907865511712, 0.9908171590872487, 0.8602246610510723, 0.6499952230309913, 0.667236545022168, 0.891216312964001, 0.9399910288813473, 0.9006904104761797, 0.8707196532938934, 0.7024692122979094, 0.7523364063094344, 0.7813364338352407, 0.9650709999184659, 0.9780863597757017, 0.9566897861531656, 0.9581211865999629, 0.7743876267843408, 0.5121091324937851, 0.7276358671415026, 0.7259593886732262, 0.5141808316856855, 0.9319913324456437, 0.7871097028690299, 0.8043217322149208, 0.691430720106373, 0.5186653970164872, 0.6475569939372363, 0.7660663291407325, 0.6615666202847955, 0.5713360130384172, 0.7541921244116196, 0.8180240505259428, 0.6722286166334251, 0.6584308649832504, 0.9282734634706542, 0.7968846474992997, 0.510433774154059, 0.9165769810925226, 0.7873527755349081, 0.9366565278258843, 0.5616202539297626, 0.979271172732559, 0.6947533005419584, 0.7734833657664607, 0.8948438544430002, 0.8478843922999667, 0.8624669609370419, 0.6295625360455405, 0.8081919070658008, 0.943569010920235, 0.7537331124777563, 0.7434044481216913, 0.7825054821463442, 0.9206259997580484, 0.5576982365744472, 0.7358322529220559, 0.6945131937110812, 0.6652999393357472, 0.8716274299804996, 0.9442282442742966, 0.5371306925421033, 0.7521744647616883, 0.6255380936861692, 0.8632980861482957, 0.6197523890632584, 0.5876045918649497, 0.6327628458818945, 0.6570506618656222, 0.8316378310024659, 0.6124844988709142, 0.98841757464248, 0.6685517616776696, 0.7422008164879147, 0.6900566542030575, 0.6058489608921296, 0.6002387341103285, 0.9784325761746402, 0.5468236635327661, 0.8807902832000358, 0.6488302249065478, 0.9669119428350782, 0.541642681796395, 0.536091596743241, 0.5132922810706837, 0.6258666410933666, 0.704452146659156, 0.7730867609399317, 0.6279964941315781, 0.6444888761766185, 0.6373477439040967, 0.9310438825108356, 0.6986846495910035, 0.9135345354304575, 0.8057185672281156, 0.7891171117184084, 0.6429426359923003, 0.6386353571945874, 0.9880506014853263, 0.5131499611453819, 0.689007235503535, 0.7079839213941013, 0.963842759838722, 0.645690560794109, 0.7970026257968266, 0.6858499177799122, 0.6344602740801861, 0.6316958994804595, 0.7414706516963778, 0.5949347762986199, 0.8741989756617251, 0.9375222217340768, 0.5506946135836601, 0.9133860726086575, 0.5792892345492967, 0.5065739111063685, 0.9599636398233774, 0.9353430144299204, 0.8970167501822496, 0.6371325347925085, 0.5607314876661917, 0.855308659394107, 0.6045967546085521, 0.5336286954553713, 0.7872283458248224, 0.8713925038260605, 0.8307750317755629, 0.5767296945639147, 0.9849013380193521, 0.8182446270644248, 0.8729729231284772, 0.7918447052351002, 0.9465145741949013, 0.8043266300472749, 0.5720981059795998, 0.5019875403111789, 0.5063978441501273, 0.965547559706204, 0.9324715239162242, 0.9280139201879956, 0.5763400443547644, 0.595981967514552, 0.959351261762003, 0.9329610979884839, 0.5692606790632095, 0.973918830825363, 0.8005413905655636, 0.6379933526660371, 0.6523453084419554, 0.8081486301283729, 0.8938616319703533, 0.6989786995677862, 0.9701645657261178, 0.5392061039584364, 0.8952892096436551, 0.5910056201567992, 0.8844057378613666, 0.547294392543939, 0.5129302991795457, 0.891035531794044, 0.7973070441141994, 0.5520511904192636, 0.965704844402396, 0.5317764356096008, 0.7528936658485155, 0.7528659731200312, 0.6141055083134842, 0.8477684324858878, 0.9714283704130011, 0.8347979635147567, 0.5061285766881781, 0.8141929476177289, 0.6482700245209269, 0.6368256590430981, 0.9959506174081396, 0.6179806329836801, 0.6384890808612476, 0.5500356160391825, 0.6957257518446708, 0.6751421608417507, 0.6341683026770178, 0.9607685932117337, 0.8751632352752526, 0.8603812190655565, 0.7925119856364251, 0.8165619717260697, 0.9930753602669073, 0.9774937121805491, 0.5496366252694442, 0.6975120763994025, 0.6689572968398276, 0.8227670684798715, 0.8636905261690977, 0.9922413515733951, 0.5692071302055028, 0.9867118271021191, 0.6133411656263881, 0.6170147751230962, 0.8300839174610333, 0.640396233461878, 0.8318112676320051, 0.9002002868239263, 0.541463249123036, 0.9204351885636652, 0.9456211455175856, 0.9595666212118356, 0.7135150901612599, 0.6465912687186364, 0.7842941519497164, 0.7079012013706201, 0.6782172492866858, 0.6221859385423356, 0.5994546312580888, 0.8678036882070499, 0.9525146458238671, 0.6473454741943929, 0.9850286127025095, 0.5572413091563413, 0.6893123017127227, 0.919277060133116, 0.925098003089408, 0.6274882575358622, 0.8690328973772381, 0.6356634944888797, 0.6405892086696023, 0.5745387911253275, 0.830698436001196, 0.5454950032551454, 0.8711053157685006, 0.9081878015305623, 0.7707427982307797, 0.596914321477272, 0.5050122998486876, 0.796933923188407, 0.8321285798315917, 0.840080454849614, 0.6257394795154131, 0.517607667631108, 0.9334099904293253, 0.6921895637361358, 0.9541648341184353, 0.6979937873660733, 0.8065241562826604, 0.526582737679339, 0.5242774968763657, 0.7644834609075959, 0.8371249102673943, 0.9726979595833369, 0.7128118197176099, 0.5436501828226064, 0.8066766863536985, 0.6616036718979259, 0.5430130072163117, 0.6676162077353863, 0.8567167008709977, 0.789517210123148, 0.5382839022735193, 0.6057484541214797, 0.5876359530744767, 0.5638961276328702, 0.5811706189403649, 0.534914137478521, 0.5264993577299133, 0.5720022203476961, 0.5562905705098902, 0.8338284060630199, 0.7482721948601616, 0.7306375676106225, 0.8394858289411373, 0.5231453739798171, 0.9392974347455081, 0.9313974260753359, 0.6906299493041653, 0.6915260089070843, 0.9332112368659499, 0.9378124498691435, 0.8579378312825913, 0.8872632217716403, 0.880944538777092, 0.6371676372474623, 0.5398432943384968, 0.5110833013236116, 0.5122703079790221, 0.5086337652908574, 0.8904955145991069, 0.6586875404083679, 0.6370201866093357, 0.7451552895962217, 0.8810900530893072, 0.7254567855720364, 0.7039767669897298, 0.9318528733347407, 0.7967775172866415, 0.8219790062675767, 0.9182920063390345, 0.5557283747698318, 0.9738148173942283, 0.932106195980199, 0.5101102665168674, 0.8038199986963188, 0.6320412021820783, 0.9635016318568482, 0.7538001837357745, 0.8646173868400031, 0.9440693338834011, 0.6507042060171464, 0.9362792282764943, 0.7904158004335213, 0.5253874767782399, 0.8486834190419366, 0.5548689070224484, 0.90370826753541, 0.9102461385982643, 0.8778410550966057, 0.7763816057407731, 0.6622987672525864, 0.9565888234256201, 0.9108241182800711, 0.6225735827146481, 0.8493087770187033, 0.9274028207195495, 0.9956032408332515, 0.9176717521706805, 0.6327948763994915, 0.6256867450881048, 0.6763444786010271, 0.8547804909672689, 0.6077532493761411, 0.936935006026436, 0.9146235607509507, 0.7521522475757751, 0.6262845321446115, 0.868034069611334, 0.7094783939738624, 0.8314006702054626, 0.7252802181147415, 0.8635088390329035, 0.566088231941807, 0.8799843303262871, 0.7350289556495408, 0.5221719061820213, 0.6554833209603161, 0.7654627855705827, 0.5669322996695396, 0.5634560542607875, 0.8940772200195084, 0.7216514223407551, 0.8637040376487974, 0.5744336351025947, 0.8213855497796667, 0.5487561601020481, 0.9922525803404885, 0.9079478980932388, 0.9018008015276013, 0.7259179594342382, 0.8546367666583521, 0.7762403600902039, 0.534161925161849, 0.718760262902612, 0.9668257920203635, 0.8021481416345985, 0.9399561944344342, 0.9303546534682272, 0.6121211322160983, 0.6835949773150779, 0.8175160684797045, 0.7135701141414973, 0.6016440154698091, 0.8091732282694211, 0.9479123982772432, 0.6835172569821869, 0.8665629670569803, 0.7686062322969802, 0.7778776914296034, 0.5536914316754034, 0.7034612022873294, 0.5195618946984116, 0.7174210773645748, 0.7086623845611206, 0.7059269206494792, 0.7742545910179844, 0.5861109361742065, 0.8973502952686365, 0.5404109038294537, 0.9082657576207449, 0.8838915762961617, 0.6690705617799371, 0.5362874448825579, 0.5883887286877734, 0.6171296264016142, 0.7359744207260277, 0.6727600910412133, 0.6968929912440754, 0.6445886987056526, 0.8969630568327993, 0.844406495007652, 0.859410639845109, 0.5542327403452594, 0.5418300788575114, 0.8229336069884807, 0.7702411148415633, 0.6992643021579763, 0.6370216302163579, 0.5935595455539987, 0.7831871918606624, 0.5003690407914905, 0.810537219887935, 0.9584480419933085, 0.9538050833206433, 0.8786074681772853, 0.9543926417561098, 0.8126576604583973, 0.7466015286697134, 0.9476327621893113, 0.6514689624391291, 0.8737260130589884, 0.8768061461398486, 0.9857655449493083, 0.7368372363497715, 0.5838994687950858, 0.8354757136823443, 0.7851187712589868, 0.6854745749379481, 0.9995384789542279, 0.6316204574463615, 0.8319038241772269, 0.8392272629628841, 0.7391079111417818, 0.9072724355525237, 0.7452399978977039, 0.5314650610375145, 0.570334482629419, 0.9251928769762323, 0.9194710871460516, 0.8298856455110102, 0.6854409977741195, 0.6239349431436012, 0.7370293219027274, 0.8881280227922368, 0.7044059062755758, 0.6266541305106641, 0.8408186219062133, 0.7097857083800778, 0.7650375450010813, 0.9008332598474058, 0.9575642758272623, 0.954949099552985, 0.6656088890424113, 0.8157987059515841, 0.5528481260129225, 0.678125638200151, 0.5300394279914213, 0.8418431539125122, 0.89269087084705, 0.5630990507550031, 0.9657617798199922, 0.5728879738077812, 0.8060947630371705, 0.6007291434375273, 0.8672558484697739, 0.8901956733711196, 0.5985282402269115, 0.8858836058901808, 0.6432052042992941, 0.83415652846205, 0.7601058975155932, 0.8001559562566796, 0.8881798414714291, 0.5462888361497189, 0.9308518029946715, 0.6171618534174912, 0.9722863282129968, 0.6338506607961437, 0.6224090051278305, 0.9492333540941631, 0.5325057182443204, 0.7166458345766431, 0.8573100146231493, 0.7563616956470051, 0.6537751504821954, 0.5372396525684561, 0.8765571181228624, 0.584634103589377, 0.5017986753946342, 0.9041155158229874, 0.8127482175792308, 0.9921790188491411, 0.947302547922207, 0.839687624671009, 0.6929239113066084, 0.6452224913626599, 0.7545092730127408, 0.7338104266763523, 0.8419344766989096, 0.5408044914327763, 0.9816741504351705, 0.7547134406922869, 0.5246371755401358, 0.6516861740256075, 0.509073752865805, 0.7243719298964837, 0.7833790351945757, 0.7754787649651959, 0.9934064162055186, 0.9130432999893378, 0.54534318866541, 0.9776684577179982, 0.6494662412937169, 0.5326428414157551, 0.9394318441181781, 0.8954115637592024, 0.9182176339694715, 0.5676089093923226, 0.8371711573428958, 0.9961707257373542, 0.7847590361338429, 0.6985191900797748, 0.731419520464657, 0.8940504394046963, 0.9124006645305888, 0.7274616501676814, 0.9889997891408733, 0.8896960648606576, 0.7626320000667504, 50000.0};
int h_B[]= {
3, 5, 7, 9, 11, 13, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 373, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 395, 397, 399, 401, 403, 405, 407, 409, 411, 413, 415, 417, 419, 421, 423, 425, 427, 429, 431, 433, 435, 437, 439, 441, 443, 445, 447, 449, 451, 453, 455, 457, 459, 461, 463, 465, 467, 469, 471, 473, 476, 478, 480, 482, 485, 487, 489, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 511, 513, 515, 517, 519, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 559, 561, 563, 565, 567, 569, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 642, 644, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 668, 670, 672, 674, 677, 679, 681, 683, 687, 689, 691, 693, 695, 697, 700, 702, 704, 706, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 768, 770, 773, 775, 778, 780, 782, 784, 786, 788, 790, 792, 795, 797, 800, 802, 805, 807, 809, 811, 813, 815, 817, 819, 822, 824, 827, 829, 831, 833, 836, 838, 840, 842, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 867, 869, 872, 874, 877, 879, 881, 883, 885, 887, 890, 892, 894, 896, 898, 900, 903, 905, 908, 910, 913, 915, 918, 920, 923, 925, 928, 930, 933, 935, 938, 940, 942, 944, 946, 948, 951, 953, 955, 957, 959, 961, 964, 966, 968, 970, 972, 974, 977, 979, 982, 984, 987, 989, 992, 994, 996, 998, 1001, 1003, 1005, 1007, 1010, 1012, 1016, 1018, 1020, 1022, 1025, 1027, 1030, 1032, 1035, 1037, 1040, 1042, 1045, 1047, 1050, 1052, 1055, 1057, 1060, 1062, 1065, 1067, 1070, 1072, 1075, 1077, 1080, 1082, 1085, 1087, 1090, 1092, 1095, 1097, 1100, 1102, 1104, 1106, 1108, 1110, 1113, 1115, 1118, 1120, 1123, 1125, 1128, 1130, 1133, 1135, 1138, 1140, 1143, 1145, 1148, 1150, 1153, 1155, 1158, 1160, 1163, 1165, 1168, 1170, 1172, 1174, 1176, 1178, 1181, 1183, 1186, 1188, 1191, 1193, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1210, 1212, 1214, 1216, 1218, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1244, 1246, 1248, 1250, 1252, 1254, 1256, 1258, 1260, 1262, 1264, 1266, 1268, 1270, 1272, 1274, 1276, 1278, 1280, 1282, 1284, 1286, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1326, 1328, 1330, 1332, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1355, 1357, 1359, 1361, 1363, 1365, 1367, 1369, 1371, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1389, 1391, 1393, 1395, 1397, 1399, 1401, 1403, 1405, 1407, 1409, 1411, 1413, 1415, 1418, 1420, 1423, 1425, 1428, 1430, 1433, 1435, 1438, 1440, 1443, 1445, 1448, 1450, 1453, 1455, 1457, 1459, 1461, 1463, 1466, 1468, 1471, 1473, 1475, 1477, 1479, 1481, 1483, 1485, 1488, 1490, 1492, 1494, 1497, 1499, 1501, 1503, 1506, 1508, 1512, 1514, 1516, 1518, 1520, 1522, 1525, 1527, 1530, 1532, 1537, 1539, 1541, 1543, 1545, 1547, 1550, 1552, 1555, 1557, 1560, 1562, 1565, 1567, 1569, 1571, 1573, 1575, 1578, 1580, 1583, 1585, 1588, 1590, 1593, 1595, 1598, 1600, 1603, 1605, 1608, 1610, 1613, 1615, 1618, 1620, 1623, 1625, 1628, 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35861, 35862, 35863, 35864, 35865, 35867, 35868, 35869, 35873, 35875, 35877, 35878, 35879, 35881, 35882, 35560, 35883, 35885, 35888, 35890, 35892, 35893, 35560, 35895, 35896, 35897, 35898, 35902, 35903, 35905, 35908, 35909, 35910, 35561, 35911, 35912, 35913, 35914, 35915, 35916, 35919, 35920, 35923, 35927, 35928, 35932, 35933, 35934, 35935, 35936, 35937, 35938, 35939, 35940, 35562, 35941, 35942, 35943, 35944, 35945, 35947, 35948, 35952, 35953, 35955, 35956, 35965, 35966, 35971, 35973, 35581, 35600, 35604, 35790, 35770, 35768, 35792, 35662, 35653, 35655, 35662, 35663, 35977, 33508, 35978, 35758, 35756, 35770, 35768, 35762, 35770, 35768, 35694, 35698, 35709, 35724, 35758, 35756, 35762, 35770, 35768, 35778, 35792, 35790, 35794, 35901, 35901, 35907, 35925, 35931, 35951, 35980, 35394, 35981, 35982, 35983, 35984, 35985, 35986, 34736, 35987, 35988, 35989, 35990, 35991, 35992, 33570, 35993, 35995, 35997, 35999, 36002, 36003, 36004, 36005, 36010, 36011, 33620, 36012, 36015, 35961, 36016, 36017, 36019, 36020, 36028, 36029, 36030, 36032, 36033, 35975, 35996, 36001, 36008, 36036, 36041, 36043, 36044, 36045, 36046, 35471, 36047, 36048, 36049, 36051, 36052, 36053, 36040, 36027, 36025, 36023, 36040, 36038, 36054, 36057, 36058, 36059, 36060, 36065, 36066, 36067, 36072, 36073, 36040, 36038, 36074, 36077, 36080, 36081, 36071, 36071, 36071, 28, 29, 30, 31, 36099, 36101, 36105, 36107, 35591, 35594, 36124, 36128, 35621, 36138, 36143, 35637, 36149, 36151, 35700, 35703, 36187, 36194, 36196, 35744, 35749, 36217, 36241, 36245, 36246, 36251, 36252, 36256, 36257, 36262, 36263, 36267, 36268, 36273, 36274, 36281, 36286, 36299, 36302, 36306, 36307, 36310, 36317, 36323, 36329, 36334, 36338, 36339, 36346, 36097, 35568, 36103, 35577, 35972, 36354, 35855, 35853, 35800, 35855, 35853, 35588, 35855, 35853, 36120, 36355, 36122, 36356, 35610, 35616, 35618, 35790, 36357, 35620, 36358, 36359, 36360, 36136, 36319, 36141, 35635, 36147, 35644, 36304, 36156, 36152, 36361, 36293, 36362, 36153, 36154, 36363, 36290, 35870, 36304, 36156, 36155, 36364, 36293, 36292, 36157, 36365, 36158, 36319, 36160, 35671, 36162, 35675, 36367, 36205, 36221, 35743, 35748, 36214, 36369, 36370, 35764, 36371, 36372, 35790, 36165, 36167, 36169, 36171, 35743, 35748, 35760, 36373, 35764, 36374, 36375, 35790, 36172, 36173, 36174, 36175, 35692, 36376, 35696, 36377, 35796, 36183, 36378, 36185, 35717, 36191, 35721, 36379, 35730, 35732, 36201, 35736, 35738, 36205, 36221, 35743, 35748, 36214, 36380, 36381, 35760, 36382, 35764, 36383, 36384, 35790, 36220, 36221, 35776, 36385, 35780, 36225, 35788, 35786, 36386, 36387, 36388, 35796, 36230, 35800, 36233, 36235, 36237, 36239, 36238, 35812, 36244, 35819, 36249, 36250, 36254, 35827, 35831, 36260, 36261, 36265, 35839, 35843, 36271, 36272, 35849, 36277, 35855, 35853, 36284, 36289, 36290, 35870, 36304, 36389, 36312, 36293, 36292, 36295, 36294, 36297, 35886, 35884, 36304, 36390, 36312, 36313, 36315, 36391, 36319, 36321, 36324, 35921, 36326, 36392, 36327, 36393, 35929, 36333, 36343, 36344, 36394, 35949, 36349, 36396, 36403, 36410, 36413, 36417, 36421, 36014, 36424, 36350, 35964, 35963, 35967, 36352, 35970, 35969, 35996, 35994, 36434, 36435, 36398, 36400, 36402, 36408, 36406, 35996, 35994, 36436, 36416, 36437, 36419, 36426, 36428, 36444, 36432, 36451, 36452, 36453, 36454, 36430, 36441, 36034, 36447, 36448, 36432, 36455, 36456, 36430, 36441, 36034, 36447, 36448, 36461, 36462, 36432, 36034, 36438, 36467, 36468, 36042, 36441, 36443, 36447, 36448, 36473, 36055, 36450, 36474, 36055, 36459, 36071, 36466, 36064, 36475, 36069, 36466, 36079, 36076, 36472, 28, 29, 30, 31, 36529, 36480, 36530, 36481, 36531, 36482, 36532, 36483, 36535, 36536, 36537, 36538, 36539, 36540, 36541, 36542, 36119, 36117, 36543, 36545, 36486, 36547, 36487, 36548, 36549, 36550, 36552, 36134, 36553, 36556, 36489, 36557, 36558, 36490, 36559, 36491, 36560, 36492, 36561, 36493, 36562, 36519, 36520, 36521, 36563, 36564, 36566, 36568, 36569, 36571, 36517, 36572, 36518, 36573, 36519, 36520, 36521, 36574, 36575, 36577, 36578, 36579, 36581, 36582, 36583, 36584, 36585, 36586, 36366, 36588, 36589, 36590, 36209, 36591, 36212, 36592, 36593, 36218, 36595, 36598, 36596, 36599, 36600, 36601, 36602, 36603, 36209, 36604, 36212, 36605, 36218, 36607, 36610, 36608, 36611, 36612, 36613, 36614, 36615, 36617, 36619, 36182, 36180, 36620, 36622, 36496, 36623, 36624, 36625, 36497, 36498, 36627, 36628, 36629, 36630, 36631, 36632, 36633, 36634, 36209, 36635, 36212, 36636, 36637, 36639, 36218, 36641, 36644, 36642, 36645, 36646, 36647, 36649, 36650, 36651, 36652, 36653, 36656, 36657, 36658, 36659, 36660, 36661, 36662, 36663, 36664, 36242, 36665, 35817, 35816, 36666, 36667, 36506, 36668, 36669, 36670, 36508, 36671, 36672, 36510, 36673, 36674, 36675, 36512, 36676, 36677, 36514, 36678, 36679, 36680, 36681, 36682, 36515, 36683, 36516, 36684, 36685, 36517, 36686, 36518, 36687, 36519, 36520, 36521, 36689, 36690, 36691, 36692, 36693, 36694, 36517, 36695, 36696, 36518, 36697, 36519, 36520, 36521, 36699, 36700, 36701, 36522, 36703, 36704, 36523, 36705, 36706, 36707, 36709, 36711, 36331, 36712, 36336, 36526, 36341, 36713, 36714, 36716, 35954, 36717, 36420, 36726, 36727, 36728, 35962, 36729, 36730, 36731, 36732, 35968, 36534, 36733, 36734, 36718, 36737, 36738, 36739, 36720, 36740, 36741, 36404, 36742, 36743, 36414, 36745, 36006, 36747, 36725, 36748, 36749, 36751, 36752, 36754, 36756, 36757, 36758, 36429, 36759, 36760, 36761, 36762, 36764, 36765, 36766, 36431, 36767, 36768, 36769, 36771, 36772, 36773, 36774, 36776, 36777, 36778, 36445, 36779, 36780, 36782, 36783, 36785, 36786, 36787, 36788, 36789, 36466, 36791, 36792, 36793, 36794, 36795, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36801, 36803, 36805, 36807, 36808, 36811, 36814, 36816, 36817, 36820, 36822, 36827, 36830, 36833, 36835, 36837, 36839, 36841, 36842, 36843, 36844, 36846, 36848, 36850, 36852, 36854, 36855, 36856, 36857, 36859, 36861, 36872, 36874, 36877, 36880, 36886, 36888, 36890, 36893, 36901, 36902, 36905, 36909, 36910, 36919, 36921, 36925, 36928, 36934, 36943, 36946, 36948, 36949, 36952, 36956, 36959, 36963, 36966, 36970, 36972, 36974, 36977, 36979, 36981, 36982, 36983, 36985, 36987, 36990, 36991, 36993, 36995, 36996, 36997, 37000, 37001, 37004, 36710, 37010, 37012, 37013, 37014, 36715, 37018, 37024, 37021, 37029, 37026, 37030, 36810, 36900, 36941, 36951, 36958, 36965, 36900, 36810, 36941, 36951, 36958, 36965, 36899, 36900, 36941, 36951, 36958, 36965, 36819, 36546, 36915, 36824, 36917, 36640, 36923, 36897, 36616, 36933, 36904, 36908, 36915, 36913, 36917, 36640, 36923, 36897, 36616, 36933, 36998, 36708, 37006, 36998, 36708, 36867, 36865, 36368, 36870, 36876, 36884, 36882, 36606, 36941, 36941, 36897, 36616, 36900, 36899, 36951, 36958, 36965, 36904, 36908, 36915, 36913, 36917, 36640, 36923, 36930, 36648, 36933, 36939, 36937, 36941, 36951, 36958, 36965, 36984, 36998, 36708, 37006, 37033, 37037, 37040, 37041, 37043, 37045, 36422, 37047, 37036, 37049, 37036, 37049, 37051, 37056, 37064, 37036, 37049, 37036, 37049, 37036, 37049, 37075, 37054, 37058, 37060, 37062, 37066, 36781, 37054, 37058, 37060, 37062, 37066, 36784, 37071, 37073, 37077, 36069, 37071, 37073, 37077, 36063, 37085, 37071, 37073, 37077, 36790, 37071, 37073, 37077, 37088, 28, 29, 30, 31, 37127, 37141, 37153, 37157, 37159, 37166, 37171, 37173, 37175, 37177, 37023, 37120, 37121, 37122, 37123, 37028, 37209, 37210, 37211, 37169, 37170, 37212, 37213, 37174, 37214, 37176, 37124, 36975, 36973, 37215, 37216, 37217, 37169, 37170, 37218, 37219, 37174, 37220, 37176, 37125, 36975, 36973, 37221, 37222, 37223, 37169, 37170, 37224, 37225, 37174, 37226, 37176, 37126, 36975, 36973, 37227, 36906, 37228, 36626, 36823, 36821, 37229, 37230, 37231, 37165, 37164, 37232, 37233, 37234, 37235, 37236, 36551, 37237, 36906, 36626, 37238, 36911, 37239, 37240, 37241, 37165, 37131, 37242, 37243, 37244, 37245, 37246, 36555, 37132, 37133, 37134, 37135, 37136, 37137, 37139, 36565, 37143, 37144, 37145, 37147, 36576, 37150, 37188, 37190, 37191, 37193, 37247, 37194, 36831, 37196, 37197, 37248, 37249, 37015, 37200, 37011, 37202, 37019, 37133, 37134, 37135, 37136, 37137, 37139, 36565, 37143, 37144, 37145, 37147, 36576, 37150, 37188, 37190, 37191, 37193, 37250, 37194, 36863, 37196, 37197, 37251, 37252, 37253, 37015, 37200, 37011, 37202, 37019, 37254, 37255, 37152, 37151, 37256, 37257, 37258, 37156, 37155, 37259, 37260, 37261, 37174, 37176, 37262, 37263, 37264, 37265, 37266, 37267, 37268, 37269, 36906, 36626, 37270, 36911, 37271, 37272, 37273, 37165, 37164, 37274, 37275, 37276, 37277, 37278, 36936, 37279, 37280, 37281, 37169, 37170, 37282, 37283, 37174, 37284, 37176, 37178, 36975, 36973, 37181, 37182, 37183, 37185, 37285, 37187, 37188, 37190, 37191, 37193, 37286, 37194, 37002, 37196, 37197, 37287, 37288, 37015, 37200, 37011, 37202, 37019, 37290, 37039, 36744, 36746, 37295, 37297, 37034, 37298, 37299, 37034, 37300, 37304, 37034, 37305, 37306, 37034, 37307, 37308, 37034, 37309, 37301, 37311, 37302, 37312, 37313, 37314, 37303, 37315, 37301, 37317, 37302, 37318, 37319, 37320, 37303, 37321, 37323, 37324, 37310, 37325, 37326, 37327, 37328, 37310, 37329, 37330, 37332, 37333, 37310, 37334, 37336, 37337, 37310, 37338, 37079, 37081, 37087, 37090, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37205, 37355, 37356, 37357, 37358, 37207, 37360, 37363, 37364, 37350, 36954, 37367, 36961, 37369, 36813, 37370, 37371, 37372, 37373, 37376, 37377, 37350, 36954, 37380, 36961, 37382, 36968, 37383, 37384, 37385, 37386, 37389, 37390, 37350, 36954, 37393, 36961, 37395, 36813, 37396, 37397, 37398, 36818, 37400, 37402, 37403, 37404, 37405, 37408, 37409, 37410, 36825, 37415, 36903, 37417, 37418, 37420, 37421, 37424, 37425, 37426, 36828, 37431, 37432, 37433, 37434, 37435, 37436, 37437, 37438, 37439, 37142, 37440, 37441, 37442, 37443, 37444, 37445, 37446, 37447, 37448, 37449, 37451, 37452, 37453, 37454, 37457, 37458, 37459, 37460, 37461, 37462, 37463, 37464, 37465, 37466, 37467, 37468, 37142, 37469, 37470, 37471, 37472, 37473, 37474, 37475, 37476, 37477, 37478, 37480, 37481, 37482, 37483, 37485, 37487, 37488, 37489, 37490, 37491, 37494, 37495, 37154, 37497, 37499, 37500, 37158, 37350, 37504, 37505, 37508, 36903, 37514, 37515, 37517, 37518, 37521, 37522, 37523, 37167, 37528, 37529, 37532, 37533, 37350, 36954, 37536, 36961, 37538, 36968, 37539, 37540, 37541, 37542, 37543, 37544, 37545, 37547, 37548, 37549, 37550, 37551, 37553, 37554, 37555, 37556, 37559, 37560, 37561, 37562, 37563, 37564, 37570, 37046, 37044, 37208, 37573, 37046, 37044, 37208, 37413, 37429, 37576, 37046, 37044, 37296, 37579, 37046, 37044, 37296, 37507, 37507, 37526, 37582, 37046, 37044, 37296, 37584, 37586, 37590, 37592, 37594, 37598, 37602, 37082, 37607, 37084, 37612, 37616, 37589, 37618, 37597, 37619, 37601, 37606, 37611, 37620, 37615, 37621, 26, 27, 28, 29, 30, 31, 37641, 37642, 37366, 37644, 37368, 37646, 37648, 37653, 37654, 37379, 37656, 37381, 37658, 37660, 37665, 37666, 37392, 37668, 37394, 37670, 37672, 37674, 37401, 37677, 37680, 37683, 37685, 37687, 37690, 37693, 37703, 37717, 37718, 37730, 37744, 37746, 37751, 37753, 37755, 37757, 37758, 37762, 37764, 37767, 37770, 37775, 37776, 37535, 37778, 37537, 37780, 37782, 37796, 37797, 37701, 37699, 37697, 37707, 37705, 37709, 37713, 37711, 37714, 37695, 37722, 37728, 37726, 37724, 37740, 37738, 37741, 37743, 37750, 37569, 37025, 37804, 37805, 37806, 37701, 37699, 37697, 37707, 37636, 37634, 37709, 37713, 37711, 37714, 37695, 37722, 37572, 37032, 37808, 37809, 37810, 37031, 37362, 37640, 37375, 37652, 37388, 37664, 37701, 37699, 37697, 37707, 37705, 37709, 37713, 37711, 37714, 37695, 37722, 37728, 37726, 37724, 37740, 37738, 37741, 37743, 37750, 37032, 37811, 37684, 37689, 37812, 37694, 37531, 37774, 37701, 37699, 37697, 37707, 37705, 37709, 37713, 37711, 37714, 37695, 37722, 37728, 37726, 37724, 37743, 37750, 37575, 36735, 37814, 37815, 37816, 37701, 37699, 37697, 37707, 37705, 37709, 37713, 37711, 37714, 37716, 37722, 37728, 37726, 37724, 37734, 37732, 37736, 37740, 37738, 37741, 37743, 37750, 37578, 36736, 37818, 37819, 37820, 37766, 37821, 37771, 37502, 37774, 37503, 37766, 37822, 37531, 37774, 37766, 37823, 37771, 37531, 37774, 37787, 37785, 37788, 37792, 37790, 37793, 37795, 37801, 37581, 37292, 37825, 37826, 37827, 37585, 37587, 37840, 37591, 37593, 37595, 37842, 37599, 37844, 37603, 37083, 37845, 37608, 37331, 37846, 37613, 37848, 37617, 35519, 35520, 35523, 35524, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37365, 37378, 37391, 37887, 37888, 37890, 37891, 37534, 37908, 37909, 37910, 37911, 37912, 37886, 37913, 37914, 37915, 37916, 37917, 37918, 37919, 37920, 37921, 37922, 37923, 37889, 37924, 37925, 37926, 37927, 37928, 37930, 37931, 37571, 37934, 37935, 37936, 37886, 37937, 37938, 37939, 37940, 37941, 37942, 37943, 37944, 37945, 37947, 37948, 37574, 37951, 37952, 37953, 37861, 37859, 37862, 37954, 37955, 37868, 37866, 37869, 37956, 37957, 37875, 37873, 37876, 37958, 37959, 37960, 37886, 37961, 37962, 37963, 37964, 37965, 37966, 37967, 37968, 37969, 37970, 37971, 37889, 37972, 37973, 37974, 37975, 37976, 37977, 37399, 37679, 37878, 37880, 37881, 37979, 37416, 37980, 37883, 37884, 37885, 37982, 37983, 37984, 37906, 37904, 37907, 37985, 37986, 37987, 37886, 37988, 37989, 37990, 37991, 37992, 37993, 37994, 37995, 37996, 37997, 37998, 37889, 37999, 38000, 38002, 38003, 37577, 38006, 38007, 38008, 37886, 38009, 38010, 38011, 38012, 38013, 38014, 38015, 38016, 38017, 38018, 38019, 37889, 38020, 38021, 38022, 38023, 38024, 38025, 38026, 38027, 38029, 38030, 37580, 37513, 38033, 37898, 37892, 37893, 37894, 37895, 38035, 38036, 38037, 37906, 37904, 37902, 37907, 38038, 37906, 37904, 37902, 37513, 38039, 37898, 37899, 37900, 38041, 38042, 37906, 37904, 37902, 37907, 37513, 38043, 37898, 37899, 37900, 38045, 38046, 38047, 37906, 37904, 37907, 38048, 38049, 38050, 38051, 38052, 38053, 38054, 38055, 38057, 38058, 37583, 38061, 38062, 38064, 38065, 38066, 38068, 38070, 38071, 38073, 38074, 38076, 38078, 38079, 38080, 38081, 38082, 28, 29, 30, 31, 38122, 38125, 38126, 38129, 38116, 38134, 38137, 38138, 38118, 38146, 38149, 38150, 38154, 38116, 38165, 38166, 37857, 38167, 38170, 38171, 37864, 38172, 38175, 38176, 37871, 38177, 38178, 38181, 38182, 38185, 38116, 38190, 38193, 38194, 38118, 38200, 38201, 38202, 38203, 38204, 38206, 38208, 38209, 38210, 38214, 38215, 37902, 38216, 38217, 38220, 38221, 38224, 38116, 38229, 38232, 38118, 38238, 38241, 38242, 38245, 38116, 38250, 38253, 38254, 38257, 38118, 38265, 38267, 38268, 38269, 38270, 38271, 38275, 38276, 38277, 38278, 38280, 38281, 38282, 38283, 38285, 38286, 38287, 38290, 38291, 38292, 38293, 38294, 38296, 38297, 38298, 38302, 38303, 37902, 38304, 38305, 38308, 38121, 38143, 38145, 38159, 38161, 38162, 38315, 38205, 38211, 38164, 38169, 38174, 38299, 38301, 38199, 38315, 38205, 38211, 38213, 38299, 38301, 38235, 38237, 38262, 38264, 38272, 38274, 38289, 38299, 38289, 38299, 38301, 38313, 38315, 38318, 38317, 38321, 38320, 38322, 35521, 38324, 35522, 38326, 38327, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38336, 38340, 38341, 38344, 38345, 38347, 38349, 38352, 38350, 38356, 38354, 38360, 38358, 38362, 38366, 38367, 38370, 38372, 38207, 38382, 38380, 38384, 38388, 38389, 38391, 38392, 38396, 38397, 38401, 38266, 38408, 38412, 38284, 38419, 38295, 38429, 38427, 38433, 38131, 38128, 38140, 38256, 38434, 38435, 38156, 38436, 38437, 38187, 38184, 38196, 38256, 38438, 38439, 38440, 38375, 38441, 38379, 38442, 38443, 38444, 38445, 38426, 38446, 38187, 38184, 38196, 38256, 38447, 38448, 38449, 38375, 38450, 38379, 38451, 38452, 38426, 38453, 38226, 38223, 38259, 38256, 38454, 38455, 38247, 38244, 38259, 38256, 38456, 38457, 38458, 38407, 38405, 38459, 38460, 38461, 38418, 38462, 38463, 38426, 38464, 38310, 38307, 38465, 38466, 38467, 38468, 38469, 38470, 38471, 38472, 38473, 38474, 38475, 38476, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38133, 38142, 38158, 38504, 38506, 38508, 38189, 38198, 38516, 38228, 38234, 38249, 38261, 38526, 38527, 38529, 38532, 38312, 38534, 38535, 38337, 38536, 38537, 38342, 38540, 38153, 38346, 38543, 38544, 38363, 38545, 38546, 38368, 38513, 38550, 38514, 38552, 38530, 38557, 38559, 38560, 38363, 38561, 38562, 38368, 38513, 38566, 38514, 38568, 38530, 38571, 38573, 38574, 38385, 38575, 38576, 38390, 38579, 38580, 38393, 38581, 38582, 38398, 38525, 38586, 38587, 38528, 38591, 38530, 38594, 38596, 38597, 38539, 38542, 38548, 38599, 38564, 38578, 38584, 38599, 38600, 38602, 38330, 38607, 38605, 38331, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38644, 38642, 38624, 38647, 38645, 38625, 38649, 38650, 38626, 38653, 38651, 38630, 38656, 38654, 38631, 38657, 38549, 38659, 38551, 38353, 38357, 38361, 38661, 38556, 38430, 38665, 38663, 38630, 38668, 38666, 38631, 38669, 38565, 38671, 38567, 38383, 38673, 38570, 38430, 38677, 38675, 38633, 38680, 38678, 38634, 38683, 38681, 38635, 38686, 38684, 38636, 38687, 38585, 38411, 38422, 38690, 38590, 38422, 38692, 38593, 38430, 38694, 38641, 38696, 38697, 38698, 38699, 38700, 38701, 38702, 38703, 38706, 38707, 38329, 38708, 38328, 38709, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38721, 38722, 38724, 38725, 38648, 38728, 38730, 38731, 38733, 38734, 38739, 38740, 38741, 38744, 38746, 38747, 38749, 38750, 38755, 38758, 38760, 38761, 38763, 38764, 38766, 38767, 38769, 38770, 38772, 38773, 38774, 38777, 38780, 38782, 38738, 38736, 38743, 38754, 38752, 38757, 38738, 38736, 38743, 38754, 38752, 38757, 38776, 38779, 38793, 38795, 38791, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38820, 38850, 38851, 38828, 38827, 38826, 38852, 38829, 38819, 38817, 38843, 38853, 38854, 38834, 38855, 38835, 38825, 38823, 38844, 38845, 38849, 38856, 38857, 38828, 38827, 38826, 38858, 38829, 38833, 38831, 38859, 38860, 38834, 38861, 38835, 38839, 38837, 38843, 38841, 38844, 38845, 38846, 38862, 38847, 38863, 38848, 38849, 38864, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38881, 38883, 38884, 38885, 38887, 38888, 38889, 38890, 38821, 38891, 38893, 38895, 38896, 38897, 38898, 38899, 38900, 38901, 38903, 38904, 38905, 38907, 38908, 38909, 38910, 38912, 38914, 38915, 38916, 38917, 38918, 38919, 38920, 38921, 38923, 38925, 38926, 38866, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38945, 38949, 38952, 38956, 38962, 38966, 38971, 38973, 38948, 38955, 38954, 38979, 38978, 38977, 38959, 38786, 38965, 38970, 38969, 38979, 38978, 38977, 38976, 38790, 38981, 25, 26, 27, 28, 29, 30, 31, 39008, 38951, 39012, 39016, 38783, 39017, 39018, 38785, 39019, 39020, 39021, 39022, 39023, 39024, 38787, 39025, 39026, 38789, 38788, 39027, 39028, 39029, 39030, 39031, 24, 25, 26, 27, 28, 29, 30, 31, 39040, 38784, 39044, 39045, 39047, 39048, 39050, 39042, 39054, 39055, 39057, 39058, 39059, 39061, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39072, 39073, 39077, 39079, 39082, 39084, 39076, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39043, 39105, 39053, 39052, 39110, 39063, 39108, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39139, 39137, 39141, 39142, 39080, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39169, 39168, 39172, 39170, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39201, 39203, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38796, 39032, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39264, 39265, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39296, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
int h_C[]= {
4, 6, 8, 10, 12, 14, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 400, 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, 468, 470, 472, 474, 477, 479, 481, 483, 486, 488, 490, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 558, 560, 562, 564, 566, 568, 570, 572, 574, 576, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 643, 645, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 669, 671, 673, 675, 678, 680, 682, 684, 688, 690, 692, 694, 696, 698, 701, 703, 705, 707, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 769, 771, 774, 776, 779, 781, 783, 785, 787, 789, 791, 793, 796, 798, 801, 803, 806, 808, 810, 812, 814, 816, 818, 820, 823, 825, 828, 830, 832, 834, 837, 839, 841, 843, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 868, 870, 873, 875, 878, 880, 882, 884, 886, 888, 891, 893, 895, 897, 899, 901, 904, 906, 909, 911, 914, 916, 919, 921, 924, 926, 929, 931, 934, 936, 939, 941, 943, 945, 947, 949, 952, 954, 956, 958, 960, 962, 965, 967, 969, 971, 973, 975, 978, 980, 983, 985, 988, 990, 993, 995, 997, 999, 1002, 1004, 1006, 1008, 1011, 1013, 1017, 1019, 1021, 1023, 1026, 1028, 1031, 1033, 1036, 1038, 1041, 1043, 1046, 1048, 1051, 1053, 1056, 1058, 1061, 1063, 1066, 1068, 1071, 1073, 1076, 1078, 1081, 1083, 1086, 1088, 1091, 1093, 1096, 1098, 1101, 1103, 1105, 1107, 1109, 1111, 1114, 1116, 1119, 1121, 1124, 1126, 1129, 1131, 1134, 1136, 1139, 1141, 1144, 1146, 1149, 1151, 1154, 1156, 1159, 1161, 1164, 1166, 1169, 1171, 1173, 1175, 1177, 1179, 1182, 1184, 1187, 1189, 1192, 1194, 1197, 1199, 1201, 1203, 1205, 1207, 1209, 1211, 1213, 1215, 1217, 1219, 1221, 1223, 1225, 1227, 1229, 1231, 1233, 1235, 1237, 1239, 1241, 1243, 1245, 1247, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 1277, 1279, 1281, 1283, 1285, 1287, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1327, 1329, 1331, 1333, 1336, 1338, 1340, 1342, 1344, 1346, 1348, 1350, 1352, 1354, 1356, 1358, 1360, 1362, 1364, 1366, 1368, 1370, 1372, 1374, 1376, 1378, 1380, 1382, 1384, 1386, 1390, 1392, 1394, 1396, 1398, 1400, 1402, 1404, 1406, 1408, 1410, 1412, 1414, 1416, 1419, 1421, 1424, 1426, 1429, 1431, 1434, 1436, 1439, 1441, 1444, 1446, 1449, 1451, 1454, 1456, 1458, 1460, 1462, 1464, 1467, 1469, 1472, 1474, 1476, 1478, 1480, 1482, 1484, 1486, 1489, 1491, 1493, 1495, 1498, 1500, 1502, 1504, 1507, 1509, 1513, 1515, 1517, 1519, 1521, 1523, 1526, 1528, 1531, 1533, 1538, 1540, 1542, 1544, 1546, 1548, 1551, 1553, 1556, 1558, 1561, 1563, 1566, 1568, 1570, 1572, 1574, 1576, 1579, 1581, 1584, 1586, 1589, 1591, 1594, 1596, 1599, 1601, 1604, 1606, 1609, 1611, 1614, 1616, 1619, 1621, 1624, 1626, 1629, 1631, 1634, 1636, 1638, 1640, 1642, 1644, 1646, 1648, 1650, 1652, 1654, 1656, 1658, 1660, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1694, 1696, 1698, 1700, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1718, 1720, 1722, 1724, 1726, 1728, 1730, 1732, 1734, 1736, 1739, 1741, 1743, 1745, 1748, 1750, 1753, 1755, 1758, 1760, 1763, 1765, 1768, 1770, 1773, 1775, 1778, 1780, 1782, 1784, 1786, 1788, 1791, 1793, 1796, 1798, 1801, 1803, 1806, 1808, 1811, 1813, 1816, 1818, 1821, 1823, 1826, 1828, 1831, 1833, 1836, 1838, 1841, 1843, 1846, 1848, 1851, 1853, 1856, 1858, 1861, 1863, 1866, 1868, 1870, 1872, 1874, 1876, 1879, 1881, 1884, 1886, 1889, 1891, 1894, 1896, 1899, 1901, 1904, 1906, 1911, 1913, 1915, 1917, 1919, 1921, 1923, 1925, 1927, 1929, 1931, 1933, 1935, 1937, 1939, 1941, 1944, 1946, 1949, 1951, 1953, 1955, 1957, 1959, 1961, 1963, 1965, 1967, 1969, 1971, 1973, 1975, 1977, 1979, 1981, 1983, 1985, 1987, 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023, 2025, 2027, 2029, 2031, 2033, 2035, 2037, 2039, 2041, 2043, 2045, 2047, 2049, 2051, 2053, 2055, 2057, 2059, 2061, 2063, 2065, 2067, 2069, 2071, 2073, 2075, 2077, 2079, 2081, 2083, 2085, 2087, 2089, 2091, 2093, 2095, 2097, 2099, 2101, 2103, 2105, 2107, 2110, 2112, 2114, 2116, 2118, 2120, 2122, 2124, 2126, 2128, 2130, 2132, 2134, 2136, 2138, 2140, 2142, 2144, 2146, 2148, 2150, 2152, 2154, 2156, 2158, 2160, 2162, 2164, 2166, 2168, 2170, 2172, 2175, 2177, 2179, 2181, 2183, 2185, 2187, 2189, 2191, 2193, 2195, 2197, 2200, 2202, 2204, 2206, 2209, 2211, 2213, 2215, 2218, 2220, 2222, 2224, 2227, 2229, 2231, 2233, 2235, 2237, 2239, 2241, 2243, 2245, 2247, 2249, 2251, 2253, 2255, 2257, 2259, 2261, 2263, 2265, 2267, 2269, 2271, 2273, 2275, 2277, 2279, 2281, 2283, 2285, 2288, 2290, 2293, 2295, 2297, 2299, 2301, 2303, 2305, 2307, 2309, 2311, 2313, 2315, 2317, 2319, 2321, 2323, 2325, 2327, 2329, 2331, 2333, 2335, 2337, 2339, 2341, 2343, 2345, 2347, 2349, 2351, 2353, 2355, 2357, 2359, 2361, 2363, 2365, 2367, 2369, 2371, 2373, 2375, 2377, 2379, 2381, 2383, 2385, 2387, 2389, 2391, 2393, 2395, 2397, 2399, 2401, 2403, 2405, 2407, 2409, 2411, 2413, 2415, 2417, 2419, 2421, 2423, 2425, 2427, 2429, 2431, 2433, 2435, 2437, 2439, 2441, 2443, 2445, 2447, 2449, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 2474, 2476, 2478, 2480, 2482, 2484, 2486, 2488, 2490, 2492, 2494, 2496, 2498, 2500, 2502, 2504, 2506, 2508, 2510, 2512, 2514, 2516, 2518, 2520, 2522, 2524, 2526, 2528, 2530, 2532, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554, 2556, 2558, 2560, 2563, 2565, 2567, 2569, 2572, 2574, 2576, 2578, 2580, 2582, 2584, 2586, 2588, 2590, 2593, 2595, 2597, 2599, 2602, 2604, 2606, 2608, 2611, 2613, 2616, 2618, 2622, 2624, 2626, 2628, 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2644, 2646, 2648, 2650, 2652, 2654, 2656, 2659, 2661, 2663, 2665, 2668, 2670, 2673, 2675, 2678, 2680, 2683, 2685, 2688, 2690, 2692, 2694, 2696, 2698, 2701, 2703, 2706, 2708, 2711, 2713, 2716, 2718, 2721, 2723, 2726, 2728, 2730, 2732, 2734, 2736, 2739, 2741, 2744, 2746, 2749, 2751, 2754, 2756, 2759, 2761, 2764, 2766, 2769, 2771, 2774, 2776, 2779, 2781, 2784, 2786, 2789, 2791, 2794, 2796, 2799, 2801, 2804, 2806, 2809, 2811, 2814, 2816, 2818, 2820, 2822, 2824, 2827, 2829, 2832, 2834, 2837, 2839, 2842, 2844, 2847, 2849, 2852, 2854, 2857, 2859, 2862, 2864, 2866, 2868, 2870, 2872, 2875, 2877, 2880, 2882, 2885, 2887, 2890, 2892, 2895, 2897, 2900, 2902, 2905, 2907, 2910, 2912, 2915, 2917, 2920, 2922, 2925, 2927, 2930, 2932, 2935, 2937, 2940, 2942, 2945, 2947, 2950, 2952, 2955, 2957, 2960, 2962, 2965, 2967, 2970, 2972, 2975, 2977, 2980, 2982, 2985, 2987, 2990, 2992, 2995, 2997, 3000, 3002, 3005, 3007, 3010, 3012, 3014, 3016, 3018, 3020, 3023, 3025, 3028, 3030, 3033, 3035, 3038, 3040, 3042, 3044, 3047, 3049, 3052, 3054, 3060, 3062, 3064, 3066, 3068, 3070, 3073, 3075, 3078, 3080, 3083, 3085, 3088, 3090, 3093, 3095, 3098, 3100, 3102, 3104, 3106, 3108, 3110, 3112, 3114, 3116, 3118, 3120, 3123, 3125, 3128, 3130, 3133, 3135, 3138, 3140, 3143, 3145, 3148, 3150, 3156, 3158, 3160, 3162, 3164, 3166, 3169, 3171, 3173, 3175, 3177, 3179, 3182, 3184, 3187, 3189, 3195, 3197, 3200, 3202, 3205, 3207, 3209, 3211, 3214, 3216, 3218, 3220, 3225, 3227, 3229, 3231, 3233, 3235, 3237, 3239, 3242, 3244, 3246, 3248, 3250, 3252, 3255, 3257, 3260, 3262, 3265, 3267, 3270, 3272, 3274, 3276, 3278, 3280, 3282, 3284, 3286, 3288, 3291, 3293, 3296, 3298, 3301, 3303, 3306, 3308, 3311, 3313, 3316, 3318, 3321, 3323, 3326, 3328, 3331, 3333, 3335, 3337, 3339, 3341, 3343, 3345, 3347, 3349, 3352, 3354, 3357, 3359, 3361, 3363, 3365, 3367, 3369, 3371, 3373, 3375, 3377, 3379, 3381, 3383, 3386, 3388, 3390, 3392, 3394, 3396, 3399, 3401, 3404, 3406, 3408, 3410, 3412, 3414, 3417, 3419, 3422, 3424, 3427, 3429, 3432, 3434, 3437, 3439, 3442, 3444, 3447, 3449, 3452, 3454, 3457, 3459, 3461, 3463, 3465, 3467, 3469, 3471, 3473, 3475, 3477, 3479, 3481, 3483, 3485, 3487, 3489, 3491, 3493, 3495, 3498, 3500, 3502, 3504, 3506, 3508, 3511, 3513, 3516, 3518, 3520, 3522, 3524, 3526, 3529, 3531, 3534, 3536, 3539, 3541, 3544, 3546, 3548, 3550, 3552, 3554, 3557, 3559, 3562, 3564, 3567, 3569, 3572, 3574, 3577, 3579, 3583, 3585, 3587, 3589, 3594, 3596, 3599, 3601, 3604, 3606, 3609, 3611, 3614, 3616, 3618, 3620, 3623, 3625, 3628, 3630, 3642, 3644, 3647, 3649, 3652, 3654, 3657, 3659, 3662, 3664, 3666, 3668, 3670, 3672, 3675, 3677, 3680, 3682, 3685, 3687, 3690, 3692, 3695, 3697, 3700, 3702, 3704, 3706, 3709, 3711, 3714, 3716, 3722, 3724, 3726, 3728, 3730, 3732, 3735, 3737, 3740, 3742, 3745, 3747, 3750, 3752, 3754, 3756, 3758, 3760, 3763, 3765, 3768, 3770, 3773, 3775, 3778, 3780, 3782, 3784, 3786, 3788, 3791, 3793, 3796, 3798, 3801, 3803, 3806, 3808, 3811, 3813, 3816, 3818, 3821, 3823, 3826, 3828, 3830, 3832, 3835, 3837, 3840, 3842, 3848, 3850, 3853, 3855, 3858, 3860, 3863, 3865, 3868, 3870, 3873, 3875, 3878, 3880, 3883, 3885, 3888, 3890, 3892, 3894, 3896, 3898, 3901, 3903, 3906, 3908, 3911, 3913, 3916, 3918, 3921, 3923, 3926, 3928, 3931, 3933, 3936, 3938, 3940, 3942, 3944, 3946, 3949, 3951, 3954, 3956, 3959, 3961, 3964, 3966, 3968, 3970, 3972, 3974, 3976, 3978, 3981, 3983, 3985, 3987, 3991, 3993, 3996, 3998, 4001, 4003, 4006, 4008, 4011, 4013, 4015, 4017, 4019, 4021, 4023, 4025, 4027, 4029, 4031, 4033, 4035, 4037, 4040, 4042, 4045, 4047, 4050, 4052, 4055, 4057, 4060, 4062, 4065, 4067, 4070, 4072, 4075, 4077, 4080, 4082, 4085, 4087, 4090, 4092, 4094, 4096, 4098, 4100, 4103, 4105, 4108, 4110, 4113, 4115, 4118, 4120, 4123, 4125, 4128, 4130, 4133, 4135, 4138, 4140, 4143, 4145, 4148, 4150, 4153, 4155, 4158, 4160, 4162, 4164, 4166, 4168, 4171, 4173, 4176, 4178, 4181, 4183, 4186, 4188, 4190, 4192, 4194, 4196, 4198, 4200, 4203, 4205, 4208, 4210, 4216, 4218, 4220, 4222, 4224, 4226, 4229, 4231, 4234, 4236, 4239, 4241, 4244, 4246, 4249, 4251, 4254, 4256, 4259, 4261, 4264, 4266, 4268, 4270, 4272, 4274, 4276, 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19337, 19339, 19341, 19343, 19345, 19347, 19349, 19351, 19353, 19355, 19357, 19359, 19361, 19363, 19365, 19367, 19369, 19371, 19373, 19375, 19377, 19379, 19381, 19383, 19385, 19387, 19389, 19391, 19393, 19395, 19397, 19399, 19401, 19403, 19405, 19407, 19409, 19411, 19413, 19415, 19417, 19419, 19421, 19423, 19425, 19427, 19429, 19431, 19433, 19435, 19437, 19439, 19441, 19443, 19445, 19447, 19449, 19451, 19453, 19455, 19457, 19459, 19461, 19463, 19465, 19467, 19469, 19471, 19473, 19475, 19477, 19479, 19481, 19483, 19485, 19487, 19489, 19491, 19493, 19495, 19497, 19499, 19501, 19503, 19505, 19507, 19509, 19511, 19513, 19515, 19517, 19519, 19521, 19523, 19525, 19527, 19529, 19531, 19533, 19535, 19537, 19539, 19541, 19543, 19545, 19547, 19549, 19551, 19553, 19555, 19557, 19559, 19561, 19563, 19565, 19567, 19569, 19571, 19573, 19575, 19577, 19579, 19581, 19583, 19585, 19587, 19589, 19591, 19593, 19595, 19597, 19599, 19601, 19603, 19605, 19607, 19609, 19611, 19613, 19615, 19617, 19619, 19621, 19623, 19625, 19627, 19629, 19631, 19633, 19635, 19637, 19639, 19641, 19643, 19645, 19647, 19649, 19651, 19653, 19655, 19657, 19659, 19661, 19663, 19665, 19667, 19669, 19671, 19673, 19675, 19677, 19679, 19681, 19683, 19685, 19687, 19689, 19691, 19693, 19695, 19697, 19699, 19701, 19703, 19705, 19707, 19709, 19711, 19713, 19715, 19717, 19719, 19721, 19723, 19725, 19727, 19729, 19731, 19733, 19735, 19737, 19739, 19741, 19743, 19745, 19747, 19749, 19751, 19753, 19755, 19757, 19759, 19761, 19763, 19765, 19767, 19769, 19771, 19773, 19775, 19777, 19779, 19781, 19783, 19785, 19787, 19789, 19791, 19793, 19795, 19797, 19799, 19801, 19803, 19805, 19807, 19809, 19811, 19813, 19815, 19817, 19819, 19821, 19823, 19825, 19827, 19829, 19831, 19833, 19835, 19837, 19839, 19841, 19843, 19845, 19847, 19849, 19851, 19853, 19855, 19857, 19859, 19861, 19863, 19865, 19867, 19869, 19871, 19873, 19875, 19877, 19879, 19881, 19883, 19885, 19887, 19889, 19891, 19893, 19895, 19897, 19899, 19901, 19903, 19905, 19907, 19909, 19911, 19913, 19915, 19917, 19919, 19921, 19923, 19925, 19927, 19929, 19931, 19933, 19935, 19937, 19939, 19941, 19943, 19945, 19947, 19949, 19951, 19953, 19955, 19957, 19959, 19961, 19963, 19965, 19967, 19969, 19971, 19973, 19975, 19977, 19979, 19981, 19983, 19985, 19987, 19989, 19991, 19993, 19995, 19997, 19999, 20001, 20003, 20005, 20007, 20009, 20011, 20013, 20015, 20017, 20019, 20021, 20023, 20025, 20027, 20029, 20031, 20033, 20035, 20037, 20039, 20041, 20043, 20045, 20047, 20049, 20051, 20053, 20055, 20057, 20059, 20061, 20063, 20065, 20067, 20069, 20071, 20073, 20075, 20077, 20079, 20081, 20083, 20085, 20087, 20089, 20091, 20093, 20095, 20097, 20099, 20101, 20103, 20105, 20107, 20109, 20111, 20113, 20115, 20117, 20119, 20121, 20123, 20125, 20127, 20129, 20131, 20133, 20135, 20137, 20139, 20141, 20143, 20145, 20147, 20149, 20151, 20153, 20155, 20157, 20159, 20161, 20163, 20165, 20167, 20169, 20171, 20173, 20175, 20177, 20179, 20181, 20183, 20185, 20187, 20189, 20191, 20193, 20195, 20197, 20199, 20201, 20203, 20205, 20207, 20209, 20211, 20213, 20215, 20217, 20219, 20221, 20223, 20225, 20227, 20229, 20231, 20233, 20235, 20237, 20239, 20241, 20243, 20245, 20247, 20249, 20251, 20253, 20255, 20257, 20259, 20261, 20263, 20265, 20267, 20269, 20271, 20273, 20275, 20277, 20279, 20281, 20283, 20285, 20287, 20289, 20291, 20293, 20295, 20297, 20299, 20301, 20303, 20305, 20307, 20309, 20311, 20313, 20315, 20317, 20319, 20321, 20323, 20325, 20327, 20329, 20331, 20333, 20335, 20337, 20339, 20341, 20343, 20345, 20347, 20349, 20351, 20353, 20355, 20357, 20359, 20361, 20363, 20365, 20367, 20369, 20371, 20373, 20375, 20377, 20379, 20381, 20383, 20385, 20387, 20389, 20391, 20393, 20395, 20397, 20399, 20401, 20403, 20405, 20407, 20409, 20411, 20413, 20415, 20417, 20419, 20421, 20423, 20425, 20427, 20429, 20431, 20433, 20435, 20437, 20439, 20441, 20443, 20445, 20447, 20449, 20451, 20453, 20455, 20457, 20459, 20461, 20463, 20465, 20467, 20469, 20471, 20473, 20475, 20477, 20479, 20481, 20483, 20485, 20487, 20489, 20491, 20493, 20495, 20497, 20499, 20501, 20503, 20505, 20507, 20509, 20511, 20513, 20515, 20517, 20519, 20521, 20523, 20525, 20527, 20529, 20531, 20533, 20535, 20537, 20539, 20541, 20543, 20545, 20547, 20549, 20551, 20553, 20555, 20557, 20559, 20561, 20563, 20565, 20567, 20569, 20571, 20573, 20575, 20577, 20579, 20581, 20583, 20585, 8006, 8007, 8008, 8009, 8010, 8011, 20593, 20595, 20597, 8026, 8027, 8032, 8033, 8034, 8035, 8036, 8037, 8038, 8039, 8042, 8043, 8046, 8047, 8050, 8051, 8113, 8114, 8121, 8122, 8131, 8132, 8155, 8156, 8159, 8160, 8166, 8167, 20627, 20629, 20631, 20633, 20635, 20637, 8193, 8194, 8195, 8196, 8199, 8200, 8202, 8203, 8226, 8227, 8230, 8231, 8278, 8279, 8282, 8285, 8296, 8297, 8320, 8323, 8336, 8339, 8367, 8368, 8371, 8372, 8376, 8377, 8378, 8379, 8380, 8381, 20671, 20673, 20675, 20677, 8404, 8405, 8408, 8409, 8410, 8411, 8412, 8413, 8427, 8428, 8429, 8430, 8431, 8432, 8433, 8434, 8453, 8454, 8481, 8483, 8579, 8580, 8590, 8593, 8609, 8610, 8611, 8612, 8613, 8614, 8634, 8637, 8646, 8649, 8727, 8729, 8787, 8788, 8795, 8796, 8799, 8800, 8803, 8804, 8807, 8808, 8825, 8826, 8833, 8834, 8837, 8838, 8841, 8842, 8845, 8846, 8931, 8934, 8943, 8944, 8947, 8948, 8951, 8952, 8955, 8956, 8973, 8986, 9064, 9065, 9068, 9069, 9083, 9084, 9091, 9092, 9096, 9097, 9103, 9104, 9109, 9110, 9113, 9114, 9185, 9186, 9187, 9188, 9189, 9190, 9208, 9209, 9211, 9212, 9219, 9222, 9223, 9224, 9225, 9226, 9227, 9228, 9229, 9230, 9244, 9245, 9248, 9249, 9274, 9275, 9278, 9279, 9317, 9320, 9362, 9365, 9368, 9369, 9401, 9402, 9405, 9406, 9413, 9414, 9425, 9426, 9429, 9430, 9437, 9438, 9451, 9452, 9471, 9472, 9475, 9476, 9479, 9480, 9496, 9497, 9500, 9501, 9504, 9507, 9510, 9511, 9525, 9527, 9531, 9532, 9534, 9535, 9552, 9553, 9556, 9557, 9597, 9600, 9606, 9609, 9618, 9619, 9622, 9623, 9695, 9696, 9697, 9698, 9710, 9711, 9714, 9715, 9724, 9725, 9728, 9729, 9734, 9735, 9738, 9739, 9767, 9768, 9771, 9772, 9787, 9788, 9791, 9792, 9817, 9818, 9834, 9835, 9851, 9853, 9855, 9857, 9859, 9862, 9865, 9866, 9868, 9869, 9879, 9880, 9883, 9884, 9893, 9894, 9897, 9898, 9904, 9905, 9914, 9915, 9918, 9919, 9925, 9926, 9943, 9944, 9950, 9951, 9956, 9957, 9960, 9961, 9983, 9986, 10004, 10005, 10008, 10011, 10014, 10015, 10084, 10085, 10087, 10088, 10104, 10121, 10133, 10134, 10137, 10138, 10145, 10148, 10154, 10155, 10158, 10159, 10162, 10163, 10166, 10167, 10192, 10193, 10196, 10197, 10236, 10237, 10247, 10248, 10251, 10252, 10292, 10293, 10298, 10301, 10307, 10310, 10311, 10314, 10330, 10331, 10334, 10335, 10362, 10364, 10376, 10378, 10393, 10394, 20961, 20963, 20965, 20967, 20969, 20971, 20973, 20975, 20977, 20979, 20981, 20983, 20985, 20987, 20989, 20991, 20993, 20995, 20997, 20999, 21001, 21003, 21005, 21007, 21009, 21011, 21013, 21015, 21017, 21019, 21021, 21023, 21025, 21027, 21029, 21031, 21033, 21035, 21037, 21039, 21041, 21043, 21045, 21047, 21049, 21051, 21053, 21055, 21057, 21059, 21061, 21063, 21065, 21067, 21069, 21071, 21073, 21075, 21077, 21079, 21081, 21083, 21085, 21087, 21089, 21091, 21093, 21095, 21097, 21099, 21101, 21103, 21105, 21107, 21109, 21111, 21113, 21115, 21117, 21119, 21121, 21123, 21125, 21127, 21129, 21131, 21133, 21135, 21137, 21139, 21141, 21143, 21145, 21147, 21149, 21151, 21153, 21155, 21157, 21159, 21161, 21163, 21165, 21167, 21169, 21171, 21173, 21175, 21177, 21179, 21181, 21183, 21185, 21187, 21189, 21191, 21193, 21195, 21197, 21199, 21201, 21203, 21205, 21207, 21209, 21211, 21213, 21215, 21217, 21219, 21221, 21223, 21225, 21227, 21229, 21231, 21233, 21235, 21237, 21239, 21241, 21243, 21245, 21247, 21249, 21251, 21253, 21255, 21257, 21259, 21261, 21263, 21265, 21267, 21269, 21271, 21273, 21275, 21277, 21279, 21281, 21283, 21285, 21287, 21289, 21291, 21293, 21295, 21297, 21299, 21301, 21303, 21305, 21307, 21309, 21311, 21313, 21315, 21317, 21319, 21321, 21323, 21325, 21327, 21329, 21331, 21333, 21335, 21337, 21339, 21341, 21343, 21345, 21347, 21349, 21351, 21353, 21355, 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21775, 21777, 21779, 21781, 21783, 21785, 21787, 21789, 21791, 21793, 21795, 21797, 21799, 21801, 21803, 21805, 21807, 21809, 21811, 21813, 21815, 21817, 21819, 21821, 21823, 21825, 21827, 21829, 21831, 21833, 21835, 21837, 21839, 21841, 21843, 21845, 21847, 21849, 21851, 21853, 21855, 21857, 21859, 21861, 21863, 21865, 21867, 21869, 21871, 21873, 21875, 21877, 21879, 21881, 21883, 21885, 21887, 21889, 21891, 21893, 21895, 21897, 21899, 21901, 21903, 21905, 21907, 21909, 21911, 21913, 21915, 21917, 21919, 21921, 21923, 21925, 21927, 21929, 21931, 21933, 21935, 21937, 21939, 21941, 21943, 21945, 21947, 21949, 21951, 21953, 21955, 21957, 21959, 21961, 21963, 21965, 21967, 21969, 21971, 21973, 21975, 21977, 21979, 21981, 21983, 21985, 21987, 21989, 21991, 21993, 21995, 21997, 21999, 22001, 22003, 22005, 22007, 22009, 22011, 22013, 22015, 22017, 22019, 22021, 22023, 22025, 22027, 22029, 22031, 22033, 22035, 22037, 22039, 22041, 22043, 22045, 22047, 22049, 22051, 22053, 14165, 14166, 14167, 14168, 14179, 14180, 14181, 14182, 14195, 14196, 14200, 14201, 14216, 14217, 22069, 22071, 22073, 22075, 22077, 22079, 22081, 22083, 22085, 22087, 22089, 22091, 22093, 22095, 22097, 22099, 22101, 22103, 22105, 22107, 22109, 22111, 22113, 22115, 22117, 22119, 22121, 22123, 14358, 14359, 22127, 22129, 22131, 22133, 22135, 22137, 22139, 22141, 22143, 22145, 22147, 22149, 22151, 22153, 22155, 22157, 22159, 22161, 22163, 22165, 22167, 22169, 22171, 22173, 22175, 22177, 22179, 22181, 22183, 15110, 15111, 15114, 15115, 15118, 15119, 15122, 15123, 15151, 15154, 15176, 15177, 15180, 15181, 15193, 15194, 15197, 15198, 15213, 15214, 15217, 15218, 22207, 22209, 22211, 22213, 22215, 22217, 22219, 22221, 22223, 22225, 22227, 22229, 22231, 22233, 22235, 22237, 22239, 22241, 22243, 22245, 22247, 22249, 22251, 22253, 22255, 22257, 22259, 22261, 22263, 22265, 22267, 22269, 22271, 22273, 22275, 22277, 22279, 22281, 22283, 22285, 22287, 22289, 22291, 15475, 15476, 15482, 15483, 15486, 15487, 15490, 15491, 15503, 15506, 15511, 15512, 15515, 15516, 15519, 15520, 15537, 15544, 15551, 15552, 15555, 15556, 15559, 15560, 15928, 15929, 15950, 15951, 15967, 15969, 16038, 16039, 16330, 16331, 22327, 22329, 22331, 22333, 22335, 22337, 22339, 22341, 22343, 22345, 22347, 22349, 22351, 22353, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 24150, 24152, 24154, 23524, 23523, 22549, 22548, 22551, 22368, 24161, 24163, 24165, 24167, 22370, 22369, 23481, 23480, 24171, 23483, 23482, 24173, 23485, 23484, 4988, 23488, 23487, 5003, 23491, 22371, 23493, 22372, 23494, 23495, 22373, 22374, 22376, 22375, 22378, 22377, 22379, 22381, 22382, 22384, 22386, 22385, 22388, 22387, 22390, 22389, 22392, 22391, 22627, 22626, 22628, 22630, 22629, 22631, 22633, 22632, 22635, 22634, 22637, 22636, 22638, 22393, 22641, 22640, 22642, 22644, 22643, 22394, 22683, 22395, 22662, 22797, 22799, 22687, 22667, 22690, 22689, 22692, 22691, 24175, 22669, 22398, 22400, 22399, 22402, 22401, 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16720, 37050, 16731, 37055, 16733, 16734, 16735, 37063, 16737, 16747, 16748, 37069, 16750, 16752, 16760, 16761, 37074, 16763, 16765, 16773, 16774, 37069, 16776, 16869, 16870, 37074, 16872, 37316, 37322, 37335, 37339, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37354, 15980, 15981, 15982, 15983, 37359, 37361, 16043, 16044, 36947, 37351, 16049, 37352, 16052, 37353, 16054, 16055, 16056, 37374, 16060, 16061, 36947, 37351, 16066, 37352, 16069, 37353, 16071, 16072, 16073, 37387, 16077, 16078, 36947, 37351, 16083, 37352, 16086, 37353, 16088, 16089, 16090, 37344, 16094, 16096, 16097, 16098, 37406, 16102, 16103, 37411, 37349, 16110, 37348, 16113, 16114, 16116, 37422, 16120, 16121, 37427, 37349, 16128, 16129, 16131, 16132, 16133, 16134, 16135, 16136, 16137, 37345, 16139, 16140, 16141, 16142, 16143, 16144, 16145, 16146, 16147, 16148, 16150, 16151, 16152, 16153, 16156, 16157, 16158, 16159, 16160, 16161, 16162, 16163, 16164, 16165, 16166, 16167, 37345, 16169, 16170, 16171, 16172, 16173, 16174, 16175, 16176, 16177, 16178, 16180, 16181, 16182, 16183, 37486, 16187, 16188, 16189, 16190, 16191, 16195, 16196, 37346, 37498, 16201, 16202, 37347, 36947, 16208, 16209, 37509, 37348, 16219, 16220, 16222, 37519, 16226, 16227, 37524, 37349, 16234, 37530, 16238, 16239, 36947, 37351, 16244, 37352, 16247, 37353, 16249, 16250, 16251, 16252, 16253, 16254, 16255, 16257, 16258, 16259, 16260, 16261, 16263, 16264, 16265, 16266, 16269, 16270, 16271, 16272, 16273, 37565, 16356, 37567, 37566, 37492, 16377, 37567, 37566, 37568, 37412, 37428, 16543, 37567, 37566, 37568, 16576, 37567, 37566, 37492, 37506, 37506, 37525, 16636, 37567, 37566, 37568, 16713, 16715, 16719, 16730, 16732, 16736, 16749, 37604, 16762, 37609, 16775, 16871, 37588, 16911, 37596, 16918, 37600, 37605, 37610, 16933, 37614, 16968, 26, 27, 28, 29, 30, 31, 16046, 16047, 37643, 16050, 37645, 16053, 37649, 16063, 16064, 37655, 16067, 37657, 16070, 37661, 16080, 16081, 37667, 16084, 37669, 16087, 37673, 16092, 37676, 37678, 37681, 16106, 16111, 37419, 37691, 16124, 16138, 37455, 37719, 16168, 37484, 37747, 37752, 16198, 37756, 16204, 16207, 16217, 37516, 37768, 16230, 16241, 16242, 37777, 16245, 37779, 16248, 37783, 37557, 37798, 37700, 37698, 37696, 37706, 37704, 37708, 37712, 37710, 37450, 37715, 37721, 37727, 37725, 37723, 37739, 37737, 37479, 37742, 37749, 37803, 37632, 16358, 16359, 16361, 37700, 37698, 37696, 37706, 37635, 37633, 37708, 37712, 37710, 37450, 37715, 37721, 37807, 37637, 16379, 16380, 16382, 37802, 37638, 37639, 37650, 37651, 37662, 37663, 37700, 37698, 37696, 37706, 37704, 37708, 37712, 37710, 37450, 37715, 37721, 37727, 37725, 37723, 37739, 37737, 37479, 37742, 37749, 37802, 16507, 37414, 37688, 16514, 37430, 37761, 37773, 37700, 37698, 37696, 37706, 37704, 37708, 37712, 37710, 37450, 37715, 37721, 37727, 37725, 37723, 37742, 37749, 37813, 37802, 16545, 16546, 16548, 37700, 37698, 37696, 37706, 37704, 37708, 37712, 37710, 37450, 37715, 37721, 37727, 37725, 37723, 37733, 37731, 37735, 37739, 37737, 37479, 37742, 37749, 37817, 37802, 16578, 16579, 16581, 37765, 16589, 37527, 37761, 37773, 37761, 37765, 16606, 37761, 37773, 37765, 16618, 37527, 37772, 37773, 37786, 37784, 37546, 37791, 37789, 37552, 37794, 37800, 37824, 37802, 16638, 16639, 16641, 37828, 37829, 16909, 37830, 37831, 37832, 16916, 37833, 16921, 37834, 37835, 16926, 37836, 37837, 16931, 37838, 16966, 37839, 37841, 37843, 37847, 37849, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37856, 37863, 37870, 37456, 37720, 37745, 37748, 37901, 37558, 37799, 16332, 16333, 16334, 37702, 16336, 16337, 16338, 16339, 16340, 16341, 16342, 16343, 16345, 16346, 16347, 37729, 16349, 16350, 16351, 16352, 16353, 16357, 37932, 37933, 16362, 16363, 16364, 37702, 16366, 16367, 16368, 16369, 16370, 16371, 16372, 16373, 16374, 16378, 37949, 37950, 16383, 16460, 16461, 37860, 37858, 37647, 16466, 16467, 37867, 37865, 37659, 16472, 16473, 37874, 37872, 37671, 16478, 16479, 16480, 37702, 16482, 16483, 16484, 16485, 16486, 16487, 16488, 16489, 16491, 16492, 16493, 37729, 16495, 16496, 16497, 16498, 16499, 16501, 37877, 37879, 37675, 37407, 37682, 16508, 37882, 16510, 37686, 37423, 37692, 16515, 16516, 16517, 37905, 37903, 37781, 16522, 16523, 16524, 37702, 16526, 16527, 16528, 16529, 16530, 16531, 16532, 16533, 16535, 16536, 16537, 37729, 16539, 16540, 16544, 38004, 38005, 16549, 16550, 16551, 37702, 16553, 16554, 16555, 16556, 16557, 16558, 16559, 16560, 16562, 16563, 16564, 37729, 16566, 16567, 16568, 16569, 16570, 16571, 16572, 16573, 16577, 38031, 38032, 37897, 16583, 37763, 37493, 37496, 37754, 37501, 16590, 16591, 16592, 37760, 37759, 37896, 37781, 16597, 37760, 37759, 37896, 37897, 16602, 37763, 37520, 37769, 16607, 16608, 37512, 37511, 37510, 37781, 37897, 16614, 37763, 37520, 37769, 16619, 16620, 16621, 37905, 37903, 37781, 16626, 16627, 16628, 16629, 16630, 16631, 16632, 16633, 16637, 38059, 38060, 16907, 16908, 16910, 16914, 16915, 16917, 16922, 16923, 16927, 16928, 16932, 16967, 16985, 16988, 16994, 17008, 28, 29, 30, 31, 38123, 16335, 38127, 38130, 38115, 38135, 16348, 38139, 38117, 38147, 16365, 38151, 38155, 38115, 16462, 16463, 38112, 16465, 16468, 16469, 38113, 16471, 16474, 16475, 38114, 16477, 38179, 16481, 38183, 38186, 38115, 38191, 16494, 38195, 38117, 16502, 16503, 16504, 16505, 16506, 16509, 16511, 16512, 16513, 16518, 16519, 38119, 16521, 38218, 16525, 38222, 38225, 38115, 38230, 16538, 38117, 38239, 16552, 38243, 38246, 38115, 38251, 16565, 38255, 38258, 38117, 16582, 16584, 16585, 16586, 16587, 16588, 16593, 16594, 16595, 16596, 16598, 16599, 16600, 16601, 16603, 16604, 16605, 16609, 16610, 16611, 16612, 16613, 16615, 16616, 16617, 16622, 16623, 38119, 16625, 38306, 38309, 38120, 37929, 38144, 37946, 38160, 38056, 38314, 37978, 37981, 38163, 38168, 38173, 38044, 38300, 38056, 38314, 37978, 37981, 38212, 38044, 38300, 38001, 38236, 38028, 38263, 38034, 38273, 38279, 38040, 38288, 38044, 38300, 38056, 38314, 38063, 38316, 38067, 38319, 38069, 38323, 38072, 38325, 38075, 38077, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38124, 16344, 38136, 16354, 38148, 38152, 16375, 16464, 38351, 16470, 38355, 16476, 38359, 38180, 16490, 38192, 16500, 38373, 38377, 16520, 38381, 38219, 16534, 38231, 16541, 38240, 16561, 38252, 16574, 38403, 38409, 38413, 38416, 38420, 38424, 16624, 38428, 16634, 38339, 38338, 38343, 38399, 16695, 16696, 38348, 16701, 16702, 38365, 38364, 38369, 38399, 16711, 16712, 16787, 38374, 16790, 38378, 16792, 16794, 16796, 16799, 38425, 16801, 38365, 38364, 38369, 38399, 16811, 16812, 16814, 38374, 16817, 38378, 16819, 16822, 38425, 16824, 38387, 38386, 38400, 38399, 16834, 16835, 38395, 38394, 38400, 38399, 16844, 16845, 16847, 38406, 38404, 16850, 16852, 16855, 38417, 16857, 16860, 38425, 16862, 38432, 38431, 16867, 16868, 16983, 16984, 16986, 16987, 16989, 16990, 16991, 16992, 16993, 17007, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38497, 38499, 38502, 38503, 38505, 38507, 38510, 38512, 38515, 38518, 38520, 38522, 38524, 38410, 38414, 38421, 38531, 38533, 16687, 16688, 38496, 16691, 16692, 38498, 16697, 38501, 38500, 16703, 16704, 38509, 16707, 16708, 38511, 38371, 16788, 38376, 16791, 38423, 16800, 16803, 16804, 38509, 16807, 16808, 38511, 38371, 16815, 38376, 16818, 38423, 16823, 16826, 16827, 38517, 16830, 16831, 38519, 16836, 16837, 38521, 16840, 16841, 38523, 38402, 16848, 16849, 38415, 16856, 38423, 16861, 16864, 16865, 38538, 38541, 38547, 38598, 38563, 38577, 38583, 38598, 38601, 38603, 38608, 38606, 38604, 38609, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 16689, 38643, 38132, 16693, 38646, 38141, 16698, 16699, 38157, 16705, 38652, 38188, 16709, 38655, 38197, 16786, 38658, 16789, 38660, 38627, 38628, 38629, 16798, 38662, 38640, 16805, 38664, 38188, 16809, 38667, 38197, 16813, 38670, 16816, 38672, 38632, 16821, 38674, 38640, 16828, 38676, 38227, 16832, 38679, 38233, 16838, 38682, 38248, 16842, 38685, 38260, 16846, 38688, 38637, 38638, 16854, 38691, 38639, 16859, 38693, 38640, 38695, 38311, 16891, 16894, 16902, 16906, 16945, 16953, 16956, 16965, 17012, 17013, 38705, 17015, 38704, 17020, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38720, 16690, 38723, 16694, 38726, 16700, 38729, 16706, 38732, 16710, 16793, 16795, 16797, 16802, 38745, 16806, 38748, 16810, 16820, 16825, 38759, 16829, 38762, 16833, 38765, 16839, 38768, 16843, 38689, 16851, 16853, 16858, 16863, 16866, 38737, 38735, 38742, 38753, 38751, 38756, 38737, 38735, 38742, 38753, 38751, 38756, 38775, 38778, 17014, 17016, 38792, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38727, 16882, 16883, 38555, 38554, 38553, 16887, 38558, 38818, 38816, 38842, 16895, 16896, 38569, 16898, 38572, 38824, 38822, 38771, 38588, 38781, 16936, 16937, 38555, 38554, 38553, 16941, 38558, 38832, 38830, 16946, 16947, 38569, 16949, 38572, 38838, 38836, 38842, 38840, 38771, 38588, 38589, 16960, 38592, 16962, 38595, 38781, 38794, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38882, 16884, 16885, 16886, 16888, 16889, 16890, 16892, 38880, 38892, 16897, 16899, 16900, 16901, 16903, 16904, 16905, 38902, 16938, 16939, 16940, 16942, 16943, 16944, 38911, 16948, 16950, 16951, 16952, 16954, 16955, 16957, 16958, 16959, 16961, 16963, 16964, 38927, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 38946, 38950, 16893, 38957, 38963, 38967, 38972, 38974, 38886, 38894, 38953, 38924, 38922, 38922, 38958, 38960, 38906, 38913, 38968, 38924, 38922, 38922, 38975, 38980, 38865, 25, 26, 27, 28, 29, 30, 31, 38947, 39010, 38964, 16971, 39009, 16975, 16976, 39011, 16978, 16979, 16980, 16981, 16982, 16995, 39013, 16998, 16999, 39015, 39014, 17002, 17003, 17004, 17005, 17006, 24, 25, 26, 27, 28, 29, 30, 31, 38944, 39041, 16974, 39046, 16977, 39049, 39051, 38961, 16997, 39056, 17000, 17001, 39060, 39062, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 16972, 16973, 39078, 16996, 39083, 39085, 39075, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39104, 39074, 39107, 39106, 17010, 39109, 39081, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 17009, 39136, 17017, 17018, 39138, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 17011, 39140, 17019, 39171, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39200, 39202, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39233, 39232, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 17021, 17022, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 39297, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
bool h_Op[]= {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
#define THREADS_PER_BLOCK 32
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 17024
#define SIZE_OF_AC 22336
__device__ void
ac(float *A, const int *B, const int *C, const bool *Op, int n_iter) {
int i= blockDim.x * blockIdx.x + threadIdx.x;
__shared__ float R[1230*THREADS_PER_BLOCK];
const int t= THREADS_PER_BLOCK;
__shared__ float final;
final=0;
R[i + 0*t] = A[i + 0*t];
R[i + 1*t] = A[i + 1*t];
R[i + 2*t] = A[i + 2*t];
R[i + 3*t] = A[i + 3*t];
R[i + 4*t] = A[i + 4*t];
R[i + 5*t] = A[i + 5*t];
R[i + 6*t] = A[i + 6*t];
R[i + 7*t] = A[i + 7*t];
R[i + 8*t] = A[i + 8*t];
R[i + 9*t] = A[i + 9*t];
R[i + 10*t] = A[i + 10*t];
R[i + 11*t] = A[i + 11*t];
R[i + 12*t] = A[i + 12*t];
R[i + 13*t] = A[i + 13*t];
R[i + 14*t] = A[i + 14*t];
R[i + 15*t] = A[i + 15*t];
R[i + 16*t] = A[i + 16*t];
R[i + 17*t] = A[i + 17*t];
R[i + 18*t] = A[i + 18*t];
R[i + 19*t] = A[i + 19*t];
R[i + 20*t] = A[i + 20*t];
R[i + 21*t] = A[i + 21*t];
R[i + 22*t] = A[i + 22*t];
R[i + 23*t] = A[i + 23*t];
R[i + 24*t] = A[i + 24*t];
R[i + 25*t] = A[i + 25*t];
R[i + 26*t] = A[i + 26*t];
R[i + 27*t] = A[i + 27*t];
R[i + 28*t] = A[i + 28*t];
R[i + 29*t] = A[i + 29*t];
R[i + 30*t] = A[i + 30*t];
R[i + 31*t] = A[i + 31*t];
R[i + 32*t] = A[i + 32*t];
R[i + 33*t] = A[i + 33*t];
R[i + 34*t] = A[i + 34*t];
R[i + 35*t] = A[i + 35*t];
R[i + 36*t] = A[i + 36*t];
R[i + 37*t] = A[i + 37*t];
R[i + 38*t] = A[i + 38*t];
R[i + 39*t] = A[i + 39*t];
R[i + 40*t] = A[i + 40*t];
R[i + 41*t] = A[i + 41*t];
R[i + 42*t] = A[i + 42*t];
R[i + 43*t] = A[i + 43*t];
R[i + 44*t] = A[i + 44*t];
R[i + 45*t] = A[i + 45*t];
R[i + 46*t] = A[i + 46*t];
R[i + 47*t] = A[i + 47*t];
R[i + 48*t] = A[i + 48*t];
R[i + 49*t] = A[i + 49*t];
R[i + 50*t] = A[i + 50*t];
R[i + 51*t] = A[i + 51*t];
R[i + 52*t] = A[i + 52*t];
R[i + 53*t] = A[i + 53*t];
R[i + 54*t] = A[i + 54*t];
R[i + 55*t] = A[i + 55*t];
R[i + 56*t] = A[i + 56*t];
R[i + 57*t] = A[i + 57*t];
R[i + 58*t] = A[i + 58*t];
R[i + 59*t] = A[i + 59*t];
R[i + 60*t] = A[i + 60*t];
R[i + 61*t] = A[i + 61*t];
R[i + 62*t] = A[i + 62*t];
R[i + 63*t] = A[i + 63*t];
R[i + 64*t] = A[i + 64*t];
R[i + 65*t] = A[i + 65*t];
R[i + 66*t] = A[i + 66*t];
R[i + 67*t] = A[i + 67*t];
R[i + 68*t] = A[i + 68*t];
R[i + 69*t] = A[i + 69*t];
R[i + 70*t] = A[i + 70*t];
R[i + 71*t] = A[i + 71*t];
R[i + 72*t] = A[i + 72*t];
R[i + 73*t] = A[i + 73*t];
R[i + 74*t] = A[i + 74*t];
R[i + 75*t] = A[i + 75*t];
R[i + 76*t] = A[i + 76*t];
R[i + 77*t] = A[i + 77*t];
R[i + 78*t] = A[i + 78*t];
R[i + 79*t] = A[i + 79*t];
R[i + 80*t] = A[i + 80*t];
R[i + 81*t] = A[i + 81*t];
R[i + 82*t] = A[i + 82*t];
R[i + 83*t] = A[i + 83*t];
R[i + 84*t] = A[i + 84*t];
R[i + 85*t] = A[i + 85*t];
R[i + 86*t] = A[i + 86*t];
R[i + 87*t] = A[i + 87*t];
R[i + 88*t] = A[i + 88*t];
R[i + 89*t] = A[i + 89*t];
R[i + 90*t] = A[i + 90*t];
R[i + 91*t] = A[i + 91*t];
R[i + 92*t] = A[i + 92*t];
R[i + 93*t] = A[i + 93*t];
R[i + 94*t] = A[i + 94*t];
R[i + 95*t] = A[i + 95*t];
R[i + 96*t] = A[i + 96*t];
R[i + 97*t] = A[i + 97*t];
R[i + 98*t] = A[i + 98*t];
R[i + 99*t] = A[i + 99*t];
R[i + 100*t] = A[i + 100*t];
R[i + 101*t] = A[i + 101*t];
R[i + 102*t] = A[i + 102*t];
R[i + 103*t] = A[i + 103*t];
R[i + 104*t] = A[i + 104*t];
R[i + 105*t] = A[i + 105*t];
R[i + 106*t] = A[i + 106*t];
R[i + 107*t] = A[i + 107*t];
R[i + 108*t] = A[i + 108*t];
R[i + 109*t] = A[i + 109*t];
R[i + 110*t] = A[i + 110*t];
R[i + 111*t] = A[i + 111*t];
R[i + 112*t] = A[i + 112*t];
R[i + 113*t] = A[i + 113*t];
R[i + 114*t] = A[i + 114*t];
R[i + 115*t] = A[i + 115*t];
R[i + 116*t] = A[i + 116*t];
R[i + 117*t] = A[i + 117*t];
R[i + 118*t] = A[i + 118*t];
R[i + 119*t] = A[i + 119*t];
R[i + 120*t] = A[i + 120*t];
R[i + 121*t] = A[i + 121*t];
R[i + 122*t] = A[i + 122*t];
R[i + 123*t] = A[i + 123*t];
R[i + 124*t] = A[i + 124*t];
R[i + 125*t] = A[i + 125*t];
R[i + 126*t] = A[i + 126*t];
R[i + 127*t] = A[i + 127*t];
R[i + 128*t] = A[i + 128*t];
R[i + 129*t] = A[i + 129*t];
R[i + 130*t] = A[i + 130*t];
R[i + 131*t] = A[i + 131*t];
R[i + 132*t] = A[i + 132*t];
R[i + 133*t] = A[i + 133*t];
R[i + 134*t] = A[i + 134*t];
R[i + 135*t] = A[i + 135*t];
R[i + 136*t] = A[i + 136*t];
R[i + 137*t] = A[i + 137*t];
R[i + 138*t] = A[i + 138*t];
R[i + 139*t] = A[i + 139*t];
R[i + 140*t] = A[i + 140*t];
R[i + 141*t] = A[i + 141*t];
R[i + 142*t] = A[i + 142*t];
R[i + 143*t] = A[i + 143*t];
R[i + 144*t] = A[i + 144*t];
R[i + 145*t] = A[i + 145*t];
R[i + 146*t] = A[i + 146*t];
R[i + 147*t] = A[i + 147*t];
R[i + 148*t] = A[i + 148*t];
R[i + 149*t] = A[i + 149*t];
R[i + 150*t] = A[i + 150*t];
R[i + 151*t] = A[i + 151*t];
R[i + 152*t] = A[i + 152*t];
R[i + 153*t] = A[i + 153*t];
R[i + 154*t] = A[i + 154*t];
R[i + 155*t] = A[i + 155*t];
R[i + 156*t] = A[i + 156*t];
R[i + 157*t] = A[i + 157*t];
R[i + 158*t] = A[i + 158*t];
R[i + 159*t] = A[i + 159*t];
R[i + 160*t] = A[i + 160*t];
R[i + 161*t] = A[i + 161*t];
R[i + 162*t] = A[i + 162*t];
R[i + 163*t] = A[i + 163*t];
R[i + 164*t] = A[i + 164*t];
R[i + 165*t] = A[i + 165*t];
R[i + 166*t] = A[i + 166*t];
R[i + 167*t] = A[i + 167*t];
R[i + 168*t] = A[i + 168*t];
R[i + 169*t] = A[i + 169*t];
R[i + 170*t] = A[i + 170*t];
R[i + 171*t] = A[i + 171*t];
R[i + 172*t] = A[i + 172*t];
R[i + 173*t] = A[i + 173*t];
R[i + 174*t] = A[i + 174*t];
R[i + 175*t] = A[i + 175*t];
R[i + 176*t] = A[i + 176*t];
R[i + 177*t] = A[i + 177*t];
R[i + 178*t] = A[i + 178*t];
R[i + 179*t] = A[i + 179*t];
R[i + 180*t] = A[i + 180*t];
R[i + 181*t] = A[i + 181*t];
R[i + 182*t] = A[i + 182*t];
R[i + 183*t] = A[i + 183*t];
R[i + 184*t] = A[i + 184*t];
R[i + 185*t] = A[i + 185*t];
R[i + 186*t] = A[i + 186*t];
R[i + 187*t] = A[i + 187*t];
R[i + 188*t] = A[i + 188*t];
R[i + 189*t] = A[i + 189*t];
R[i + 190*t] = A[i + 190*t];
R[i + 191*t] = A[i + 191*t];
R[i + 192*t] = A[i + 192*t];
R[i + 193*t] = A[i + 193*t];
R[i + 194*t] = A[i + 194*t];
R[i + 195*t] = A[i + 195*t];
R[i + 196*t] = A[i + 196*t];
R[i + 197*t] = A[i + 197*t];
R[i + 198*t] = A[i + 198*t];
R[i + 199*t] = A[i + 199*t];
R[i + 200*t] = A[i + 200*t];
R[i + 201*t] = A[i + 201*t];
R[i + 202*t] = A[i + 202*t];
R[i + 203*t] = A[i + 203*t];
R[i + 204*t] = A[i + 204*t];
R[i + 205*t] = A[i + 205*t];
R[i + 206*t] = A[i + 206*t];
R[i + 207*t] = A[i + 207*t];
R[i + 208*t] = A[i + 208*t];
R[i + 209*t] = A[i + 209*t];
R[i + 210*t] = A[i + 210*t];
R[i + 211*t] = A[i + 211*t];
R[i + 212*t] = A[i + 212*t];
R[i + 213*t] = A[i + 213*t];
R[i + 214*t] = A[i + 214*t];
R[i + 215*t] = A[i + 215*t];
R[i + 216*t] = A[i + 216*t];
R[i + 217*t] = A[i + 217*t];
R[i + 218*t] = A[i + 218*t];
R[i + 219*t] = A[i + 219*t];
R[i + 220*t] = A[i + 220*t];
R[i + 221*t] = A[i + 221*t];
R[i + 222*t] = A[i + 222*t];
R[i + 223*t] = A[i + 223*t];
R[i + 224*t] = A[i + 224*t];
R[i + 225*t] = A[i + 225*t];
R[i + 226*t] = A[i + 226*t];
R[i + 227*t] = A[i + 227*t];
R[i + 228*t] = A[i + 228*t];
R[i + 229*t] = A[i + 229*t];
R[i + 230*t] = A[i + 230*t];
R[i + 231*t] = A[i + 231*t];
R[i + 232*t] = A[i + 232*t];
R[i + 233*t] = A[i + 233*t];
R[i + 234*t] = A[i + 234*t];
R[i + 235*t] = A[i + 235*t];
R[i + 236*t] = A[i + 236*t];
R[i + 237*t] = A[i + 237*t];
R[i + 238*t] = A[i + 238*t];
R[i + 239*t] = A[i + 239*t];
R[i + 240*t] = A[i + 240*t];
R[i + 241*t] = A[i + 241*t];
R[i + 242*t] = A[i + 242*t];
R[i + 243*t] = A[i + 243*t];
R[i + 244*t] = A[i + 244*t];
R[i + 245*t] = A[i + 245*t];
R[i + 246*t] = A[i + 246*t];
R[i + 247*t] = A[i + 247*t];
R[i + 248*t] = A[i + 248*t];
R[i + 249*t] = A[i + 249*t];
R[i + 250*t] = A[i + 250*t];
R[i + 251*t] = A[i + 251*t];
R[i + 252*t] = A[i + 252*t];
R[i + 253*t] = A[i + 253*t];
R[i + 254*t] = A[i + 254*t];
R[i + 255*t] = A[i + 255*t];
R[i + 256*t] = A[i + 256*t];
R[i + 257*t] = A[i + 257*t];
R[i + 258*t] = A[i + 258*t];
R[i + 259*t] = A[i + 259*t];
R[i + 260*t] = A[i + 260*t];
R[i + 261*t] = A[i + 261*t];
R[i + 262*t] = A[i + 262*t];
R[i + 263*t] = A[i + 263*t];
R[i + 264*t] = A[i + 264*t];
R[i + 265*t] = A[i + 265*t];
R[i + 266*t] = A[i + 266*t];
R[i + 267*t] = A[i + 267*t];
R[i + 268*t] = A[i + 268*t];
R[i + 269*t] = A[i + 269*t];
R[i + 270*t] = A[i + 270*t];
R[i + 271*t] = A[i + 271*t];
R[i + 272*t] = A[i + 272*t];
R[i + 273*t] = A[i + 273*t];
R[i + 274*t] = A[i + 274*t];
R[i + 275*t] = A[i + 275*t];
R[i + 276*t] = A[i + 276*t];
R[i + 277*t] = A[i + 277*t];
R[i + 278*t] = A[i + 278*t];
R[i + 279*t] = A[i + 279*t];
R[i + 280*t] = A[i + 280*t];
R[i + 281*t] = A[i + 281*t];
R[i + 282*t] = A[i + 282*t];
R[i + 283*t] = A[i + 283*t];
R[i + 284*t] = A[i + 284*t];
R[i + 285*t] = A[i + 285*t];
R[i + 286*t] = A[i + 286*t];
R[i + 287*t] = A[i + 287*t];
R[i + 288*t] = A[i + 288*t];
R[i + 289*t] = A[i + 289*t];
R[i + 290*t] = A[i + 290*t];
R[i + 291*t] = A[i + 291*t];
R[i + 292*t] = A[i + 292*t];
R[i + 293*t] = A[i + 293*t];
R[i + 294*t] = A[i + 294*t];
R[i + 295*t] = A[i + 295*t];
R[i + 296*t] = A[i + 296*t];
R[i + 297*t] = A[i + 297*t];
R[i + 298*t] = A[i + 298*t];
R[i + 299*t] = A[i + 299*t];
R[i + 300*t] = A[i + 300*t];
R[i + 301*t] = A[i + 301*t];
R[i + 302*t] = A[i + 302*t];
R[i + 303*t] = A[i + 303*t];
R[i + 304*t] = A[i + 304*t];
R[i + 305*t] = A[i + 305*t];
R[i + 306*t] = A[i + 306*t];
R[i + 307*t] = A[i + 307*t];
R[i + 308*t] = A[i + 308*t];
R[i + 309*t] = A[i + 309*t];
R[i + 310*t] = A[i + 310*t];
R[i + 311*t] = A[i + 311*t];
R[i + 312*t] = A[i + 312*t];
R[i + 313*t] = A[i + 313*t];
R[i + 314*t] = A[i + 314*t];
R[i + 315*t] = A[i + 315*t];
R[i + 316*t] = A[i + 316*t];
R[i + 317*t] = A[i + 317*t];
R[i + 318*t] = A[i + 318*t];
R[i + 319*t] = A[i + 319*t];
R[i + 320*t] = A[i + 320*t];
R[i + 321*t] = A[i + 321*t];
R[i + 322*t] = A[i + 322*t];
R[i + 323*t] = A[i + 323*t];
R[i + 324*t] = A[i + 324*t];
R[i + 325*t] = A[i + 325*t];
R[i + 326*t] = A[i + 326*t];
R[i + 327*t] = A[i + 327*t];
R[i + 328*t] = A[i + 328*t];
R[i + 329*t] = A[i + 329*t];
R[i + 330*t] = A[i + 330*t];
R[i + 331*t] = A[i + 331*t];
R[i + 332*t] = A[i + 332*t];
R[i + 333*t] = A[i + 333*t];
R[i + 334*t] = A[i + 334*t];
R[i + 335*t] = A[i + 335*t];
R[i + 336*t] = A[i + 336*t];
R[i + 337*t] = A[i + 337*t];
R[i + 338*t] = A[i + 338*t];
R[i + 339*t] = A[i + 339*t];
R[i + 340*t] = A[i + 340*t];
R[i + 341*t] = A[i + 341*t];
R[i + 342*t] = A[i + 342*t];
R[i + 343*t] = A[i + 343*t];
R[i + 344*t] = A[i + 344*t];
R[i + 345*t] = A[i + 345*t];
R[i + 346*t] = A[i + 346*t];
R[i + 347*t] = A[i + 347*t];
R[i + 348*t] = A[i + 348*t];
R[i + 349*t] = A[i + 349*t];
R[i + 350*t] = A[i + 350*t];
R[i + 351*t] = A[i + 351*t];
R[i + 352*t] = A[i + 352*t];
R[i + 353*t] = A[i + 353*t];
R[i + 354*t] = A[i + 354*t];
R[i + 355*t] = A[i + 355*t];
R[i + 356*t] = A[i + 356*t];
R[i + 357*t] = A[i + 357*t];
R[i + 358*t] = A[i + 358*t];
R[i + 359*t] = A[i + 359*t];
R[i + 360*t] = A[i + 360*t];
R[i + 361*t] = A[i + 361*t];
R[i + 362*t] = A[i + 362*t];
R[i + 363*t] = A[i + 363*t];
R[i + 364*t] = A[i + 364*t];
R[i + 365*t] = A[i + 365*t];
R[i + 366*t] = A[i + 366*t];
R[i + 367*t] = A[i + 367*t];
R[i + 368*t] = A[i + 368*t];
R[i + 369*t] = A[i + 369*t];
R[i + 370*t] = A[i + 370*t];
R[i + 371*t] = A[i + 371*t];
R[i + 372*t] = A[i + 372*t];
R[i + 373*t] = A[i + 373*t];
R[i + 374*t] = A[i + 374*t];
R[i + 375*t] = A[i + 375*t];
R[i + 376*t] = A[i + 376*t];
R[i + 377*t] = A[i + 377*t];
R[i + 378*t] = A[i + 378*t];
R[i + 379*t] = A[i + 379*t];
R[i + 380*t] = A[i + 380*t];
R[i + 381*t] = A[i + 381*t];
R[i + 382*t] = A[i + 382*t];
R[i + 383*t] = A[i + 383*t];
R[i + 384*t] = A[i + 384*t];
R[i + 385*t] = A[i + 385*t];
R[i + 386*t] = A[i + 386*t];
R[i + 387*t] = A[i + 387*t];
R[i + 388*t] = A[i + 388*t];
R[i + 389*t] = A[i + 389*t];
R[i + 390*t] = A[i + 390*t];
R[i + 391*t] = A[i + 391*t];
R[i + 392*t] = A[i + 392*t];
R[i + 393*t] = A[i + 393*t];
R[i + 394*t] = A[i + 394*t];
R[i + 395*t] = A[i + 395*t];
R[i + 396*t] = A[i + 396*t];
R[i + 397*t] = A[i + 397*t];
R[i + 398*t] = A[i + 398*t];
R[i + 399*t] = A[i + 399*t];
R[i + 400*t] = A[i + 400*t];
R[i + 401*t] = A[i + 401*t];
R[i + 402*t] = A[i + 402*t];
R[i + 403*t] = A[i + 403*t];
R[i + 404*t] = A[i + 404*t];
R[i + 405*t] = A[i + 405*t];
R[i + 406*t] = A[i + 406*t];
R[i + 407*t] = A[i + 407*t];
R[i + 408*t] = A[i + 408*t];
R[i + 409*t] = A[i + 409*t];
R[i + 410*t] = A[i + 410*t];
R[i + 411*t] = A[i + 411*t];
R[i + 412*t] = A[i + 412*t];
R[i + 413*t] = A[i + 413*t];
R[i + 414*t] = A[i + 414*t];
R[i + 415*t] = A[i + 415*t];
R[i + 416*t] = A[i + 416*t];
R[i + 417*t] = A[i + 417*t];
R[i + 418*t] = A[i + 418*t];
R[i + 419*t] = A[i + 419*t];
R[i + 420*t] = A[i + 420*t];
R[i + 421*t] = A[i + 421*t];
R[i + 422*t] = A[i + 422*t];
R[i + 423*t] = A[i + 423*t];
R[i + 424*t] = A[i + 424*t];
R[i + 425*t] = A[i + 425*t];
R[i + 426*t] = A[i + 426*t];
R[i + 427*t] = A[i + 427*t];
R[i + 428*t] = A[i + 428*t];
R[i + 429*t] = A[i + 429*t];
R[i + 430*t] = A[i + 430*t];
R[i + 431*t] = A[i + 431*t];
R[i + 432*t] = A[i + 432*t];
R[i + 433*t] = A[i + 433*t];
R[i + 434*t] = A[i + 434*t];
R[i + 435*t] = A[i + 435*t];
R[i + 436*t] = A[i + 436*t];
R[i + 437*t] = A[i + 437*t];
R[i + 438*t] = A[i + 438*t];
R[i + 439*t] = A[i + 439*t];
R[i + 440*t] = A[i + 440*t];
R[i + 441*t] = A[i + 441*t];
R[i + 442*t] = A[i + 442*t];
R[i + 443*t] = A[i + 443*t];
R[i + 444*t] = A[i + 444*t];
R[i + 445*t] = A[i + 445*t];
R[i + 446*t] = A[i + 446*t];
R[i + 447*t] = A[i + 447*t];
R[i + 448*t] = A[i + 448*t];
R[i + 449*t] = A[i + 449*t];
R[i + 450*t] = A[i + 450*t];
R[i + 451*t] = A[i + 451*t];
R[i + 452*t] = A[i + 452*t];
R[i + 453*t] = A[i + 453*t];
R[i + 454*t] = A[i + 454*t];
R[i + 455*t] = A[i + 455*t];
R[i + 456*t] = A[i + 456*t];
R[i + 457*t] = A[i + 457*t];
R[i + 458*t] = A[i + 458*t];
R[i + 459*t] = A[i + 459*t];
R[i + 460*t] = A[i + 460*t];
R[i + 461*t] = A[i + 461*t];
R[i + 462*t] = A[i + 462*t];
R[i + 463*t] = A[i + 463*t];
R[i + 464*t] = A[i + 464*t];
R[i + 465*t] = A[i + 465*t];
R[i + 466*t] = A[i + 466*t];
R[i + 467*t] = A[i + 467*t];
R[i + 468*t] = A[i + 468*t];
R[i + 469*t] = A[i + 469*t];
R[i + 470*t] = A[i + 470*t];
R[i + 471*t] = A[i + 471*t];
R[i + 472*t] = A[i + 472*t];
R[i + 473*t] = A[i + 473*t];
R[i + 474*t] = A[i + 474*t];
R[i + 475*t] = A[i + 475*t];
R[i + 476*t] = A[i + 476*t];
R[i + 477*t] = A[i + 477*t];
R[i + 478*t] = A[i + 478*t];
R[i + 479*t] = A[i + 479*t];
R[i + 480*t] = A[i + 480*t];
R[i + 481*t] = A[i + 481*t];
R[i + 482*t] = A[i + 482*t];
R[i + 483*t] = A[i + 483*t];
R[i + 484*t] = A[i + 484*t];
R[i + 485*t] = A[i + 485*t];
R[i + 486*t] = A[i + 486*t];
R[i + 487*t] = A[i + 487*t];
R[i + 488*t] = A[i + 488*t];
R[i + 489*t] = A[i + 489*t];
R[i + 490*t] = A[i + 490*t];
R[i + 491*t] = A[i + 491*t];
R[i + 492*t] = A[i + 492*t];
R[i + 493*t] = A[i + 493*t];
R[i + 494*t] = A[i + 494*t];
R[i + 495*t] = A[i + 495*t];
R[i + 496*t] = A[i + 496*t];
R[i + 497*t] = A[i + 497*t];
R[i + 498*t] = A[i + 498*t];
R[i + 499*t] = A[i + 499*t];
R[i + 500*t] = A[i + 500*t];
R[i + 501*t] = A[i + 501*t];
R[i + 502*t] = A[i + 502*t];
R[i + 503*t] = A[i + 503*t];
R[i + 504*t] = A[i + 504*t];
R[i + 505*t] = A[i + 505*t];
R[i + 506*t] = A[i + 506*t];
R[i + 507*t] = A[i + 507*t];
R[i + 508*t] = A[i + 508*t];
R[i + 509*t] = A[i + 509*t];
R[i + 510*t] = A[i + 510*t];
R[i + 511*t] = A[i + 511*t];
R[i + 512*t] = A[i + 512*t];
R[i + 513*t] = A[i + 513*t];
R[i + 514*t] = A[i + 514*t];
R[i + 515*t] = A[i + 515*t];
R[i + 516*t] = A[i + 516*t];
R[i + 517*t] = A[i + 517*t];
R[i + 518*t] = A[i + 518*t];
R[i + 519*t] = A[i + 519*t];
R[i + 520*t] = A[i + 520*t];
R[i + 521*t] = A[i + 521*t];
R[i + 522*t] = A[i + 522*t];
R[i + 523*t] = A[i + 523*t];
R[i + 524*t] = A[i + 524*t];
R[i + 525*t] = A[i + 525*t];
R[i + 526*t] = A[i + 526*t];
R[i + 527*t] = A[i + 527*t];
R[i + 528*t] = A[i + 528*t];
R[i + 529*t] = A[i + 529*t];
R[i + 530*t] = A[i + 530*t];
R[i + 531*t] = A[i + 531*t];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 532*t] = Op[i + 0*t] ? R[B[i + 0*t]] * R[C[i + 0*t]] : R[B[i + 0*t]] + R[C[i + 0*t]];
R[i + 533*t] = Op[i + 1*t] ? R[B[i + 1*t]] * R[C[i + 1*t]] : R[B[i + 1*t]] + R[C[i + 1*t]];
R[i + 534*t] = Op[i + 2*t] ? R[B[i + 2*t]] * R[C[i + 2*t]] : R[B[i + 2*t]] + R[C[i + 2*t]];
R[i + 535*t] = Op[i + 3*t] ? R[B[i + 3*t]] * R[C[i + 3*t]] : R[B[i + 3*t]] + R[C[i + 3*t]];
R[i + 536*t] = Op[i + 4*t] ? R[B[i + 4*t]] * R[C[i + 4*t]] : R[B[i + 4*t]] + R[C[i + 4*t]];
R[i + 537*t] = Op[i + 5*t] ? R[B[i + 5*t]] * R[C[i + 5*t]] : R[B[i + 5*t]] + R[C[i + 5*t]];
R[i + 538*t] = Op[i + 6*t] ? R[B[i + 6*t]] * R[C[i + 6*t]] : R[B[i + 6*t]] + R[C[i + 6*t]];
R[i + 539*t] = Op[i + 7*t] ? R[B[i + 7*t]] * R[C[i + 7*t]] : R[B[i + 7*t]] + R[C[i + 7*t]];
R[i + 540*t] = Op[i + 8*t] ? R[B[i + 8*t]] * R[C[i + 8*t]] : R[B[i + 8*t]] + R[C[i + 8*t]];
R[i + 541*t] = Op[i + 9*t] ? R[B[i + 9*t]] * R[C[i + 9*t]] : R[B[i + 9*t]] + R[C[i + 9*t]];
R[i + 542*t] = Op[i + 10*t] ? R[B[i + 10*t]] * R[C[i + 10*t]] : R[B[i + 10*t]] + R[C[i + 10*t]];
R[i + 543*t] = Op[i + 11*t] ? R[B[i + 11*t]] * R[C[i + 11*t]] : R[B[i + 11*t]] + R[C[i + 11*t]];
R[i + 544*t] = Op[i + 12*t] ? R[B[i + 12*t]] * R[C[i + 12*t]] : R[B[i + 12*t]] + R[C[i + 12*t]];
R[i + 545*t] = Op[i + 13*t] ? R[B[i + 13*t]] * R[C[i + 13*t]] : R[B[i + 13*t]] + R[C[i + 13*t]];
R[i + 546*t] = Op[i + 14*t] ? R[B[i + 14*t]] * R[C[i + 14*t]] : R[B[i + 14*t]] + R[C[i + 14*t]];
R[i + 547*t] = Op[i + 15*t] ? R[B[i + 15*t]] * R[C[i + 15*t]] : R[B[i + 15*t]] + R[C[i + 15*t]];
R[i + 548*t] = Op[i + 16*t] ? R[B[i + 16*t]] * R[C[i + 16*t]] : R[B[i + 16*t]] + R[C[i + 16*t]];
R[i + 549*t] = Op[i + 17*t] ? R[B[i + 17*t]] * R[C[i + 17*t]] : R[B[i + 17*t]] + R[C[i + 17*t]];
R[i + 550*t] = Op[i + 18*t] ? R[B[i + 18*t]] * R[C[i + 18*t]] : R[B[i + 18*t]] + R[C[i + 18*t]];
R[i + 551*t] = Op[i + 19*t] ? R[B[i + 19*t]] * R[C[i + 19*t]] : R[B[i + 19*t]] + R[C[i + 19*t]];
R[i + 552*t] = Op[i + 20*t] ? R[B[i + 20*t]] * R[C[i + 20*t]] : R[B[i + 20*t]] + R[C[i + 20*t]];
R[i + 553*t] = Op[i + 21*t] ? R[B[i + 21*t]] * R[C[i + 21*t]] : R[B[i + 21*t]] + R[C[i + 21*t]];
R[i + 554*t] = Op[i + 22*t] ? R[B[i + 22*t]] * R[C[i + 22*t]] : R[B[i + 22*t]] + R[C[i + 22*t]];
R[i + 555*t] = Op[i + 23*t] ? R[B[i + 23*t]] * R[C[i + 23*t]] : R[B[i + 23*t]] + R[C[i + 23*t]];
R[i + 556*t] = Op[i + 24*t] ? R[B[i + 24*t]] * R[C[i + 24*t]] : R[B[i + 24*t]] + R[C[i + 24*t]];
R[i + 557*t] = Op[i + 25*t] ? R[B[i + 25*t]] * R[C[i + 25*t]] : R[B[i + 25*t]] + R[C[i + 25*t]];
R[i + 558*t] = Op[i + 26*t] ? R[B[i + 26*t]] * R[C[i + 26*t]] : R[B[i + 26*t]] + R[C[i + 26*t]];
R[i + 559*t] = Op[i + 27*t] ? R[B[i + 27*t]] * R[C[i + 27*t]] : R[B[i + 27*t]] + R[C[i + 27*t]];
R[i + 560*t] = Op[i + 28*t] ? R[B[i + 28*t]] * R[C[i + 28*t]] : R[B[i + 28*t]] + R[C[i + 28*t]];
R[i + 561*t] = Op[i + 29*t] ? R[B[i + 29*t]] * R[C[i + 29*t]] : R[B[i + 29*t]] + R[C[i + 29*t]];
R[i + 562*t] = Op[i + 30*t] ? R[B[i + 30*t]] * R[C[i + 30*t]] : R[B[i + 30*t]] + R[C[i + 30*t]];
R[i + 563*t] = Op[i + 31*t] ? R[B[i + 31*t]] * R[C[i + 31*t]] : R[B[i + 31*t]] + R[C[i + 31*t]];
R[i + 564*t] = Op[i + 32*t] ? R[B[i + 32*t]] * R[C[i + 32*t]] : R[B[i + 32*t]] + R[C[i + 32*t]];
R[i + 565*t] = Op[i + 33*t] ? R[B[i + 33*t]] * R[C[i + 33*t]] : R[B[i + 33*t]] + R[C[i + 33*t]];
R[i + 566*t] = Op[i + 34*t] ? R[B[i + 34*t]] * R[C[i + 34*t]] : R[B[i + 34*t]] + R[C[i + 34*t]];
R[i + 567*t] = Op[i + 35*t] ? R[B[i + 35*t]] * R[C[i + 35*t]] : R[B[i + 35*t]] + R[C[i + 35*t]];
R[i + 568*t] = Op[i + 36*t] ? R[B[i + 36*t]] * R[C[i + 36*t]] : R[B[i + 36*t]] + R[C[i + 36*t]];
R[i + 569*t] = Op[i + 37*t] ? R[B[i + 37*t]] * R[C[i + 37*t]] : R[B[i + 37*t]] + R[C[i + 37*t]];
R[i + 570*t] = Op[i + 38*t] ? R[B[i + 38*t]] * R[C[i + 38*t]] : R[B[i + 38*t]] + R[C[i + 38*t]];
R[i + 571*t] = Op[i + 39*t] ? R[B[i + 39*t]] * R[C[i + 39*t]] : R[B[i + 39*t]] + R[C[i + 39*t]];
R[i + 572*t] = Op[i + 40*t] ? R[B[i + 40*t]] * R[C[i + 40*t]] : R[B[i + 40*t]] + R[C[i + 40*t]];
R[i + 573*t] = Op[i + 41*t] ? R[B[i + 41*t]] * R[C[i + 41*t]] : R[B[i + 41*t]] + R[C[i + 41*t]];
R[i + 574*t] = Op[i + 42*t] ? R[B[i + 42*t]] * R[C[i + 42*t]] : R[B[i + 42*t]] + R[C[i + 42*t]];
R[i + 575*t] = Op[i + 43*t] ? R[B[i + 43*t]] * R[C[i + 43*t]] : R[B[i + 43*t]] + R[C[i + 43*t]];
R[i + 576*t] = Op[i + 44*t] ? R[B[i + 44*t]] * R[C[i + 44*t]] : R[B[i + 44*t]] + R[C[i + 44*t]];
R[i + 577*t] = Op[i + 45*t] ? R[B[i + 45*t]] * R[C[i + 45*t]] : R[B[i + 45*t]] + R[C[i + 45*t]];
R[i + 578*t] = Op[i + 46*t] ? R[B[i + 46*t]] * R[C[i + 46*t]] : R[B[i + 46*t]] + R[C[i + 46*t]];
R[i + 579*t] = Op[i + 47*t] ? R[B[i + 47*t]] * R[C[i + 47*t]] : R[B[i + 47*t]] + R[C[i + 47*t]];
R[i + 580*t] = Op[i + 48*t] ? R[B[i + 48*t]] * R[C[i + 48*t]] : R[B[i + 48*t]] + R[C[i + 48*t]];
R[i + 581*t] = Op[i + 49*t] ? R[B[i + 49*t]] * R[C[i + 49*t]] : R[B[i + 49*t]] + R[C[i + 49*t]];
R[i + 582*t] = Op[i + 50*t] ? R[B[i + 50*t]] * R[C[i + 50*t]] : R[B[i + 50*t]] + R[C[i + 50*t]];
R[i + 583*t] = Op[i + 51*t] ? R[B[i + 51*t]] * R[C[i + 51*t]] : R[B[i + 51*t]] + R[C[i + 51*t]];
R[i + 584*t] = Op[i + 52*t] ? R[B[i + 52*t]] * R[C[i + 52*t]] : R[B[i + 52*t]] + R[C[i + 52*t]];
R[i + 585*t] = Op[i + 53*t] ? R[B[i + 53*t]] * R[C[i + 53*t]] : R[B[i + 53*t]] + R[C[i + 53*t]];
R[i + 586*t] = Op[i + 54*t] ? R[B[i + 54*t]] * R[C[i + 54*t]] : R[B[i + 54*t]] + R[C[i + 54*t]];
R[i + 587*t] = Op[i + 55*t] ? R[B[i + 55*t]] * R[C[i + 55*t]] : R[B[i + 55*t]] + R[C[i + 55*t]];
R[i + 588*t] = Op[i + 56*t] ? R[B[i + 56*t]] * R[C[i + 56*t]] : R[B[i + 56*t]] + R[C[i + 56*t]];
R[i + 589*t] = Op[i + 57*t] ? R[B[i + 57*t]] * R[C[i + 57*t]] : R[B[i + 57*t]] + R[C[i + 57*t]];
R[i + 590*t] = Op[i + 58*t] ? R[B[i + 58*t]] * R[C[i + 58*t]] : R[B[i + 58*t]] + R[C[i + 58*t]];
R[i + 591*t] = Op[i + 59*t] ? R[B[i + 59*t]] * R[C[i + 59*t]] : R[B[i + 59*t]] + R[C[i + 59*t]];
R[i + 592*t] = Op[i + 60*t] ? R[B[i + 60*t]] * R[C[i + 60*t]] : R[B[i + 60*t]] + R[C[i + 60*t]];
R[i + 593*t] = Op[i + 61*t] ? R[B[i + 61*t]] * R[C[i + 61*t]] : R[B[i + 61*t]] + R[C[i + 61*t]];
R[i + 594*t] = Op[i + 62*t] ? R[B[i + 62*t]] * R[C[i + 62*t]] : R[B[i + 62*t]] + R[C[i + 62*t]];
R[i + 595*t] = Op[i + 63*t] ? R[B[i + 63*t]] * R[C[i + 63*t]] : R[B[i + 63*t]] + R[C[i + 63*t]];
R[i + 596*t] = Op[i + 64*t] ? R[B[i + 64*t]] * R[C[i + 64*t]] : R[B[i + 64*t]] + R[C[i + 64*t]];
R[i + 597*t] = Op[i + 65*t] ? R[B[i + 65*t]] * R[C[i + 65*t]] : R[B[i + 65*t]] + R[C[i + 65*t]];
R[i + 598*t] = Op[i + 66*t] ? R[B[i + 66*t]] * R[C[i + 66*t]] : R[B[i + 66*t]] + R[C[i + 66*t]];
R[i + 599*t] = Op[i + 67*t] ? R[B[i + 67*t]] * R[C[i + 67*t]] : R[B[i + 67*t]] + R[C[i + 67*t]];
R[i + 600*t] = Op[i + 68*t] ? R[B[i + 68*t]] * R[C[i + 68*t]] : R[B[i + 68*t]] + R[C[i + 68*t]];
R[i + 601*t] = Op[i + 69*t] ? R[B[i + 69*t]] * R[C[i + 69*t]] : R[B[i + 69*t]] + R[C[i + 69*t]];
R[i + 602*t] = Op[i + 70*t] ? R[B[i + 70*t]] * R[C[i + 70*t]] : R[B[i + 70*t]] + R[C[i + 70*t]];
R[i + 603*t] = Op[i + 71*t] ? R[B[i + 71*t]] * R[C[i + 71*t]] : R[B[i + 71*t]] + R[C[i + 71*t]];
R[i + 604*t] = Op[i + 72*t] ? R[B[i + 72*t]] * R[C[i + 72*t]] : R[B[i + 72*t]] + R[C[i + 72*t]];
R[i + 605*t] = Op[i + 73*t] ? R[B[i + 73*t]] * R[C[i + 73*t]] : R[B[i + 73*t]] + R[C[i + 73*t]];
R[i + 606*t] = Op[i + 74*t] ? R[B[i + 74*t]] * R[C[i + 74*t]] : R[B[i + 74*t]] + R[C[i + 74*t]];
R[i + 607*t] = Op[i + 75*t] ? R[B[i + 75*t]] * R[C[i + 75*t]] : R[B[i + 75*t]] + R[C[i + 75*t]];
R[i + 608*t] = Op[i + 76*t] ? R[B[i + 76*t]] * R[C[i + 76*t]] : R[B[i + 76*t]] + R[C[i + 76*t]];
R[i + 609*t] = Op[i + 77*t] ? R[B[i + 77*t]] * R[C[i + 77*t]] : R[B[i + 77*t]] + R[C[i + 77*t]];
R[i + 610*t] = Op[i + 78*t] ? R[B[i + 78*t]] * R[C[i + 78*t]] : R[B[i + 78*t]] + R[C[i + 78*t]];
R[i + 611*t] = Op[i + 79*t] ? R[B[i + 79*t]] * R[C[i + 79*t]] : R[B[i + 79*t]] + R[C[i + 79*t]];
R[i + 612*t] = Op[i + 80*t] ? R[B[i + 80*t]] * R[C[i + 80*t]] : R[B[i + 80*t]] + R[C[i + 80*t]];
R[i + 613*t] = Op[i + 81*t] ? R[B[i + 81*t]] * R[C[i + 81*t]] : R[B[i + 81*t]] + R[C[i + 81*t]];
R[i + 614*t] = Op[i + 82*t] ? R[B[i + 82*t]] * R[C[i + 82*t]] : R[B[i + 82*t]] + R[C[i + 82*t]];
R[i + 615*t] = Op[i + 83*t] ? R[B[i + 83*t]] * R[C[i + 83*t]] : R[B[i + 83*t]] + R[C[i + 83*t]];
R[i + 616*t] = Op[i + 84*t] ? R[B[i + 84*t]] * R[C[i + 84*t]] : R[B[i + 84*t]] + R[C[i + 84*t]];
R[i + 617*t] = Op[i + 85*t] ? R[B[i + 85*t]] * R[C[i + 85*t]] : R[B[i + 85*t]] + R[C[i + 85*t]];
R[i + 618*t] = Op[i + 86*t] ? R[B[i + 86*t]] * R[C[i + 86*t]] : R[B[i + 86*t]] + R[C[i + 86*t]];
R[i + 619*t] = Op[i + 87*t] ? R[B[i + 87*t]] * R[C[i + 87*t]] : R[B[i + 87*t]] + R[C[i + 87*t]];
R[i + 620*t] = Op[i + 88*t] ? R[B[i + 88*t]] * R[C[i + 88*t]] : R[B[i + 88*t]] + R[C[i + 88*t]];
R[i + 621*t] = Op[i + 89*t] ? R[B[i + 89*t]] * R[C[i + 89*t]] : R[B[i + 89*t]] + R[C[i + 89*t]];
R[i + 622*t] = Op[i + 90*t] ? R[B[i + 90*t]] * R[C[i + 90*t]] : R[B[i + 90*t]] + R[C[i + 90*t]];
R[i + 623*t] = Op[i + 91*t] ? R[B[i + 91*t]] * R[C[i + 91*t]] : R[B[i + 91*t]] + R[C[i + 91*t]];
R[i + 624*t] = Op[i + 92*t] ? R[B[i + 92*t]] * R[C[i + 92*t]] : R[B[i + 92*t]] + R[C[i + 92*t]];
R[i + 625*t] = Op[i + 93*t] ? R[B[i + 93*t]] * R[C[i + 93*t]] : R[B[i + 93*t]] + R[C[i + 93*t]];
R[i + 626*t] = Op[i + 94*t] ? R[B[i + 94*t]] * R[C[i + 94*t]] : R[B[i + 94*t]] + R[C[i + 94*t]];
R[i + 627*t] = Op[i + 95*t] ? R[B[i + 95*t]] * R[C[i + 95*t]] : R[B[i + 95*t]] + R[C[i + 95*t]];
R[i + 628*t] = Op[i + 96*t] ? R[B[i + 96*t]] * R[C[i + 96*t]] : R[B[i + 96*t]] + R[C[i + 96*t]];
R[i + 629*t] = Op[i + 97*t] ? R[B[i + 97*t]] * R[C[i + 97*t]] : R[B[i + 97*t]] + R[C[i + 97*t]];
R[i + 630*t] = Op[i + 98*t] ? R[B[i + 98*t]] * R[C[i + 98*t]] : R[B[i + 98*t]] + R[C[i + 98*t]];
R[i + 631*t] = Op[i + 99*t] ? R[B[i + 99*t]] * R[C[i + 99*t]] : R[B[i + 99*t]] + R[C[i + 99*t]];
R[i + 632*t] = Op[i + 100*t] ? R[B[i + 100*t]] * R[C[i + 100*t]] : R[B[i + 100*t]] + R[C[i + 100*t]];
R[i + 633*t] = Op[i + 101*t] ? R[B[i + 101*t]] * R[C[i + 101*t]] : R[B[i + 101*t]] + R[C[i + 101*t]];
R[i + 634*t] = Op[i + 102*t] ? R[B[i + 102*t]] * R[C[i + 102*t]] : R[B[i + 102*t]] + R[C[i + 102*t]];
R[i + 635*t] = Op[i + 103*t] ? R[B[i + 103*t]] * R[C[i + 103*t]] : R[B[i + 103*t]] + R[C[i + 103*t]];
R[i + 636*t] = Op[i + 104*t] ? R[B[i + 104*t]] * R[C[i + 104*t]] : R[B[i + 104*t]] + R[C[i + 104*t]];
R[i + 637*t] = Op[i + 105*t] ? R[B[i + 105*t]] * R[C[i + 105*t]] : R[B[i + 105*t]] + R[C[i + 105*t]];
R[i + 638*t] = Op[i + 106*t] ? R[B[i + 106*t]] * R[C[i + 106*t]] : R[B[i + 106*t]] + R[C[i + 106*t]];
R[i + 639*t] = Op[i + 107*t] ? R[B[i + 107*t]] * R[C[i + 107*t]] : R[B[i + 107*t]] + R[C[i + 107*t]];
R[i + 640*t] = Op[i + 108*t] ? R[B[i + 108*t]] * R[C[i + 108*t]] : R[B[i + 108*t]] + R[C[i + 108*t]];
R[i + 641*t] = Op[i + 109*t] ? R[B[i + 109*t]] * R[C[i + 109*t]] : R[B[i + 109*t]] + R[C[i + 109*t]];
R[i + 642*t] = Op[i + 110*t] ? R[B[i + 110*t]] * R[C[i + 110*t]] : R[B[i + 110*t]] + R[C[i + 110*t]];
R[i + 643*t] = Op[i + 111*t] ? R[B[i + 111*t]] * R[C[i + 111*t]] : R[B[i + 111*t]] + R[C[i + 111*t]];
R[i + 644*t] = Op[i + 112*t] ? R[B[i + 112*t]] * R[C[i + 112*t]] : R[B[i + 112*t]] + R[C[i + 112*t]];
R[i + 645*t] = Op[i + 113*t] ? R[B[i + 113*t]] * R[C[i + 113*t]] : R[B[i + 113*t]] + R[C[i + 113*t]];
R[i + 646*t] = Op[i + 114*t] ? R[B[i + 114*t]] * R[C[i + 114*t]] : R[B[i + 114*t]] + R[C[i + 114*t]];
R[i + 647*t] = Op[i + 115*t] ? R[B[i + 115*t]] * R[C[i + 115*t]] : R[B[i + 115*t]] + R[C[i + 115*t]];
R[i + 648*t] = Op[i + 116*t] ? R[B[i + 116*t]] * R[C[i + 116*t]] : R[B[i + 116*t]] + R[C[i + 116*t]];
R[i + 649*t] = Op[i + 117*t] ? R[B[i + 117*t]] * R[C[i + 117*t]] : R[B[i + 117*t]] + R[C[i + 117*t]];
R[i + 650*t] = Op[i + 118*t] ? R[B[i + 118*t]] * R[C[i + 118*t]] : R[B[i + 118*t]] + R[C[i + 118*t]];
R[i + 651*t] = Op[i + 119*t] ? R[B[i + 119*t]] * R[C[i + 119*t]] : R[B[i + 119*t]] + R[C[i + 119*t]];
R[i + 652*t] = Op[i + 120*t] ? R[B[i + 120*t]] * R[C[i + 120*t]] : R[B[i + 120*t]] + R[C[i + 120*t]];
R[i + 653*t] = Op[i + 121*t] ? R[B[i + 121*t]] * R[C[i + 121*t]] : R[B[i + 121*t]] + R[C[i + 121*t]];
R[i + 654*t] = Op[i + 122*t] ? R[B[i + 122*t]] * R[C[i + 122*t]] : R[B[i + 122*t]] + R[C[i + 122*t]];
R[i + 655*t] = Op[i + 123*t] ? R[B[i + 123*t]] * R[C[i + 123*t]] : R[B[i + 123*t]] + R[C[i + 123*t]];
R[i + 656*t] = Op[i + 124*t] ? R[B[i + 124*t]] * R[C[i + 124*t]] : R[B[i + 124*t]] + R[C[i + 124*t]];
R[i + 657*t] = Op[i + 125*t] ? R[B[i + 125*t]] * R[C[i + 125*t]] : R[B[i + 125*t]] + R[C[i + 125*t]];
R[i + 658*t] = Op[i + 126*t] ? R[B[i + 126*t]] * R[C[i + 126*t]] : R[B[i + 126*t]] + R[C[i + 126*t]];
R[i + 659*t] = Op[i + 127*t] ? R[B[i + 127*t]] * R[C[i + 127*t]] : R[B[i + 127*t]] + R[C[i + 127*t]];
R[i + 660*t] = Op[i + 128*t] ? R[B[i + 128*t]] * R[C[i + 128*t]] : R[B[i + 128*t]] + R[C[i + 128*t]];
R[i + 661*t] = Op[i + 129*t] ? R[B[i + 129*t]] * R[C[i + 129*t]] : R[B[i + 129*t]] + R[C[i + 129*t]];
R[i + 662*t] = Op[i + 130*t] ? R[B[i + 130*t]] * R[C[i + 130*t]] : R[B[i + 130*t]] + R[C[i + 130*t]];
R[i + 663*t] = Op[i + 131*t] ? R[B[i + 131*t]] * R[C[i + 131*t]] : R[B[i + 131*t]] + R[C[i + 131*t]];
R[i + 664*t] = Op[i + 132*t] ? R[B[i + 132*t]] * R[C[i + 132*t]] : R[B[i + 132*t]] + R[C[i + 132*t]];
R[i + 665*t] = Op[i + 133*t] ? R[B[i + 133*t]] * R[C[i + 133*t]] : R[B[i + 133*t]] + R[C[i + 133*t]];
R[i + 666*t] = Op[i + 134*t] ? R[B[i + 134*t]] * R[C[i + 134*t]] : R[B[i + 134*t]] + R[C[i + 134*t]];
R[i + 667*t] = Op[i + 135*t] ? R[B[i + 135*t]] * R[C[i + 135*t]] : R[B[i + 135*t]] + R[C[i + 135*t]];
R[i + 668*t] = Op[i + 136*t] ? R[B[i + 136*t]] * R[C[i + 136*t]] : R[B[i + 136*t]] + R[C[i + 136*t]];
R[i + 669*t] = Op[i + 137*t] ? R[B[i + 137*t]] * R[C[i + 137*t]] : R[B[i + 137*t]] + R[C[i + 137*t]];
R[i + 670*t] = Op[i + 138*t] ? R[B[i + 138*t]] * R[C[i + 138*t]] : R[B[i + 138*t]] + R[C[i + 138*t]];
R[i + 671*t] = Op[i + 139*t] ? R[B[i + 139*t]] * R[C[i + 139*t]] : R[B[i + 139*t]] + R[C[i + 139*t]];
R[i + 672*t] = Op[i + 140*t] ? R[B[i + 140*t]] * R[C[i + 140*t]] : R[B[i + 140*t]] + R[C[i + 140*t]];
R[i + 673*t] = Op[i + 141*t] ? R[B[i + 141*t]] * R[C[i + 141*t]] : R[B[i + 141*t]] + R[C[i + 141*t]];
R[i + 674*t] = Op[i + 142*t] ? R[B[i + 142*t]] * R[C[i + 142*t]] : R[B[i + 142*t]] + R[C[i + 142*t]];
R[i + 675*t] = Op[i + 143*t] ? R[B[i + 143*t]] * R[C[i + 143*t]] : R[B[i + 143*t]] + R[C[i + 143*t]];
R[i + 676*t] = Op[i + 144*t] ? R[B[i + 144*t]] * R[C[i + 144*t]] : R[B[i + 144*t]] + R[C[i + 144*t]];
R[i + 677*t] = Op[i + 145*t] ? R[B[i + 145*t]] * R[C[i + 145*t]] : R[B[i + 145*t]] + R[C[i + 145*t]];
R[i + 678*t] = Op[i + 146*t] ? R[B[i + 146*t]] * R[C[i + 146*t]] : R[B[i + 146*t]] + R[C[i + 146*t]];
R[i + 679*t] = Op[i + 147*t] ? R[B[i + 147*t]] * R[C[i + 147*t]] : R[B[i + 147*t]] + R[C[i + 147*t]];
R[i + 680*t] = Op[i + 148*t] ? R[B[i + 148*t]] * R[C[i + 148*t]] : R[B[i + 148*t]] + R[C[i + 148*t]];
R[i + 681*t] = Op[i + 149*t] ? R[B[i + 149*t]] * R[C[i + 149*t]] : R[B[i + 149*t]] + R[C[i + 149*t]];
R[i + 682*t] = Op[i + 150*t] ? R[B[i + 150*t]] * R[C[i + 150*t]] : R[B[i + 150*t]] + R[C[i + 150*t]];
R[i + 683*t] = Op[i + 151*t] ? R[B[i + 151*t]] * R[C[i + 151*t]] : R[B[i + 151*t]] + R[C[i + 151*t]];
R[i + 684*t] = Op[i + 152*t] ? R[B[i + 152*t]] * R[C[i + 152*t]] : R[B[i + 152*t]] + R[C[i + 152*t]];
R[i + 685*t] = Op[i + 153*t] ? R[B[i + 153*t]] * R[C[i + 153*t]] : R[B[i + 153*t]] + R[C[i + 153*t]];
R[i + 686*t] = Op[i + 154*t] ? R[B[i + 154*t]] * R[C[i + 154*t]] : R[B[i + 154*t]] + R[C[i + 154*t]];
R[i + 687*t] = Op[i + 155*t] ? R[B[i + 155*t]] * R[C[i + 155*t]] : R[B[i + 155*t]] + R[C[i + 155*t]];
R[i + 688*t] = Op[i + 156*t] ? R[B[i + 156*t]] * R[C[i + 156*t]] : R[B[i + 156*t]] + R[C[i + 156*t]];
R[i + 689*t] = Op[i + 157*t] ? R[B[i + 157*t]] * R[C[i + 157*t]] : R[B[i + 157*t]] + R[C[i + 157*t]];
R[i + 690*t] = Op[i + 158*t] ? R[B[i + 158*t]] * R[C[i + 158*t]] : R[B[i + 158*t]] + R[C[i + 158*t]];
R[i + 691*t] = Op[i + 159*t] ? R[B[i + 159*t]] * R[C[i + 159*t]] : R[B[i + 159*t]] + R[C[i + 159*t]];
R[i + 692*t] = Op[i + 160*t] ? R[B[i + 160*t]] * R[C[i + 160*t]] : R[B[i + 160*t]] + R[C[i + 160*t]];
R[i + 693*t] = Op[i + 161*t] ? R[B[i + 161*t]] * R[C[i + 161*t]] : R[B[i + 161*t]] + R[C[i + 161*t]];
R[i + 694*t] = Op[i + 162*t] ? R[B[i + 162*t]] * R[C[i + 162*t]] : R[B[i + 162*t]] + R[C[i + 162*t]];
R[i + 695*t] = Op[i + 163*t] ? R[B[i + 163*t]] * R[C[i + 163*t]] : R[B[i + 163*t]] + R[C[i + 163*t]];
R[i + 696*t] = Op[i + 164*t] ? R[B[i + 164*t]] * R[C[i + 164*t]] : R[B[i + 164*t]] + R[C[i + 164*t]];
R[i + 697*t] = Op[i + 165*t] ? R[B[i + 165*t]] * R[C[i + 165*t]] : R[B[i + 165*t]] + R[C[i + 165*t]];
R[i + 698*t] = Op[i + 166*t] ? R[B[i + 166*t]] * R[C[i + 166*t]] : R[B[i + 166*t]] + R[C[i + 166*t]];
__syncthreads();
R[i + 699*t] = Op[i + 167*t] ? R[B[i + 167*t]] * R[C[i + 167*t]] : R[B[i + 167*t]] + R[C[i + 167*t]];
R[i + 700*t] = Op[i + 168*t] ? R[B[i + 168*t]] * R[C[i + 168*t]] : R[B[i + 168*t]] + R[C[i + 168*t]];
R[i + 701*t] = Op[i + 169*t] ? R[B[i + 169*t]] * R[C[i + 169*t]] : R[B[i + 169*t]] + R[C[i + 169*t]];
R[i + 702*t] = Op[i + 170*t] ? R[B[i + 170*t]] * R[C[i + 170*t]] : R[B[i + 170*t]] + R[C[i + 170*t]];
R[i + 703*t] = Op[i + 171*t] ? R[B[i + 171*t]] * R[C[i + 171*t]] : R[B[i + 171*t]] + R[C[i + 171*t]];
R[i + 704*t] = Op[i + 172*t] ? R[B[i + 172*t]] * R[C[i + 172*t]] : R[B[i + 172*t]] + R[C[i + 172*t]];
R[i + 705*t] = Op[i + 173*t] ? R[B[i + 173*t]] * R[C[i + 173*t]] : R[B[i + 173*t]] + R[C[i + 173*t]];
R[i + 706*t] = Op[i + 174*t] ? R[B[i + 174*t]] * R[C[i + 174*t]] : R[B[i + 174*t]] + R[C[i + 174*t]];
R[i + 707*t] = Op[i + 175*t] ? R[B[i + 175*t]] * R[C[i + 175*t]] : R[B[i + 175*t]] + R[C[i + 175*t]];
R[i + 708*t] = Op[i + 176*t] ? R[B[i + 176*t]] * R[C[i + 176*t]] : R[B[i + 176*t]] + R[C[i + 176*t]];
R[i + 709*t] = Op[i + 177*t] ? R[B[i + 177*t]] * R[C[i + 177*t]] : R[B[i + 177*t]] + R[C[i + 177*t]];
R[i + 710*t] = Op[i + 178*t] ? R[B[i + 178*t]] * R[C[i + 178*t]] : R[B[i + 178*t]] + R[C[i + 178*t]];
R[i + 711*t] = Op[i + 179*t] ? R[B[i + 179*t]] * R[C[i + 179*t]] : R[B[i + 179*t]] + R[C[i + 179*t]];
R[i + 712*t] = Op[i + 180*t] ? R[B[i + 180*t]] * R[C[i + 180*t]] : R[B[i + 180*t]] + R[C[i + 180*t]];
R[i + 713*t] = Op[i + 181*t] ? R[B[i + 181*t]] * R[C[i + 181*t]] : R[B[i + 181*t]] + R[C[i + 181*t]];
R[i + 714*t] = Op[i + 182*t] ? R[B[i + 182*t]] * R[C[i + 182*t]] : R[B[i + 182*t]] + R[C[i + 182*t]];
R[i + 715*t] = Op[i + 183*t] ? R[B[i + 183*t]] * R[C[i + 183*t]] : R[B[i + 183*t]] + R[C[i + 183*t]];
R[i + 716*t] = Op[i + 184*t] ? R[B[i + 184*t]] * R[C[i + 184*t]] : R[B[i + 184*t]] + R[C[i + 184*t]];
R[i + 717*t] = Op[i + 185*t] ? R[B[i + 185*t]] * R[C[i + 185*t]] : R[B[i + 185*t]] + R[C[i + 185*t]];
R[i + 718*t] = Op[i + 186*t] ? R[B[i + 186*t]] * R[C[i + 186*t]] : R[B[i + 186*t]] + R[C[i + 186*t]];
R[i + 719*t] = Op[i + 187*t] ? R[B[i + 187*t]] * R[C[i + 187*t]] : R[B[i + 187*t]] + R[C[i + 187*t]];
R[i + 720*t] = Op[i + 188*t] ? R[B[i + 188*t]] * R[C[i + 188*t]] : R[B[i + 188*t]] + R[C[i + 188*t]];
R[i + 721*t] = Op[i + 189*t] ? R[B[i + 189*t]] * R[C[i + 189*t]] : R[B[i + 189*t]] + R[C[i + 189*t]];
R[i + 722*t] = Op[i + 190*t] ? R[B[i + 190*t]] * R[C[i + 190*t]] : R[B[i + 190*t]] + R[C[i + 190*t]];
R[i + 723*t] = Op[i + 191*t] ? R[B[i + 191*t]] * R[C[i + 191*t]] : R[B[i + 191*t]] + R[C[i + 191*t]];
R[i + 724*t] = Op[i + 192*t] ? R[B[i + 192*t]] * R[C[i + 192*t]] : R[B[i + 192*t]] + R[C[i + 192*t]];
R[i + 725*t] = Op[i + 193*t] ? R[B[i + 193*t]] * R[C[i + 193*t]] : R[B[i + 193*t]] + R[C[i + 193*t]];
R[i + 726*t] = Op[i + 194*t] ? R[B[i + 194*t]] * R[C[i + 194*t]] : R[B[i + 194*t]] + R[C[i + 194*t]];
R[i + 727*t] = Op[i + 195*t] ? R[B[i + 195*t]] * R[C[i + 195*t]] : R[B[i + 195*t]] + R[C[i + 195*t]];
R[i + 728*t] = Op[i + 196*t] ? R[B[i + 196*t]] * R[C[i + 196*t]] : R[B[i + 196*t]] + R[C[i + 196*t]];
R[i + 729*t] = Op[i + 197*t] ? R[B[i + 197*t]] * R[C[i + 197*t]] : R[B[i + 197*t]] + R[C[i + 197*t]];
R[i + 730*t] = Op[i + 198*t] ? R[B[i + 198*t]] * R[C[i + 198*t]] : R[B[i + 198*t]] + R[C[i + 198*t]];
R[i + 731*t] = Op[i + 199*t] ? R[B[i + 199*t]] * R[C[i + 199*t]] : R[B[i + 199*t]] + R[C[i + 199*t]];
R[i + 732*t] = Op[i + 200*t] ? R[B[i + 200*t]] * R[C[i + 200*t]] : R[B[i + 200*t]] + R[C[i + 200*t]];
R[i + 733*t] = Op[i + 201*t] ? R[B[i + 201*t]] * R[C[i + 201*t]] : R[B[i + 201*t]] + R[C[i + 201*t]];
R[i + 734*t] = Op[i + 202*t] ? R[B[i + 202*t]] * R[C[i + 202*t]] : R[B[i + 202*t]] + R[C[i + 202*t]];
R[i + 735*t] = Op[i + 203*t] ? R[B[i + 203*t]] * R[C[i + 203*t]] : R[B[i + 203*t]] + R[C[i + 203*t]];
R[i + 736*t] = Op[i + 204*t] ? R[B[i + 204*t]] * R[C[i + 204*t]] : R[B[i + 204*t]] + R[C[i + 204*t]];
R[i + 737*t] = Op[i + 205*t] ? R[B[i + 205*t]] * R[C[i + 205*t]] : R[B[i + 205*t]] + R[C[i + 205*t]];
R[i + 738*t] = Op[i + 206*t] ? R[B[i + 206*t]] * R[C[i + 206*t]] : R[B[i + 206*t]] + R[C[i + 206*t]];
R[i + 739*t] = Op[i + 207*t] ? R[B[i + 207*t]] * R[C[i + 207*t]] : R[B[i + 207*t]] + R[C[i + 207*t]];
R[i + 740*t] = Op[i + 208*t] ? R[B[i + 208*t]] * R[C[i + 208*t]] : R[B[i + 208*t]] + R[C[i + 208*t]];
R[i + 741*t] = Op[i + 209*t] ? R[B[i + 209*t]] * R[C[i + 209*t]] : R[B[i + 209*t]] + R[C[i + 209*t]];
R[i + 742*t] = Op[i + 210*t] ? R[B[i + 210*t]] * R[C[i + 210*t]] : R[B[i + 210*t]] + R[C[i + 210*t]];
R[i + 743*t] = Op[i + 211*t] ? R[B[i + 211*t]] * R[C[i + 211*t]] : R[B[i + 211*t]] + R[C[i + 211*t]];
R[i + 744*t] = Op[i + 212*t] ? R[B[i + 212*t]] * R[C[i + 212*t]] : R[B[i + 212*t]] + R[C[i + 212*t]];
R[i + 745*t] = Op[i + 213*t] ? R[B[i + 213*t]] * R[C[i + 213*t]] : R[B[i + 213*t]] + R[C[i + 213*t]];
R[i + 746*t] = Op[i + 214*t] ? R[B[i + 214*t]] * R[C[i + 214*t]] : R[B[i + 214*t]] + R[C[i + 214*t]];
R[i + 747*t] = Op[i + 215*t] ? R[B[i + 215*t]] * R[C[i + 215*t]] : R[B[i + 215*t]] + R[C[i + 215*t]];
R[i + 748*t] = Op[i + 216*t] ? R[B[i + 216*t]] * R[C[i + 216*t]] : R[B[i + 216*t]] + R[C[i + 216*t]];
R[i + 749*t] = Op[i + 217*t] ? R[B[i + 217*t]] * R[C[i + 217*t]] : R[B[i + 217*t]] + R[C[i + 217*t]];
R[i + 750*t] = Op[i + 218*t] ? R[B[i + 218*t]] * R[C[i + 218*t]] : R[B[i + 218*t]] + R[C[i + 218*t]];
R[i + 751*t] = Op[i + 219*t] ? R[B[i + 219*t]] * R[C[i + 219*t]] : R[B[i + 219*t]] + R[C[i + 219*t]];
R[i + 752*t] = Op[i + 220*t] ? R[B[i + 220*t]] * R[C[i + 220*t]] : R[B[i + 220*t]] + R[C[i + 220*t]];
R[i + 753*t] = Op[i + 221*t] ? R[B[i + 221*t]] * R[C[i + 221*t]] : R[B[i + 221*t]] + R[C[i + 221*t]];
R[i + 754*t] = Op[i + 222*t] ? R[B[i + 222*t]] * R[C[i + 222*t]] : R[B[i + 222*t]] + R[C[i + 222*t]];
R[i + 755*t] = Op[i + 223*t] ? R[B[i + 223*t]] * R[C[i + 223*t]] : R[B[i + 223*t]] + R[C[i + 223*t]];
R[i + 756*t] = Op[i + 224*t] ? R[B[i + 224*t]] * R[C[i + 224*t]] : R[B[i + 224*t]] + R[C[i + 224*t]];
R[i + 757*t] = Op[i + 225*t] ? R[B[i + 225*t]] * R[C[i + 225*t]] : R[B[i + 225*t]] + R[C[i + 225*t]];
R[i + 758*t] = Op[i + 226*t] ? R[B[i + 226*t]] * R[C[i + 226*t]] : R[B[i + 226*t]] + R[C[i + 226*t]];
R[i + 759*t] = Op[i + 227*t] ? R[B[i + 227*t]] * R[C[i + 227*t]] : R[B[i + 227*t]] + R[C[i + 227*t]];
R[i + 760*t] = Op[i + 228*t] ? R[B[i + 228*t]] * R[C[i + 228*t]] : R[B[i + 228*t]] + R[C[i + 228*t]];
R[i + 761*t] = Op[i + 229*t] ? R[B[i + 229*t]] * R[C[i + 229*t]] : R[B[i + 229*t]] + R[C[i + 229*t]];
R[i + 762*t] = Op[i + 230*t] ? R[B[i + 230*t]] * R[C[i + 230*t]] : R[B[i + 230*t]] + R[C[i + 230*t]];
R[i + 763*t] = Op[i + 231*t] ? R[B[i + 231*t]] * R[C[i + 231*t]] : R[B[i + 231*t]] + R[C[i + 231*t]];
R[i + 764*t] = Op[i + 232*t] ? R[B[i + 232*t]] * R[C[i + 232*t]] : R[B[i + 232*t]] + R[C[i + 232*t]];
R[i + 765*t] = Op[i + 233*t] ? R[B[i + 233*t]] * R[C[i + 233*t]] : R[B[i + 233*t]] + R[C[i + 233*t]];
R[i + 766*t] = Op[i + 234*t] ? R[B[i + 234*t]] * R[C[i + 234*t]] : R[B[i + 234*t]] + R[C[i + 234*t]];
R[i + 767*t] = Op[i + 235*t] ? R[B[i + 235*t]] * R[C[i + 235*t]] : R[B[i + 235*t]] + R[C[i + 235*t]];
R[i + 768*t] = Op[i + 236*t] ? R[B[i + 236*t]] * R[C[i + 236*t]] : R[B[i + 236*t]] + R[C[i + 236*t]];
R[i + 769*t] = Op[i + 237*t] ? R[B[i + 237*t]] * R[C[i + 237*t]] : R[B[i + 237*t]] + R[C[i + 237*t]];
R[i + 770*t] = Op[i + 238*t] ? R[B[i + 238*t]] * R[C[i + 238*t]] : R[B[i + 238*t]] + R[C[i + 238*t]];
R[i + 771*t] = Op[i + 239*t] ? R[B[i + 239*t]] * R[C[i + 239*t]] : R[B[i + 239*t]] + R[C[i + 239*t]];
R[i + 772*t] = Op[i + 240*t] ? R[B[i + 240*t]] * R[C[i + 240*t]] : R[B[i + 240*t]] + R[C[i + 240*t]];
R[i + 773*t] = Op[i + 241*t] ? R[B[i + 241*t]] * R[C[i + 241*t]] : R[B[i + 241*t]] + R[C[i + 241*t]];
R[i + 774*t] = Op[i + 242*t] ? R[B[i + 242*t]] * R[C[i + 242*t]] : R[B[i + 242*t]] + R[C[i + 242*t]];
R[i + 775*t] = Op[i + 243*t] ? R[B[i + 243*t]] * R[C[i + 243*t]] : R[B[i + 243*t]] + R[C[i + 243*t]];
R[i + 776*t] = Op[i + 244*t] ? R[B[i + 244*t]] * R[C[i + 244*t]] : R[B[i + 244*t]] + R[C[i + 244*t]];
R[i + 777*t] = Op[i + 245*t] ? R[B[i + 245*t]] * R[C[i + 245*t]] : R[B[i + 245*t]] + R[C[i + 245*t]];
R[i + 778*t] = Op[i + 246*t] ? R[B[i + 246*t]] * R[C[i + 246*t]] : R[B[i + 246*t]] + R[C[i + 246*t]];
R[i + 779*t] = Op[i + 247*t] ? R[B[i + 247*t]] * R[C[i + 247*t]] : R[B[i + 247*t]] + R[C[i + 247*t]];
R[i + 780*t] = Op[i + 248*t] ? R[B[i + 248*t]] * R[C[i + 248*t]] : R[B[i + 248*t]] + R[C[i + 248*t]];
R[i + 781*t] = Op[i + 249*t] ? R[B[i + 249*t]] * R[C[i + 249*t]] : R[B[i + 249*t]] + R[C[i + 249*t]];
R[i + 782*t] = Op[i + 250*t] ? R[B[i + 250*t]] * R[C[i + 250*t]] : R[B[i + 250*t]] + R[C[i + 250*t]];
R[i + 783*t] = Op[i + 251*t] ? R[B[i + 251*t]] * R[C[i + 251*t]] : R[B[i + 251*t]] + R[C[i + 251*t]];
R[i + 784*t] = Op[i + 252*t] ? R[B[i + 252*t]] * R[C[i + 252*t]] : R[B[i + 252*t]] + R[C[i + 252*t]];
R[i + 785*t] = Op[i + 253*t] ? R[B[i + 253*t]] * R[C[i + 253*t]] : R[B[i + 253*t]] + R[C[i + 253*t]];
R[i + 786*t] = Op[i + 254*t] ? R[B[i + 254*t]] * R[C[i + 254*t]] : R[B[i + 254*t]] + R[C[i + 254*t]];
R[i + 787*t] = Op[i + 255*t] ? R[B[i + 255*t]] * R[C[i + 255*t]] : R[B[i + 255*t]] + R[C[i + 255*t]];
R[i + 788*t] = Op[i + 256*t] ? R[B[i + 256*t]] * R[C[i + 256*t]] : R[B[i + 256*t]] + R[C[i + 256*t]];
R[i + 789*t] = Op[i + 257*t] ? R[B[i + 257*t]] * R[C[i + 257*t]] : R[B[i + 257*t]] + R[C[i + 257*t]];
R[i + 790*t] = Op[i + 258*t] ? R[B[i + 258*t]] * R[C[i + 258*t]] : R[B[i + 258*t]] + R[C[i + 258*t]];
R[i + 791*t] = Op[i + 259*t] ? R[B[i + 259*t]] * R[C[i + 259*t]] : R[B[i + 259*t]] + R[C[i + 259*t]];
__syncthreads();
R[i + 792*t] = Op[i + 260*t] ? R[B[i + 260*t]] * R[C[i + 260*t]] : R[B[i + 260*t]] + R[C[i + 260*t]];
R[i + 793*t] = Op[i + 261*t] ? R[B[i + 261*t]] * R[C[i + 261*t]] : R[B[i + 261*t]] + R[C[i + 261*t]];
R[i + 794*t] = Op[i + 262*t] ? R[B[i + 262*t]] * R[C[i + 262*t]] : R[B[i + 262*t]] + R[C[i + 262*t]];
R[i + 795*t] = Op[i + 263*t] ? R[B[i + 263*t]] * R[C[i + 263*t]] : R[B[i + 263*t]] + R[C[i + 263*t]];
R[i + 796*t] = Op[i + 264*t] ? R[B[i + 264*t]] * R[C[i + 264*t]] : R[B[i + 264*t]] + R[C[i + 264*t]];
R[i + 797*t] = Op[i + 265*t] ? R[B[i + 265*t]] * R[C[i + 265*t]] : R[B[i + 265*t]] + R[C[i + 265*t]];
R[i + 798*t] = Op[i + 266*t] ? R[B[i + 266*t]] * R[C[i + 266*t]] : R[B[i + 266*t]] + R[C[i + 266*t]];
R[i + 799*t] = Op[i + 267*t] ? R[B[i + 267*t]] * R[C[i + 267*t]] : R[B[i + 267*t]] + R[C[i + 267*t]];
R[i + 800*t] = Op[i + 268*t] ? R[B[i + 268*t]] * R[C[i + 268*t]] : R[B[i + 268*t]] + R[C[i + 268*t]];
R[i + 801*t] = Op[i + 269*t] ? R[B[i + 269*t]] * R[C[i + 269*t]] : R[B[i + 269*t]] + R[C[i + 269*t]];
R[i + 802*t] = Op[i + 270*t] ? R[B[i + 270*t]] * R[C[i + 270*t]] : R[B[i + 270*t]] + R[C[i + 270*t]];
R[i + 803*t] = Op[i + 271*t] ? R[B[i + 271*t]] * R[C[i + 271*t]] : R[B[i + 271*t]] + R[C[i + 271*t]];
R[i + 804*t] = Op[i + 272*t] ? R[B[i + 272*t]] * R[C[i + 272*t]] : R[B[i + 272*t]] + R[C[i + 272*t]];
R[i + 805*t] = Op[i + 273*t] ? R[B[i + 273*t]] * R[C[i + 273*t]] : R[B[i + 273*t]] + R[C[i + 273*t]];
R[i + 806*t] = Op[i + 274*t] ? R[B[i + 274*t]] * R[C[i + 274*t]] : R[B[i + 274*t]] + R[C[i + 274*t]];
R[i + 807*t] = Op[i + 275*t] ? R[B[i + 275*t]] * R[C[i + 275*t]] : R[B[i + 275*t]] + R[C[i + 275*t]];
R[i + 808*t] = Op[i + 276*t] ? R[B[i + 276*t]] * R[C[i + 276*t]] : R[B[i + 276*t]] + R[C[i + 276*t]];
R[i + 809*t] = Op[i + 277*t] ? R[B[i + 277*t]] * R[C[i + 277*t]] : R[B[i + 277*t]] + R[C[i + 277*t]];
R[i + 810*t] = Op[i + 278*t] ? R[B[i + 278*t]] * R[C[i + 278*t]] : R[B[i + 278*t]] + R[C[i + 278*t]];
R[i + 811*t] = Op[i + 279*t] ? R[B[i + 279*t]] * R[C[i + 279*t]] : R[B[i + 279*t]] + R[C[i + 279*t]];
R[i + 812*t] = Op[i + 280*t] ? R[B[i + 280*t]] * R[C[i + 280*t]] : R[B[i + 280*t]] + R[C[i + 280*t]];
R[i + 813*t] = Op[i + 281*t] ? R[B[i + 281*t]] * R[C[i + 281*t]] : R[B[i + 281*t]] + R[C[i + 281*t]];
R[i + 814*t] = Op[i + 282*t] ? R[B[i + 282*t]] * R[C[i + 282*t]] : R[B[i + 282*t]] + R[C[i + 282*t]];
R[i + 815*t] = Op[i + 283*t] ? R[B[i + 283*t]] * R[C[i + 283*t]] : R[B[i + 283*t]] + R[C[i + 283*t]];
R[i + 816*t] = Op[i + 284*t] ? R[B[i + 284*t]] * R[C[i + 284*t]] : R[B[i + 284*t]] + R[C[i + 284*t]];
R[i + 817*t] = Op[i + 285*t] ? R[B[i + 285*t]] * R[C[i + 285*t]] : R[B[i + 285*t]] + R[C[i + 285*t]];
R[i + 818*t] = Op[i + 286*t] ? R[B[i + 286*t]] * R[C[i + 286*t]] : R[B[i + 286*t]] + R[C[i + 286*t]];
R[i + 819*t] = Op[i + 287*t] ? R[B[i + 287*t]] * R[C[i + 287*t]] : R[B[i + 287*t]] + R[C[i + 287*t]];
R[i + 820*t] = Op[i + 288*t] ? R[B[i + 288*t]] * R[C[i + 288*t]] : R[B[i + 288*t]] + R[C[i + 288*t]];
R[i + 821*t] = Op[i + 289*t] ? R[B[i + 289*t]] * R[C[i + 289*t]] : R[B[i + 289*t]] + R[C[i + 289*t]];
R[i + 822*t] = Op[i + 290*t] ? R[B[i + 290*t]] * R[C[i + 290*t]] : R[B[i + 290*t]] + R[C[i + 290*t]];
R[i + 823*t] = Op[i + 291*t] ? R[B[i + 291*t]] * R[C[i + 291*t]] : R[B[i + 291*t]] + R[C[i + 291*t]];
R[i + 824*t] = Op[i + 292*t] ? R[B[i + 292*t]] * R[C[i + 292*t]] : R[B[i + 292*t]] + R[C[i + 292*t]];
R[i + 825*t] = Op[i + 293*t] ? R[B[i + 293*t]] * R[C[i + 293*t]] : R[B[i + 293*t]] + R[C[i + 293*t]];
R[i + 826*t] = Op[i + 294*t] ? R[B[i + 294*t]] * R[C[i + 294*t]] : R[B[i + 294*t]] + R[C[i + 294*t]];
R[i + 827*t] = Op[i + 295*t] ? R[B[i + 295*t]] * R[C[i + 295*t]] : R[B[i + 295*t]] + R[C[i + 295*t]];
R[i + 828*t] = Op[i + 296*t] ? R[B[i + 296*t]] * R[C[i + 296*t]] : R[B[i + 296*t]] + R[C[i + 296*t]];
R[i + 829*t] = Op[i + 297*t] ? R[B[i + 297*t]] * R[C[i + 297*t]] : R[B[i + 297*t]] + R[C[i + 297*t]];
R[i + 830*t] = Op[i + 298*t] ? R[B[i + 298*t]] * R[C[i + 298*t]] : R[B[i + 298*t]] + R[C[i + 298*t]];
R[i + 831*t] = Op[i + 299*t] ? R[B[i + 299*t]] * R[C[i + 299*t]] : R[B[i + 299*t]] + R[C[i + 299*t]];
R[i + 832*t] = Op[i + 300*t] ? R[B[i + 300*t]] * R[C[i + 300*t]] : R[B[i + 300*t]] + R[C[i + 300*t]];
R[i + 833*t] = Op[i + 301*t] ? R[B[i + 301*t]] * R[C[i + 301*t]] : R[B[i + 301*t]] + R[C[i + 301*t]];
R[i + 834*t] = Op[i + 302*t] ? R[B[i + 302*t]] * R[C[i + 302*t]] : R[B[i + 302*t]] + R[C[i + 302*t]];
R[i + 835*t] = Op[i + 303*t] ? R[B[i + 303*t]] * R[C[i + 303*t]] : R[B[i + 303*t]] + R[C[i + 303*t]];
R[i + 836*t] = Op[i + 304*t] ? R[B[i + 304*t]] * R[C[i + 304*t]] : R[B[i + 304*t]] + R[C[i + 304*t]];
R[i + 837*t] = Op[i + 305*t] ? R[B[i + 305*t]] * R[C[i + 305*t]] : R[B[i + 305*t]] + R[C[i + 305*t]];
R[i + 838*t] = Op[i + 306*t] ? R[B[i + 306*t]] * R[C[i + 306*t]] : R[B[i + 306*t]] + R[C[i + 306*t]];
R[i + 839*t] = Op[i + 307*t] ? R[B[i + 307*t]] * R[C[i + 307*t]] : R[B[i + 307*t]] + R[C[i + 307*t]];
R[i + 840*t] = Op[i + 308*t] ? R[B[i + 308*t]] * R[C[i + 308*t]] : R[B[i + 308*t]] + R[C[i + 308*t]];
R[i + 841*t] = Op[i + 309*t] ? R[B[i + 309*t]] * R[C[i + 309*t]] : R[B[i + 309*t]] + R[C[i + 309*t]];
R[i + 842*t] = Op[i + 310*t] ? R[B[i + 310*t]] * R[C[i + 310*t]] : R[B[i + 310*t]] + R[C[i + 310*t]];
R[i + 843*t] = Op[i + 311*t] ? R[B[i + 311*t]] * R[C[i + 311*t]] : R[B[i + 311*t]] + R[C[i + 311*t]];
R[i + 844*t] = Op[i + 312*t] ? R[B[i + 312*t]] * R[C[i + 312*t]] : R[B[i + 312*t]] + R[C[i + 312*t]];
R[i + 845*t] = Op[i + 313*t] ? R[B[i + 313*t]] * R[C[i + 313*t]] : R[B[i + 313*t]] + R[C[i + 313*t]];
R[i + 846*t] = Op[i + 314*t] ? R[B[i + 314*t]] * R[C[i + 314*t]] : R[B[i + 314*t]] + R[C[i + 314*t]];
R[i + 847*t] = Op[i + 315*t] ? R[B[i + 315*t]] * R[C[i + 315*t]] : R[B[i + 315*t]] + R[C[i + 315*t]];
R[i + 848*t] = Op[i + 316*t] ? R[B[i + 316*t]] * R[C[i + 316*t]] : R[B[i + 316*t]] + R[C[i + 316*t]];
R[i + 849*t] = Op[i + 317*t] ? R[B[i + 317*t]] * R[C[i + 317*t]] : R[B[i + 317*t]] + R[C[i + 317*t]];
R[i + 850*t] = Op[i + 318*t] ? R[B[i + 318*t]] * R[C[i + 318*t]] : R[B[i + 318*t]] + R[C[i + 318*t]];
R[i + 851*t] = Op[i + 319*t] ? R[B[i + 319*t]] * R[C[i + 319*t]] : R[B[i + 319*t]] + R[C[i + 319*t]];
R[i + 852*t] = Op[i + 320*t] ? R[B[i + 320*t]] * R[C[i + 320*t]] : R[B[i + 320*t]] + R[C[i + 320*t]];
R[i + 853*t] = Op[i + 321*t] ? R[B[i + 321*t]] * R[C[i + 321*t]] : R[B[i + 321*t]] + R[C[i + 321*t]];
R[i + 854*t] = Op[i + 322*t] ? R[B[i + 322*t]] * R[C[i + 322*t]] : R[B[i + 322*t]] + R[C[i + 322*t]];
R[i + 855*t] = Op[i + 323*t] ? R[B[i + 323*t]] * R[C[i + 323*t]] : R[B[i + 323*t]] + R[C[i + 323*t]];
R[i + 856*t] = Op[i + 324*t] ? R[B[i + 324*t]] * R[C[i + 324*t]] : R[B[i + 324*t]] + R[C[i + 324*t]];
R[i + 857*t] = Op[i + 325*t] ? R[B[i + 325*t]] * R[C[i + 325*t]] : R[B[i + 325*t]] + R[C[i + 325*t]];
R[i + 858*t] = Op[i + 326*t] ? R[B[i + 326*t]] * R[C[i + 326*t]] : R[B[i + 326*t]] + R[C[i + 326*t]];
R[i + 859*t] = Op[i + 327*t] ? R[B[i + 327*t]] * R[C[i + 327*t]] : R[B[i + 327*t]] + R[C[i + 327*t]];
R[i + 860*t] = Op[i + 328*t] ? R[B[i + 328*t]] * R[C[i + 328*t]] : R[B[i + 328*t]] + R[C[i + 328*t]];
R[i + 861*t] = Op[i + 329*t] ? R[B[i + 329*t]] * R[C[i + 329*t]] : R[B[i + 329*t]] + R[C[i + 329*t]];
R[i + 862*t] = Op[i + 330*t] ? R[B[i + 330*t]] * R[C[i + 330*t]] : R[B[i + 330*t]] + R[C[i + 330*t]];
R[i + 863*t] = Op[i + 331*t] ? R[B[i + 331*t]] * R[C[i + 331*t]] : R[B[i + 331*t]] + R[C[i + 331*t]];
R[i + 864*t] = Op[i + 332*t] ? R[B[i + 332*t]] * R[C[i + 332*t]] : R[B[i + 332*t]] + R[C[i + 332*t]];
R[i + 865*t] = Op[i + 333*t] ? R[B[i + 333*t]] * R[C[i + 333*t]] : R[B[i + 333*t]] + R[C[i + 333*t]];
R[i + 866*t] = Op[i + 334*t] ? R[B[i + 334*t]] * R[C[i + 334*t]] : R[B[i + 334*t]] + R[C[i + 334*t]];
R[i + 867*t] = Op[i + 335*t] ? R[B[i + 335*t]] * R[C[i + 335*t]] : R[B[i + 335*t]] + R[C[i + 335*t]];
R[i + 868*t] = Op[i + 336*t] ? R[B[i + 336*t]] * R[C[i + 336*t]] : R[B[i + 336*t]] + R[C[i + 336*t]];
R[i + 869*t] = Op[i + 337*t] ? R[B[i + 337*t]] * R[C[i + 337*t]] : R[B[i + 337*t]] + R[C[i + 337*t]];
R[i + 870*t] = Op[i + 338*t] ? R[B[i + 338*t]] * R[C[i + 338*t]] : R[B[i + 338*t]] + R[C[i + 338*t]];
R[i + 871*t] = Op[i + 339*t] ? R[B[i + 339*t]] * R[C[i + 339*t]] : R[B[i + 339*t]] + R[C[i + 339*t]];
R[i + 872*t] = Op[i + 340*t] ? R[B[i + 340*t]] * R[C[i + 340*t]] : R[B[i + 340*t]] + R[C[i + 340*t]];
R[i + 873*t] = Op[i + 341*t] ? R[B[i + 341*t]] * R[C[i + 341*t]] : R[B[i + 341*t]] + R[C[i + 341*t]];
R[i + 874*t] = Op[i + 342*t] ? R[B[i + 342*t]] * R[C[i + 342*t]] : R[B[i + 342*t]] + R[C[i + 342*t]];
R[i + 875*t] = Op[i + 343*t] ? R[B[i + 343*t]] * R[C[i + 343*t]] : R[B[i + 343*t]] + R[C[i + 343*t]];
R[i + 876*t] = Op[i + 344*t] ? R[B[i + 344*t]] * R[C[i + 344*t]] : R[B[i + 344*t]] + R[C[i + 344*t]];
R[i + 877*t] = Op[i + 345*t] ? R[B[i + 345*t]] * R[C[i + 345*t]] : R[B[i + 345*t]] + R[C[i + 345*t]];
R[i + 878*t] = Op[i + 346*t] ? R[B[i + 346*t]] * R[C[i + 346*t]] : R[B[i + 346*t]] + R[C[i + 346*t]];
R[i + 879*t] = Op[i + 347*t] ? R[B[i + 347*t]] * R[C[i + 347*t]] : R[B[i + 347*t]] + R[C[i + 347*t]];
R[i + 880*t] = Op[i + 348*t] ? R[B[i + 348*t]] * R[C[i + 348*t]] : R[B[i + 348*t]] + R[C[i + 348*t]];
R[i + 881*t] = Op[i + 349*t] ? R[B[i + 349*t]] * R[C[i + 349*t]] : R[B[i + 349*t]] + R[C[i + 349*t]];
R[i + 882*t] = Op[i + 350*t] ? R[B[i + 350*t]] * R[C[i + 350*t]] : R[B[i + 350*t]] + R[C[i + 350*t]];
R[i + 883*t] = Op[i + 351*t] ? R[B[i + 351*t]] * R[C[i + 351*t]] : R[B[i + 351*t]] + R[C[i + 351*t]];
R[i + 884*t] = Op[i + 352*t] ? R[B[i + 352*t]] * R[C[i + 352*t]] : R[B[i + 352*t]] + R[C[i + 352*t]];
R[i + 885*t] = Op[i + 353*t] ? R[B[i + 353*t]] * R[C[i + 353*t]] : R[B[i + 353*t]] + R[C[i + 353*t]];
R[i + 886*t] = Op[i + 354*t] ? R[B[i + 354*t]] * R[C[i + 354*t]] : R[B[i + 354*t]] + R[C[i + 354*t]];
__syncthreads();
R[i + 887*t] = Op[i + 355*t] ? R[B[i + 355*t]] * R[C[i + 355*t]] : R[B[i + 355*t]] + R[C[i + 355*t]];
R[i + 888*t] = Op[i + 356*t] ? R[B[i + 356*t]] * R[C[i + 356*t]] : R[B[i + 356*t]] + R[C[i + 356*t]];
R[i + 889*t] = Op[i + 357*t] ? R[B[i + 357*t]] * R[C[i + 357*t]] : R[B[i + 357*t]] + R[C[i + 357*t]];
R[i + 890*t] = Op[i + 358*t] ? R[B[i + 358*t]] * R[C[i + 358*t]] : R[B[i + 358*t]] + R[C[i + 358*t]];
R[i + 891*t] = Op[i + 359*t] ? R[B[i + 359*t]] * R[C[i + 359*t]] : R[B[i + 359*t]] + R[C[i + 359*t]];
R[i + 892*t] = Op[i + 360*t] ? R[B[i + 360*t]] * R[C[i + 360*t]] : R[B[i + 360*t]] + R[C[i + 360*t]];
R[i + 893*t] = Op[i + 361*t] ? R[B[i + 361*t]] * R[C[i + 361*t]] : R[B[i + 361*t]] + R[C[i + 361*t]];
R[i + 894*t] = Op[i + 362*t] ? R[B[i + 362*t]] * R[C[i + 362*t]] : R[B[i + 362*t]] + R[C[i + 362*t]];
R[i + 895*t] = Op[i + 363*t] ? R[B[i + 363*t]] * R[C[i + 363*t]] : R[B[i + 363*t]] + R[C[i + 363*t]];
R[i + 896*t] = Op[i + 364*t] ? R[B[i + 364*t]] * R[C[i + 364*t]] : R[B[i + 364*t]] + R[C[i + 364*t]];
R[i + 897*t] = Op[i + 365*t] ? R[B[i + 365*t]] * R[C[i + 365*t]] : R[B[i + 365*t]] + R[C[i + 365*t]];
R[i + 898*t] = Op[i + 366*t] ? R[B[i + 366*t]] * R[C[i + 366*t]] : R[B[i + 366*t]] + R[C[i + 366*t]];
R[i + 899*t] = Op[i + 367*t] ? R[B[i + 367*t]] * R[C[i + 367*t]] : R[B[i + 367*t]] + R[C[i + 367*t]];
R[i + 900*t] = Op[i + 368*t] ? R[B[i + 368*t]] * R[C[i + 368*t]] : R[B[i + 368*t]] + R[C[i + 368*t]];
R[i + 901*t] = Op[i + 369*t] ? R[B[i + 369*t]] * R[C[i + 369*t]] : R[B[i + 369*t]] + R[C[i + 369*t]];
R[i + 902*t] = Op[i + 370*t] ? R[B[i + 370*t]] * R[C[i + 370*t]] : R[B[i + 370*t]] + R[C[i + 370*t]];
R[i + 903*t] = Op[i + 371*t] ? R[B[i + 371*t]] * R[C[i + 371*t]] : R[B[i + 371*t]] + R[C[i + 371*t]];
R[i + 904*t] = Op[i + 372*t] ? R[B[i + 372*t]] * R[C[i + 372*t]] : R[B[i + 372*t]] + R[C[i + 372*t]];
R[i + 905*t] = Op[i + 373*t] ? R[B[i + 373*t]] * R[C[i + 373*t]] : R[B[i + 373*t]] + R[C[i + 373*t]];
R[i + 906*t] = Op[i + 374*t] ? R[B[i + 374*t]] * R[C[i + 374*t]] : R[B[i + 374*t]] + R[C[i + 374*t]];
R[i + 907*t] = Op[i + 375*t] ? R[B[i + 375*t]] * R[C[i + 375*t]] : R[B[i + 375*t]] + R[C[i + 375*t]];
R[i + 908*t] = Op[i + 376*t] ? R[B[i + 376*t]] * R[C[i + 376*t]] : R[B[i + 376*t]] + R[C[i + 376*t]];
R[i + 909*t] = Op[i + 377*t] ? R[B[i + 377*t]] * R[C[i + 377*t]] : R[B[i + 377*t]] + R[C[i + 377*t]];
R[i + 910*t] = Op[i + 378*t] ? R[B[i + 378*t]] * R[C[i + 378*t]] : R[B[i + 378*t]] + R[C[i + 378*t]];
R[i + 911*t] = Op[i + 379*t] ? R[B[i + 379*t]] * R[C[i + 379*t]] : R[B[i + 379*t]] + R[C[i + 379*t]];
R[i + 912*t] = Op[i + 380*t] ? R[B[i + 380*t]] * R[C[i + 380*t]] : R[B[i + 380*t]] + R[C[i + 380*t]];
R[i + 913*t] = Op[i + 381*t] ? R[B[i + 381*t]] * R[C[i + 381*t]] : R[B[i + 381*t]] + R[C[i + 381*t]];
R[i + 914*t] = Op[i + 382*t] ? R[B[i + 382*t]] * R[C[i + 382*t]] : R[B[i + 382*t]] + R[C[i + 382*t]];
R[i + 915*t] = Op[i + 383*t] ? R[B[i + 383*t]] * R[C[i + 383*t]] : R[B[i + 383*t]] + R[C[i + 383*t]];
R[i + 916*t] = Op[i + 384*t] ? R[B[i + 384*t]] * R[C[i + 384*t]] : R[B[i + 384*t]] + R[C[i + 384*t]];
R[i + 917*t] = Op[i + 385*t] ? R[B[i + 385*t]] * R[C[i + 385*t]] : R[B[i + 385*t]] + R[C[i + 385*t]];
R[i + 918*t] = Op[i + 386*t] ? R[B[i + 386*t]] * R[C[i + 386*t]] : R[B[i + 386*t]] + R[C[i + 386*t]];
R[i + 919*t] = Op[i + 387*t] ? R[B[i + 387*t]] * R[C[i + 387*t]] : R[B[i + 387*t]] + R[C[i + 387*t]];
R[i + 920*t] = Op[i + 388*t] ? R[B[i + 388*t]] * R[C[i + 388*t]] : R[B[i + 388*t]] + R[C[i + 388*t]];
R[i + 921*t] = Op[i + 389*t] ? R[B[i + 389*t]] * R[C[i + 389*t]] : R[B[i + 389*t]] + R[C[i + 389*t]];
R[i + 922*t] = Op[i + 390*t] ? R[B[i + 390*t]] * R[C[i + 390*t]] : R[B[i + 390*t]] + R[C[i + 390*t]];
R[i + 923*t] = Op[i + 391*t] ? R[B[i + 391*t]] * R[C[i + 391*t]] : R[B[i + 391*t]] + R[C[i + 391*t]];
R[i + 924*t] = Op[i + 392*t] ? R[B[i + 392*t]] * R[C[i + 392*t]] : R[B[i + 392*t]] + R[C[i + 392*t]];
R[i + 925*t] = Op[i + 393*t] ? R[B[i + 393*t]] * R[C[i + 393*t]] : R[B[i + 393*t]] + R[C[i + 393*t]];
R[i + 926*t] = Op[i + 394*t] ? R[B[i + 394*t]] * R[C[i + 394*t]] : R[B[i + 394*t]] + R[C[i + 394*t]];
R[i + 927*t] = Op[i + 395*t] ? R[B[i + 395*t]] * R[C[i + 395*t]] : R[B[i + 395*t]] + R[C[i + 395*t]];
R[i + 928*t] = Op[i + 396*t] ? R[B[i + 396*t]] * R[C[i + 396*t]] : R[B[i + 396*t]] + R[C[i + 396*t]];
R[i + 929*t] = Op[i + 397*t] ? R[B[i + 397*t]] * R[C[i + 397*t]] : R[B[i + 397*t]] + R[C[i + 397*t]];
R[i + 930*t] = Op[i + 398*t] ? R[B[i + 398*t]] * R[C[i + 398*t]] : R[B[i + 398*t]] + R[C[i + 398*t]];
R[i + 931*t] = Op[i + 399*t] ? R[B[i + 399*t]] * R[C[i + 399*t]] : R[B[i + 399*t]] + R[C[i + 399*t]];
R[i + 932*t] = Op[i + 400*t] ? R[B[i + 400*t]] * R[C[i + 400*t]] : R[B[i + 400*t]] + R[C[i + 400*t]];
R[i + 933*t] = Op[i + 401*t] ? R[B[i + 401*t]] * R[C[i + 401*t]] : R[B[i + 401*t]] + R[C[i + 401*t]];
R[i + 934*t] = Op[i + 402*t] ? R[B[i + 402*t]] * R[C[i + 402*t]] : R[B[i + 402*t]] + R[C[i + 402*t]];
R[i + 935*t] = Op[i + 403*t] ? R[B[i + 403*t]] * R[C[i + 403*t]] : R[B[i + 403*t]] + R[C[i + 403*t]];
R[i + 936*t] = Op[i + 404*t] ? R[B[i + 404*t]] * R[C[i + 404*t]] : R[B[i + 404*t]] + R[C[i + 404*t]];
R[i + 937*t] = Op[i + 405*t] ? R[B[i + 405*t]] * R[C[i + 405*t]] : R[B[i + 405*t]] + R[C[i + 405*t]];
R[i + 938*t] = Op[i + 406*t] ? R[B[i + 406*t]] * R[C[i + 406*t]] : R[B[i + 406*t]] + R[C[i + 406*t]];
R[i + 939*t] = Op[i + 407*t] ? R[B[i + 407*t]] * R[C[i + 407*t]] : R[B[i + 407*t]] + R[C[i + 407*t]];
R[i + 940*t] = Op[i + 408*t] ? R[B[i + 408*t]] * R[C[i + 408*t]] : R[B[i + 408*t]] + R[C[i + 408*t]];
R[i + 941*t] = Op[i + 409*t] ? R[B[i + 409*t]] * R[C[i + 409*t]] : R[B[i + 409*t]] + R[C[i + 409*t]];
R[i + 942*t] = Op[i + 410*t] ? R[B[i + 410*t]] * R[C[i + 410*t]] : R[B[i + 410*t]] + R[C[i + 410*t]];
R[i + 943*t] = Op[i + 411*t] ? R[B[i + 411*t]] * R[C[i + 411*t]] : R[B[i + 411*t]] + R[C[i + 411*t]];
R[i + 944*t] = Op[i + 412*t] ? R[B[i + 412*t]] * R[C[i + 412*t]] : R[B[i + 412*t]] + R[C[i + 412*t]];
R[i + 945*t] = Op[i + 413*t] ? R[B[i + 413*t]] * R[C[i + 413*t]] : R[B[i + 413*t]] + R[C[i + 413*t]];
R[i + 946*t] = Op[i + 414*t] ? R[B[i + 414*t]] * R[C[i + 414*t]] : R[B[i + 414*t]] + R[C[i + 414*t]];
R[i + 947*t] = Op[i + 415*t] ? R[B[i + 415*t]] * R[C[i + 415*t]] : R[B[i + 415*t]] + R[C[i + 415*t]];
R[i + 948*t] = Op[i + 416*t] ? R[B[i + 416*t]] * R[C[i + 416*t]] : R[B[i + 416*t]] + R[C[i + 416*t]];
R[i + 949*t] = Op[i + 417*t] ? R[B[i + 417*t]] * R[C[i + 417*t]] : R[B[i + 417*t]] + R[C[i + 417*t]];
R[i + 950*t] = Op[i + 418*t] ? R[B[i + 418*t]] * R[C[i + 418*t]] : R[B[i + 418*t]] + R[C[i + 418*t]];
R[i + 951*t] = Op[i + 419*t] ? R[B[i + 419*t]] * R[C[i + 419*t]] : R[B[i + 419*t]] + R[C[i + 419*t]];
R[i + 952*t] = Op[i + 420*t] ? R[B[i + 420*t]] * R[C[i + 420*t]] : R[B[i + 420*t]] + R[C[i + 420*t]];
R[i + 953*t] = Op[i + 421*t] ? R[B[i + 421*t]] * R[C[i + 421*t]] : R[B[i + 421*t]] + R[C[i + 421*t]];
R[i + 954*t] = Op[i + 422*t] ? R[B[i + 422*t]] * R[C[i + 422*t]] : R[B[i + 422*t]] + R[C[i + 422*t]];
R[i + 955*t] = Op[i + 423*t] ? R[B[i + 423*t]] * R[C[i + 423*t]] : R[B[i + 423*t]] + R[C[i + 423*t]];
R[i + 956*t] = Op[i + 424*t] ? R[B[i + 424*t]] * R[C[i + 424*t]] : R[B[i + 424*t]] + R[C[i + 424*t]];
R[i + 957*t] = Op[i + 425*t] ? R[B[i + 425*t]] * R[C[i + 425*t]] : R[B[i + 425*t]] + R[C[i + 425*t]];
R[i + 958*t] = Op[i + 426*t] ? R[B[i + 426*t]] * R[C[i + 426*t]] : R[B[i + 426*t]] + R[C[i + 426*t]];
R[i + 959*t] = Op[i + 427*t] ? R[B[i + 427*t]] * R[C[i + 427*t]] : R[B[i + 427*t]] + R[C[i + 427*t]];
R[i + 960*t] = Op[i + 428*t] ? R[B[i + 428*t]] * R[C[i + 428*t]] : R[B[i + 428*t]] + R[C[i + 428*t]];
R[i + 961*t] = Op[i + 429*t] ? R[B[i + 429*t]] * R[C[i + 429*t]] : R[B[i + 429*t]] + R[C[i + 429*t]];
R[i + 962*t] = Op[i + 430*t] ? R[B[i + 430*t]] * R[C[i + 430*t]] : R[B[i + 430*t]] + R[C[i + 430*t]];
R[i + 963*t] = Op[i + 431*t] ? R[B[i + 431*t]] * R[C[i + 431*t]] : R[B[i + 431*t]] + R[C[i + 431*t]];
R[i + 964*t] = Op[i + 432*t] ? R[B[i + 432*t]] * R[C[i + 432*t]] : R[B[i + 432*t]] + R[C[i + 432*t]];
R[i + 965*t] = Op[i + 433*t] ? R[B[i + 433*t]] * R[C[i + 433*t]] : R[B[i + 433*t]] + R[C[i + 433*t]];
R[i + 966*t] = Op[i + 434*t] ? R[B[i + 434*t]] * R[C[i + 434*t]] : R[B[i + 434*t]] + R[C[i + 434*t]];
R[i + 967*t] = Op[i + 435*t] ? R[B[i + 435*t]] * R[C[i + 435*t]] : R[B[i + 435*t]] + R[C[i + 435*t]];
R[i + 968*t] = Op[i + 436*t] ? R[B[i + 436*t]] * R[C[i + 436*t]] : R[B[i + 436*t]] + R[C[i + 436*t]];
R[i + 969*t] = Op[i + 437*t] ? R[B[i + 437*t]] * R[C[i + 437*t]] : R[B[i + 437*t]] + R[C[i + 437*t]];
R[i + 970*t] = Op[i + 438*t] ? R[B[i + 438*t]] * R[C[i + 438*t]] : R[B[i + 438*t]] + R[C[i + 438*t]];
R[i + 971*t] = Op[i + 439*t] ? R[B[i + 439*t]] * R[C[i + 439*t]] : R[B[i + 439*t]] + R[C[i + 439*t]];
R[i + 972*t] = Op[i + 440*t] ? R[B[i + 440*t]] * R[C[i + 440*t]] : R[B[i + 440*t]] + R[C[i + 440*t]];
R[i + 973*t] = Op[i + 441*t] ? R[B[i + 441*t]] * R[C[i + 441*t]] : R[B[i + 441*t]] + R[C[i + 441*t]];
R[i + 974*t] = Op[i + 442*t] ? R[B[i + 442*t]] * R[C[i + 442*t]] : R[B[i + 442*t]] + R[C[i + 442*t]];
__syncthreads();
R[i + 975*t] = Op[i + 443*t] ? R[B[i + 443*t]] * R[C[i + 443*t]] : R[B[i + 443*t]] + R[C[i + 443*t]];
R[i + 976*t] = Op[i + 444*t] ? R[B[i + 444*t]] * R[C[i + 444*t]] : R[B[i + 444*t]] + R[C[i + 444*t]];
R[i + 977*t] = Op[i + 445*t] ? R[B[i + 445*t]] * R[C[i + 445*t]] : R[B[i + 445*t]] + R[C[i + 445*t]];
R[i + 978*t] = Op[i + 446*t] ? R[B[i + 446*t]] * R[C[i + 446*t]] : R[B[i + 446*t]] + R[C[i + 446*t]];
R[i + 979*t] = Op[i + 447*t] ? R[B[i + 447*t]] * R[C[i + 447*t]] : R[B[i + 447*t]] + R[C[i + 447*t]];
R[i + 980*t] = Op[i + 448*t] ? R[B[i + 448*t]] * R[C[i + 448*t]] : R[B[i + 448*t]] + R[C[i + 448*t]];
R[i + 981*t] = Op[i + 449*t] ? R[B[i + 449*t]] * R[C[i + 449*t]] : R[B[i + 449*t]] + R[C[i + 449*t]];
R[i + 982*t] = Op[i + 450*t] ? R[B[i + 450*t]] * R[C[i + 450*t]] : R[B[i + 450*t]] + R[C[i + 450*t]];
R[i + 983*t] = Op[i + 451*t] ? R[B[i + 451*t]] * R[C[i + 451*t]] : R[B[i + 451*t]] + R[C[i + 451*t]];
R[i + 984*t] = Op[i + 452*t] ? R[B[i + 452*t]] * R[C[i + 452*t]] : R[B[i + 452*t]] + R[C[i + 452*t]];
R[i + 985*t] = Op[i + 453*t] ? R[B[i + 453*t]] * R[C[i + 453*t]] : R[B[i + 453*t]] + R[C[i + 453*t]];
R[i + 986*t] = Op[i + 454*t] ? R[B[i + 454*t]] * R[C[i + 454*t]] : R[B[i + 454*t]] + R[C[i + 454*t]];
R[i + 987*t] = Op[i + 455*t] ? R[B[i + 455*t]] * R[C[i + 455*t]] : R[B[i + 455*t]] + R[C[i + 455*t]];
R[i + 988*t] = Op[i + 456*t] ? R[B[i + 456*t]] * R[C[i + 456*t]] : R[B[i + 456*t]] + R[C[i + 456*t]];
R[i + 989*t] = Op[i + 457*t] ? R[B[i + 457*t]] * R[C[i + 457*t]] : R[B[i + 457*t]] + R[C[i + 457*t]];
R[i + 990*t] = Op[i + 458*t] ? R[B[i + 458*t]] * R[C[i + 458*t]] : R[B[i + 458*t]] + R[C[i + 458*t]];
R[i + 991*t] = Op[i + 459*t] ? R[B[i + 459*t]] * R[C[i + 459*t]] : R[B[i + 459*t]] + R[C[i + 459*t]];
R[i + 992*t] = Op[i + 460*t] ? R[B[i + 460*t]] * R[C[i + 460*t]] : R[B[i + 460*t]] + R[C[i + 460*t]];
R[i + 993*t] = Op[i + 461*t] ? R[B[i + 461*t]] * R[C[i + 461*t]] : R[B[i + 461*t]] + R[C[i + 461*t]];
R[i + 994*t] = Op[i + 462*t] ? R[B[i + 462*t]] * R[C[i + 462*t]] : R[B[i + 462*t]] + R[C[i + 462*t]];
R[i + 995*t] = Op[i + 463*t] ? R[B[i + 463*t]] * R[C[i + 463*t]] : R[B[i + 463*t]] + R[C[i + 463*t]];
R[i + 996*t] = Op[i + 464*t] ? R[B[i + 464*t]] * R[C[i + 464*t]] : R[B[i + 464*t]] + R[C[i + 464*t]];
R[i + 997*t] = Op[i + 465*t] ? R[B[i + 465*t]] * R[C[i + 465*t]] : R[B[i + 465*t]] + R[C[i + 465*t]];
R[i + 998*t] = Op[i + 466*t] ? R[B[i + 466*t]] * R[C[i + 466*t]] : R[B[i + 466*t]] + R[C[i + 466*t]];
R[i + 999*t] = Op[i + 467*t] ? R[B[i + 467*t]] * R[C[i + 467*t]] : R[B[i + 467*t]] + R[C[i + 467*t]];
R[i + 1000*t] = Op[i + 468*t] ? R[B[i + 468*t]] * R[C[i + 468*t]] : R[B[i + 468*t]] + R[C[i + 468*t]];
R[i + 1001*t] = Op[i + 469*t] ? R[B[i + 469*t]] * R[C[i + 469*t]] : R[B[i + 469*t]] + R[C[i + 469*t]];
R[i + 1002*t] = Op[i + 470*t] ? R[B[i + 470*t]] * R[C[i + 470*t]] : R[B[i + 470*t]] + R[C[i + 470*t]];
R[i + 1003*t] = Op[i + 471*t] ? R[B[i + 471*t]] * R[C[i + 471*t]] : R[B[i + 471*t]] + R[C[i + 471*t]];
R[i + 1004*t] = Op[i + 472*t] ? R[B[i + 472*t]] * R[C[i + 472*t]] : R[B[i + 472*t]] + R[C[i + 472*t]];
R[i + 1005*t] = Op[i + 473*t] ? R[B[i + 473*t]] * R[C[i + 473*t]] : R[B[i + 473*t]] + R[C[i + 473*t]];
R[i + 1006*t] = Op[i + 474*t] ? R[B[i + 474*t]] * R[C[i + 474*t]] : R[B[i + 474*t]] + R[C[i + 474*t]];
R[i + 1007*t] = Op[i + 475*t] ? R[B[i + 475*t]] * R[C[i + 475*t]] : R[B[i + 475*t]] + R[C[i + 475*t]];
R[i + 1008*t] = Op[i + 476*t] ? R[B[i + 476*t]] * R[C[i + 476*t]] : R[B[i + 476*t]] + R[C[i + 476*t]];
R[i + 1009*t] = Op[i + 477*t] ? R[B[i + 477*t]] * R[C[i + 477*t]] : R[B[i + 477*t]] + R[C[i + 477*t]];
R[i + 1010*t] = Op[i + 478*t] ? R[B[i + 478*t]] * R[C[i + 478*t]] : R[B[i + 478*t]] + R[C[i + 478*t]];
R[i + 1011*t] = Op[i + 479*t] ? R[B[i + 479*t]] * R[C[i + 479*t]] : R[B[i + 479*t]] + R[C[i + 479*t]];
R[i + 1012*t] = Op[i + 480*t] ? R[B[i + 480*t]] * R[C[i + 480*t]] : R[B[i + 480*t]] + R[C[i + 480*t]];
R[i + 1013*t] = Op[i + 481*t] ? R[B[i + 481*t]] * R[C[i + 481*t]] : R[B[i + 481*t]] + R[C[i + 481*t]];
R[i + 1014*t] = Op[i + 482*t] ? R[B[i + 482*t]] * R[C[i + 482*t]] : R[B[i + 482*t]] + R[C[i + 482*t]];
R[i + 1015*t] = Op[i + 483*t] ? R[B[i + 483*t]] * R[C[i + 483*t]] : R[B[i + 483*t]] + R[C[i + 483*t]];
__syncthreads();
R[i + 1016*t] = Op[i + 484*t] ? R[B[i + 484*t]] * R[C[i + 484*t]] : R[B[i + 484*t]] + R[C[i + 484*t]];
R[i + 1017*t] = Op[i + 485*t] ? R[B[i + 485*t]] * R[C[i + 485*t]] : R[B[i + 485*t]] + R[C[i + 485*t]];
R[i + 1018*t] = Op[i + 486*t] ? R[B[i + 486*t]] * R[C[i + 486*t]] : R[B[i + 486*t]] + R[C[i + 486*t]];
R[i + 1019*t] = Op[i + 487*t] ? R[B[i + 487*t]] * R[C[i + 487*t]] : R[B[i + 487*t]] + R[C[i + 487*t]];
R[i + 1020*t] = Op[i + 488*t] ? R[B[i + 488*t]] * R[C[i + 488*t]] : R[B[i + 488*t]] + R[C[i + 488*t]];
R[i + 1021*t] = Op[i + 489*t] ? R[B[i + 489*t]] * R[C[i + 489*t]] : R[B[i + 489*t]] + R[C[i + 489*t]];
R[i + 1022*t] = Op[i + 490*t] ? R[B[i + 490*t]] * R[C[i + 490*t]] : R[B[i + 490*t]] + R[C[i + 490*t]];
R[i + 1023*t] = Op[i + 491*t] ? R[B[i + 491*t]] * R[C[i + 491*t]] : R[B[i + 491*t]] + R[C[i + 491*t]];
R[i + 1024*t] = Op[i + 492*t] ? R[B[i + 492*t]] * R[C[i + 492*t]] : R[B[i + 492*t]] + R[C[i + 492*t]];
R[i + 1025*t] = Op[i + 493*t] ? R[B[i + 493*t]] * R[C[i + 493*t]] : R[B[i + 493*t]] + R[C[i + 493*t]];
R[i + 1026*t] = Op[i + 494*t] ? R[B[i + 494*t]] * R[C[i + 494*t]] : R[B[i + 494*t]] + R[C[i + 494*t]];
R[i + 1027*t] = Op[i + 495*t] ? R[B[i + 495*t]] * R[C[i + 495*t]] : R[B[i + 495*t]] + R[C[i + 495*t]];
R[i + 1028*t] = Op[i + 496*t] ? R[B[i + 496*t]] * R[C[i + 496*t]] : R[B[i + 496*t]] + R[C[i + 496*t]];
R[i + 1029*t] = Op[i + 497*t] ? R[B[i + 497*t]] * R[C[i + 497*t]] : R[B[i + 497*t]] + R[C[i + 497*t]];
R[i + 1030*t] = Op[i + 498*t] ? R[B[i + 498*t]] * R[C[i + 498*t]] : R[B[i + 498*t]] + R[C[i + 498*t]];
R[i + 1031*t] = Op[i + 499*t] ? R[B[i + 499*t]] * R[C[i + 499*t]] : R[B[i + 499*t]] + R[C[i + 499*t]];
R[i + 1032*t] = Op[i + 500*t] ? R[B[i + 500*t]] * R[C[i + 500*t]] : R[B[i + 500*t]] + R[C[i + 500*t]];
R[i + 1033*t] = Op[i + 501*t] ? R[B[i + 501*t]] * R[C[i + 501*t]] : R[B[i + 501*t]] + R[C[i + 501*t]];
R[i + 1034*t] = Op[i + 502*t] ? R[B[i + 502*t]] * R[C[i + 502*t]] : R[B[i + 502*t]] + R[C[i + 502*t]];
R[i + 1035*t] = Op[i + 503*t] ? R[B[i + 503*t]] * R[C[i + 503*t]] : R[B[i + 503*t]] + R[C[i + 503*t]];
R[i + 1036*t] = Op[i + 504*t] ? R[B[i + 504*t]] * R[C[i + 504*t]] : R[B[i + 504*t]] + R[C[i + 504*t]];
R[i + 1037*t] = Op[i + 505*t] ? R[B[i + 505*t]] * R[C[i + 505*t]] : R[B[i + 505*t]] + R[C[i + 505*t]];
R[i + 1038*t] = Op[i + 506*t] ? R[B[i + 506*t]] * R[C[i + 506*t]] : R[B[i + 506*t]] + R[C[i + 506*t]];
R[i + 1039*t] = Op[i + 507*t] ? R[B[i + 507*t]] * R[C[i + 507*t]] : R[B[i + 507*t]] + R[C[i + 507*t]];
R[i + 1040*t] = Op[i + 508*t] ? R[B[i + 508*t]] * R[C[i + 508*t]] : R[B[i + 508*t]] + R[C[i + 508*t]];
R[i + 1041*t] = Op[i + 509*t] ? R[B[i + 509*t]] * R[C[i + 509*t]] : R[B[i + 509*t]] + R[C[i + 509*t]];
R[i + 1042*t] = Op[i + 510*t] ? R[B[i + 510*t]] * R[C[i + 510*t]] : R[B[i + 510*t]] + R[C[i + 510*t]];
R[i + 1043*t] = Op[i + 511*t] ? R[B[i + 511*t]] * R[C[i + 511*t]] : R[B[i + 511*t]] + R[C[i + 511*t]];
R[i + 1044*t] = Op[i + 512*t] ? R[B[i + 512*t]] * R[C[i + 512*t]] : R[B[i + 512*t]] + R[C[i + 512*t]];
R[i + 1045*t] = Op[i + 513*t] ? R[B[i + 513*t]] * R[C[i + 513*t]] : R[B[i + 513*t]] + R[C[i + 513*t]];
R[i + 1046*t] = Op[i + 514*t] ? R[B[i + 514*t]] * R[C[i + 514*t]] : R[B[i + 514*t]] + R[C[i + 514*t]];
R[i + 1047*t] = Op[i + 515*t] ? R[B[i + 515*t]] * R[C[i + 515*t]] : R[B[i + 515*t]] + R[C[i + 515*t]];
R[i + 1048*t] = Op[i + 516*t] ? R[B[i + 516*t]] * R[C[i + 516*t]] : R[B[i + 516*t]] + R[C[i + 516*t]];
R[i + 1049*t] = Op[i + 517*t] ? R[B[i + 517*t]] * R[C[i + 517*t]] : R[B[i + 517*t]] + R[C[i + 517*t]];
R[i + 1050*t] = Op[i + 518*t] ? R[B[i + 518*t]] * R[C[i + 518*t]] : R[B[i + 518*t]] + R[C[i + 518*t]];
R[i + 1051*t] = Op[i + 519*t] ? R[B[i + 519*t]] * R[C[i + 519*t]] : R[B[i + 519*t]] + R[C[i + 519*t]];
R[i + 1052*t] = Op[i + 520*t] ? R[B[i + 520*t]] * R[C[i + 520*t]] : R[B[i + 520*t]] + R[C[i + 520*t]];
R[i + 1053*t] = Op[i + 521*t] ? R[B[i + 521*t]] * R[C[i + 521*t]] : R[B[i + 521*t]] + R[C[i + 521*t]];
R[i + 1054*t] = Op[i + 522*t] ? R[B[i + 522*t]] * R[C[i + 522*t]] : R[B[i + 522*t]] + R[C[i + 522*t]];
R[i + 1055*t] = Op[i + 523*t] ? R[B[i + 523*t]] * R[C[i + 523*t]] : R[B[i + 523*t]] + R[C[i + 523*t]];
R[i + 1056*t] = Op[i + 524*t] ? R[B[i + 524*t]] * R[C[i + 524*t]] : R[B[i + 524*t]] + R[C[i + 524*t]];
__syncthreads();
R[i + 1057*t] = Op[i + 525*t] ? R[B[i + 525*t]] * R[C[i + 525*t]] : R[B[i + 525*t]] + R[C[i + 525*t]];
R[i + 1058*t] = Op[i + 526*t] ? R[B[i + 526*t]] * R[C[i + 526*t]] : R[B[i + 526*t]] + R[C[i + 526*t]];
R[i + 1059*t] = Op[i + 527*t] ? R[B[i + 527*t]] * R[C[i + 527*t]] : R[B[i + 527*t]] + R[C[i + 527*t]];
R[i + 1060*t] = Op[i + 528*t] ? R[B[i + 528*t]] * R[C[i + 528*t]] : R[B[i + 528*t]] + R[C[i + 528*t]];
R[i + 1061*t] = Op[i + 529*t] ? R[B[i + 529*t]] * R[C[i + 529*t]] : R[B[i + 529*t]] + R[C[i + 529*t]];
R[i + 1062*t] = Op[i + 530*t] ? R[B[i + 530*t]] * R[C[i + 530*t]] : R[B[i + 530*t]] + R[C[i + 530*t]];
R[i + 1063*t] = Op[i + 531*t] ? R[B[i + 531*t]] * R[C[i + 531*t]] : R[B[i + 531*t]] + R[C[i + 531*t]];
R[i + 1064*t] = Op[i + 532*t] ? R[B[i + 532*t]] * R[C[i + 532*t]] : R[B[i + 532*t]] + R[C[i + 532*t]];
R[i + 1065*t] = Op[i + 533*t] ? R[B[i + 533*t]] * R[C[i + 533*t]] : R[B[i + 533*t]] + R[C[i + 533*t]];
R[i + 1066*t] = Op[i + 534*t] ? R[B[i + 534*t]] * R[C[i + 534*t]] : R[B[i + 534*t]] + R[C[i + 534*t]];
R[i + 1067*t] = Op[i + 535*t] ? R[B[i + 535*t]] * R[C[i + 535*t]] : R[B[i + 535*t]] + R[C[i + 535*t]];
R[i + 1068*t] = Op[i + 536*t] ? R[B[i + 536*t]] * R[C[i + 536*t]] : R[B[i + 536*t]] + R[C[i + 536*t]];
R[i + 1069*t] = Op[i + 537*t] ? R[B[i + 537*t]] * R[C[i + 537*t]] : R[B[i + 537*t]] + R[C[i + 537*t]];
R[i + 1070*t] = Op[i + 538*t] ? R[B[i + 538*t]] * R[C[i + 538*t]] : R[B[i + 538*t]] + R[C[i + 538*t]];
R[i + 1071*t] = Op[i + 539*t] ? R[B[i + 539*t]] * R[C[i + 539*t]] : R[B[i + 539*t]] + R[C[i + 539*t]];
R[i + 1072*t] = Op[i + 540*t] ? R[B[i + 540*t]] * R[C[i + 540*t]] : R[B[i + 540*t]] + R[C[i + 540*t]];
R[i + 1073*t] = Op[i + 541*t] ? R[B[i + 541*t]] * R[C[i + 541*t]] : R[B[i + 541*t]] + R[C[i + 541*t]];
R[i + 1074*t] = Op[i + 542*t] ? R[B[i + 542*t]] * R[C[i + 542*t]] : R[B[i + 542*t]] + R[C[i + 542*t]];
R[i + 1075*t] = Op[i + 543*t] ? R[B[i + 543*t]] * R[C[i + 543*t]] : R[B[i + 543*t]] + R[C[i + 543*t]];
R[i + 1076*t] = Op[i + 544*t] ? R[B[i + 544*t]] * R[C[i + 544*t]] : R[B[i + 544*t]] + R[C[i + 544*t]];
R[i + 1077*t] = Op[i + 545*t] ? R[B[i + 545*t]] * R[C[i + 545*t]] : R[B[i + 545*t]] + R[C[i + 545*t]];
R[i + 1078*t] = Op[i + 546*t] ? R[B[i + 546*t]] * R[C[i + 546*t]] : R[B[i + 546*t]] + R[C[i + 546*t]];
R[i + 1079*t] = Op[i + 547*t] ? R[B[i + 547*t]] * R[C[i + 547*t]] : R[B[i + 547*t]] + R[C[i + 547*t]];
R[i + 1080*t] = Op[i + 548*t] ? R[B[i + 548*t]] * R[C[i + 548*t]] : R[B[i + 548*t]] + R[C[i + 548*t]];
R[i + 1081*t] = Op[i + 549*t] ? R[B[i + 549*t]] * R[C[i + 549*t]] : R[B[i + 549*t]] + R[C[i + 549*t]];
R[i + 1082*t] = Op[i + 550*t] ? R[B[i + 550*t]] * R[C[i + 550*t]] : R[B[i + 550*t]] + R[C[i + 550*t]];
R[i + 1083*t] = Op[i + 551*t] ? R[B[i + 551*t]] * R[C[i + 551*t]] : R[B[i + 551*t]] + R[C[i + 551*t]];
R[i + 1084*t] = Op[i + 552*t] ? R[B[i + 552*t]] * R[C[i + 552*t]] : R[B[i + 552*t]] + R[C[i + 552*t]];
R[i + 1085*t] = Op[i + 553*t] ? R[B[i + 553*t]] * R[C[i + 553*t]] : R[B[i + 553*t]] + R[C[i + 553*t]];
R[i + 1086*t] = Op[i + 554*t] ? R[B[i + 554*t]] * R[C[i + 554*t]] : R[B[i + 554*t]] + R[C[i + 554*t]];
R[i + 1087*t] = Op[i + 555*t] ? R[B[i + 555*t]] * R[C[i + 555*t]] : R[B[i + 555*t]] + R[C[i + 555*t]];
R[i + 1088*t] = Op[i + 556*t] ? R[B[i + 556*t]] * R[C[i + 556*t]] : R[B[i + 556*t]] + R[C[i + 556*t]];
R[i + 1089*t] = Op[i + 557*t] ? R[B[i + 557*t]] * R[C[i + 557*t]] : R[B[i + 557*t]] + R[C[i + 557*t]];
R[i + 1090*t] = Op[i + 558*t] ? R[B[i + 558*t]] * R[C[i + 558*t]] : R[B[i + 558*t]] + R[C[i + 558*t]];
R[i + 1091*t] = Op[i + 559*t] ? R[B[i + 559*t]] * R[C[i + 559*t]] : R[B[i + 559*t]] + R[C[i + 559*t]];
R[i + 1092*t] = Op[i + 560*t] ? R[B[i + 560*t]] * R[C[i + 560*t]] : R[B[i + 560*t]] + R[C[i + 560*t]];
__syncthreads();
R[i + 1093*t] = Op[i + 561*t] ? R[B[i + 561*t]] * R[C[i + 561*t]] : R[B[i + 561*t]] + R[C[i + 561*t]];
R[i + 1094*t] = Op[i + 562*t] ? R[B[i + 562*t]] * R[C[i + 562*t]] : R[B[i + 562*t]] + R[C[i + 562*t]];
R[i + 1095*t] = Op[i + 563*t] ? R[B[i + 563*t]] * R[C[i + 563*t]] : R[B[i + 563*t]] + R[C[i + 563*t]];
R[i + 1096*t] = Op[i + 564*t] ? R[B[i + 564*t]] * R[C[i + 564*t]] : R[B[i + 564*t]] + R[C[i + 564*t]];
R[i + 1097*t] = Op[i + 565*t] ? R[B[i + 565*t]] * R[C[i + 565*t]] : R[B[i + 565*t]] + R[C[i + 565*t]];
R[i + 1098*t] = Op[i + 566*t] ? R[B[i + 566*t]] * R[C[i + 566*t]] : R[B[i + 566*t]] + R[C[i + 566*t]];
R[i + 1099*t] = Op[i + 567*t] ? R[B[i + 567*t]] * R[C[i + 567*t]] : R[B[i + 567*t]] + R[C[i + 567*t]];
R[i + 1100*t] = Op[i + 568*t] ? R[B[i + 568*t]] * R[C[i + 568*t]] : R[B[i + 568*t]] + R[C[i + 568*t]];
R[i + 1101*t] = Op[i + 569*t] ? R[B[i + 569*t]] * R[C[i + 569*t]] : R[B[i + 569*t]] + R[C[i + 569*t]];
R[i + 1102*t] = Op[i + 570*t] ? R[B[i + 570*t]] * R[C[i + 570*t]] : R[B[i + 570*t]] + R[C[i + 570*t]];
R[i + 1103*t] = Op[i + 571*t] ? R[B[i + 571*t]] * R[C[i + 571*t]] : R[B[i + 571*t]] + R[C[i + 571*t]];
R[i + 1104*t] = Op[i + 572*t] ? R[B[i + 572*t]] * R[C[i + 572*t]] : R[B[i + 572*t]] + R[C[i + 572*t]];
R[i + 1105*t] = Op[i + 573*t] ? R[B[i + 573*t]] * R[C[i + 573*t]] : R[B[i + 573*t]] + R[C[i + 573*t]];
R[i + 1106*t] = Op[i + 574*t] ? R[B[i + 574*t]] * R[C[i + 574*t]] : R[B[i + 574*t]] + R[C[i + 574*t]];
R[i + 1107*t] = Op[i + 575*t] ? R[B[i + 575*t]] * R[C[i + 575*t]] : R[B[i + 575*t]] + R[C[i + 575*t]];
R[i + 1108*t] = Op[i + 576*t] ? R[B[i + 576*t]] * R[C[i + 576*t]] : R[B[i + 576*t]] + R[C[i + 576*t]];
R[i + 1109*t] = Op[i + 577*t] ? R[B[i + 577*t]] * R[C[i + 577*t]] : R[B[i + 577*t]] + R[C[i + 577*t]];
R[i + 1110*t] = Op[i + 578*t] ? R[B[i + 578*t]] * R[C[i + 578*t]] : R[B[i + 578*t]] + R[C[i + 578*t]];
__syncthreads();
R[i + 1111*t] = Op[i + 579*t] ? R[B[i + 579*t]] * R[C[i + 579*t]] : R[B[i + 579*t]] + R[C[i + 579*t]];
R[i + 1112*t] = Op[i + 580*t] ? R[B[i + 580*t]] * R[C[i + 580*t]] : R[B[i + 580*t]] + R[C[i + 580*t]];
R[i + 1113*t] = Op[i + 581*t] ? R[B[i + 581*t]] * R[C[i + 581*t]] : R[B[i + 581*t]] + R[C[i + 581*t]];
R[i + 1114*t] = Op[i + 582*t] ? R[B[i + 582*t]] * R[C[i + 582*t]] : R[B[i + 582*t]] + R[C[i + 582*t]];
R[i + 1115*t] = Op[i + 583*t] ? R[B[i + 583*t]] * R[C[i + 583*t]] : R[B[i + 583*t]] + R[C[i + 583*t]];
R[i + 1116*t] = Op[i + 584*t] ? R[B[i + 584*t]] * R[C[i + 584*t]] : R[B[i + 584*t]] + R[C[i + 584*t]];
R[i + 1117*t] = Op[i + 585*t] ? R[B[i + 585*t]] * R[C[i + 585*t]] : R[B[i + 585*t]] + R[C[i + 585*t]];
R[i + 1118*t] = Op[i + 586*t] ? R[B[i + 586*t]] * R[C[i + 586*t]] : R[B[i + 586*t]] + R[C[i + 586*t]];
R[i + 1119*t] = Op[i + 587*t] ? R[B[i + 587*t]] * R[C[i + 587*t]] : R[B[i + 587*t]] + R[C[i + 587*t]];
R[i + 1120*t] = Op[i + 588*t] ? R[B[i + 588*t]] * R[C[i + 588*t]] : R[B[i + 588*t]] + R[C[i + 588*t]];
R[i + 1121*t] = Op[i + 589*t] ? R[B[i + 589*t]] * R[C[i + 589*t]] : R[B[i + 589*t]] + R[C[i + 589*t]];
R[i + 1122*t] = Op[i + 590*t] ? R[B[i + 590*t]] * R[C[i + 590*t]] : R[B[i + 590*t]] + R[C[i + 590*t]];
R[i + 1123*t] = Op[i + 591*t] ? R[B[i + 591*t]] * R[C[i + 591*t]] : R[B[i + 591*t]] + R[C[i + 591*t]];
R[i + 1124*t] = Op[i + 592*t] ? R[B[i + 592*t]] * R[C[i + 592*t]] : R[B[i + 592*t]] + R[C[i + 592*t]];
R[i + 1125*t] = Op[i + 593*t] ? R[B[i + 593*t]] * R[C[i + 593*t]] : R[B[i + 593*t]] + R[C[i + 593*t]];
R[i + 1126*t] = Op[i + 594*t] ? R[B[i + 594*t]] * R[C[i + 594*t]] : R[B[i + 594*t]] + R[C[i + 594*t]];
R[i + 1127*t] = Op[i + 595*t] ? R[B[i + 595*t]] * R[C[i + 595*t]] : R[B[i + 595*t]] + R[C[i + 595*t]];
__syncthreads();
R[i + 1128*t] = Op[i + 596*t] ? R[B[i + 596*t]] * R[C[i + 596*t]] : R[B[i + 596*t]] + R[C[i + 596*t]];
R[i + 1129*t] = Op[i + 597*t] ? R[B[i + 597*t]] * R[C[i + 597*t]] : R[B[i + 597*t]] + R[C[i + 597*t]];
R[i + 1130*t] = Op[i + 598*t] ? R[B[i + 598*t]] * R[C[i + 598*t]] : R[B[i + 598*t]] + R[C[i + 598*t]];
R[i + 1131*t] = Op[i + 599*t] ? R[B[i + 599*t]] * R[C[i + 599*t]] : R[B[i + 599*t]] + R[C[i + 599*t]];
R[i + 1132*t] = Op[i + 600*t] ? R[B[i + 600*t]] * R[C[i + 600*t]] : R[B[i + 600*t]] + R[C[i + 600*t]];
R[i + 1133*t] = Op[i + 601*t] ? R[B[i + 601*t]] * R[C[i + 601*t]] : R[B[i + 601*t]] + R[C[i + 601*t]];
R[i + 1134*t] = Op[i + 602*t] ? R[B[i + 602*t]] * R[C[i + 602*t]] : R[B[i + 602*t]] + R[C[i + 602*t]];
R[i + 1135*t] = Op[i + 603*t] ? R[B[i + 603*t]] * R[C[i + 603*t]] : R[B[i + 603*t]] + R[C[i + 603*t]];
R[i + 1136*t] = Op[i + 604*t] ? R[B[i + 604*t]] * R[C[i + 604*t]] : R[B[i + 604*t]] + R[C[i + 604*t]];
R[i + 1137*t] = Op[i + 605*t] ? R[B[i + 605*t]] * R[C[i + 605*t]] : R[B[i + 605*t]] + R[C[i + 605*t]];
R[i + 1138*t] = Op[i + 606*t] ? R[B[i + 606*t]] * R[C[i + 606*t]] : R[B[i + 606*t]] + R[C[i + 606*t]];
R[i + 1139*t] = Op[i + 607*t] ? R[B[i + 607*t]] * R[C[i + 607*t]] : R[B[i + 607*t]] + R[C[i + 607*t]];
__syncthreads();
R[i + 1140*t] = Op[i + 608*t] ? R[B[i + 608*t]] * R[C[i + 608*t]] : R[B[i + 608*t]] + R[C[i + 608*t]];
R[i + 1141*t] = Op[i + 609*t] ? R[B[i + 609*t]] * R[C[i + 609*t]] : R[B[i + 609*t]] + R[C[i + 609*t]];
R[i + 1142*t] = Op[i + 610*t] ? R[B[i + 610*t]] * R[C[i + 610*t]] : R[B[i + 610*t]] + R[C[i + 610*t]];
R[i + 1143*t] = Op[i + 611*t] ? R[B[i + 611*t]] * R[C[i + 611*t]] : R[B[i + 611*t]] + R[C[i + 611*t]];
R[i + 1144*t] = Op[i + 612*t] ? R[B[i + 612*t]] * R[C[i + 612*t]] : R[B[i + 612*t]] + R[C[i + 612*t]];
R[i + 1145*t] = Op[i + 613*t] ? R[B[i + 613*t]] * R[C[i + 613*t]] : R[B[i + 613*t]] + R[C[i + 613*t]];
R[i + 1146*t] = Op[i + 614*t] ? R[B[i + 614*t]] * R[C[i + 614*t]] : R[B[i + 614*t]] + R[C[i + 614*t]];
R[i + 1147*t] = Op[i + 615*t] ? R[B[i + 615*t]] * R[C[i + 615*t]] : R[B[i + 615*t]] + R[C[i + 615*t]];
R[i + 1148*t] = Op[i + 616*t] ? R[B[i + 616*t]] * R[C[i + 616*t]] : R[B[i + 616*t]] + R[C[i + 616*t]];
R[i + 1149*t] = Op[i + 617*t] ? R[B[i + 617*t]] * R[C[i + 617*t]] : R[B[i + 617*t]] + R[C[i + 617*t]];
__syncthreads();
R[i + 1150*t] = Op[i + 618*t] ? R[B[i + 618*t]] * R[C[i + 618*t]] : R[B[i + 618*t]] + R[C[i + 618*t]];
R[i + 1151*t] = Op[i + 619*t] ? R[B[i + 619*t]] * R[C[i + 619*t]] : R[B[i + 619*t]] + R[C[i + 619*t]];
R[i + 1152*t] = Op[i + 620*t] ? R[B[i + 620*t]] * R[C[i + 620*t]] : R[B[i + 620*t]] + R[C[i + 620*t]];
R[i + 1153*t] = Op[i + 621*t] ? R[B[i + 621*t]] * R[C[i + 621*t]] : R[B[i + 621*t]] + R[C[i + 621*t]];
R[i + 1154*t] = Op[i + 622*t] ? R[B[i + 622*t]] * R[C[i + 622*t]] : R[B[i + 622*t]] + R[C[i + 622*t]];
R[i + 1155*t] = Op[i + 623*t] ? R[B[i + 623*t]] * R[C[i + 623*t]] : R[B[i + 623*t]] + R[C[i + 623*t]];
R[i + 1156*t] = Op[i + 624*t] ? R[B[i + 624*t]] * R[C[i + 624*t]] : R[B[i + 624*t]] + R[C[i + 624*t]];
R[i + 1157*t] = Op[i + 625*t] ? R[B[i + 625*t]] * R[C[i + 625*t]] : R[B[i + 625*t]] + R[C[i + 625*t]];
R[i + 1158*t] = Op[i + 626*t] ? R[B[i + 626*t]] * R[C[i + 626*t]] : R[B[i + 626*t]] + R[C[i + 626*t]];
R[i + 1159*t] = Op[i + 627*t] ? R[B[i + 627*t]] * R[C[i + 627*t]] : R[B[i + 627*t]] + R[C[i + 627*t]];
__syncthreads();
R[i + 1160*t] = Op[i + 628*t] ? R[B[i + 628*t]] * R[C[i + 628*t]] : R[B[i + 628*t]] + R[C[i + 628*t]];
R[i + 1161*t] = Op[i + 629*t] ? R[B[i + 629*t]] * R[C[i + 629*t]] : R[B[i + 629*t]] + R[C[i + 629*t]];
R[i + 1162*t] = Op[i + 630*t] ? R[B[i + 630*t]] * R[C[i + 630*t]] : R[B[i + 630*t]] + R[C[i + 630*t]];
R[i + 1163*t] = Op[i + 631*t] ? R[B[i + 631*t]] * R[C[i + 631*t]] : R[B[i + 631*t]] + R[C[i + 631*t]];
R[i + 1164*t] = Op[i + 632*t] ? R[B[i + 632*t]] * R[C[i + 632*t]] : R[B[i + 632*t]] + R[C[i + 632*t]];
R[i + 1165*t] = Op[i + 633*t] ? R[B[i + 633*t]] * R[C[i + 633*t]] : R[B[i + 633*t]] + R[C[i + 633*t]];
R[i + 1166*t] = Op[i + 634*t] ? R[B[i + 634*t]] * R[C[i + 634*t]] : R[B[i + 634*t]] + R[C[i + 634*t]];
__syncthreads();
R[i + 1167*t] = Op[i + 635*t] ? R[B[i + 635*t]] * R[C[i + 635*t]] : R[B[i + 635*t]] + R[C[i + 635*t]];
R[i + 1168*t] = Op[i + 636*t] ? R[B[i + 636*t]] * R[C[i + 636*t]] : R[B[i + 636*t]] + R[C[i + 636*t]];
R[i + 1169*t] = Op[i + 637*t] ? R[B[i + 637*t]] * R[C[i + 637*t]] : R[B[i + 637*t]] + R[C[i + 637*t]];
R[i + 1170*t] = Op[i + 638*t] ? R[B[i + 638*t]] * R[C[i + 638*t]] : R[B[i + 638*t]] + R[C[i + 638*t]];
R[i + 1171*t] = Op[i + 639*t] ? R[B[i + 639*t]] * R[C[i + 639*t]] : R[B[i + 639*t]] + R[C[i + 639*t]];
R[i + 1172*t] = Op[i + 640*t] ? R[B[i + 640*t]] * R[C[i + 640*t]] : R[B[i + 640*t]] + R[C[i + 640*t]];
R[i + 1173*t] = Op[i + 641*t] ? R[B[i + 641*t]] * R[C[i + 641*t]] : R[B[i + 641*t]] + R[C[i + 641*t]];
R[i + 1174*t] = Op[i + 642*t] ? R[B[i + 642*t]] * R[C[i + 642*t]] : R[B[i + 642*t]] + R[C[i + 642*t]];
R[i + 1175*t] = Op[i + 643*t] ? R[B[i + 643*t]] * R[C[i + 643*t]] : R[B[i + 643*t]] + R[C[i + 643*t]];
__syncthreads();
R[i + 1176*t] = Op[i + 644*t] ? R[B[i + 644*t]] * R[C[i + 644*t]] : R[B[i + 644*t]] + R[C[i + 644*t]];
R[i + 1177*t] = Op[i + 645*t] ? R[B[i + 645*t]] * R[C[i + 645*t]] : R[B[i + 645*t]] + R[C[i + 645*t]];
R[i + 1178*t] = Op[i + 646*t] ? R[B[i + 646*t]] * R[C[i + 646*t]] : R[B[i + 646*t]] + R[C[i + 646*t]];
R[i + 1179*t] = Op[i + 647*t] ? R[B[i + 647*t]] * R[C[i + 647*t]] : R[B[i + 647*t]] + R[C[i + 647*t]];
R[i + 1180*t] = Op[i + 648*t] ? R[B[i + 648*t]] * R[C[i + 648*t]] : R[B[i + 648*t]] + R[C[i + 648*t]];
R[i + 1181*t] = Op[i + 649*t] ? R[B[i + 649*t]] * R[C[i + 649*t]] : R[B[i + 649*t]] + R[C[i + 649*t]];
R[i + 1182*t] = Op[i + 650*t] ? R[B[i + 650*t]] * R[C[i + 650*t]] : R[B[i + 650*t]] + R[C[i + 650*t]];
__syncthreads();
R[i + 1183*t] = Op[i + 651*t] ? R[B[i + 651*t]] * R[C[i + 651*t]] : R[B[i + 651*t]] + R[C[i + 651*t]];
R[i + 1184*t] = Op[i + 652*t] ? R[B[i + 652*t]] * R[C[i + 652*t]] : R[B[i + 652*t]] + R[C[i + 652*t]];
R[i + 1185*t] = Op[i + 653*t] ? R[B[i + 653*t]] * R[C[i + 653*t]] : R[B[i + 653*t]] + R[C[i + 653*t]];
R[i + 1186*t] = Op[i + 654*t] ? R[B[i + 654*t]] * R[C[i + 654*t]] : R[B[i + 654*t]] + R[C[i + 654*t]];
R[i + 1187*t] = Op[i + 655*t] ? R[B[i + 655*t]] * R[C[i + 655*t]] : R[B[i + 655*t]] + R[C[i + 655*t]];
R[i + 1188*t] = Op[i + 656*t] ? R[B[i + 656*t]] * R[C[i + 656*t]] : R[B[i + 656*t]] + R[C[i + 656*t]];
R[i + 1189*t] = Op[i + 657*t] ? R[B[i + 657*t]] * R[C[i + 657*t]] : R[B[i + 657*t]] + R[C[i + 657*t]];
R[i + 1190*t] = Op[i + 658*t] ? R[B[i + 658*t]] * R[C[i + 658*t]] : R[B[i + 658*t]] + R[C[i + 658*t]];
__syncthreads();
R[i + 1191*t] = Op[i + 659*t] ? R[B[i + 659*t]] * R[C[i + 659*t]] : R[B[i + 659*t]] + R[C[i + 659*t]];
R[i + 1192*t] = Op[i + 660*t] ? R[B[i + 660*t]] * R[C[i + 660*t]] : R[B[i + 660*t]] + R[C[i + 660*t]];
R[i + 1193*t] = Op[i + 661*t] ? R[B[i + 661*t]] * R[C[i + 661*t]] : R[B[i + 661*t]] + R[C[i + 661*t]];
R[i + 1194*t] = Op[i + 662*t] ? R[B[i + 662*t]] * R[C[i + 662*t]] : R[B[i + 662*t]] + R[C[i + 662*t]];
R[i + 1195*t] = Op[i + 663*t] ? R[B[i + 663*t]] * R[C[i + 663*t]] : R[B[i + 663*t]] + R[C[i + 663*t]];
R[i + 1196*t] = Op[i + 664*t] ? R[B[i + 664*t]] * R[C[i + 664*t]] : R[B[i + 664*t]] + R[C[i + 664*t]];
R[i + 1197*t] = Op[i + 665*t] ? R[B[i + 665*t]] * R[C[i + 665*t]] : R[B[i + 665*t]] + R[C[i + 665*t]];
__syncthreads();
R[i + 1198*t] = Op[i + 666*t] ? R[B[i + 666*t]] * R[C[i + 666*t]] : R[B[i + 666*t]] + R[C[i + 666*t]];
R[i + 1199*t] = Op[i + 667*t] ? R[B[i + 667*t]] * R[C[i + 667*t]] : R[B[i + 667*t]] + R[C[i + 667*t]];
R[i + 1200*t] = Op[i + 668*t] ? R[B[i + 668*t]] * R[C[i + 668*t]] : R[B[i + 668*t]] + R[C[i + 668*t]];
R[i + 1201*t] = Op[i + 669*t] ? R[B[i + 669*t]] * R[C[i + 669*t]] : R[B[i + 669*t]] + R[C[i + 669*t]];
R[i + 1202*t] = Op[i + 670*t] ? R[B[i + 670*t]] * R[C[i + 670*t]] : R[B[i + 670*t]] + R[C[i + 670*t]];
__syncthreads();
R[i + 1203*t] = Op[i + 671*t] ? R[B[i + 671*t]] * R[C[i + 671*t]] : R[B[i + 671*t]] + R[C[i + 671*t]];
R[i + 1204*t] = Op[i + 672*t] ? R[B[i + 672*t]] * R[C[i + 672*t]] : R[B[i + 672*t]] + R[C[i + 672*t]];
R[i + 1205*t] = Op[i + 673*t] ? R[B[i + 673*t]] * R[C[i + 673*t]] : R[B[i + 673*t]] + R[C[i + 673*t]];
R[i + 1206*t] = Op[i + 674*t] ? R[B[i + 674*t]] * R[C[i + 674*t]] : R[B[i + 674*t]] + R[C[i + 674*t]];
__syncthreads();
R[i + 1207*t] = Op[i + 675*t] ? R[B[i + 675*t]] * R[C[i + 675*t]] : R[B[i + 675*t]] + R[C[i + 675*t]];
R[i + 1208*t] = Op[i + 676*t] ? R[B[i + 676*t]] * R[C[i + 676*t]] : R[B[i + 676*t]] + R[C[i + 676*t]];
R[i + 1209*t] = Op[i + 677*t] ? R[B[i + 677*t]] * R[C[i + 677*t]] : R[B[i + 677*t]] + R[C[i + 677*t]];
__syncthreads();
R[i + 1210*t] = Op[i + 678*t] ? R[B[i + 678*t]] * R[C[i + 678*t]] : R[B[i + 678*t]] + R[C[i + 678*t]];
R[i + 1211*t] = Op[i + 679*t] ? R[B[i + 679*t]] * R[C[i + 679*t]] : R[B[i + 679*t]] + R[C[i + 679*t]];
R[i + 1212*t] = Op[i + 680*t] ? R[B[i + 680*t]] * R[C[i + 680*t]] : R[B[i + 680*t]] + R[C[i + 680*t]];
__syncthreads();
R[i + 1213*t] = Op[i + 681*t] ? R[B[i + 681*t]] * R[C[i + 681*t]] : R[B[i + 681*t]] + R[C[i + 681*t]];
R[i + 1214*t] = Op[i + 682*t] ? R[B[i + 682*t]] * R[C[i + 682*t]] : R[B[i + 682*t]] + R[C[i + 682*t]];
__syncthreads();
R[i + 1215*t] = Op[i + 683*t] ? R[B[i + 683*t]] * R[C[i + 683*t]] : R[B[i + 683*t]] + R[C[i + 683*t]];
R[i + 1216*t] = Op[i + 684*t] ? R[B[i + 684*t]] * R[C[i + 684*t]] : R[B[i + 684*t]] + R[C[i + 684*t]];
__syncthreads();
R[i + 1217*t] = Op[i + 685*t] ? R[B[i + 685*t]] * R[C[i + 685*t]] : R[B[i + 685*t]] + R[C[i + 685*t]];
R[i + 1218*t] = Op[i + 686*t] ? R[B[i + 686*t]] * R[C[i + 686*t]] : R[B[i + 686*t]] + R[C[i + 686*t]];
__syncthreads();
R[i + 1219*t] = Op[i + 687*t] ? R[B[i + 687*t]] * R[C[i + 687*t]] : R[B[i + 687*t]] + R[C[i + 687*t]];
__syncthreads();
R[i + 1220*t] = Op[i + 688*t] ? R[B[i + 688*t]] * R[C[i + 688*t]] : R[B[i + 688*t]] + R[C[i + 688*t]];
__syncthreads();
R[i + 1221*t] = Op[i + 689*t] ? R[B[i + 689*t]] * R[C[i + 689*t]] : R[B[i + 689*t]] + R[C[i + 689*t]];
__syncthreads();
R[i + 1222*t] = Op[i + 690*t] ? R[B[i + 690*t]] * R[C[i + 690*t]] : R[B[i + 690*t]] + R[C[i + 690*t]];
__syncthreads();
R[i + 1223*t] = Op[i + 691*t] ? R[B[i + 691*t]] * R[C[i + 691*t]] : R[B[i + 691*t]] + R[C[i + 691*t]];
__syncthreads();
R[i + 1224*t] = Op[i + 692*t] ? R[B[i + 692*t]] * R[C[i + 692*t]] : R[B[i + 692*t]] + R[C[i + 692*t]];
__syncthreads();
R[i + 1225*t] = Op[i + 693*t] ? R[B[i + 693*t]] * R[C[i + 693*t]] : R[B[i + 693*t]] + R[C[i + 693*t]];
__syncthreads();
R[i + 1226*t] = Op[i + 694*t] ? R[B[i + 694*t]] * R[C[i + 694*t]] : R[B[i + 694*t]] + R[C[i + 694*t]];
__syncthreads();
R[i + 1227*t] = Op[i + 695*t] ? R[B[i + 695*t]] * R[C[i + 695*t]] : R[B[i + 695*t]] + R[C[i + 695*t]];
__syncthreads();
R[i + 1228*t] = Op[i + 696*t] ? R[B[i + 696*t]] * R[C[i + 696*t]] : R[B[i + 696*t]] + R[C[i + 696*t]];
__syncthreads();
R[i + 1229*t] = Op[i + 697*t] ? R[B[i + 697*t]] * R[C[i + 697*t]] : R[B[i + 697*t]] + R[C[i + 697*t]];
if (i==0) { final += R[1229*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
19,470 | #include "includes.h"
__global__ void InvolveVector(float* input, float* output, int inputSize)
{
int threadId = blockDim.x*blockIdx.y*gridDim.x //rows preceeding current row in grid
+ blockDim.x*blockIdx.x //blocks preceeding current block
+ threadIdx.x;
if(threadId < inputSize - 1)
{
output[0] = input[0];
output[threadId + 1] = input[inputSize - threadId - 1];
}
} |
19,471 | #include <stdlib.h>
#include <stdio.h>
#include <iostream>
#include <unistd.h>
#include <sys/time.h>
__global__ void add(float *array, int dimensions, int num_elements)
{
int index = (threadIdx.x + blockIdx.x * blockDim.x) * dimensions;
// + blockIdx.y * blockDim.y + blockIdx.z * blockDim.z;
if (threadIdx.x + blockIdx.x * blockDim.x < num_elements) {
for (int d = 0; d < dimensions; d++) {
array[index + d] = index + d + 1;
}
}
}
__global__ void makezero(float * __restrict__ array, int dimensions, int num_elements)
{
int index = (threadIdx.x + blockIdx.x * blockDim.x) * dimensions;
//int index = threadIdx.x + threadIdx.y + threadIdx.z + blockIdx.x * blockDim.x + blockIdx.y * blockDim.y + blockIdx.z * blockDim.z;
if (threadIdx.x + blockIdx.x * blockDim.x < num_elements) {
for (int d = 0; d < dimensions; d++)
{
array[index + d] = 0;
}
}
}
//works, don't change
__global__ void distance(float * __restrict__ solution, float * __restrict__ array, int dimensions, int size)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
float dist;
if (index < size) {
for (int i = 0; i < size; i++) {
solution[index*size + i] = 0;
for (int d = 0; d < dimensions; d++) {
dist = array[i*dimensions + d] - array[index * dimensions + d];
solution[index*size + i] += dist * dist;
}
solution[index*size + i] = sqrt(solution[index*size + i]);
}
}
}
//works, don't change
__global__ void distance2(float * __restrict__ solution, float * __restrict__ array, int dimensions, int size)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
int i = index/size + index;
float dist;
while (i < size) {
solution[index * size + i] = 0;
for (int d = 0; d < dimensions; d++) {
dist = (array[i * dimensions + d] - array[index * dimensions + d]);
solution[index * size + i] += dist * dist;
}
solution[index*size + i] = sqrt(solution[index*size + i]);
i++;
}
}
//works, don't change
__global__ void distance3(float * __restrict__ solution, float * __restrict__ array, int dimensions, int x_size)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
if (index < (x_size * (x_size - 1)) / 2) {
float dist;
int column;
int row;
int rev_index = (x_size * (x_size - 1)) / 2 - index - 1;
x_size += -1;
column = - ((-1 + sqrt(double (1 + 8 * rev_index))) / 2);
column = column + x_size - 1;
row = index + column + 1 - column * x_size + (column * (column - 1)) / 2;
solution[index] = 0;
for (int d = 0; d < dimensions; d++) {
dist = (array[column * dimensions + d] - array[row * dimensions + d]);
solution[index] += dist * dist;
}
solution[index] = sqrt(solution[index]);
}
}
float array_sum(float * array, int array_length)
{
int i = 0;
float array_sum = 0;
for (i = 0; i < array_length; i++) {
array_sum += array[i];
}
return array_sum;
}
int check_solution(float * array_solution, int array_size, float test_value)
{
float array_summation = 0;
array_summation = array_sum(array_solution, array_size);
if (array_summation == test_value) {
printf("Array solution is correct\n");
} else {
printf("Array solution is NOT correct: %f \n",array_summation);
}
return 0;
}
void print_cuda_error()
{
cudaError_t error = cudaGetLastError();
if(error != cudaSuccess)
{
// print the CUDA error message and exit
printf("\nCUDA error: %s\n", cudaGetErrorString(error));
} else {
printf("\nno CUDA errors, all good\n");
}
}
float * centroid(float * array, int array_length, int dimensions)
{
float * average;
average = new float [dimensions];
for (int i = 0; i < array_length; i += dimensions) {
for (int d = 0; d < dimensions; d ++) {
average[d] += array[i + d];
}
}
for (int d = 0; d < dimensions; d ++) {
average[d] = average[d] / (array_length / dimensions);
//printf ("%f", average[d]);
}
return average;
}
__global__ void move(float * __restrict__ array, float * vector, int dimensions, int x_size)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
if (index < x_size) {
for (int d = 0; d < dimensions; d++) {
array[index + d] += vector[d];
}
}
}
int main(void)
{
cudaEvent_t startEvent, stopEvent;
float ms = 0;
int num_elements = 5;
int dimensions = 3;
int repeats = 1000;
//int num_bytes = num_elements * dimensions * sizeof(float);
struct timeval tv1, tv2;
float *device_array = 0;
float *host_array = 0;
float *host_solution = 0;
float *device_solution = 0;
// allocate memory for CPU
host_array = (float*)malloc(num_elements * dimensions * sizeof(float)); //array on the computer
host_solution = (float*)malloc(num_elements * num_elements * sizeof(float)); //distance matrix on the computer
// allocate memory for GPU
cudaMalloc((void**)&device_array, num_elements * dimensions * sizeof(float)); //GPU array
cudaMalloc((void**)&device_solution, num_elements * num_elements * sizeof(float)); // GPU distance matrix
// test if solution was correctly initialized
check_solution(host_solution, num_elements*num_elements, 0);
// create grid
int max_gridsize = 1024;
dim3 grid_size; // two dimensional grid
grid_size.y = 1;
grid_size.z = 1;
if (num_elements < max_gridsize) {
grid_size.x = num_elements;
} else {
grid_size.x = max_gridsize;
}
// create blocks
int blocks = (num_elements + grid_size.x - 1)/grid_size.x;
dim3 block_size;
block_size.x = blocks;
block_size.y = 1;
block_size.z = 1;
// print CUDA variables
printf("Blocks: %i \n",block_size.x);
printf("Grid size: %i \n",grid_size.x);
printf("Number of elements: %i \n", num_elements);
printf("Dimensions: %i \n", dimensions);
printf("\n###########\n");
printf("\nStarted\n");
printf("\n###########\n");
makezero<<<grid_size.x, blocks>>>(device_array, dimensions, num_elements);
cudaMemcpy(host_array, device_array, num_elements * dimensions * sizeof(float), cudaMemcpyDeviceToHost);
add<<<grid_size.x, blocks>>>(device_array, dimensions, num_elements);
cudaEventCreate(&startEvent);
cudaEventCreate(&stopEvent);
cudaMemcpy(host_array, device_array, num_elements * dimensions * sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(device_solution, host_solution, num_elements * num_elements * sizeof(float), cudaMemcpyHostToDevice);
cudaEventRecord(startEvent,0);
//gettimeofday(&tv1, NULL);
//distance<<<1,1024>>>(device_solution, device_array, dimensions, num_elements);
//distance<<<1,1>>>(device_solution, device_array, dimensions, num_elements);
//cudaMemcpy(host_solution, device_solution, num_elements * num_elements * sizeof(float), cudaMemcpyDeviceToHost);
printf ("%i blocks\n",blocks);
printf ("%i grid_size.x\n",grid_size.x);
////////////////////////////////////////////////////////////////////////////////////////
gettimeofday(&tv1, NULL);
for (int i = 0; i < repeats; i++) {
distance<<<blocks,grid_size.x>>>(device_solution, device_array, dimensions, num_elements);
}
cudaDeviceSynchronize();
gettimeofday(&tv2, NULL);
cudaMemcpy(host_solution, device_solution, num_elements * num_elements * sizeof(float), cudaMemcpyDeviceToHost);
cudaEventRecord(stopEvent,0);
cudaEventElapsedTime(&ms, startEvent, stopEvent);
cudaMemcpy(host_solution, device_solution, (num_elements*num_elements-num_elements)/2 * sizeof(float), cudaMemcpyDeviceToHost);
printf("GPU1 solution: %12.0f \n", array_sum(host_solution, num_elements*num_elements));
printf("Time needed by GPU1: %8.0f millisecs\n", double (tv2.tv_sec-tv1.tv_sec)*1000 + (tv2.tv_usec-tv1.tv_usec)/1000);
print_cuda_error();
printf ("%i blocks\n",blocks);
printf ("%i grid_size.x\n",grid_size.x);
////////////////////////////////////////////////////////////////////////////////////////
gettimeofday(&tv1, NULL);
for (int i = 0; i < repeats; i++) {
distance2<<<blocks,grid_size.x>>>(device_solution, device_array, dimensions, num_elements);
}
cudaDeviceSynchronize();
gettimeofday(&tv2, NULL);
cudaMemcpy(host_solution, device_solution, num_elements * num_elements * sizeof(float), cudaMemcpyDeviceToHost);
cudaEventRecord(stopEvent,0);
cudaEventElapsedTime(&ms, startEvent, stopEvent);
cudaMemcpy(host_solution, device_solution, (num_elements*num_elements-num_elements)/2 * sizeof(float), cudaMemcpyDeviceToHost);
printf ("%i blocks\n",blocks);
printf("%i grid_size.x\n", grid_size.x);
printf("GPU2 solution: %12.0f \n", array_sum(host_solution, num_elements*num_elements));
printf("Time needed by GPU2 %8.0f millisecs\n", double (tv2.tv_sec-tv1.tv_sec)*1000 + (tv2.tv_usec-tv1.tv_usec)/1000);
print_cuda_error();
cudaMemcpy(device_solution, host_solution, (num_elements*num_elements-num_elements)/2 * sizeof(float), cudaMemcpyHostToDevice);
////////////////////////////////////////////////////////////////////////////////////////
makezero<<<grid_size.x, blocks>>>(device_solution, dimensions, num_elements);
gettimeofday(&tv1, NULL);
if ((num_elements*num_elements-num_elements)/2 < max_gridsize) {
grid_size.x = num_elements*(num_elements-1)/2;
} else {
grid_size.x = max_gridsize;
}
blocks = ((num_elements*num_elements-num_elements)/2 + grid_size.x - 1)/grid_size.x;
for (int i = 0; i < repeats; i++) {
distance3<<<blocks,grid_size.x>>>(device_solution, device_array, dimensions, num_elements);
}
cudaDeviceSynchronize();
gettimeofday(&tv2, NULL);
cudaMemcpy(host_solution, device_solution, (num_elements*num_elements-num_elements)/2 * sizeof(float), cudaMemcpyDeviceToHost);
printf ("\n%i blocks\n",blocks);
printf ("%i grid_size.x\n",grid_size.x);
printf("GPU3 solution: %12.0f \n", array_sum(host_solution, num_elements*(num_elements-1)/2));
printf("Time needed by GPU3: %8.0f millisecs\n", double (tv2.tv_sec-tv1.tv_sec)*1000 + (tv2.tv_usec-tv1.tv_usec)/1000);
print_cuda_error();
//prints array
printf("####array######\n");
for(int i = 0; i < num_elements*dimensions && i < 60; i++)
{
printf("%4.2f", host_array[i]);
printf(".. ");
}
printf("\n");
// prints solution
printf("####solution###\n");
for(int i = 0; i < 25 && i<(num_elements*num_elements-num_elements)/2; i++)
{
printf("%4.2f", host_solution[i]);
printf(".. ");
}
for (int i = 0; i < num_elements; i++) {
for (int j = 0; j < num_elements; j++) {
host_solution[i * num_elements + j] = 0;
}
}
gettimeofday(&tv1, NULL);
//speed test CPU
for (int r = 0; r < repeats/10; r++) {
for (int i = 0; i < num_elements; i++) {
for (int j = i + 1; j < num_elements; j++) {
host_solution[i * num_elements + j] = 0;
for (int d = 0; d < dimensions; d++) {
host_solution[i * num_elements + j] = host_solution[i*num_elements + j] + (host_array[i*dimensions+d]-host_array[j*dimensions+d]) * (host_array[i*dimensions+d]-host_array[j*dimensions+d]);
}
host_solution[i * num_elements + j] = sqrt(host_solution[i * num_elements + j]);
}
}
}
for(int i = 0; i < 50 && i<(num_elements*num_elements-num_elements)/2; i++)
{
printf("%4.2f", host_solution[i]);
printf(".. ");
}
gettimeofday(&tv2, NULL);
printf("\n");
printf("CPU solution: %12.0f \n", array_sum(host_solution, num_elements*num_elements));
printf("Time needed by CPU: %8.0f millisecs\n", double (tv2.tv_sec-tv1.tv_sec)*10000 + (tv2.tv_usec-tv1.tv_usec)/100);
//test whether GPU output is correct
//for (int i = 0; i < num_elements; i++) {
//for (int j = i; j < num_elements; j++) {
//if (host_solution[i * num_elements + j] > 0.0000) {
//printf("%f", host_solution[i*num_elements + j]);
//printf("error\n");
//}
//}
//}
printf("centroid: \n");
float * center_point;
center_point = new float [dimensions - 1];
center_point = centroid(host_array, num_elements * dimensions, dimensions);
printf("%f \n", center_point[0]);
printf("%f \n", center_point[1]);
printf("%f \n", center_point[2]);
// deallocate memory
free(host_array);
free(host_solution);
cudaFree(device_array);
cudaFree(device_solution);
cudaEventDestroy(startEvent);
cudaEventDestroy(stopEvent);
printf("\ndone\n");
return 0;
}
|
19,472 | #include <iostream>
#include <cuda.h>
static void HandleError( cudaError_t err, const char *file, int line) {
if (err != cudaSuccess) {
std::cout << "Error Name: " << cudaGetErrorName( err ) << std::endl;
std::cout << cudaGetErrorString( err ) << " in " << file << " line " << line << std::endl;
exit(EXIT_FAILURE);
}
}
#define HANDLE_ERROR(err)(HandleError(err, __FILE__, __LINE__))
class Base {
public:
__device__ virtual void fun1() const {
//__device__ void fun1() const {
printf("Base fun1\n");
}
__device__ void fun2() const {
printf("Base fun2\n");
fun1();
}
};
class Derived : public Base {
public:
__device__ void fun1() const override {
//__device__ void fun1() const {
printf("Derived fun2\n");
}
};
static __global__ void kernel() {
//Base *obj = new Base();
Base *obj = new Derived();
printf("inside kernel\n");
obj->fun1();
obj->fun2();
}
int main() {
cudaDeviceProp prop;
int dev;
memset(&prop, 0, sizeof(cudaDeviceProp));
prop.major = 1;
prop.minor = 0;
HANDLE_ERROR(
cudaChooseDevice(&dev,&prop)
);
float grids = 1;
float threads = 1;
kernel<<<grids,threads>>>();
HANDLE_ERROR(cudaDeviceSynchronize());
return 0;
}
|
19,473 |
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
// a = b * c
__global__ void mmult(float *a, float *b, float *c, int N)
{
int row = blockIdx.y;
int col = blockIdx.x*32 + threadIdx.x;
float sum = 0.0f;
for (int n = 0; n < N; ++n)
{
sum += a[row*N+n]*b[n*N+col];
}
c[row*N+col] = sum;
}
// a[][] = 0, b[][] = 1; c[][] = 1
void minit(float *a, float *b, float *c, int N)
{
int i, j;
for (j = 0; j < N; j++)
for (i = 0; i < N; i++)
{
a[i+N*j] = 0.0f;
b[i+N*j] = 1.0f;
c[i+N*j] = 1.0f;
}
}
void mprint(float *a, int N, int M)
{
int i, j;
for (j = 0; j < M; j++)
{
for (i = 0; i < M; i++)
printf("%.2f ", a[i+N*j]);
printf("...\n");
}
printf("...\n");
}
int main(int argc, char* argv[])
{
long int N = 4096;
int T = 32;
if (argc == 2)
T = atoi(argv[1]);
cudaEvent_t start, stop;
float time, flop, gflops;
float *a = (float *)malloc(N*N*sizeof(float));
float *b = (float *)malloc(N*N*sizeof(float));
float *c = (float *)malloc(N*N*sizeof(float));
minit(a, b, c, N);
// allocate device memory
float *devPtrA, *devPtrB, *devPtrC;
cudaMalloc((void**)&devPtrA, N*N*sizeof(float));
cudaMalloc((void**)&devPtrB, N*N*sizeof(float));
cudaMalloc((void**)&devPtrC, N*N*sizeof(float));
// copy input arrays to the device memory
cudaMemcpy(devPtrB, b, N*N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(devPtrC, c, N*N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemset(devPtrA, 0, N*N*sizeof(float));
// define grid and thread block sizes
dim3 threads(T);
dim3 grid(N/T, N);
printf("matrix %ix%i\n", N, N);
printf("grid %ix%i\n", grid.x, grid.y);
printf("block %ix%ix%i\n", threads.x, threads.y, threads.z);
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
// launch GPU kernel
mmult<<<grid, threads>>>(devPtrA, devPtrB, devPtrC, N);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
// copy results to host
cudaMemcpy(a, devPtrA, N*N*sizeof(float), cudaMemcpyDeviceToHost);
mprint(a, N, 5);
// free device memory
cudaFree(devPtrA);
cudaFree(devPtrB);
cudaFree(devPtrC);
free(a);
free(b);
free(c);
cudaEventElapsedTime(&time, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
time /= 1000.0f; // convert from milliseconds to seconds
flop = N*N*N*2.0f;
gflops = flop / time / 1E9;
printf("sec = %.2f GFLOPS = %.3f\n", time, gflops);
return EXIT_SUCCESS;
}
|
19,474 | //
// include files
//
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
//
// template kernel routine
//
template <typename T>
__global__ void my_first_kernel(T *x)
{
int tid = threadIdx.x + blockDim.x*blockIdx.x;
x[tid] = threadIdx.x;
}
//
// CUDA routine to be called by main code
//
extern
int prac6(int nblocks, int nthreads)
{
float *h_x, *d_x;
int *h_i, *d_i;
double *h_xx, *d_xx;
int nsize, n;
// allocate memory for arrays
nsize = nblocks*nthreads ;
h_xx = (double *)malloc(nsize*sizeof(double));
cudaMalloc((void **)&d_xx, nsize*sizeof(double));
h_x = (float *)malloc(nsize*sizeof(float));
cudaMalloc((void **)&d_x, nsize*sizeof(float));
h_i = (int *)malloc(nsize*sizeof(int));
cudaMalloc((void **)&d_i, nsize*sizeof(int));
// execute kernel for double
my_first_kernel<<<nblocks,nthreads>>>(d_xx);
cudaMemcpy(h_xx,d_xx,nsize*sizeof(double),cudaMemcpyDeviceToHost);
for (n=0; n<nsize; n++) printf(" n, x = %d %f \n",n,h_xx[n]);
// execute kernel for float
my_first_kernel<<<nblocks,nthreads>>>(d_x);
cudaMemcpy(h_x,d_x,nsize*sizeof(float),cudaMemcpyDeviceToHost);
for (n=0; n<nsize; n++) printf(" n, x = %d %f \n",n,h_x[n]);
// execute kernel for ints
my_first_kernel<<<nblocks,nthreads>>>(d_i);
cudaMemcpy(h_i,d_i,nsize*sizeof(int),cudaMemcpyDeviceToHost);
for (n=0; n<nsize; n++) printf(" n, i = %d %d \n",n,h_i[n]);
// free memory
cudaFree(d_xx);
free(h_xx);
cudaFree(d_x);
free(h_x);
cudaFree(d_i);
free(h_i);
return 0;
}
|
19,475 | #include "includes.h"
__global__ void blendFloatImageFloatLabelToRGBA_kernel( uchar4 *out_image, const float *in_image, const float *label, int width, int height, float lowerLim, float upperLim) {
const int x = __mul24(blockIdx.x, blockDim.x) + threadIdx.x;
const int y = __mul24(blockIdx.y, blockDim.y) + threadIdx.y;
uchar4 temp;
if (x < width && y < height) {
unsigned char img =
(unsigned char)(0.5f * in_image[__mul24(y, width) + x] + 128.0f);
float val = label[__mul24(y, width) + x];
// draw everything invalid or out of lim in white
if (!isfinite(val) || (val < lowerLim) || (val > upperLim)) {
// don't blend
temp.x = img;
temp.y = img;
temp.z = img;
temp.w = 255;
} else {
// blend
temp.x = 0.6f * img;
temp.y = 0.6f * img;
temp.z = img;
temp.w = 255;
}
out_image[__mul24(y, width) + x] = temp;
}
} |
19,476 | #include <stdio.h>
#include <chrono>
#define block_size_x 256
#define num_blocks 1024
//a naive summation in C
float sum_floats(float *in_array, int n) {
float sum = 0.0;
for (int i=0; i<n; i++) {
sum += in_array[i];
}
return sum;
}
//Kahan summation to avoid floating-point precision errors
float sum_floats_kahan(float *in_array, int n) {
float sum = 0.0;
float c = 0.0;
for (int i=0; i<n; i++) {
float v = in_array[i] - c;
float t = sum + v;
c = (t - sum) - v;
sum = t;
}
return sum;
}
//CUDA kernel for parallel reduction
extern "C" __global__ void reduce_kernel(float *out_array, float *in_array, int n) {
int ti = threadIdx.x;
int x = blockIdx.x * block_size_x + threadIdx.x;
int step_size = gridDim.x * block_size_x;
float sum = 0.0f;
//cooperatively (with all threads in all thread blocks) iterate over input array
for (int i=x; i<n; i+=step_size) {
sum += in_array[i];
}
//at the point we have reduced the number of values to be summed from n to
//the total number of threads in all thread blocks combined
//the goal is now to reduce the values within each thread block to a single
//value per thread block for this we will need shared memory
//declare shared memory array, how much shared memory do we need?
//__shared__ float ...;
//make every thread store its thread-local sum to the array in shared memory
//... = sum;
//now let's call syncthreads() to make sure all threads have finished
//storing their local sums to shared memory
__syncthreads();
//now this interesting looking loop will do the following:
//it iterates over the block_size_x with the following values for s:
//if block_size_x is 256, 's' will be powers of 2 from 128, 64, 32, down to 1.
//these decreasing offsets can be used to reduce the number
//of values within the thread block in only a few steps.
#pragma unroll
for (unsigned int s=block_size_x/2; s>0; s/=2) {
//you are to write the code inside this loop such that
//threads will add the sums of other threads that are 's' away
//do this iteratively such that together the threads compute the
//sum of all thread-local sums
//use shared memory to access the values of other threads
//and store the new value in shared memory to be used in the next round
//be careful that values that should be read are
//not overwritten before they are read
//make sure to call __syncthreads() when needed
}
//write back one value per thread block
if (ti == 0) {
//out_array[blockIdx.x] = ; //store the per-thread block reduced value to global memory
}
}
int main() {
int n = (int)5e7; //problem size
float time;
cudaError_t err;
//allocate arrays and fill them
float *in_array = (float *) malloc(n * sizeof(float));
float *out_array = (float *) malloc(num_blocks * sizeof(float));
for (int i=0; i<n; i++) {
in_array[i] = (rand() % 10000) / 100000.0;
}
memset(out_array, 0, num_blocks * sizeof(float));
//measure the CPU function
auto start = std::chrono::high_resolution_clock::now();
float sum = sum_floats(in_array, n);
auto stop = std::chrono::high_resolution_clock::now();
time = (float)std::chrono::duration_cast<std::chrono::microseconds>(stop-start).count()/1000.0;
printf("sum_floats took %.3f ms\n", time);
//allocate GPU memory
float *d_in; float *d_out;
err = cudaMalloc((void **)&d_in, n*sizeof(float));
if (err != cudaSuccess) fprintf(stderr, "Error in cudaMalloc: %s\n", cudaGetErrorString( err ));
err = cudaMalloc((void **)&d_out, num_blocks*sizeof(float));
if (err != cudaSuccess) fprintf(stderr, "Error in cudaMalloc: %s\n", cudaGetErrorString( err ));
//copy the input data to the GPU
err = cudaMemcpy(d_in, in_array, n*sizeof(float), cudaMemcpyHostToDevice);
if (err != cudaSuccess) fprintf(stderr, "Error in cudaMemcpy host to device: %s\n", cudaGetErrorString( err ));
//zero the output array
err = cudaMemset(d_out, 0, num_blocks*sizeof(float));
if (err != cudaSuccess) fprintf(stderr, "Error in cudaMemset: %s\n", cudaGetErrorString( err ));
//setup the grid and thread blocks
dim3 grid(num_blocks, 1);
dim3 grid2(1, 1);
dim3 threads(block_size_x, 1, 1);
//measure the GPU function
cudaDeviceSynchronize();
start = std::chrono::high_resolution_clock::now();
reduce_kernel<<<grid, threads>>>(d_out, d_in, n);
reduce_kernel<<<grid2, threads>>>(d_out, d_out, num_blocks); //call the kernel again with only 1 thread block
cudaDeviceSynchronize();
stop = std::chrono::high_resolution_clock::now();
time = (float)std::chrono::duration_cast<std::chrono::microseconds>(stop-start).count()/1000.0;
printf("reduce_kernel took %.3f ms\n", time);
//check to see if all went well
err = cudaGetLastError();
if (err != cudaSuccess) fprintf(stderr, "Error during kernel launch: %s\n", cudaGetErrorString( err ));
//copy the result back to host memory
err = cudaMemcpy(out_array, d_out, 1*sizeof(float), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) fprintf(stderr, "Error in cudaMemcpy device to host: %s\n", cudaGetErrorString( err ));
//compute a reliable reference answer on the host
float sum2 = sum_floats_kahan(in_array, n);
//check the result
float diff = abs(*out_array - sum2);
printf("cpu: %f, corrected: %f\n", sum, sum2);
printf("gpu: %f\n", *out_array);
if (diff < 1.0) {
printf("TEST PASSED!\n");
} else {
printf("TEST FAILED!\n");
}
//clean up
cudaFree(d_in);
cudaFree(d_out);
free(in_array);
free(out_array);
return 0;
}
|
19,477 | #include <cstdio>
__global__ void my_kernel() {
printf("Hello from block %i of %i and thread %i \n ", blockIdx.x, blockDim.x, threadIdx.x);
}
int main() {
my_kernel <<<16, 16 >>> ();
cudaError_t cuda_err = cudaDeviceSynchronize();
if (cuda_err != cudaSuccess)
printf("kernel launch failed with error \"%s\".\n",
cudaGetErrorString(cuda_err));
return 0;
}
|
19,478 | // CUDA multiple threads
#include <cuda.h>
#include <cuda_runtime.h>
__global__
void rgb2grey_kernel(const uchar4* const rgbaImage,
unsigned char* const greyImage,
int numRows, int numCols)
{
int idx = threadIdx.x+ blockIdx.x* blockDim.x;
if (idx < numCols*numRows) {
uchar4 rgba = rgbaImage[idx];
float grey = .299f * rgba.x + .587f * rgba.y + .114f * rgba.z;
greyImage[idx] = grey;
}
}
void rgb2grey_cuda( const uchar4* const h_rgbaImage,
unsigned char *const h_greyImage,
size_t numRows, size_t numCols)
{
uchar4 *d_rgbaImage;
unsigned char *d_greyImage;
size_t numPixels= numRows*numCols;
// Alloc memory
cudaMalloc((void **) &d_rgbaImage, sizeof(uchar4) * numPixels);
cudaMalloc((void **) &d_greyImage, sizeof(uchar4) * numPixels);
// Copy from Host to Device
cudaMemset(d_greyImage, 0, numPixels * sizeof(unsigned char));
cudaMemcpy(d_rgbaImage, h_rgbaImage, sizeof(uchar4) * numPixels, cudaMemcpyHostToDevice);
// Kernel Launch
const int threadPerBlock = 512;
const int numBlock = numPixels/threadPerBlock+1;
rgb2grey_kernel<<<numBlock, threadPerBlock>>>(d_rgbaImage, d_greyImage, numRows, numCols);
// Copy from Device to Host
cudaMemcpy(h_greyImage, d_greyImage, sizeof(unsigned char) * numPixels, cudaMemcpyDeviceToHost);
// Free memory
cudaDeviceSynchronize();
cudaFree(d_greyImage);
cudaFree(d_rgbaImage);
}
|
19,479 | //
// Created by harshvardhanchandirasekar on 8/1/20.
//
#include "planet.cuh"
int main()
{
return 0;
} |
19,480 | #include "includes.h"
__global__ void main_set(float *data, float val) {
data[threadIdx.x] = val;
} |
19,481 | #include<cstdio>
#include<fstream>
#include<cmath>
#include<cuda.h>
int threshold=256;
int xthread=32;
__global__ void multiply(float* A,float* B,float* C,int jump,int jump1,int jump2,int iter)
{
__shared__ float A1[32][32],B1[32][32];
int posy=blockIdx.y*blockDim.y+threadIdx.y;
int posx=blockIdx.x*blockDim.x+threadIdx.x;
int row=posy*jump+threadIdx.x;
int col=posx+threadIdx.y*jump1;
int place=posy*jump2+posx;
for(int i=0;i<iter;i++)
{
A1[threadIdx.y][threadIdx.x]=A[row];
B1[threadIdx.y][threadIdx.x]=B[col];
__syncthreads();
for(int i=0;i<blockDim.x;i++)
C[place]+=A1[threadIdx.y][i]*B1[i][threadIdx.x];
row+=blockDim.x;
col+=blockDim.y*jump1;
__syncthreads();
}
}
//non_square matrix multiplication
__global__ void kernel1(float* A,int jump1,int n1,float* B,int jump2,int n2,float* C,int jump3,float* D,int jump4,int m1,int m2,int m3)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
int row=blockIdx.y * blockDim.y+threadIdx.y;
if(col<m2 && row<m1)
C[col+jump3*row]=A[col+jump1*row]+A[n1+col+jump1*row];
if(col<m3 && row<m2)
D[col+jump4*row]=B[col+jump2*row]+B[n2+col+jump2*row];
}
__global__ void kernel7(float* A,int jump1,int n1,float* B,int jump2,int n2,float* C,int jump3,float* D,int jump4,int m1,int m2,int m3)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
int row=blockIdx.y * blockDim.y+threadIdx.y;
if(col<m2 && row<m1)
C[col+jump3*row]=A[col+jump1*row]-A[n1+col+jump1*row];
if(col<m3 && row<m2)
D[col+jump4*row]=B[col+jump2*row]+B[n2+col+jump2*row];
}
__global__ void kernel2(float* A,int jump,int n1,int n2,float* B,int jump1,float* C,int jump2,int n3,int m2,int m3)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
if(col<m3)
{
if(3*blockIdx.y<gridDim.y)
{
int point=col+(blockIdx.y * blockDim.y + threadIdx.y)*jump;
A[point+n2]+=A[point];
}
else if(3*blockIdx.y<2*gridDim.y)
{
int point=col+((blockIdx.y-gridDim.y/3) * blockDim.y + threadIdx.y)*jump;
A[point]+=A[point+n1];
A[point+n1]=0;
}
}
if(col<m2 && 3*blockIdx.y>=2*gridDim.y)
{
int row=(blockIdx.y-2*gridDim.y/3) * blockDim.y + threadIdx.y;
B[col+jump1*row]=C[col+jump2*row]+C[col+jump2*row+n3];
}
}
__global__ void kernel3(float* A,int jump,int n,float* B,int jump1,float* C,int jump2,int n1,int m2,int m3)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
int row=blockIdx.y * blockDim.y + threadIdx.y;
if(col<m2)
A[col+jump*row+n]-=A[col+jump*row];
if(col<m3)
B[col+jump1*row]=C[col+jump2*row]+C[col+jump2*row+n1];
}
__global__ void kernel4(float* A,int jump,int n1,float* B,int jump1,float* C,int jump2,float* D,int jump3,int n2,int m1,int m2)
{
int col=blockIdx.x * blockDim.x+threadIdx.x,row;
if(2*blockIdx.y<gridDim.y)
{
row=blockIdx.y * blockDim.y + threadIdx.y;
if(row<m1)
{
A[col+jump*row]+=B[col+jump1*row];
A[col+jump*row+n1]+=B[col+jump1*row];
B[col+jump1*row]=0;
}
}
else
{
row=(blockIdx.y-gridDim.y/2) * blockDim.y + threadIdx.y;
if(row<m2)
C[col+jump2*row]=D[col+jump3*row+n2]-D[col+jump3*row];
}
}
__global__ void kernel5(float* A,int jump,int n1,float* B,int jump1)
{
int point=(blockIdx.x * blockDim.x+threadIdx.x)+(blockIdx.y * blockDim.y + threadIdx.y)*jump;
int point1=(blockIdx.x * blockDim.x+threadIdx.x)+(blockIdx.y * blockDim.y + threadIdx.y)*jump1;
A[point]+=B[point1];
A[point+n1]+=B[point1];
}
__global__ void kernel6(float* A,int jump,float* B,int jump1,int n1,float* C,int jump2,float* D,int jump3,int n2,int m1,int m2,int m3)
{
int col=blockIdx.x*blockDim.x+threadIdx.x;
if(col<m2 && 3*blockIdx.y<gridDim.y)
{
int row=blockIdx.y*blockDim.y+threadIdx.y;
if(row<m1)
A[col+row*jump]=0;
}
if(col<m3 && 3*blockIdx.y>=gridDim.y && 3*blockIdx.y<2*gridDim.y)
{
int row=(blockIdx.y-gridDim.y/3) * blockDim.y + threadIdx.y;
if(row<m1)
B[col+row*jump1]-=B[col+row*jump1+n1];
}
if(col<m3 && 3*blockIdx.y>2*gridDim.y)
{
int row=(blockIdx.y-(2*gridDim.y/3)) * blockDim.y + threadIdx.y;
if(row<m2)
C[col+jump2*row]=D[col+jump3*row]-D[col+jump3*row+n2];
}
}
void strassen(float* A,int jump,float* B,int jump1,float* C,int jump2,int n1,int n2,int n3,float* temp1,float* temp2,int n,int n_)
{
if(n1<=threshold || n2<=threshold || n3<=threshold)
{
multiply <<<dim3(n3/xthread,n1/xthread),dim3(xthread,xthread)>>> (A,B,C,jump,jump1,jump2,n2/xthread);
}
else
{
n1/=2;n2/=2;n3/=2;
n/=2;n_/=2;
//M1
kernel1 <<<dim3(n_/xthread,n/xthread),dim3(xthread,xthread)>>> (A,jump,jump*n1+n2,B,jump1,jump1*n2+n3,temp1,n2,temp2,n3,n1,n2,n3); //temp1=A11+A22 //temp2=B11+B22
strassen(temp1,n2,temp2,n3,C,jump2,n1,n2,n3,temp1+n1*n2,temp2+n2*n3,n,n_); //C11=temp1*temp2
//M6
kernel7 <<<dim3(n_/xthread,n/xthread),dim3(xthread,xthread)>>> (A+jump*n1,jump,-jump*n1,B,jump1,n3,temp1,n2,temp2,n3,n1,n2,n3); //temp1=A21-A11//temp2=B11+B12
strassen(temp1,n2,temp2,n3,C+jump2*n1+n3,jump2,n1,n2,n3,temp1+n1*n2,temp2+n2*n3,n,n_); //C22=temp1*temp2
//M7
kernel7 <<<dim3(n_/xthread,n/xthread),dim3(xthread,xthread)>>> (A+n2,jump,jump*n1,B+jump1*n2,jump1,n3,temp1,n2,temp2,n3,n1,n2,n3);//temp1=A12-A22 //temp2=B21+B22
strassen(temp1,n2,temp2,n3,C+n3,jump2,n1,n2,n3,temp1+n1*n2,temp2+n2*n3,n,n_); //C12+=temp1*temp2
kernel2 <<<dim3(n_/xthread,(3*n1)/xthread),dim3(xthread,xthread)>>> (C,jump2,n3,jump2*n1+n3,temp1,n2,A+jump*n1,jump,n2,n2,n3); //C22+=C11//C11+=C12 //temp1=A21+A22 //C12=0
//M2
strassen(temp1,n2,B,jump1,C+jump2*n1,jump2,n1,n2,n3,temp1+n1*n2,temp2+n2*n3,n,n_); //C21=temp1*B11
kernel3 <<<dim3(n_/xthread,n1/xthread),dim3(xthread,xthread)>>> (C+jump2*n1,jump2,n3,temp1,n2,A,jump,n2,n2,n3); //C22-=C21 //temp1=A11+A12
//M5
strassen(temp1,n2,B+jump1*n2+n3,jump1,C+n3,jump2,n1,n2,n3,temp1+n1*n2,temp2+n2*n3,n,n_); //C12=temp1*B22
kernel6<<<dim3(n_/xthread,(3*n)/xthread),dim3(xthread,xthread)>>>(temp1,n2,C,jump2,n3,temp2,n3,B+n3,jump1,jump1*n2,n1,n2,n3); //C11-=C12 //temp2=B12-B22 //temp1=0
//M3
strassen(A,jump,temp2,n3,temp1,n3,n1,n2,n3,temp1+n1*n3,temp2+n2*n3,n,n_); //temp1=A11*temp2
kernel4 <<<dim3(n3/xthread,(2*n)/xthread),dim3(xthread,xthread)>>> (C+n3,jump2,jump2*n1,temp1,n2,temp2,n3,B,jump1,jump1*n2,n1,n2); //C12=C12+temp1//C22=C22+temp1 //temp2=B21-B11 //temp1=0
//M4
strassen(A+jump*n1+n2,jump,temp2,n3,temp1,n3,n1,n2,n3,temp1+n1*n3,temp2+n2*n3,n,n_); //temp1=A22*temp2
kernel5 <<<dim3(n3/xthread,n1/xthread),dim3(xthread,xthread)>>> (C,jump2,jump2*n1,temp1,n2); //C21=C21+temp1//C11=C11+temp1
}
}
//Square matrix multiplication
__global__ void kernel1_sq(float* A,int jump1,int n1,float* B,int jump2,int n2,float* C,float* D,int jump)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
int row=blockIdx.y * blockDim.y+threadIdx.y;
C[col+jump*row]=A[col+jump1*row]+A[n1+col+jump1*row];
D[col+jump*row]=B[col+jump2*row]+B[n2+col+jump2*row];
}
__global__ void kernel7_sq(float* A,int jump1,int n1,float* B,int jump2,int n2,float* C,float* D,int jump)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
int row=blockIdx.y * blockDim.y+threadIdx.y;
C[col+jump*row]=A[col+jump1*row]-A[n1+col+jump1*row];
D[col+jump*row]=B[col+jump2*row]+B[n2+col+jump2*row];
}
__global__ void kernel2_sq(float* A,int jump,int n1,int n2,float* B,int jump1,float* C,int jump2,int n3)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
if(3*blockIdx.y<gridDim.y)
{
int point=col+(blockIdx.y * blockDim.y + threadIdx.y)*jump;
A[point+n2]+=A[point];
}
else if(3*blockIdx.y<2*gridDim.y)
{
int point=col+((blockIdx.y-gridDim.y/3) * blockDim.y + threadIdx.y)*jump;
A[point]+=A[point+n1];
A[point+n1]=0;
}
else
{
int row=(blockIdx.y-2*gridDim.y/3) * blockDim.y + threadIdx.y;
B[col+jump1*row]=C[col+jump2*row]+C[col+jump2*row+n3];
}
}
__global__ void kernel3_sq(float* A,int jump,int n,float* B,int jump1,float* C,int jump2,int n1)
{
int col=blockIdx.x * blockDim.x+threadIdx.x;
int row=blockIdx.y * blockDim.y + threadIdx.y;
A[col+jump*row+n]-=A[col+jump*row];
B[col+jump1*row]=C[col+jump2*row]+C[col+jump2*row+n1];
}
__global__ void kernel4_sq(float* A,int jump,int n1,float* B,int jump1,float* C,int jump2,float* D,int jump3,int n2)
{
int col=blockIdx.x * blockDim.x+threadIdx.x,row;
if(2*blockIdx.y<gridDim.y)
{
row=blockIdx.y * blockDim.y + threadIdx.y;
A[col+jump*row]+=B[col+jump1*row];
A[col+jump*row+n1]+=B[col+jump1*row];
B[col+jump1*row]=0;
}
else
{
row=(blockIdx.y-gridDim.y/2) * blockDim.y + threadIdx.y;
C[col+jump2*row]=D[col+jump3*row+n2]-D[col+jump3*row];
}
}
__global__ void kernel6_sq(float* A,int jump,float* B,int jump1,int n1,float* C,int jump2,float* D,int jump3,int n2)
{
int col=blockIdx.x*blockDim.x+threadIdx.x;
if(3*blockIdx.y<gridDim.y)
{
int row=blockIdx.y*blockDim.y+threadIdx.y;
A[col+row*jump]=0;
}
else if(3*blockIdx.y<2*gridDim.y)
{
int row=(blockIdx.y-gridDim.y/3) * blockDim.y + threadIdx.y;
B[col+row*jump1]-=B[col+row*jump1+n1];
}
else
{
int row=(blockIdx.y-(2*gridDim.y/3)) * blockDim.y + threadIdx.y;
C[col+jump2*row]=D[col+jump3*row]-D[col+jump3*row+n2];
}
}
void strassen_sq(float* A,int jump,float* B,int jump1,float* C,int jump2,int n,float* temp1,float* temp2,int block_len)
{
if(n<=threshold)
multiply<<<dim3(block_len,block_len),dim3(xthread,xthread)>>>(A,B,C,jump,jump1,jump2,n/xthread);
else
{
n/=2;
block_len/=2;
kernel1_sq<<<dim3(block_len,block_len),dim3(xthread,xthread)>>>(A,jump,jump*n+n,B,jump1,jump1*n+n,temp1,temp2,n);
strassen_sq(temp1,n,temp2,n,C,jump2,n,temp1+n*n,temp2+n*n,block_len);
kernel7_sq<<<dim3(block_len,block_len),dim3(xthread,xthread)>>>(A+jump*n,jump,-jump*n,B,jump1,n,temp1,temp2,n);
strassen_sq(temp1,n,temp2,n,C+jump2*n+n,jump2,n,temp1+n*n,temp2+n*n,block_len);
kernel7_sq<<<dim3(block_len,block_len),dim3(xthread,xthread)>>>(A+n,jump,jump*n,B+jump1*n,jump1,n,temp1,temp2,n);
strassen_sq(temp1,n,temp2,n,C+n,jump2,n,temp1+n*n,temp2+n*n,block_len);
kernel2_sq<<<dim3(block_len,3*block_len),dim3(xthread,xthread)>>>(C,jump2,n,jump2*n+n,temp1,n,A+jump*n,jump,n);
strassen_sq(temp1,n,B,jump1,C+jump2*n,jump2,n,temp1+n*n,temp2+n*n,block_len);
kernel3_sq<<<dim3(block_len,block_len),dim3(xthread,xthread)>>>(C+jump2*n,jump2,n,temp1,n,A,jump,n);
strassen_sq(temp1,n,B+jump1*n+n,jump1,C+n,jump2,n,temp1+n*n,temp2+n*n,block_len);
kernel6_sq<<<dim3(block_len,3*block_len),dim3(xthread,xthread)>>>(temp1,n,C,jump2,n,temp2,n,B+n,jump1,jump1*n);
strassen_sq(A,jump,temp2,n,temp1,n,n,temp1+n*n,temp2+n*n,block_len);
kernel4_sq<<<dim3(block_len,2*block_len),dim3(xthread,xthread)>>>(C+n,jump2,jump2*n,temp1,n,temp2,n,B,jump1,jump1*n);
strassen_sq(A+jump*n+n,jump,temp2,n,temp1,n,n,temp1+n*n,temp2+n*n,block_len);
kernel5<<<dim3(block_len,block_len),dim3(xthread,xthread)>>>(C,jump2,jump2*n,temp1,n);
}
}
int nearest_ideal(int &n,int &temp)
{
int temp1=(xthread-n%xthread)%xthread;
int pow=1;
n+=temp1;
int m=n/xthread;
while(m>threshold/xthread)
{
if(m%2==1)
{
temp+=pow;
m++;
}
m/=2;
pow*=2;
}
n+=temp*xthread;
temp=temp*xthread;
temp+=temp1;
return pow;
}
int main(int argc,char** argv)
{
std::ifstream in(argv[1]);
std::ifstream in1(argv[2]);
std::ofstream out(argv[3]);
float *A,*B,*C;
int n1,n2,n3;
int temp1=0,temp2=0,temp3=0;
in>>n1>>n2;
in1>>n2>>n3;
out<<n1<<'\t'<<n3<<'\n';
int power=nearest_ideal(n1,temp1);
power=std::min(power,nearest_ideal(n2,temp2));
power=std::min(power,nearest_ideal(n3,temp3));
float factor=0;
for(int i=power;i>1;i/=2)
factor+=1/(float)(i*i);
A=new float[n1*n2];
B=new float[n2*n3];
C=new float[n1*n3];
for(int i=0; i<n1-temp1; i++)
{
for(int j=0; j<n2-temp2; j++)
in>>A[i*n2+j];
for(int j=n2-temp2;j<n2;j++)
A[i*n2+j]=0;
}
for(int i=(n1-temp1)*n2;i<n1*n2;i++)
A[i]=0;
in.close();
for(int i=0; i<n2-temp2; i++)
{
for(int j=0; j<n3-temp3; j++)
in1>>B[i*n3+j];
for(int j=n3-temp3;j<n3;j++)
B[i*n3+j]=0;
}
for(int i=(n2-temp2)*n3;i<n2*n3;i++)
B[i]=0;
in1.close();
struct timespec start,end;
int size_temp1,size_temp2,n_,n;
n=n2>n3?n2:n3;
n_=n1>n2?n1:n2;
size_temp1=(int)(n1*n_)*factor;
size_temp2=(int)(n2*n3)*factor;
float *d_A, *d_B, *d_C,*temp1_,*temp2_;
cudaMalloc( (void **) &d_A, sizeof(float)*n1*n2);
cudaMalloc( (void **) &d_B, sizeof(float)*n2*n3);
cudaMalloc( (void **) &d_C, sizeof(float)*n1*n3);
cudaMalloc( (void **) &temp1_,sizeof(float)*size_temp1);
cudaMalloc( (void **) &temp2_,sizeof(float)*size_temp2);
//copy from host to device
cudaMemcpy (d_A, A, sizeof(float)*n1*n2, cudaMemcpyHostToDevice);
cudaMemcpy (d_B, B, sizeof(float)*n2*n3, cudaMemcpyHostToDevice);
cudaMemset(d_C,0,sizeof(float)*n1*n3);
cudaMemset(temp1_,0,sizeof(float)*size_temp1);
cudaMemset(temp2_,0,sizeof(float)*size_temp2);
clock_gettime(CLOCK_PROCESS_CPUTIME_ID,&start);
if(n1!=n2 || n3!=n2)
strassen(d_A,n2,d_B,n3,d_C,n3,n1,n2,n3,temp1_,temp2_,n,n_);
else
strassen_sq(d_A,n1,d_B,n1,d_C,n1,n1,temp1_,temp2_,n1/xthread);
cudaDeviceSynchronize();
clock_gettime(CLOCK_PROCESS_CPUTIME_ID,&end);
cudaMemcpy (C, d_C, sizeof(float)*n1*n3, cudaMemcpyDeviceToHost);
printf("Error %s \n",cudaGetErrorString(cudaGetLastError()));
double time_taken = (end.tv_nsec-start.tv_nsec)+1e+9*(end.tv_sec-start.tv_sec);
printf("StrassenRec - Time taken: %f\n",time_taken);
for(int i=0;i<n1-temp1;i++)
{
for(int j=0;j<n3-temp3;j++)
out<<C[i*n3+j]<<'\t';
out<<'\n';
}
std::ofstream ofile;
ofile.open(argv[4],std::ios_base::app);
ofile<<"strassenRec - Time taken (ns): "<<time_taken<<"\n";
ofile.close();
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
cudaFree(temp1_);
cudaFree(temp2_);
delete C,A,B;
}
|
19,482 | #include <stdio.h>
//__global__ void kernel( void ) {
// does nothing
//}
int main(int argc, char** argv) {
// default the loop count to equal 1
int loopCount = 1;
// take in a command line arg to set the loop count
if(argc > 1){
loopCount = atoi(argv[1]);
}
// delcare two variables
int *dev_a;
// get the size of an int for the cuda malloc
int size = 1;
// malloc on the device
// loop over the loop count and copy to device
for(int i = 0; i < loopCount; i++){
cudaMalloc((void **)&dev_a, size);
//cudaFree(dev_a);
}
return 0;
}
|
19,483 | /*
kernel.cu
Holds the kernel for the main program
*/
#include <iostream>
#define BLOCK_WIDTH 32
#define cuda_check_errors(val) check( (val), #val, __FILE__, __LINE__)
using namespace std;
/*
Reports the location of the occured error and exits the program
*/
template<typename T>
void check(T err, const char* const func, const char* const file,
const int line) {
if (err != cudaSuccess) {
cerr << "CUDA error at: " << file << ":" << line << endl;
cerr << cudaGetErrorString(err) << " " << func << endl;
exit(1);
}
}
/*
The primary kernel (heart of the program!)
Each pixel p in the RGBA image is a struct of four unsigned chars:
- p.x Which is the red channel number.
- p.y The green channel.
- p.z The blue channel.
- p.w The alpha channel (which we ignore).
For each greyscale pixel to be created we calculate this formula:
p = .299*p.x + .587*p.y + .114*p.z
The output is a single char because we only have one channel.
In the kernel each thread is responsible for calculating the mentioned
formula for each pixel and the put the result into the greyscale
image placehold.
First we find out where the thread (pixel) is and then we do the
above.
*/
__global__
void rgba_to_grey(uchar4 *const d_rgba, unsigned char *const d_grey,
size_t rows, size_t cols) {
size_t j = blockIdx.y * blockDim.y + threadIdx.y;
size_t i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= rows || j >= cols)
return;
uchar4 p = d_rgba[i * cols + j];
d_grey[i * cols + j] = (unsigned char) (0.299f * p.x + 0.587f * p.y + 0.114f * p.z);
}
/*
The image is divided into number of blocks.
Each block holds BLOCK_WIDTH*BLOCK_WIDTH threads and in total we have
(rows/BLOCK_WIDTH)*(cols/BLOCK_WIDTH) blocks.
*/
void rgba_to_grey_launcher(uchar4 *const d_rgba, unsigned char *const d_grey,
size_t rows, size_t cols) {
const dim3 block_size (BLOCK_WIDTH, BLOCK_WIDTH, 1);
unsigned int grid_x = (unsigned int) (rows / BLOCK_WIDTH + 1);
unsigned int grid_y = (unsigned int) (cols / BLOCK_WIDTH + 1);
const dim3 grid_size (grid_x, grid_y, 1);
rgba_to_grey<<<grid_size, block_size>>>(d_rgba, d_grey, rows, cols);
cudaDeviceSynchronize();
cuda_check_errors(cudaGetLastError());
} |
19,484 | #include <iostream>
int main()
{
cudaDeviceProp prop;
int devcount;
cudaGetDeviceCount(&devcount);
std::cout << "Devices found: " << devcount << std::endl;
for(int i=0; i<devcount; i++)
{
cudaGetDeviceProperties(&prop, i);
std::cout << "------------------" << std::endl;
std::cout << "Device: " << i << std::endl;
std::cout << "------------------" << std::endl;
std::cout << "Name:\t\t\t" << prop.name << std::endl;
std::cout << "GlobalMemory:\t\t" << prop.totalGlobalMem << std::endl;
std::cout << "WarpSize:\t\t" << prop.warpSize << std::endl;
std::cout << "MaxThreadsPerBlock:\t" << prop.maxThreadsPerBlock << std::endl;
std::cout << "MaxThreadsDim:\t\t" << prop.maxThreadsDim[0] << " : " << prop.maxThreadsDim[1] << " : " << prop.maxThreadsDim[2] << std::endl;
std::cout << "MaxGridSize:\t\t" << prop.maxGridSize[0] << " : " << prop.maxGridSize[1] << " : " << prop.maxGridSize[2] << std::endl;
std::cout << "MultiProcessorCount:\t" << prop.multiProcessorCount << std::endl;
}
return 0;
} |
19,485 | #include <chrono>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cuda_runtime.h>
#include <iomanip>
#include <iostream>
// helper for time measurement
typedef std::chrono::duration<double, std::milli> d_ms;
const auto &now = std::chrono::high_resolution_clock::now;
// Define Error Checking Macro
#define CU_CHK(ERRORCODE) \
{ \
cudaError_t error = ERRORCODE; \
if (error != 0) { \
std::cerr << cudaGetErrorName(error) << ": " \
<< cudaGetErrorString(error) << " at " << __FILE__ << ":" \
<< __LINE__ << "\n"; \
} \
}
// Constants
const static int DEFAULT_NUM_DATA = 1024;
const static int DEFAULT_NUM_CLUSTER = 3;
const static int DEFAULT_DIMENSIONS = 2;
const static int DEFAULT_NUM_ITERATIONS = 5;
const static int DEFAULT_BLOCK_DIM = 1024;
extern void kmeans_cluster_assignment_wrapper(int grid_size, int block_size,
float *data, int *data_ca,
float *centroids, int numData,
int numCluster,
int numDimensions);
extern void kmeans_centroid_sum_wrapper(int grid_size, int block_size,
float *data, int *data_ca,
float *centroids, int *cluster_count,
int numData, int numCluster,
int numDimensions);
extern void kmeans_centriod_update_wrapper(int grid_size, int block_size,
float *centroids, int *cluster_count,
int numCluster, int numDimensions);
// Main
int main(int argc, char *argv[]) {
// Process Arguments
if (argc < 2 or std::string(argv[1]) == "-h") {
std::cout << "Usage:\n\t" << argv[0]
<< " <data points> [iterations] [dimensions]\n";
return 1;
}
int numData = 0;
numData = std::stoi(argv[1]);
numData = numData > 0 ? numData : DEFAULT_NUM_DATA;
int numIterations = 0;
if (argc > 2)
numIterations = std::stoi(argv[2]);
numIterations = numIterations != 0 ? numIterations : DEFAULT_NUM_ITERATIONS;
int numDimensions = 0;
if (argc > 3)
numDimensions = std::stoi(argv[2]);
numDimensions = numDimensions != 0 ? numDimensions : DEFAULT_DIMENSIONS;
int numCluster = DEFAULT_NUM_CLUSTER;
// Allocate Memory
// Host
float *h_data;
CU_CHK(cudaMallocHost(&h_data,
(size_t)(numData * numDimensions * sizeof(float))));
float *h_centroids;
CU_CHK(cudaMallocHost(&h_centroids,
(size_t)(numCluster * numDimensions * sizeof(float))));
// Init
srand(0); // Always the same random numbers
for (int i = 0; i < numData; ++i)
for (int d = 0; d < numDimensions; ++d)
h_centroids[i * numDimensions + d] = (float)rand() / (double)RAND_MAX;
for (int c = 0; c < numCluster; ++c)
for (int d = 0; d < numDimensions; ++d)
h_centroids[c * numDimensions + d] = (float)rand() / (double)RAND_MAX;
// Device Memory
float *d_data;
CU_CHK(cudaMalloc(&d_data,
(size_t)(numData * numDimensions * sizeof(float))));
int *d_data_ca;
CU_CHK(cudaMalloc(&d_data_ca, (size_t)(numData * sizeof(int))));
float *d_centroids;
CU_CHK(cudaMalloc(&d_centroids,
(size_t)(numCluster * numDimensions * sizeof(float))));
int *d_cluster_count;
CU_CHK(cudaMalloc(&d_cluster_count, (size_t)(numCluster * sizeof(int))));
// Copy Data to the Device
auto t1 = now();
CU_CHK(cudaMemcpy(d_data, h_data,
(size_t)(numData * numDimensions * sizeof(float)),
cudaMemcpyHostToDevice));
CU_CHK(cudaMemcpy(d_centroids, d_centroids,
(size_t)(numCluster * numDimensions * sizeof(float)),
cudaMemcpyHostToDevice));
CU_CHK(cudaMemset(d_cluster_count, 0, numCluster * sizeof(int)));
auto t2 = now();
// Block Dimension / Threads per Block
int block_dim = DEFAULT_BLOCK_DIM;
int grid_dim =
ceil(static_cast<float>(numData) / static_cast<float>(block_dim));
std::cout << "Computing kmeans with " << numData << " elements and "
<< numIterations << " iterations\n";
std::cout << "Launch kernel with " << grid_dim << " blocks and " << block_dim
<< " threads per block\n";
auto t3 = now();
for (int i = 0; i < numIterations; i++) {
kmeans_cluster_assignment_wrapper(grid_dim, block_dim, d_data, d_data_ca,
d_centroids, numData, numCluster,
numDimensions);
CU_CHK(
cudaMemset(d_centroids, 0, numCluster * numDimensions * sizeof(float)));
CU_CHK(cudaMemset(d_cluster_count, 0, numCluster * sizeof(int)));
kmeans_centroid_sum_wrapper(grid_dim, block_dim, d_data, d_data_ca,
d_centroids, d_cluster_count, numData,
numCluster, numDimensions);
kmeans_centriod_update_wrapper(grid_dim, numCluster * numDimensions,
d_centroids, d_cluster_count, numCluster,
numDimensions);
}
// Synchronize
CU_CHK(cudaDeviceSynchronize());
auto t4 = now();
// Compute time for copies and kernel
d_ms time_copyH2D = t2 - t1;
d_ms time_kernel = t4 - t3;
// Free Memory
CU_CHK(cudaFreeHost(h_data));
CU_CHK(cudaFreeHost(h_centroids));
CU_CHK(cudaFree(d_data));
CU_CHK(cudaFree(d_centroids));
CU_CHK(cudaFree(d_data_ca));
CU_CHK(cudaFree(d_cluster_count));
// Print Meassurement Results
std::cout << "Results:\n"
<< "H2D [ms], kernel [ms]\n"
<< time_copyH2D.count() << ", " << time_kernel.count() << "\n";
return 0;
}
|
19,486 | #include "includes.h"
__global__ static void kernelFindMax4(const int* dataArray, int arraySize, int* maxVal)
{
__shared__ extern int cache[];
int cacheIndex = threadIdx.x;
int arrayIndex1 = (int)(blockDim.x * blockIdx.x + threadIdx.x); // グローバルメモリの1つ目の要素番号
int arrayIndex2 = arrayIndex1 + gridDim.x * blockDim.x; // グローバルメモリの2つ目の要素番号
cache[cacheIndex] = INT_MIN;
if (arrayIndex1 < arraySize)
{
cache[cacheIndex] = max(cache[cacheIndex] , dataArray[arrayIndex1]); // シェアードメモリと比較
}
if (arrayIndex2 < arraySize)
{
cache[cacheIndex] = max(cache[cacheIndex] , dataArray[arrayIndex2]); // シェアードメモリと比較
}
__syncthreads();
int blockSize = blockDim.x;
for (int offset = blockSize >> 1; offset > 0; offset >>= 1)
{
if (cacheIndex < offset)
{
cache[cacheIndex] = max(cache[cacheIndex], cache[cacheIndex ^ offset]);
}
__syncthreads();
}
if (cacheIndex == 0)
{
atomicMax(maxVal, cache[0]);
}
} |
19,487 | #include <cuda_runtime.h>
#include <stdio.h>
constexpr size_t N = 512;
__global__ void add_one(size_t n, float* x) {
int i = threadIdx.x;
if (i < n) {
x[i] = x[i] + 1;
}
}
void switch_device() {
// select device 0
size_t size = N * sizeof(float);
cudaSetDevice(0); // Set device 0 as current
float* p0;
cudaMalloc(&p0, size);
add_one<<<1, N>>>(N, p0); // Launch kernel on device 0
// switch to device 1
cudaSetDevice(1); // Set device 1 as current
float* p1;
cudaMalloc(&p1, size);
add_one<<<1, N>>>(N, p1); // Launch kernel on device 1
}
void p2p_memory_access() {
cudaSetDevice(0); // Set device 0 as current
float* p0;
size_t size = N * sizeof(float);
cudaMalloc(&p0, size); // Allocate memory on device 0
add_one<<<1, N>>>(N, p0); // Launch kernel on device 0
cudaSetDevice(1); // Set device 1 as current
cudaDeviceEnablePeerAccess(0, 0); // Enable peer-to-peer access with device 0
// Launch kernel on device 1
// This kernel launch can access memory on device 0 at address p0
add_one<<<1, N>>>(N, p0);
}
void p2p_memory_copy() {
cudaSetDevice(0); // Set device 0 as current
float* p0;
size_t size = N * sizeof(float);
cudaMalloc(&p0, size); // Allocate memory on device 0
cudaSetDevice(1); // Set device 1 as current
float* p1;
cudaMalloc(&p1, size); // Allocate memory on device 1
cudaSetDevice(0); // Set device 0 as current
add_one<<<1, N>>>(N, p0); // Launch kernel on device 0
cudaSetDevice(1); // Set device 1 as current
/**
* implicit barrier
* cudaMemcpyPeer starts after all commands on both devices finish
* all commands on both devices issue after cudaMemcpyPeer finish
*/
cudaMemcpyPeer(p1, 1, p0, 0, size); // Copy p0 to p1
add_one<<<1, N>>>(N, p1); // Launch kernel on device 1
}
int main(void) {
// enumerate these devices
int deviceCount;
cudaGetDeviceCount(&deviceCount);
int device;
for (device = 0; device < deviceCount; ++device) {
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, device);
printf("Device %d has compute capability %d.%d.\n", device, deviceProp.major, deviceProp.minor);
}
switch_device();
p2p_memory_access();
}
|
19,488 | // Dan Rolfe
#define BLOCKSIZE 32
/**
* cuda vector add function
**/
// there is a problem here, running this ruins the add
__global__ void d_add( float* __restrict__ x, float* __restrict__ y, float* __restrict__ z, int size)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
if(index < size)
z[index] = x[index] + y[index];
}
/**
* mul:
* cuda vector multiply function
**/
__global__ void d_mul( float* __restrict__ x, float* __restrict__ y, float* __restrict__ z, int size)
{
int index = threadIdx.x + blockIdx.x * blockDim.x;
if(index < size)
z[index] = x[index] * y[index];
}
|
19,489 | #include <iostream>
struct fields
{
unsigned a : 4;
unsigned b : 4;
unsigned c : 4;
unsigned d : 4;
unsigned e : 4;
unsigned f : 4;
unsigned g : 4;
unsigned h : 4;
};
union u
{
unsigned int i;
fields f;
};
__device__ __forceinline__
unsigned int bfe(unsigned int x, unsigned int bit, unsigned int numBits) {
unsigned int ret;
asm volatile ("{ \n\t"
" bfe.u32 %0, %1, %2, %3;\n\t"
"} \n\t" : "=r"(ret) : "r"(x), "r"(bit), "r"(numBits));
return ret;
}
__device__ __forceinline__
unsigned int c_extract(unsigned int in, int which)
{
u x;
x.i = in;
if (which == 0)
return x.f.a;
else if (which == 1)
return x.f.b;
else if (which == 2)
return x.f.c;
else if (which == 3)
return x.f.d;
else return 0;
}
__device__ __forceinline__
unsigned int d_extract(unsigned int in, int which)
{
if (which == 0)
return bfe(in, 0, 4);
else if (which == 1)
return bfe(in, 4, 4);
else if (which == 2)
return bfe(in, 8, 4);
else if (which == 3)
return bfe(in, 12, 4);
else return 0;
}
__global__ void fun_c(int * mem, int size)
{
for (int i = 0; i < size; ++i)
{
mem[i] = c_extract(mem[i], 0);
}
}
__global__ void fun_d(int * mem, int size)
{
for (int i = 0; i < size; ++i)
{
mem[i] = d_extract(mem[i], 0);
}
}
int main()
{
u y;
y.f.a = 0xf; std::cout << std::hex << y.i << "\n";
y.f.b = 0xf; std::cout << std::hex << y.i << "\n";
y.f.c = 0xf; std::cout << std::hex << y.i << "\n";
y.f.d = 0xf; std::cout << std::hex << y.i << "\n";
y.f.e = 0xf; std::cout << std::hex << y.i << "\n";
y.f.f = 0xf; std::cout << std::hex << y.i << "\n";
y.f.g = 0xf; std::cout << std::hex << y.i << "\n";
y.f.h = 0xf; std::cout << std::hex << y.i << "\n";
int * h;
int * d;
int n = 50;
h = (int*)malloc(n * sizeof(int));
cudaMalloc(&d, n * sizeof(int));
{
for (int i = 0; i < n; ++i)
h[i] = i;
cudaMemcpy(d, h, n * sizeof(int), cudaMemcpyHostToDevice);
fun_c<<<1,1>>>(d, n);
cudaThreadSynchronize();
int rv = cudaGetLastError();
cudaMemcpy(h, d, n * sizeof(int), cudaMemcpyDeviceToHost);
std::cout << "Result:\n";
for (int i = 0; i < n; ++i)
std::cout << std::hex << h[i] << "\n";
}
{
for (int i = 0; i < n; ++i)
h[i] = i;
cudaMemcpy(d, h, n * sizeof(int), cudaMemcpyHostToDevice);
fun_d<<<1,1>>>(d, n);
cudaThreadSynchronize();
int rv = cudaGetLastError();
cudaMemcpy(h, d, n * sizeof(int), cudaMemcpyDeviceToHost);
std::cout << "Result:\n";
for (int i = 0; i < n; ++i)
std::cout << std::hex << h[i] << "\n";
}
return 0;
}
|
19,490 | #include <thrust/random.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/transform.h>
#include <thrust/device_vector.h>
#include <thrust/functional.h>
#include <iostream>
struct randf :public thrust::unary_function<int, float> {
int seed;
randf(int seed_)
:seed(seed_){}
__device__ __host__
float operator()(int ind){
thrust::default_random_engine rng(seed);
rng.discard(1 * ind);
thrust::uniform_real_distribution<float> u01(0,1);
return u01(rng);
}
};
int main(void){
thrust::device_vector<float> output(2);
for(int i=0; i<10; i++){
thrust::transform(thrust::counting_iterator<int>(0),
thrust::counting_iterator<int>(2),
output.begin(),
randf(i+1)); // seed of 0 and 1 somehow returns the same number
std::cout << output[0] << " " <<
output[1] << std::endl;
}
return 0;
}
|
19,491 | // file esempio querydevice
#include "stdio.h"
static void HandleError( cudaError_t err, const char *file, int line) {
if (err != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString( err ), file, line);
exit(EXIT_FAILURE);
}
}
#define HANDLE_ERROR(err)(HandleError(err, __FILE__, __LINE__))
int main( void ) {
cudaDeviceProp prop;
int count;
HANDLE_ERROR( cudaGetDeviceCount( &count) );
for (int i=0; i<count; i++) {
HANDLE_ERROR( cudaGetDeviceProperties( &prop, i ));
printf(" -- General Information for device %d --\n", i);
printf("Name: %s\n", prop.name);
printf("Compute capability: %d.%d\n", prop.major, prop.minor);
printf("Clock rate: %d\n", prop.clockRate);
printf("Device copy overlap: ");
if(prop.deviceOverlap)
printf("Enabled\n");
else
printf("Disabled\n");
printf("Kernel executionn timeout: ");
if (prop.kernelExecTimeoutEnabled)
printf("Enabled\n");
else
printf("Disabled\n");
printf(" -- Memory Information for device %d --\n", i);
printf("Total global mem: %ld\n", prop.totalGlobalMem);
printf("Total constant mem: %ld\n", prop.totalConstMem);
printf("Max mem pitch: %ld\n", prop.textureAlignment);
printf(" -- MP Information for device %d --\n", i);
printf("Multiprocessor count: %d\n", prop.multiProcessorCount);
printf("Shared mem per mp: %ld\n", prop.sharedMemPerMultiprocessor);
printf("Register per mp: %d\n", prop.regsPerMultiprocessor);
printf("Threads in warp: %d\n", prop.warpSize);
printf("Max threads per block: %d\n", prop.maxThreadsPerBlock);
printf("Max thread dimensions: (%d, %d, %d)\n",
prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);
printf("Max grid dimensions: (%d, %d, %d)\n",
prop.maxGridSize[0], prop.maxGridSize[1], prop.maxGridSize[2]);
printf("\n");
}
return 0;
}
|
19,492 | #include <cuda.h>
extern "C"
__global__ void kern(int *out)
{
out[0] = 1;
}
|
19,493 | #include "includes.h"
__global__ void MatrixMulKernel (float* Md, float* Nd, float* Pd, int ncols) {
int row = blockIdx.y*blockDim.y + threadIdx.y;
int col = blockIdx.x*blockDim.x + threadIdx.x;
// Pvalue is used to store the element of the output matrix
// that is computed by the thread
float Pvalue = 0;
for (int k=0; k < ncols; ++k) {
float Melement = Md[row*ncols+k];
float Nelement = Nd[k*ncols+col];
Pvalue += Melement * Nelement;
}
Pd[row*ncols+col] = Pvalue;
} |
19,494 | #include "includes.h"
__global__ void MatrixTranspose(const float *A_elements, float *B_elements, const int A_width, const int A_height)
{
int strideRow = blockDim.y * gridDim.y;
int strideCol = blockDim.x * gridDim.x;
for(int row = blockIdx.y * blockDim.y + threadIdx.y; row < A_width; row += strideRow)
for(int col = blockIdx.x * blockDim.x + threadIdx.x; col < A_height; col += strideCol)
{
B_elements[row * A_height + col] = A_elements[col * A_width + row];
}
} |
19,495 | // nvcc -arch=sm_21 -o cuda_dstar cuda_dstar.cu -lrt -lm
#include <cstdio>
#include <cstdlib>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define MAPROW 5
#define MAPCOL 5
#define MAX 9999
#define STRCOL 0
#define STRROW 0
#define DSTCOL 4
#define DSTROW 4
#define BLOCK 5
typedef struct {
int key1; // min(g(x), rhs(x) + h(x))
int key2; // min(g(x), rhs(x))
} keyvalue;
typedef struct {
int x, y; // coordinate
int px, py; // parents
int g, h, rhs; // parameters
keyvalue key; // keyvalue
int type; // terrain: to be defined, i.e. ostacle / river / mountain / ground
int inlist; // used in openlist
} point;
// Initiation can be done parallelly.
// Parallel A* Kernel
// In each kernel, a starting and destination point is given and the optimal path is calculate.
// Path should be stored in a list. In the meantime, cost between the two points should be stored.
__device__ void CalculateKey(point *p)
{
point *t = p;
int temp;
temp = t->rhs + t->h;
t->key.key1 = (t->g > temp) ? temp : t->g;
t->key.key2 = (t->g > t->rhs) ? t->rhs : t->g;
//printf("key %d\t%d\t%d\t%d\n", p.y, p.x, p.key.key1, p.key.key2);
}
__device__ void InitMap(point *map) // can be done in GPU
{
int i, j;
for (i = 0; i < MAPROW; i++)
for (j = 0; j < MAPCOL; j++)
{
map[i*BLOCK+j].g = MAX; // set an extremely large number
map[i*BLOCK+j].h = (i > j) ? i : j; // set the origin point as starting point
map[i*BLOCK+j].rhs = MAX; // set an extremely large number
map[i*BLOCK+j].inlist = 0; // not in list
map[i*BLOCK+j].key.key1 = MAX;
map[i*BLOCK+j].key.key2 = MAX;
}
}
__device__ void CalculateCost(point dst, point src, int *cst)
{
int flag, cost;
flag = dst.type * src.type;
switch (flag)
{
case 1: cost = 1; break; // ground to ground
case 2: cost = 5; break; // ground to water / water to ground
case 3: cost = 10; break; // ground to mount / mount to ground
case 4: cost = 2; break; // water to water
case 6: cost = 20; break; // mount to water / water to mount
case 9: cost = 15; break; // mount to mount
default: cost = MAX; break;
}
*cst = cost;
}
__device__ void UpdateVertex(point p, point *map)
{
// Update RHS
int i, j, temp, cost, row, col;
int r = p.rhs; // set a large value at first
for (i = -1; i < 2; i++)
{
row = p.y + i;
if (row >=0 && row < MAPROW)
for (j = -1; j < 2; j++)
{
col = p.x + j;
if (col >=0 && col < MAPCOL)
{
if (i == 0 && j == 0) cost = 0;
else CalculateCost(p, map[row*BLOCK+col], &cost);
temp = map[row*BLOCK+col].g + cost;
if (r >= temp)
{
r = temp;
if (col != p.x || row != p.y)
{
p.px = col;
p.py = row;
}
}
}
}
}
p.rhs = r;
if (p.rhs != p.g)
{
p.inlist = 1;
CalculateKey(&p);
}
else p.inlist = 0;
map[p.y*BLOCK+p.x] = p;
//printf("update debug: %d %d %d %d\n", p.y, p.x, p.py, p.px);
//printf("%d\t%d\t%d\t%d\t%d\t%d\t%d\n", p.y, p.x, p.g, p.rhs, p.key.key1, p.key.key2, p.inlist);
}
__device__ void FindKey(point *map, int *cx, int *ry)
{
int i, j, x, y;
point *data = map;
int temp1 = MAX;
int temp2 = MAX;
x = 0;
y = 0;
for (i = 0; i < MAPROW; i++)
{
for (j = 0; j < MAPCOL; j++)
{
if (data[i*BLOCK+j].inlist == 1) // only points inlist will be compared
{
keyvalue kk = data[i*BLOCK+j].key;
if (temp1 > kk.key1)
{
temp1 = kk.key1;
temp2 = kk.key2;
x = j;
y = i;
}
else if (temp1 == kk.key1)
{
if (temp2 > kk.key2)
{
temp2 = kk.key2;
x = j;
y = i;
}
}
}
}
}
*cx = x;
*ry = y;
//printf("update: %d\t%d\n", y, x);
}
__device__ void ShortestPath(int cx, int ry, point *map)
{
int position; // Top Key
point *data = map;
int i, j, row, col;
point cur = data[ry*BLOCK+cx];
//printf("debug: %d %d %d %d\n", cur.x, cur.y, cur.key.key1, cur.key.key2);
if (cur.g > cur.rhs)
{
data[ry*BLOCK+cx].g = data[ry*BLOCK+cx].rhs;
for (i = -1; i <= 1; i++)
{
row = ry + i;
if (row >= 0 && row < MAPROW)
for (j = -1; j <=1 ; j++)
{
col = cx + j;
if (col >= 0 && col < MAPCOL)
{
//if (i != 0 || j!= 0)
//{
//printf("debug neighbors: %d %d\n", row, col);
point temp = data[row*BLOCK+col];
if (temp.type > 0) UpdateVertex(temp, map);
//}
}
}
}
}
else // update itself as well
{
data[ry*BLOCK+cx].g = MAX; // set a large value
for (i = -1; i < 2; i++)
{
row = ry + i;
if (row >= 0 && row < MAPROW)
for (j = -1; j < 2; j++)
{
col = cx + j;
if (col >= 0 && col < MAPCOL)
{
point temp = data[row*BLOCK+col];
if (temp.type > 0) UpdateVertex(temp, map);
}
}
}
}
}
// cuda kernel
__global__ void kernel_path(point *map, int mx, int my, int sx, int sy, int dx, int dy)
{
point top = map[dy*BLOCK+dx];
point goal = map[sy*BLOCK+sx];
int row, col, i, j, it, ry, cx;
int flag = 0;
InitMap(map);
cx = dx;
ry = dy;
map[ry*BLOCK+cx].inlist = 1; // put start point into list
map[ry*BLOCK+cx].rhs = 0; // set start point rhs as 0
map[ry*BLOCK+cx].key.key1 = map[(my-1)*BLOCK+mx-1].h;
map[ry*BLOCK+cx].key.key2 = 0;
keyvalue topkey = top.key;
keyvalue goalkey = goal.key;
printf("kernel test: %d\n", map[0].key.key1);
if (topkey.key1 < goalkey.key1 || (topkey.key1 == goalkey.key1 && topkey.key2 < goalkey.key2) || goal.rhs != goal.g) flag = 1;
else flag = 0;
it = 0;
while (flag)
{
printf("iter %d\t%d\t%d\n", it, ry, cx);
ShortestPath(cx, ry, map);
//printf("dst inlist %d\n", map[MAPROW-1][MAPCOL-1].inlist);
FindKey(map, &cx, &ry);
//printf("iter %d\t%d\t%d\n", it, ry, cx);
top = map[ry*BLOCK+cx];
goal = map[STRROW*BLOCK+STRCOL];
topkey = top.key;
goalkey = goal.key;
if (topkey.key1 < goalkey.key1 || (topkey.key1 == goalkey.key1 && topkey.key2 < goalkey.key2) || goal.rhs != goal.g) flag = 1;
else flag = 0;
it++;
}
}
int main()
{
int i, j, row, col;
point *map, *d_map;
const size_t map_size = sizeof(point) * MAPROW * MAPCOL;
cudaMallocHost((void**)&map, map_size);
FILE *fp = fopen("map2.csv", "r");
if (fp != NULL)
{
for (i = 0; i < MAPROW; i++)
for (j = 0; j < MAPCOL; j++)
{
fscanf(fp, "%d,%d,%d\n", &map[i*BLOCK+j].y, &map[i*BLOCK+j].x, &map[i*BLOCK+j].type);
//printf("%d\t%d\t%d\n", map[i][j].x, map[i][j].y, map[i][j].type);
}
}
fclose(fp);
cudaMalloc((void **)&d_map, map_size);
cudaMemcpy(d_map, map, map_size, cudaMemcpyHostToDevice);
printf("cpu test: %d\n", map[0].type);
dim3 gridSize(1, 1, 1);
//dim3 blockSize();
kernel_path<<<gridSize, 1>>>(d_map, MAPCOL, MAPROW, STRCOL, STRROW, DSTCOL, DSTROW);
cudaDeviceSynchronize();
cudaMemcpy(map, d_map, map_size, cudaMemcpyDeviceToHost);
// row = STRROW;
// col = STRCOL;
// point path = map[row*BLOCK+col];
// while (row != DSTROW || col != DSTCOL)
// {
// printf("Coordinate[%d, %d]\n", row, col);
// row = path.py;
// col = path.px;
// path = map[row*BLOCK+col];
// }
// printf("Coordinate[%d, %d]\n", row, col);
cudaFreeHost(map);
cudaFree(d_map);
} |
19,496 |
#include <cstdio>
#define DLIMIT 99999999
// Cluster Center
//
// float* f; // vector of size #channels
// float x, y, z;
//
#define __min(a, b) (((a) < (b)) ? (a) : (b))
#define __max(a, b) (((a) >= (b)) ? (a) : (b))
/*
* P = point
* S = data shape
* F = data # features
*/
__device__
float at(const float* data, const int4& P, const int3& S) {
long s2d = S.y * S.x, s3d = S.z * S.y * S.x;
return data[P.w * s3d + P.z * s2d + P.y * S.x + P.x];
}
__device__
float gradient(const float* data, int4& P, const int3& S, int nf) {
float d;
float3 diff;
int4 q; q.z = P.z; q.y = P.y; q.x = P.x;
for ( int k = 0; k < nf; k++ ) {
q.w = P.w = k;
q.x = P.x + 1;
d = at(data, P, S) - at(data, q, S);
diff.x += d * d;
q.x = P.x; q.y = P.y + 1;
d = at(data, P, S) - at(data, q, S);
diff.y += d * d;
q.y = P.y; q.z = P.z + 1;
d = at(data, P, S) - at(data, q, S);
diff.z += d * d;
}
return diff.x + diff.y + diff.z;
}
__global__
void init_clusters(const float* data,
float* centers,
const int n_clusters,
const int n_features,
const int3 sp_grid,
const int3 sp_shape,
const int3 im_shape)
{
long lidx = threadIdx.x + (blockIdx.x * blockDim.x);
if ( lidx >= n_clusters ) {
return;
}
int3 idx;
int plane = sp_grid.y * sp_grid.x;
idx.z = lidx / plane;
int aux = lidx % plane;
idx.y = aux / sp_grid.x;
idx.x = aux % sp_grid.x;
int3 jdx;
int volume_linear_idx = lidx;
jdx.z = volume_linear_idx / (sp_grid.x * sp_grid.y);
int plane_linear_idx = volume_linear_idx - jdx.z * sp_grid.x * sp_grid.y;
jdx.y = plane_linear_idx / sp_grid.x;
jdx.x = plane_linear_idx % sp_grid.x;
int4 p, q, r;
p.z = r.z = idx.z * sp_shape.z + sp_shape.z / 2;
p.y = r.y = idx.y * sp_shape.y + sp_shape.y / 2;
p.x = r.x = idx.x * sp_shape.x + sp_shape.x / 2;
int shift = n_features + 3;
centers[lidx * shift + n_features + 0] = r.z;
centers[lidx * shift + n_features + 1] = r.y;
centers[lidx * shift + n_features + 2] = r.x;
}
__global__
void expectation(const float* data,
const float* centers,
unsigned int* labels,
const float m, const float S,
const int n_clusters,
const int n_features,
const float3 spacing,
const int3 sp_grid,
const int3 sp_shape,
const int3 im_shape)
{
int4 idx, p, q;
long gidx = threadIdx.x + (blockIdx.x * blockDim.x);
if ( gidx >= im_shape.x * im_shape.y * im_shape.z ) {
return;
}
// linear index to 3D pixel index transformation
int plane = im_shape.y * im_shape.x;
int aux = gidx % plane;
idx.z = gidx / plane;
idx.y = aux / im_shape.x;
idx.x = aux % im_shape.x;
// approx center grid positoin
p.z = __max(0, __min(idx.z / sp_shape.z, sp_grid.z - 1));
p.y = __max(0, __min(idx.y / sp_shape.y, sp_grid.y - 1));
p.x = __max(0, __min(idx.x / sp_shape.x, sp_grid.x - 1));
float min_d = DLIMIT, d, dist, adiff, pdiff;
int R = 2, cshift = n_features + 3;
long cidx, ridx = 0;
for ( int k = -R; k <= R; k++ ) {
q.z = p.z + k;
if ( q.z < 0 || q.z >= sp_grid.z ) {continue;}
for ( int i = -R; i <= R; i++ ) {
q.y = p.y + i;
if ( q.y < 0 || q.y >= sp_grid.y ) {continue;}
for ( int j = -R; j <= R; j++ ) {
q.x = p.x + j;
if ( q.x < 0 || q.x >= sp_grid.x ) {continue;}
cidx = q.z * sp_grid.y * sp_grid.x + q.y * sp_grid.x + q.x;
if ( centers[cidx * cshift] == DLIMIT ) {
continue;
}
// Appearance diff
adiff = 0;
for ( int w = 0; w < n_features; w++ ) {
idx.w = w;
d = at(data, idx, im_shape) - centers[cidx * cshift + w];
adiff += d * d;
}
// Position diff
float3 pd;
pd.z = (idx.z - centers[cidx * cshift + n_features + 0]) * spacing.z;
pd.y = (idx.y - centers[cidx * cshift + n_features + 1]) * spacing.y;
pd.x = (idx.x - centers[cidx * cshift + n_features + 2]) * spacing.x;
pdiff = pd.z * pd.z + pd.y * pd.y + pd.x * pd.x;
dist = adiff / (m * m * n_features * n_features) + pdiff / (S * S);
// Wrapup
if ( dist < min_d ) {
min_d = dist;
ridx = cidx;
}
}
}
}
labels[gidx] = ridx + 1;
}
__global__
void maximization(const float* data,
const unsigned int* labels,
float* centers,
int n_clusters,
int n_features,
const int3 sp_grid,
const int3 sp_shape,
const int3 im_shape)
{
long lidx = threadIdx.x + (blockIdx.x * blockDim.x);
if ( lidx >= n_clusters ) {
return;
}
long cshift = n_features + 3;
int3 cidx;
cidx.z = (int) centers[lidx * cshift + n_features + 0];
cidx.y = (int) centers[lidx * cshift + n_features + 1];
cidx.x = (int) centers[lidx * cshift + n_features + 2];
float ratio = 2.0f;
int3 from;
from.z = __max(cidx.z - sp_shape.z * ratio, 0);
from.y = __max(cidx.y - sp_shape.y * ratio, 0);
from.x = __max(cidx.x - sp_shape.x * ratio, 0);
int3 to;
to.z = __min(cidx.z + sp_shape.z * ratio, im_shape.z);
to.y = __min(cidx.y + sp_shape.y * ratio, im_shape.y);
to.x = __min(cidx.x + sp_shape.x * ratio, im_shape.x);
int4 p;
float* f = new float[cshift];
for ( int k = 0; k < cshift; k++ ) {f[k] = 0;}
long count = 0, offset, s2d = im_shape.x * im_shape.y;
for ( p.z = from.z; p.z < to.z; p.z++ ) {
for ( p.y = from.y; p.y < to.y; p.y++ ) {
for ( p.x = from.x; p.x < to.x; p.x++ ) {
offset = p.z * s2d + p.y * im_shape.x + p.x;
if ( labels[offset] == lidx + 1 ) {
for ( int w = 0; w < n_features; w++ ) {
p.w = w;
f[w] += at(data, p, im_shape);
}
f[n_features + 0] += p.z;
f[n_features + 1] += p.y;
f[n_features + 2] += p.x;
count += 1;
}
}
}
}
if ( count > 0 ) {
for ( int w = 0; w < cshift; w++ ) {
centers[lidx * cshift + w] = f[w] / count;
}
} else {
centers[lidx * cshift] = DLIMIT;
}
delete[] f;
} |
19,497 | #include <stdio.h>
#define N 3
#define M 2
__global__ void add(int *a, int *b, int *c)
{
int tid = threadIdx.x;
// if(tid < N)
for(int i = 0; i < N; i++)
c[tid * N + i] = a[tid * N + i] + b[tid * N + i];
}
int main()
{
// int *a, *b, *c;
int a[M * N], b[M * N], c[M * N];
// host copies of variables a, b & c
int *d_a, *d_b, *d_c;
// device copies of variables a, b & c
int size = sizeof(int) * M * N;
// a = (int *)malloc(sizeof(int) * N);
// b = (int *)malloc(sizeof(int) * N);
// c = (int *)malloc(sizeof(int) * N);
// Allocate space for device copies a, b & c
cudaMalloc((void**)&d_a, size);
cudaMalloc((void**)&d_b, size);
cudaMalloc((void**)&d_c, size);
// Setup input values
printf("Enter values for a: ");
for(int i = 0; i < M; i++)
for(int j = 0; j < N; j++)
scanf("%d", &a[i * N + j]);
printf("Enter values for b: ");
for(int i = 0; i < M; i++)
for(int j = 0; j < N; j++)
scanf("%d", &b[i * N + j]);
// Copy inputs to device
cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
// Launch add() kernel on GPU
add<<<1, N>>>(d_a, d_b, d_c);
// Copy result back to host
cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost);
// print result
for(int i = 0; i < M; i++)
{
for(int j = 0; j < N; j++)
printf("%d + %d = %d\n", a[i * N + j], b[i * N + j], c[i * N + j]);
}
// Cleanup
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
return 0;
}
|
19,498 | #include <stdio.h>
#include <time.h>
#include <unistd.h>
#include <stdlib.h>
#include <math.h>
using namespace std;
__device__ void _2Dstencil_(int *d_e,int *d_r,float* c_coeff,int X,int Y,int k, int x, int y,int GX,int Gx,int Gy)
{
int h_e_i;
int h_r_i = x + ( y * (X) );
h_e_i = h_r_i;
int temp = d_e[h_r_i];
temp *= c_coeff[0];
for(int lk =1;lk<(k/2)+1;lk++)
{
h_e_i = (x+lk) + ( (y) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
h_e_i = (x-lk) + ( (y) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
h_e_i = (x) + ( (y+lk) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
h_e_i = (x) + ( (y-lk) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
}
h_r_i = Gx + ( (Gy) * (GX) );
d_r[h_r_i] = temp;
}
__global__ void _2Dstencil_global(int *d_e,int *d_r,float *c_coeff,int X,int Y,int k,int times){
int x,y;//,h_e_i,h_r_i,Xs,Ys,Dx,Dy;
x = threadIdx.x + (blockIdx.x*blockDim.x);
y = threadIdx.y + (blockIdx.y*blockDim.y);
int k2 = k/2*times;
extern __shared__ int shared[];
int blockThreadIndex = threadIdx.x + threadIdx.y*blockDim.x;
// Xs = threadIdx.x;
// Ys = threadIdx.y;
int Dx = blockDim.x+(k*times);
int Dy = blockDim.y+(k*times);
int sharedTam = Dx*Dy;
int * sharedRes = &shared[sharedTam];
for(int stride=blockThreadIndex;stride<sharedTam;stride+=(blockDim.x*blockDim.y))
{
int globalIdx = (blockIdx.x*blockDim.x)-k2+stride%Dx + ((blockIdx.y*blockDim.y)-k2+stride/Dx)*X;
if(globalIdx > 0 && (blockIdx.x*blockDim.x)-k2+stride%Dx < X && ((blockIdx.y*blockDim.y)-k2+stride/Dx)<Y)
shared[stride] = d_e[globalIdx];
else
shared[stride] = 0;
}
__syncthreads();
for(int t=times-1;t>0;t--)
{
//_2Dstencil_(shared,sharedRes,c_coeff,Dx,Dy,k,threadIdx.x+k2,threadIdx.y+k2,Dx,threadIdx.x+k2,threadIdx.y+k2);
int tDx = blockDim.x+(t*k);
int tDy = blockDim.y+(t*k);
int tk2 = (times-t)*k/2;
// int tDx = blockDim.x+(1*k);
// int tDy = blockDim.y+(1*k);
// int tk2 = (1)*k/2;
int tSharedTam = tDx * tDy;
for(int stride=blockThreadIndex;stride<tSharedTam;stride+=(blockDim.x*blockDim.y))
{
_2Dstencil_(shared,sharedRes,c_coeff,Dx,Dy,k,(stride%tDx)+tk2,(stride/tDx)+tk2,Dx,(stride%tDx)+tk2,(stride/tDx)+tk2);
}
__syncthreads();
for(int stride=blockThreadIndex;stride<sharedTam;stride+=(blockDim.x*blockDim.y))
{
shared[stride]=sharedRes[stride];
}
__syncthreads();
}
_2Dstencil_(shared,d_r,c_coeff,Dx,Dy,k,threadIdx.x+k2,threadIdx.y+k2,X,x,y);
// for(int stride=blockThreadIndex;stride<sharedTam;stride+=(blockDim.x*blockDim.y))
// {
// int globalIdx = (blockIdx.x*blockDim.x)-k2+stride%Dx + ((blockIdx.y*blockDim.y)-k2+stride/Dx)*X;
// if(globalIdx > 0 && (blockIdx.x*blockDim.x)-k2+stride%Dx < X && ((blockIdx.y*blockDim.y)-k2+stride/Dx)<Y)
// d_r[globalIdx] = sharedRes[stride];
// }
}
int main(int argc, char* argv[]) {
int *h_e,*h_r;
int *d_e, *d_r;
int size,tam,sharedSize,sharedTam;
int X=32;
int Y=32;
int k=4;
int times = 1;
int BX=32;
int BY=32;
int GX=1;
int GY=1;
float *c_coeff,*d_c_coeff;
if(argc > 1)
{
X = atoi(argv[1]);
Y = X;
}
if(argc > 2)
{
k = atoi(argv[2]);
}
if(argc > 3)
{
times = atoi(argv[3]);
}
if(X>32)
{
GX = ceil((float)X/(float)32);
BX = 32;
}
if(Y>32)
{
GY = ceil((float)Y/(float)32);
BY = 32;
}
dim3 block_dim(BX,BY,1);
dim3 grid_dim(GX,GY,1);
//sharedSize = ((block_dim.x+k)*(block_dim.y+k))*sizeof(int);
sharedSize = ((block_dim.x+(k*times))*(block_dim.y+(k*times)))*sizeof(int)*2;
//sharedTam = ((block_dim.x+(k*2))*(block_dim.y+(k*2)));
size = X * Y * sizeof(int);
tam = X * Y;
h_e = (int*) malloc(size);
h_r = (int*) malloc(size);
c_coeff = (float*)malloc((k/2+1)*sizeof(float));
cudaMalloc(&d_e, size);
cudaMalloc(&d_r, size);
cudaMalloc(&d_c_coeff,(k/2+1)*sizeof(float));
printf("\n coefs \n");
for(int i=0;i<(k/2+1);i++)
{
c_coeff[i]=(float)((k/2+1)-i)/(float)(k/2+1);
}
for(int i=0;i<(k/2+1);i++)
{
printf(" %f",c_coeff[i]);
}
printf("\n coefs \n");
FILE *arq;
arq = fopen("entrada.txt", "rt");
for(int i=0;i<X;i++)
for(int j=0;j<Y;j++)
fscanf(arq," %d",&h_e[i+j*X]);
fclose(arq);
/* Copy vectors from host memory to device memory */
cudaMemcpy(d_e, h_e, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_c_coeff, c_coeff, (k/2+1)*sizeof(float), cudaMemcpyHostToDevice);
cudaEvent_t start, stop;
cudaEventCreate (&start);
cudaEventCreate (&stop);
cudaEventRecord (start, 0);
/******************
*** Kernel Call ***
*******************/
//_3Dstencil_global<<<blks,th_p_blk>>>(d_e,d_r,X,Y,Z);
_2Dstencil_global<<<grid_dim,block_dim,sharedSize>>>(d_e,d_r,d_c_coeff,X,Y,k,times);
cudaError_t err = cudaSuccess;
err = cudaGetLastError();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to launch _3Dstencil_global kernel (error code %s)!\n", cudaGetErrorString(err));
}
/******************
*** Kernel Call ***
*******************/
cudaDeviceSynchronize();
cudaEventRecord (stop, 0);
cudaEventSynchronize (stop);
float elapsedTime;
cudaEventElapsedTime (&elapsedTime, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
printf("X %d || Y %d \nBX %d || BY %d \n",X,Y,BX,BY);
printf ("[%d,%.5f],\n", tam,elapsedTime);
cudaMemcpy(h_r, d_r, size, cudaMemcpyDeviceToHost);
arq = fopen("resultado.txt", "wt");
for(int i=0;i<X;i++)
{
for(int j=0;j<Y;j++)
{
fprintf(arq," %d",h_r[i+j*X]);
}
fprintf(arq,"\n");
}
fclose(arq);
cudaFree(d_e);
cudaFree(d_r);
cudaFree(d_c_coeff);
std::free(h_e);
std::free(h_r);
std::free(c_coeff);
return 0;
} /* main */
/*
for(int lk = 1;lk<(k/2)+1;lk++)
{
if(x+lk < X)
{
if((x+lk)/Dx == blockIdx.x)
{
h_e_i = ((x+lk)%Dx) + ( (Ys) * (Dx) );
temp += shared[h_e_i]*c_coeff[lk];
}else
{
h_e_i = (x+lk) + ( (y) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
}
}
if(x-lk >= 0)
{
if((x-lk)/Dx == blockIdx.x)
{
h_e_i = ((x-lk)%Dx) + ( (Ys) * (Dx) );
temp += shared[h_e_i]*c_coeff[lk];
}
else
{
h_e_i = (x-lk) + ( (y) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
}
}
if(y+lk < Y)
{
if((y+lk)/Dy == blockIdx.y)
{
h_e_i = ((Xs) + ( ((y+lk)%Dy) * (Dx) ));
temp += shared[h_e_i]*c_coeff[lk];
}
else
{
h_e_i = (x) + ( (y+lk) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
}
}
if(y-lk >= 0)
{
if((y-lk)/Dy == blockIdx.y)
{
h_e_i = ((Xs) + ( ((y-lk)%Dy) * (Dx) ));
temp += shared[h_e_i]*c_coeff[lk];
}
else
{
h_e_i = (x) + ( (y-lk) * (X) );
temp += d_e[h_e_i]*c_coeff[lk];
}
}
}
d_r[h_r_i] = temp;
*/ |
19,499 | #define TUD 2
#define BPW 3
#define WST 8
#define WSU 8
#define WSV 8
#define WS (WST*WSU*WSV)
#define CELL_LENGTH 4
#define CELL_SIZE (4*4*4)
#define BLOCK_SIZE (WS*CELL_SIZE)
#define BS_NUINT (BLOCK_SIZE/4)
#define WLT (WST*CELL_LENGTH)
#define WLU (WSU*CELL_LENGTH)
#define WLV (WSV*CELL_LENGTH)
#define TUV_MASK 0x421
#define T_MASK 0x0079
#define U_MASK 0x0782
#define V_MASK 0x7804
#define CELL_MASK 0x88f
#include <stdint.h>
#define uint uint32_t
// #define SLAB_MASK 0x3001
__device__ uint NextPrev(uint site, uint* lattice);
__device__ uint GlobalSite(uint lSite, uint lid);
__device__ uint EdgeOutside(uint site, uint next);
__device__ uint OutOfBounds(uint site, uint next);
__device__ uint OnEdge(uint site);
__device__ uint RelPos(uint unit);
__device__ uint AddUnitToSite(uint unit, uint site);
__device__ uint GPUValidateAddUnitVectors(uint a, uint b, uint* c);
__device__ uint GPUAddUnitVectors(uint a, uint b);
__device__ void TransMove(uint* lattice, uint site, uint* slab, uint* trans, uint4* rngState);
__device__ void DiffuseSL(uint* lattice, uint site, uint* slab);
__device__ int LocalSiteToSite(uint localSite);
__device__ void SetEdges(uint* slab, uint* lattice, uint lidSiteStart);
__device__ uint Rng4(uint4* state){
uint b;
b = ((((*state).x << 6)^(*state).x) >> 13);
(*state).x = ((((*state).x & 4294967294) << 18) ^b);
b = ((((*state).y << 2)^(*state).y) >> 27);
(*state).y = ((((*state).y & 4294967288) << 2) ^b);
b = ((((*state).z << 13)^(*state).z) >> 21);
(*state).z = ((((*state).z & 4294967280) << 7) ^b);
b = ((((*state).w << 3)^(*state).w) >> 12);
(*state).w = ((((*state).w & 4294967168) << 13)^b);
return ((*state).x^(*state).y^(*state).z^(*state).w);
}
__device__ uint NextPrev(uint site, uint* lattice){
return (lattice[site>>2]>>(8*(site&0x3)))&0xff;
}
__device__ uint AddUnitToSite(uint unit, uint site){
uint flip;
uint add;
flip = (unit&0x8)?((unit&0x7)^0x7):unit;
add = (unit&0x8)?((flip^site)&flip):(flip&site);
add = ((add&0x1)<<3)|((add&0x2)<<6)|((add&0x4)<<9);
site += (unit&0x8)?(-add):(add);
site ^= flip;
return site;
}
__device__ uint GPUValidateAddUnitVectors(uint a, uint b, uint* c){
uint r, valid;
if((a|b) != 0xf && (a&b))
return 0;
r = (((a|b)==0xf)?(a&b):(a|b));
valid = (r==0x3||r==0xc)?0:1;
*c = r;
return valid;
}
__device__ uint GPUAddUnitVectors(uint a, uint b){
return (((a|b)==0xf)?(a&b):(a|b));
}
__device__ void TransMove(uint* lattice, uint site, uint* slab, uint* trans, uint4* rngState){
uint next;
uint prev;
uint bond, temp;
uint oldNextSite, oldPrevSite, bondSite, newSite;
uint nNext, nPrev;
uint prevBond, rand;
uint latSiteComplete, nextPrev, slabSite, latNew, latBond, latPrev, latNext;
uint slabBond, label;
latSiteComplete = lattice[site/4];
nextPrev = (latSiteComplete>>(8*(site&0x3)))&0xff;
prev = nextPrev>>4; next=nextPrev&0xf;
slabSite = slab[site/8];
if(!nextPrev) return;
rand = Rng4(rngState);
if((slabSite>>(16|(site&0x7)))&0x1){
// return;
///Forward: SL-> no SL
prevBond = (rand&0x4)>>2;
bond = (nextPrev>>(prevBond*4))&0xf;
if(EdgeOutside(site, bond^(0xf*prevBond)))
return;
nPrev = (trans[bond/4]>>(4*(2*(bond%4)+(rand&0x1))))&0xf;
nNext = GPUAddUnitVectors((~nPrev)&0xf, bond);
temp = nPrev;
nPrev = (rand&0x2)?nPrev:nNext;
nNext = (rand&0x2)?nNext:temp;
bondSite = AddUnitToSite(bond^(0xf*prevBond), site);
oldNextSite = (prevBond)?site:bondSite;
oldPrevSite = (prevBond)?bondSite:site;
newSite = AddUnitToSite(nPrev, oldPrevSite);
if(EdgeOutside(oldPrevSite, nPrev))
return;
latNew = lattice[newSite/4];
if(latNew&(0xff<<((newSite&0x3)*8)))
return;
latNew |= (nNext|(nPrev<<4))<<((newSite&0x3)*8);
latSiteComplete &= ~(0xf<<((site&0x3)*8+4*prevBond));
latSiteComplete |= ((prevBond)?(nNext<<4):nPrev)<<((site&0x3)*8);
if(newSite/4 == site/4){
lattice[site/4] = (latSiteComplete&(0xff<<((site&0x3)*8))) | (latNew&(~(0xff<<((site&0x3)*8))));
}
else{
lattice[site/4] = latSiteComplete;
lattice[newSite/4] = latNew;
}
latBond = lattice[bondSite/4];
latBond &= ~(0xf<<((bondSite&0x3)*8+4*(prevBond^0x1)));
latBond |= ((prevBond)?(nPrev):(nNext<<4))<<((bondSite&0x3)*8);
lattice[bondSite/4] = latBond;
label = (slabSite >> (((site&0x7)*2)|prevBond))&0x1;
slabSite &= ~((1<<((site&0x7)|16))|(1<<(((site&0x7)*2)|prevBond)));
if(!prevBond){
slabSite |= (slabSite&(1<<((site&0x7)*2+1)))>>1;
slabSite &= ~(1<<((site&0x7)*2+1));
}
slab[site/8] = slabSite;
slab[newSite/8] |= label<<((newSite&0x7)*2);
}
else{
///Backward: no SL -> SL
if(((rand>>4)&0x1) != 0) return;
prevBond = rand&0x1;
if(EdgeOutside(site, (~prev)&0xf)|EdgeOutside(site,next))
return;
if(! GPUValidateAddUnitVectors(next,prev,&bond))
return;
oldNextSite = AddUnitToSite(next, site);
oldPrevSite = AddUnitToSite((~prev)&0xf, site);
bondSite = (prevBond)?oldPrevSite:oldNextSite;
slabBond = slab[bondSite/8];
if(slabBond & (1<<((bondSite&0x7)|16)))
return;
slab[bondSite/8] = slabBond | (1<<((bondSite&0x7)|16));
slabSite = slab[site/8];
label = (slabSite>>(2*(site&0x7)))&0x1;
slab[site/8] = slabSite&(~(1<<(2*(site&0x7))));
slabBond = slab[bondSite/8];
if(prevBond){
slabBond |= (slabBond&(1<<(2*(bondSite&0x7))))<<1;
slabBond &= ~(1<<(2*(bondSite&0x7)));
}
slab[bondSite/8] = slabBond | (label<<((2*(bondSite&0x7))|(prevBond^0x1)));
lattice[site/4] = latSiteComplete &(~(0xff<<((site&0x3)*8)));
latPrev = lattice[oldPrevSite/4];
latPrev &= ~(0xf<<((oldPrevSite%4)*8));
latPrev |= (bond<<((oldPrevSite%4)*8));
lattice[oldPrevSite/4] = latPrev;
latNext = lattice[oldNextSite/4];
latNext &= ~(0xf0<<((oldNextSite%4)*8));
latNext |= (bond<<((oldNextSite%4)*8+4));
lattice[oldNextSite/4] = latNext;
}
}
__device__ void DiffuseSL(uint* lattice, uint site, uint* slab){
uint next, nextSite, tsl;
uint bit, nBit, lBit, lnBit;
uint slabSite, slabNext, label;
next = NextPrev(site, lattice);
next &= 0xf;
if(!next || EdgeOutside(site, next))
return;
nextSite = AddUnitToSite(next,site);
slabSite = slab[site/8]; slabNext = slab[nextSite/8];
bit = 16|(site&0x7); nBit = 16|(nextSite&0x7);
lBit = (site&0x7)*2; lnBit = (nextSite&0x7)*2;
if((slabSite&(1<<bit)) && !(slabNext&(1<<nBit))){
label = slabSite&(0x3<<lBit);
slabSite &= ~(0x3<<lBit);
slabSite |= (label&(0x2<<lBit))>>1;
label = (label>>lBit)&0x1;
slabNext |= label<<(lnBit+1);
}
else if(!(slabSite&(1<<bit)) && (slabNext&(1<<nBit))){
label = (slabNext>>(lnBit+1))&0x1;
slabNext &= ~(1<<(lnBit+1));
slabSite |= (slabSite&(1<<lBit))<<1;
slabSite &= ~(1<<lBit);
slabSite |= label<<lBit;
}
else
return;
tsl = ((slabSite>>bit) & 0x1)<<nBit;
slabSite &= ~(1<<bit);
slabSite |= ((slabNext>>nBit)&0x1)<<bit;
slabNext &= ~(1<<nBit);
slabNext |= tsl;
if(site/8 == nextSite/8){
slab[site/8] = (slabSite&((0x3<<lBit)|(0x1<<bit)))|(slabNext&(~((0x3<<lBit)|(0x1<<bit))));
}
else{
slab[site/8] = slabSite;
slab[nextSite/8] = slabNext;
}
}
__device__ uint EdgeOutside(uint site, uint next){
int t,u,v,w;
w = next>>3;
t = (next&0x1) -w;
u = ((next&0x2)>>1)-w;
v = ((next&0x4)>>2)-w;
if((site&T_MASK) == 0 && t<0)
return 1;
if((site&T_MASK) == T_MASK && t>0)
return 1;
if((site&U_MASK) == 0 && u<0)
return 1;
if((site&U_MASK) == U_MASK && u>0)
return 1;
if((site&V_MASK) == 0 && v<0)
return 1;
if((site&V_MASK) == V_MASK && v>0)
return 1;
return 0;
}
__device__ int LocalSiteToSite(uint localSite){
localSite |= (localSite&0x10)<<3;
localSite |= (localSite&0x20)<<6;
localSite &= CELL_MASK;
return localSite;
}
__device__ void SetEdges(uint* sledge, uint* lattice, uint lidSiteStart){
uint site, lSite;
uint next,prev;
for(lSite=0; lSite<CELL_SIZE; lSite++){
site = lidSiteStart|LocalSiteToSite(lSite);
next = NextPrev(site, lattice);
prev = (~(next>>4))&0xf;
next &= 0xf;
sledge[site>>3] |= EdgeOutside(site, next)<<(2*(site&0x7));
sledge[site>>3] |= EdgeOutside(site, prev)<<(2*(site&0x7)+1);
}
}
__global__ void polmove(uint nStep, uint4* seeds, uint* gLattice, uint* gTrans, uint tuvOffset, uint NWT, uint NWU, uint NWV){
__shared__ uint lattice[BLOCK_SIZE/4];
__shared__ uint slab[BLOCK_SIZE/8];
uint trans[4];
uint locId, localOffset,t,u,v, destId, memAddr, unit;
uint tOffset, uOffset, vOffset;
uint lid = threadIdx.x;
uint wid = blockIdx.x;
uint gid = wid * blockDim.x + lid;
uint i;
uint lidSiteStart;
uint4 rngl;
uint4 rngp;
uint site, lSite;
for(i=0; i<4; i++) trans[i] = gTrans[i];
tOffset = tuvOffset%WLT;
uOffset = (tuvOffset/WLT)%(WLU);
vOffset = tuvOffset/(WLT*WLU);
tOffset += ((lid&0x7)<<2)|((wid%NWT)*CELL_LENGTH*WST);
uOffset += ((lid&0x38)>>1)|(((wid/NWT)%NWU)*CELL_LENGTH*WSU);
vOffset += ((lid&0x1c0)>>4)|((wid/NWU/NWT)*CELL_LENGTH*WSV);
localOffset = ((lid&0x7)<<4)|((lid&0x38)<<5)|((lid&0x1c0)<<6);
for(locId=0; locId<CELL_SIZE/4; locId++){
destId=(locId&0x3)|((locId&0x4)<<3)|((locId&0x8)<<6);
destId |= localOffset>>2;
lattice[destId]=0;
}
for(locId=0; locId<CELL_SIZE/8; locId++){
destId=(locId&0x1)|((locId&0x2)<<3)|((locId&0x4)<<6);
destId |= localOffset>>3;
slab[destId]=0;
}
for(locId=0; locId<CELL_SIZE; locId++){
t = tOffset+(locId&0x3);
u = uOffset+((locId>>2)&0x3);
v = vOffset+((locId>>4)&0x3);
t %= NWT*CELL_LENGTH*WST;
u %= NWU*CELL_LENGTH*WSU;
v %= NWV*CELL_LENGTH*WSV;
memAddr = ((t&0x3)<<9)|((t&0x1c)>>2) | ((u&0x3)<<11) | ((u&0x1c)<<1) | ((v&0x3)<<13) | ((v&0x1c)<<4);
memAddr |= ((t>>5)+(u>>5)*NWT+(v>>5)*NWT*NWU)<<15;
unit = gLattice[memAddr];
destId = (locId&0x1)|((locId&0x2)<<2)|((locId&0x4)>>1)|((locId&0x8)<<4)|((locId&0x10)>>2)|((locId&0x20)<<6);
destId |= localOffset;
lattice[destId/4] |= (unit&0xff)<<(8*(destId%4));
slab[destId/8] |= ((unit&0x100)>>8)<<(16|(destId&0x7));
slab[destId/8] |= ((unit&0x600)>>9)<<((destId&0x7)*2);
}
lidSiteStart = ((lid&0x7)<<4)|((lid&0x38)<<5)|((lid&0x1c0)<<6);
// SetEdges(sledge, lattice, lidSiteStart);
rngp = seeds[gid*2];
rngl = seeds[gid*2+1];
for(i=0; i<nStep; i++){
__syncthreads();
site = lidSiteStart | (Rng4(&rngl)&CELL_MASK);
DiffuseSL(lattice, site, slab);
__syncthreads();
site = lidSiteStart | (Rng4(&rngl)&CELL_MASK);
TransMove(lattice, site, slab, trans, &rngp);
}
__syncthreads();
for(locId=0; locId<CELL_SIZE; locId++){
t = tOffset+(locId&0x3);
u = uOffset+((locId>>2)&0x3);
v = vOffset+((locId>>4)&0x3);
t %= NWT*CELL_LENGTH*WST;
u %= NWU*CELL_LENGTH*WSU;
v %= NWV*CELL_LENGTH*WSV;
memAddr = ((t&0x3)<<9)|((t&0x1c)>>2) | ((u&0x3)<<11) | ((u&0x1c)<<1) | ((v&0x3)<<13) | ((v&0x1c)<<4);
memAddr |= ((t>>5)+(u>>5)*NWT+(v>>5)*NWT*NWU)<<15;
destId = (locId&0x1)|((locId&0x2)<<2)|((locId&0x4)>>1)|((locId&0x8)<<4)|((locId&0x10)>>2)|((locId&0x20)<<6);
destId |= localOffset;
unit = (lattice[destId/4]>>(8*(destId%4)))&0xff;
unit |= ((slab[destId/8]>>(16|(destId&0x7)))&0x1)<<8;
unit |= ((slab[destId/8]>>(2*(destId&0x7)))&0x3)<<9;
gLattice[memAddr]=unit;
}
seeds[gid*2]=rngp;
seeds[gid*2+1]=rngl;
__syncthreads();
} |
19,500 | /******************************************************************************
* PROGRAM: copyStruture
* PURPOSE: This program is a test which test the ability to transfer multilevel
* C++ structured data from host to device, modify them and transfer back.
*
*
* NAME: Vuong Pham-Duy.
* College student.
* Faculty of Computer Science and Technology.
* Ho Chi Minh University of Technology, Viet Nam.
* vuongpd95@gmail.com
*
* DATE: 5/10/2017
*
******************************************************************************/
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true)
{
if (code != cudaSuccess)
{
fprintf(stderr, "GPUassert: %s %s %d\n", \
cudaGetErrorString(code), file, line);
if (abort)
exit(code);
}
}
__global__ void func(int *value) {
printf("value[%d] = %d\nvalue[%d] = %d\n", \
value[0], value[0], value[1], value[1]);
}
int main(int argc, char *argv[])
{
int *value;
gpuErrchk(cudaMallocManaged(&value, 2 * sizeof(int)));
value[0] = 0;
value[1] = 1;
func<<<1, 1>>>(value);
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk(cudaFree(value));
return 0;
}
|
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