serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
6,701 | #include <iostream>
#include <pthread.h>
#include <cfloat>
#include <chrono>
#include <sys/time.h>
#define threadsPerBlock_x 16
#define threadsPerBlock_y 16
struct timeval stop, start;
typedef struct _thread_param{
int cities;
int num_threads;
int thread_id;
float* distance;
float* thread_minimum;
int* p_tour;
}thread_param;
void read_data(int cities, float* x_pos, float* y_pos, FILE* fp){
ssize_t read;
size_t len = 0;
char* line = NULL;
char* ch = NULL;
for(int i = 0; i < cities; i++){
read = getline(&line, &len, fp);
if(read == -1){
fprintf(stderr, "Error in reading file");
exit(1);
}
ch = strtok(line," ");
ch = strtok(NULL, " ");
x_pos[i] = atof(ch);
ch = strtok(NULL, " ");
y_pos[i] = atof(ch);
}
}
void cal_dist(float* distance, float* x_pos, float* y_pos, int cities){
float x_diff;
float y_diff;
for(int i = 0; i < cities; i++){
for(int j = 0; j < cities; j++){
x_diff = x_pos[i] - x_pos[j];
y_diff = y_pos[i] - y_pos[j];
distance[i * cities + j] = sqrt(x_diff * x_diff + y_diff * y_diff);
}
}
}
void read_files(FILE* fp, float* distance, int cities){
float* x_pos = new float[cities];
float* y_pos = new float[cities];
if(fp == NULL){
fprintf(stderr, "Can't open the file.\n");
exit(1);
}
read_data(cities, x_pos, y_pos, fp);
cal_dist(distance, x_pos, y_pos, cities);
delete[] x_pos;
delete[] y_pos;
}
__global__ void gpu_two_opt(int cities, int* tour, float* distance, float* gpu_minimum, int* gpu_head_tail){
__shared__ float thread_minimum[threadsPerBlock_x * threadsPerBlock_y];
__shared__ int thread_head_tail[threadsPerBlock_x * threadsPerBlock_y];
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int stride_x = blockDim.x * gridDim.x;
int stride_y = blockDim.y * gridDim.y;
float thread_min_diff = 0;
int head_tail = 0;
for(int i = x + 1; i < cities - 1; i += stride_x){
for(int j = y + 1; j < cities; j += stride_y){
if(i < j){
float diff_dist = distance[tour[i] * cities + tour[j + 1]] +
distance[tour[i - 1] * cities + tour[j]] -
distance[tour[i - 1] * cities + tour[i]] -
distance[tour[j] * cities + tour[j + 1]];
if(diff_dist < thread_min_diff){
thread_min_diff = diff_dist;
head_tail = i * cities + j;
}
}
}
}
thread_minimum[blockDim.x * threadIdx.y + threadIdx.x] = thread_min_diff;
thread_head_tail[blockDim.x * threadIdx.y + threadIdx.x] = head_tail;
__syncthreads();
if(threadIdx.x == 0 && threadIdx.y == 0){
float block_minimum = 0;
int block_head_tail = 0;
for(int i = 0; i < blockDim.x * blockDim.y; i++){
if(thread_minimum[i] < block_minimum){
block_minimum = thread_minimum[i];
block_head_tail = thread_head_tail[i];
}
}
gpu_minimum[blockIdx.y * gridDim.x + blockIdx.x] = block_minimum;
gpu_head_tail[blockIdx.y * gridDim.x + blockIdx.x] = block_head_tail;
}
__syncthreads();
}
// function to be called by each thread
void* pthread_tsp(void* _param){
// read pthread parameters
thread_param* param = (thread_param *) _param;
int cities = param->cities;
int num_threads = param->num_threads;
int thread_id = param->thread_id;
float* distance = param->distance;
float* thread_minimum = param->thread_minimum;
int* p_tour = param->p_tour;
// setup gpu
cudaSetDevice(thread_id);
dim3 threadsPerBlock(threadsPerBlock_x, threadsPerBlock_y);
dim3 numBlocks(cities / threadsPerBlock_x + ((cities % threadsPerBlock_x) ? 1 : 0),
cities / threadsPerBlock_y + ((cities % threadsPerBlock_y) ? 1 : 0));
float* gpu_distance = NULL;
cudaMalloc(&gpu_distance, cities * cities * sizeof(float));
cudaMemcpy(gpu_distance, distance, cities * cities * sizeof(float), cudaMemcpyHostToDevice);
int* cpu_tour = new int[cities + 1], *gpu_tour = NULL;
cudaMalloc(&gpu_tour, (cities + 1) * sizeof(int));
float* cpu_minimum = new float[numBlocks.x * numBlocks.y], *gpu_minimum = NULL;
cudaMalloc(&gpu_minimum, numBlocks.x * numBlocks.y * sizeof(float));
int* cpu_head_tail = new int[numBlocks.x * numBlocks.y], *gpu_head_tail = NULL;
cudaMalloc(&gpu_head_tail, numBlocks.x * numBlocks.y * sizeof(int));
// measure per pthread execution time
gettimeofday(&start, NULL);
for(int i = thread_id; i < cities; i += num_threads){
// create initial solution
for(int j = 0; j < cities; j++){
cpu_tour[j] = (i + j) % cities;
}
cpu_tour[cities] = i;
// calculate initial solution cost
float base_dist = 0;
for(int j = 0; j < cities; j++){
base_dist += distance[cpu_tour[j] * cities + cpu_tour[j + 1]];
}
float maximum_diff = 0;
do{
base_dist += maximum_diff;
maximum_diff = 0;
// run 2-opt on device
cudaMemcpy(gpu_tour, cpu_tour, (cities + 1) * sizeof(int), cudaMemcpyHostToDevice);
gpu_two_opt<<<numBlocks, threadsPerBlock>>>(cities, gpu_tour, gpu_distance, gpu_minimum, gpu_head_tail);
cudaDeviceSynchronize();
// copy block minimums from device to host
cudaMemcpy(cpu_minimum, gpu_minimum, numBlocks.x * numBlocks.y * sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(cpu_head_tail, gpu_head_tail, numBlocks.x * numBlocks.y * sizeof(int), cudaMemcpyDeviceToHost);
int head = 0, tail = 0;
// find the smallest cost in block minimums
for(int j = 0; j < numBlocks.x * numBlocks.y; j++){
if(cpu_minimum[j] < maximum_diff){
maximum_diff = cpu_minimum[j];
head = cpu_head_tail[j] / cities;
tail = cpu_head_tail[j] % cities;
}
}
// reverse solution array
while(head < tail){
int temp = cpu_tour[head];
cpu_tour[head] = cpu_tour[tail];
cpu_tour[tail] = temp;
head++;
tail--;
}
// check whether get better solution
}while(maximum_diff <= -0.1);
// update global minimum
if(base_dist < *thread_minimum){
*thread_minimum = base_dist;
for(int j = 0; j < cities + 1; j++){
p_tour[j] = cpu_tour[j];
}
}
}
gettimeofday(&stop, NULL);
printf("pthread %d : %lu microseconds\n", thread_id, (stop.tv_sec - start.tv_sec) * 1000000 + stop.tv_usec - start.tv_usec);
// free cuda memory
cudaFree(gpu_distance);
cudaFree(gpu_tour);
cudaFree(gpu_minimum);
cudaFree(gpu_head_tail);
return NULL;
}
int main(int argc, char* argv[])
{
int cities = atoi(argv[1]);
int num_threads = atoi(argv[2]);
FILE* fp=fopen(argv[3], "r");
//read the data from the file
float* distance = new float[cities * cities];
int* tour = new int[cities + 1];
read_files(fp, distance, cities);
fclose(fp);
// pthread parameters struct
thread_param* params = new thread_param[num_threads];
pthread_t* threads = new pthread_t[num_threads];
// measure total execution time
auto start = std::chrono::high_resolution_clock::now();
// set parameters and create pthread
for(int i = 0; i < num_threads; i++){
params[i].cities = cities;
params[i].num_threads = num_threads;
params[i].thread_id = i;
params[i].distance = distance;
params[i].thread_minimum = new float[1];
*(params[i].thread_minimum) = FLT_MAX;
params[i].p_tour = new int[cities + 1];
pthread_create(&threads[i], NULL, pthread_tsp, ¶ms[i]);
}
// thread join
for(int i = 0; i < num_threads; i++){
pthread_join(threads[i], NULL);
}
// show total execution time
auto stop = std::chrono::high_resolution_clock::now();
std::cout << "Total time taken by function: "
<< std::chrono::duration_cast<std::chrono::microseconds>(stop - start).count()
<< " microseconds" << std::endl;
// show solution of each thread
for(int i = 0; i < num_threads; i++){
printf("p_thread %d's optimal solution: %f\n", i, *(params[i].thread_minimum));
}
return 0;
} |
6,702 | #include <cuda_runtime_api.h>
#include <iostream>
#include <list>
#include "tbb/concurrent_queue.h"
#include "math.h"
#define nThreadsPerBlocks 512
__global__ void assign(int *d_a, int *d_b, int size) {
int index = threadIdx.x;
if (index < size) {
d_b[index] = d_a[0] ;
}
}
void cudaPrintError(std::string m) {
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
std::cerr<<m<<" : "<<cudaGetErrorString(err)<<std::endl;
}
}
int main(int argc, char* argv[]){
int GPU1 = 0;
int GPU2 = 1;
int size = 200;
int *a;
int *d_a0;
int *b, *c;
int *d_b1;
cudaSetDevice(GPU1);
a = (int *) malloc(1 * sizeof(int));
a[0] = 15 ;
cudaMalloc((void**)&d_a0, 1*sizeof(int)) ;
cudaMemcpy(d_a0, a, 1*sizeof(int), cudaMemcpyHostToDevice);
cudaPrintError("after GPU1");
cudaDeviceEnablePeerAccess(GPU1,0);
cudaSetDevice(GPU2);
cudaMalloc((void**)&d_b1, size*sizeof(int)) ;
cudaDeviceEnablePeerAccess(GPU1,0);
cudaPrintError("after GPU2");
assign<<<1, nThreadsPerBlocks>>>(d_a0,d_b1,size);
cudaPrintError("after KERNEL");
c = (int *) malloc(size * sizeof(int));
cudaMemcpy(c, d_b1, size*sizeof(int), cudaMemcpyDeviceToHost);
cudaPrintError("after getData");
for(int i=0; i<size; i++ )
std::cout << i << " " << c[i] << std::endl;
cudaFree(d_a0);
cudaFree(d_b1);
return 0;
}
|
6,703 | #include <stdio.h>
#include <stdlib.h>
__global__
void cudaMultVectorsKernel(int N, float *x, float *y, float *z)
{
int idx = blockIdx.x*blockDim.x + threadIdx.x;
if (idx < N) {
z[idx] = x[idx] * y[idx];
}
// idx = idx + blockDim.x * gridDim.x; // we will discuss this later...
}
// extern "C" is necessary because nvcc uses c++ compiler to compile cuda code
// hence applies name mangling. Because we use gcc for linking, we should
// prevent name mangling.
extern "C"
void runKernel(int N, float *x, float *y, float *z) {
cudaMultVectorsKernel<<<(N+511)/512, 512>>>(N, x, y, z);
}
|
6,704 | #include <stdlib.h>
#include <stdio.h>
__global__ void avg_kernel(double *heights,
int nx, int ny,
double Radius,
double *output){
int i,j, ind;
int ix, iy;
int ixmin, ixmax, iymin, iymax;
double h;
int N;
double ave;
i = blockIdx.x * blockDim.x + threadIdx.x;
j = blockIdx.y * blockDim.y + threadIdx.y;
// check the array boundaries
if (i >= nx || j >= ny) return;
ind = j * nx + i; // location in the array
h = heights[ind];
output [ind] = 0;
ixmin = max( i - int(Radius) , 0);
ixmax = min( i + int(Radius), nx-1);
iymin = max( j - int(Radius) , 0);
iymax = min( j + int(Radius), ny-1);
N=0;
ave = 0;
for(ix = ixmin; ix <= ixmax; ix++){
for(iy = iymin; iy <= iymax; iy++){
if ((ix-i)*(ix-i) + (iy-j)*(iy-j) <= Radius*Radius ){
N++;
ave = ave + heights[iy*nx+ix];
}
}
}
if (N > 0) output [ind] = ave/N;
return;
}
#define BLOCK_SIZE 16
extern "C"{
void avg_cuda(double *heights,
int nx,
int ny,
double r,
double *output){
dim3 nThreads(BLOCK_SIZE,BLOCK_SIZE);
dim3 nBlocks ( (nx-1)/BLOCK_SIZE + 1, (ny-1)/BLOCK_SIZE + 1);
double *d_heights;
double *d_output;
// allocate memory on GPU
cudaMalloc((void**) &d_heights, nx*ny * sizeof(double));
cudaMalloc((void**) &d_output, nx*ny * sizeof(double));
// copy input array:
cudaMemcpy(d_heights, heights, nx*ny*sizeof(double),cudaMemcpyHostToDevice);
// execute Kernel
avg_kernel<<<nBlocks,nThreads>>>(d_heights, nx, ny, r, d_output);
// copy output array back to the CPU
cudaMemcpy(output, d_output, nx*ny*sizeof(double),cudaMemcpyDeviceToHost);
// free the memory
cudaFree(d_heights);
cudaFree(d_output);
}
}
|
6,705 | #include <stdio.h>
// Note: Needs compute capability > 2.0, so compile with:
// nvcc hello_world_01.cu -arch=compute_20 -code=sm_20,compute_20 -o hello_world_01.out
// Other notes: can have trouble when N is large...
// Default buffer is ~8MB
// See hello_world_02.cu for details.
#include <cuda.h>
#include <cuda_runtime.h>
#define N 20000
#define GRID_D1 20
#define GRID_D2 2
#define BLOCK_D1 512
#define BLOCK_D2 1
#define BLOCK_D3 1
__global__ void hello(void)
{
int myblock = blockIdx.x + blockIdx.y * gridDim.x;
int blocksize = blockDim.x * blockDim.y * blockDim.z;
int subthread = threadIdx.z*(blockDim.x * blockDim.y) + threadIdx.y*blockDim.x + threadIdx.x;
int idx = myblock * blocksize + subthread;
if (idx < N){
printf("Hello world! My block index is (%d,%d) [Grid dims=(%d,%d)], 3D-thread index within block=(%d,%d,%d) => thread index=%d\n", blockIdx.x, blockIdx.y, gridDim.x, gridDim.y, threadIdx.x, threadIdx.y, threadIdx.y, idx);
} else {
printf("Hello world! My block index is (%d,%d) [Grid dims=(%d,%d)], 3D-thread index within block=(%d,%d,%d) => thread index=%d [### this thread would not be used for N=%d ###]\n", blockIdx.x, blockIdx.y, gridDim.x, gridDim.y, threadIdx.x, threadIdx.y, threadIdx.y, idx, N);
}
}
int main(int argc,char **argv)
{
const dim3 blockSize(BLOCK_D1, BLOCK_D2, BLOCK_D3);
const dim3 gridSize(GRID_D1, GRID_D2, 1);
int nthreads = BLOCK_D1*BLOCK_D2*BLOCK_D3*GRID_D1*GRID_D2;
if (nthreads < N){
printf("\n============ NOT ENOUGH THREADS TO COVER N=%d ===============\n\n",N);
} else {
printf("Launching %d threads (N=%d)\n",nthreads,N);
}
// launch the kernel
hello<<<gridSize, blockSize>>>();
// Need to flush prints...
cudaError_t cudaerr = cudaDeviceSynchronize();
if (cudaerr){
printf("kernel launch failed with error \"%s\".\n",
cudaGetErrorString(cudaerr));
} else {
printf("kernel launch success!\n");
}
printf("That's all!\n");
return 0;
}
|
6,706 | // nvcc SeayJohnnyHW1.cu -o SeayJohnnyHW1; ./'SeayJohnnyHW1'
#include <stdio.h>
int main ( void ) {
/*
The CUDA runtime returns the properties of devices in a structure of type
cudaDeviceProp (pg. 28). In other words, this struct is built into the
CUDA runtime and we can reference it directly. Visit
https://docs.nvidia.com/cuda/cuda-runtime-api/structcudaDeviceProp.html
for more documentation.
*/
cudaDeviceProp prop;
int count;
cudaGetDeviceCount( &count );
for(int i=0; i<count; i++) {
cudaGetDeviceProperties( &prop, i); // cudaGetDeviceProperties( ptr cudaDeviceProp struct, int device )
printf(" --- General Information for device %d ---\n", i );
printf("Name:\t\t\t\t%s\n", prop.name );
printf("Compute capability:\t\t%d.%d\n", prop.major, prop.minor );
printf("Integrated:\t\t\t%s\n", prop.integrated ? "Yes":"Not Integrated");
printf("Total global memory:\t\t%lu B\n", prop.totalGlobalMem );
printf("Max shared memory per block:\t%lu B\n", prop.sharedMemPerBlock );
printf("Max threads per block:\t\t%d\n", prop.maxThreadsPerBlock );
printf("Clock rate:\t\t\t%d kHz\n", prop.clockRate );
printf("Number of multiprocessors:\t%d\n", prop.multiProcessorCount);
printf("Shared memory avaible per \n multiprocessor:\t\t%lu B\n", prop.sharedMemPerMultiprocessor);
printf("\n");
}
}
|
6,707 | #include "includes.h"
__global__ void average_snips_v3(const double *Params, const int *ioff, const int *id, const float *uproj, const float *cmax, float *bigArray){
// jic, version to work with Nfeatures threads
// have made a big array of Nfeature*NfeatW*Nfilters so projections
// onto each Nfeature can be summed without collisions
// after running this, need to sum up each set of Nfeature subArrays
// to calculate the final NfeatW*Nfilters array
int tid, bid, ind, Nspikes, Nfeatures, NfeatW;
float xsum = 0.0f;
Nspikes = (int) Params[0];
Nfeatures = (int) Params[1];
NfeatW = (int) Params[4];
tid = threadIdx.x; //feature index
bid = blockIdx.x; //filter index
for(ind=0; ind<Nspikes;ind++) {
if (id[ind]==bid){
//uproj is Nfeatures x Nspikes
xsum = uproj[tid + Nfeatures * ind];
//add this to the Nfeature-th array of NfeatW at the offset for this spike
bigArray[ioff[ind] + tid + tid*NfeatW + Nfeatures*NfeatW * bid] += xsum;
} //end of if block for match
} //end of loop over spikes
} |
6,708 | #include<stdio.h>
#include <sys/time.h>
float timedifference_msec(struct timeval t0, struct timeval t1)
{
return (t1.tv_sec - t0.tv_sec) * 1000.0f + (t1.tv_usec - t0.tv_usec) / 1000.0f;
}
__global__ void addArray(long long n,float* ga,float *gb){
int id = blockIdx.x*blockDim.x+threadIdx.x;
if(id < n)
ga[id] += gb[id];
}
int main(void)
{
struct timeval t0;
struct timeval t1;
struct timeval t2;
struct timeval t3;
float elapsed,processing_time;
gettimeofday(&t0, 0);
unsigned long long int n = 1000000000;
float *a,*b,*ga,*gb;
a = (float*)malloc(n*sizeof(float));
b = (float*)malloc(n*sizeof(float));
for (unsigned long long int i = 0; i < n; i++)
{
b[i] = 1.1f;
a[i] = 2.2f;
}
cudaMalloc(&ga,n*sizeof(float));
cudaMalloc(&gb,n*sizeof(float));
cudaMemcpy(ga,a,n*sizeof(float),cudaMemcpyHostToDevice);
cudaMemcpy(gb,b,n*sizeof(float),cudaMemcpyHostToDevice);
int blockSize = 2;
int gridSize = (int)ceil((float)n/blockSize);
gettimeofday(&t1, 0);
addArray<<<gridSize,blockSize>>>(n,ga,gb);
gettimeofday(&t2, 0);
cudaMemcpy(a,ga,n*sizeof(float),cudaMemcpyDeviceToHost);
free(a);
free(b);
cudaFree(ga);
cudaFree(gb);
gettimeofday(&t3, 0);
elapsed = timedifference_msec(t0, t3);
processing_time = timedifference_msec(t1, t2);
printf("\nTotal Time Elasped:%f \nProcessing Time:%f \n\n",elapsed,processing_time);
return 0;
} |
6,709 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <sys/time.h>
#include <cuda_runtime.h>
// RG*RG*MAXN must fit within mytype
#define MAXN 100000
#define RG 10
#define USECPSEC 1000000ULL
#define nTPB 256
static inline int64_t div_up(int64_t a, int64_t b) {
return (a + b - 1) / b;
}
typedef double mytype;
unsigned long long dtime_usec(unsigned long long prev){
timeval tv1;
gettimeofday(&tv1,0);
return ((tv1.tv_sec * USECPSEC)+tv1.tv_usec) - prev;
}
__global__ void gemm_Kernel2(const mytype * A, const mytype * B, mytype *out, const int N){
int x = threadIdx.x+blockDim.x*blockIdx.x;
int y = blockIdx.y;
if (x <= N && y <= N){
mytype my_sum = 0;
for (int i = 0; i < N; i++) {
my_sum += __ldg(A + i + y * N) * __ldg(B + x + i * N);
}
out[x + y * N] = my_sum;
}
}
__global__ void gemm_Kernel1(const mytype * A, const mytype * B, mytype *out, const int N){
int x = threadIdx.x+blockDim.x*blockIdx.x;
int y = blockIdx.y;
if (x <= N && y <= N){
mytype my_sum = 0;
for (int i = 0; i < N; i++) {
my_sum += A[i + y * N] * B[x + i * N];
}
out[x + y * N] = my_sum;
}
}
int main(int argc, char *argv[]){
mytype *d1_A, *d1_result, *result, *d1_B, *A, *B;
mytype *d2_A, *d2_result, *d2_B;
if (argc != 2) {printf("must specify N on the command line\n"); return 1;}
int my_N = atoi(argv[1]);
if ((my_N < 1) || (my_N > MAXN)) {printf("N out of range\n"); return 1;}
B = (mytype *)malloc(my_N*my_N*sizeof(mytype));
A = (mytype *)malloc(my_N*my_N*sizeof(mytype));
result = (mytype *)malloc(my_N*my_N*sizeof(mytype));
cudaMalloc(&d1_B, my_N*my_N*sizeof(mytype));
cudaMalloc(&d1_A, my_N*my_N*sizeof(mytype));
cudaMalloc(&d1_result, my_N*my_N*sizeof(mytype));
cudaMalloc(&d2_B, my_N*my_N*sizeof(mytype));
cudaMalloc(&d2_A, my_N*my_N*sizeof(mytype));
cudaMalloc(&d2_result, my_N*my_N*sizeof(mytype));
for (int i=0; i < my_N*my_N; i++){
A[i] = rand()%RG;
B[i] = rand()%RG;
}
for (int i=0; i < my_N*my_N; i++){
result[i] = 0;
}
cudaMemset(d1_result, 0, my_N*my_N*sizeof(mytype));
cudaMemset(d2_result, 0, my_N*my_N*sizeof(mytype));
int loop = 100;
unsigned long long k1_time = 0;
dim3 grid(div_up(my_N, nTPB), my_N/1);
for (int i = 0; i < loop; i++) {
unsigned long long gpu_time = dtime_usec(0);
cudaMemcpy(d1_A, A, my_N*my_N*sizeof(mytype), cudaMemcpyHostToDevice);
cudaMemcpy(d1_B, B, my_N*my_N*sizeof(mytype), cudaMemcpyHostToDevice);
gemm_Kernel1<<<grid,nTPB>>>(d1_A, d1_B, d1_result, my_N);
cudaDeviceSynchronize();
cudaMemcpy(result, d1_result, my_N*my_N*sizeof(mytype), cudaMemcpyDeviceToHost);
gpu_time = dtime_usec(gpu_time);
k1_time += gpu_time;
}
unsigned long long k2_time = 0;
for (int i = 0; i < loop; i++) {
unsigned long long gpu_time = dtime_usec(0);
cudaMemcpy(d2_A, A, my_N * my_N*sizeof(mytype), cudaMemcpyHostToDevice);
cudaMemcpy(d2_B, B, my_N * my_N* sizeof(mytype), cudaMemcpyHostToDevice);
gemm_Kernel2<<<grid,nTPB>>>(d2_A, d2_B, d2_result, my_N);
cudaDeviceSynchronize();
cudaMemcpy(result, d2_result, my_N*my_N*sizeof(mytype), cudaMemcpyDeviceToHost);
gpu_time = dtime_usec(gpu_time);
k2_time += gpu_time;
}
printf("Finished. gemm1(without ldg) time: %ldus, gemm2(with ldg) time: %ldus\n", k1_time/100, k2_time/100);
return 0;
}
|
6,710 |
#include <stdlib.h>
#include <stdio.h>
#include <time.h>
#include <string.h>
#include <unistd.h>
__global__ void sumOnDevice(float *A, float *B, float *C, unsigned N) {
unsigned i = threadIdx.x;
if (i<N) {
C[i] = A[i] + B[i];
}
}
void sumOnHost(float *A, float *B, float *C, unsigned N) {
for (unsigned i = 0; i<N; i++) {
C[i] = A[i] + B[i];
}
}
void initData(float * arr, unsigned sz) {
srand((unsigned) time(NULL));
for (unsigned i = 0; i < sz; i++) {
arr[i] = (float) (( rand() & 0xFF)/10.0f);
}
}
void printArr(float * arr, unsigned sz) {
for (unsigned i = 0; i < sz-1; i++) {
printf("%f\t", arr[i]);
}
printf("%f\n", arr[sz-1]);
}
int main(){
unsigned N = 10;
float A[N], B[N], C[N];
initData(A, N);
sleep(1);
initData(B, N);
printArr(A, N);
printArr(B, N);
//CPU calculation
sumOnHost(A, B, C, N);
printArr(C, N);
float d_A[N], d_B[N], d_C[N];
unsigned byteSize = N * sizeof(float);
cudaMalloc((float **) &d_A, byteSize);
cudaMalloc((float**) &d_B, byteSize);
cudaMalloc((float**) &d_C, byteSize);
cudaMemcpy(d_A, A, byteSize, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, B, byteSize, cudaMemcpyHostToDevice);
sumOnDevice<<<1,1>>>(d_A, d_B, d_C, N);
cudaMemcpy(C, d_C, byteSize, cudaMemcpyDeviceToHost);
printArr(C, N);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
return 0;
}
|
6,711 | #include <stdio.h>
#include <math.h>
__global__ void kernel(float *a, int offset)
{
int i = offset + threadIdx.x + blockIdx.x * blockDim.x;
float x = (float)i;
float s = sinf(x);
float c = cosf(x);
a[i] += sqrtf(s*s + c*c);
}
float maxError(float *a, int n)
{
float maxE = 0;
for (int i = 0; i < n; i++)
{
float error = fabs(a[i]-1.0f);
if (error > maxE) maxE = error;
}
return maxE;
}
int main(int argc, char **argv)
{
const int blockSize = 256, nStreams = 4;
const int n = 256 * 1024 * blockSize * nStreams;
const int streamSize = n / nStreams; // How many go into each stream
const int streamBytes = streamSize * sizeof(float); // Num bytes for each stream
const int bytes = n * sizeof(float); // Total num bytes for all n
// Allocate pinned host memory and device memory
float *a, *d_a;
cudaMallocHost((void**)&a, bytes); // On the host
cudaMalloc((void**)&d_a, bytes); // On the device
float ms;
cudaEvent_t startEvent, stopEvent, dummyEvent;
cudaStream_t stream[nStreams]; // holds all the streams
cudaEventCreate(&startEvent);
cudaEventCreate(&stopEvent);
cudaEventCreate(&dummyEvent);
for (int i = 0; i < nStreams; i++)
{
cudaStreamCreate(&stream[i]); // Makes all the streams
}
// Baseline case - everything follows in sequence
memset(a, 0, bytes);
cudaEventRecord(startEvent, 0);
cudaMemcpy(d_a, a, bytes, cudaMemcpyHostToDevice);
// kernel<<<#THREADS IN A BLOCK, SIZE OF BLOCK>>>(...)
kernel<<<n/blockSize, blockSize>>>(d_a, 0);
cudaMemcpy(a, d_a, bytes, cudaMemcpyDeviceToHost);
cudaEventRecord(stopEvent, 0);
cudaEventSynchronize(stopEvent);
cudaEventElapsedTime(&ms, startEvent, stopEvent);
printf("Time for sequential (baseline) transfer and execute: %f ms\n", ms);
printf("Max error: %e\n", maxError(a, n));
// Asynchronous version 1: loop over {copy to d, kernel, copy to h}
memset(a, 0, bytes);
cudaEventRecord(startEvent, 0);
for (int i = 0; i < nStreams; ++i) // So for each stream individually
{
int offset = i * streamSize; // Ex: stream 2 starts where stream 2 ends
cudaMemcpyAsync(&d_a[offset], &a[offset], streamBytes, cudaMemcpyHostToDevice, stream[i]);
kernel<<<streamSize/blockSize, blockSize, 0, stream[i]>>>(d_a, offset);
cudaMemcpyAsync(&a[offset], &d_a[offset], streamBytes, cudaMemcpyDeviceToHost, stream[i]);
}
cudaEventRecord(stopEvent, 0);
cudaEventSynchronize(stopEvent);
cudaEventElapsedTime(&ms, startEvent, stopEvent);
printf("Time for aysnc V1 transfer and execute: %f ms\n", ms);
printf("Max error: %e\n", maxError(a, n));
// Asynchronous version 2: loop over all copy, all kernel, all copy
memset(a, 0, bytes);
cudaEventRecord(startEvent, 0);
for (int i = 0; i < nStreams; ++i)
{
int offset = i * streamSize;
cudaMemcpyAsync(&d_a[offset], &a[offset], streamBytes, cudaMemcpyHostToDevice, stream[i]);
}
for (int i = 0; i < nStreams; ++i)
{
int offset = i * streamSize;
kernel<<<streamSize/blockSize, blockSize, 0, stream[i]>>>(d_a, offset);
}
for (int i = 0; i < nStreams; ++i)
{
int offset = i * streamSize;
cudaMemcpyAsync(&a[offset], &d_a[offset], streamBytes, cudaMemcpyDeviceToHost, stream[i]);
}
cudaEventRecord(stopEvent, 0);
cudaEventSynchronize(stopEvent);
cudaEventElapsedTime(&ms, startEvent, stopEvent);
printf("Time for async V2 transfer and execute: %f ms\n", ms);
printf("Max error: %e\n", maxError(a, n));
cudaFree(d_a);
cudaFreeHost(a);
}
|
6,712 | #include <stdbool.h>
#include <stdio.h>
#include <string.h>
#include <getopt.h>
#include <curand_kernel.h>
#include <stdlib.h>
#include <cuda.h>
#include <sys/time.h>
#include "LSTMDeltaKernelBPTT.cu"
#include<chrono>
#include<iostream>
using namespace std;
using namespace std::chrono;
int blocks_[20][2] = {{8,8},{16,16},{24,24},{32,32},{1,64},{1,128},{1,192},{1,256},{1,320},{1,384},{1,448},{1,512},{1,576},{1,640},{1,704},{1,768},{1,832},{1,896},{1,960},{1,1024}};
int matrices_[7][2] = {{240,240},{496,496},{784,784},{1016,1016},{1232,1232},{1680,1680},{2024,2024}};
int main(int argc, char **argv) {
cudaSetDevice(0);
char* p;int matrix_len=strtol(argv[1], &p, 10);
for(int matrix_looper=0;matrix_looper<matrix_len;matrix_looper++){
for(int block_looper=0;block_looper<20;block_looper++){
int XSIZE=matrices_[matrix_looper][0],YSIZE=matrices_[matrix_looper][1],BLOCKX=blocks_[block_looper][0],BLOCKY=blocks_[block_looper][1];
float *deltas = NULL;
cudaMalloc(&deltas, XSIZE*YSIZE);
float *cellStates = NULL;
cudaMalloc(&cellStates, XSIZE*YSIZE);
float *previousCellStates = NULL;
cudaMalloc(&previousCellStates, XSIZE*YSIZE);
float *cellStateErrors = NULL;
cudaMalloc(&cellStateErrors, XSIZE*YSIZE);
float *nextCellStateErrors = NULL;
cudaMalloc(&nextCellStateErrors, XSIZE*YSIZE);
float *outputGateDeltas = NULL;
cudaMalloc(&outputGateDeltas, XSIZE*YSIZE);
float *forgetGateDeltas = NULL;
cudaMalloc(&forgetGateDeltas, XSIZE*YSIZE);
float *nextForgetGateDeltas = NULL;
cudaMalloc(&nextForgetGateDeltas, XSIZE*YSIZE);
float *inputGateDeltas = NULL;
cudaMalloc(&inputGateDeltas, XSIZE*YSIZE);
float *nextInputGateDeltas = NULL;
cudaMalloc(&nextInputGateDeltas, XSIZE*YSIZE);
float *cellInputDeltas = NULL;
cudaMalloc(&cellInputDeltas, XSIZE*YSIZE);
float *cellInputActivations = NULL;
cudaMalloc(&cellInputActivations, XSIZE*YSIZE);
float *cellStateActivations = NULL;
cudaMalloc(&cellStateActivations, XSIZE*YSIZE);
float *outputGateActivations = NULL;
cudaMalloc(&outputGateActivations, XSIZE*YSIZE);
float *nextForgetGateActivations = NULL;
cudaMalloc(&nextForgetGateActivations, XSIZE*YSIZE);
float *inputGateActivations = NULL;
cudaMalloc(&inputGateActivations, XSIZE*YSIZE);
float *cellInputActivationDerivatives = NULL;
cudaMalloc(&cellInputActivationDerivatives, XSIZE*YSIZE);
float *cellStateActivationDerivatives = NULL;
cudaMalloc(&cellStateActivationDerivatives, XSIZE*YSIZE);
float *outputGateActivationDerivatives = NULL;
cudaMalloc(&outputGateActivationDerivatives, XSIZE*YSIZE);
float *forgetGateActivationDerivatives = NULL;
cudaMalloc(&forgetGateActivationDerivatives, XSIZE*YSIZE);
float *inputGateActivationDerivatives = NULL;
cudaMalloc(&inputGateActivationDerivatives, XSIZE*YSIZE);
float *cellInputWeights = NULL;
cudaMalloc(&cellInputWeights, XSIZE*YSIZE);
float *outputGateWeights = NULL;
cudaMalloc(&outputGateWeights, XSIZE*YSIZE);
float *forgetGateWeights = NULL;
cudaMalloc(&forgetGateWeights, XSIZE*YSIZE);
float *inputGateWeights = NULL;
cudaMalloc(&inputGateWeights, XSIZE*YSIZE);
int inputCount = 1;
int cellCount = 1;
int cellsPerBlock = 1;
int iXSIZE= XSIZE;
int iYSIZE= YSIZE;
while(iXSIZE%BLOCKX!=0)
{
iXSIZE++;
}
while(iYSIZE%BLOCKY!=0)
{
iYSIZE++;
}
dim3 gridBlock(iXSIZE/BLOCKX, iYSIZE/BLOCKY);
dim3 threadBlock(BLOCKX, BLOCKY);
cudaFree(0);
LSTMDeltaKernelBPTT<<<gridBlock,threadBlock>>>(deltas,cellStates,previousCellStates,cellStateErrors,nextCellStateErrors,outputGateDeltas,forgetGateDeltas,nextForgetGateDeltas,inputGateDeltas,nextInputGateDeltas,cellInputDeltas,cellInputActivations,cellStateActivations,outputGateActivations,nextForgetGateActivations,inputGateActivations,cellInputActivationDerivatives,cellStateActivationDerivatives,outputGateActivationDerivatives,forgetGateActivationDerivatives,inputGateActivationDerivatives,cellInputWeights,outputGateWeights,forgetGateWeights,inputGateWeights,inputCount,cellCount,cellsPerBlock);
cudaDeviceSynchronize();
for (int loop_counter = 0; loop_counter < 10; ++loop_counter) {
LSTMDeltaKernelBPTT<<<gridBlock,threadBlock>>>(deltas,cellStates,previousCellStates,cellStateErrors,nextCellStateErrors,outputGateDeltas,forgetGateDeltas,nextForgetGateDeltas,inputGateDeltas,nextInputGateDeltas,cellInputDeltas,cellInputActivations,cellStateActivations,outputGateActivations,nextForgetGateActivations,inputGateActivations,cellInputActivationDerivatives,cellStateActivationDerivatives,outputGateActivationDerivatives,forgetGateActivationDerivatives,inputGateActivationDerivatives,cellInputWeights,outputGateWeights,forgetGateWeights,inputGateWeights,inputCount,cellCount,cellsPerBlock);
}
auto start = steady_clock::now();
for (int loop_counter = 0; loop_counter < 1000; loop_counter++) {
LSTMDeltaKernelBPTT<<<gridBlock,threadBlock>>>(deltas,cellStates,previousCellStates,cellStateErrors,nextCellStateErrors,outputGateDeltas,forgetGateDeltas,nextForgetGateDeltas,inputGateDeltas,nextInputGateDeltas,cellInputDeltas,cellInputActivations,cellStateActivations,outputGateActivations,nextForgetGateActivations,inputGateActivations,cellInputActivationDerivatives,cellStateActivationDerivatives,outputGateActivationDerivatives,forgetGateActivationDerivatives,inputGateActivationDerivatives,cellInputWeights,outputGateWeights,forgetGateWeights,inputGateWeights,inputCount,cellCount,cellsPerBlock);
}
auto end = steady_clock::now();
auto usecs = duration_cast<duration<float, microseconds::period> >(end - start);
cout <<'['<<usecs.count()<<','<<'('<<BLOCKX<<','<<BLOCKY<<')' << ','<<'('<<XSIZE<<','<<YSIZE<<')'<<']' << endl;
}
}} |
6,713 | extern "C"
__global__ void prepare(float *positions, int *usageIndexes, int count, int iterations,
float *random, float minScale, float maxScale,
float minX, float maxX, float minY)
{
int id = blockIdx.x * blockDim.x + threadIdx.x, jump = gridDim.x * blockDim.x, i,
row = iterations * 2 + 1;
float x, y, scale;
// for each snowflake
for (i = id; i < count; i += jump)
{
// reset usageIndex
usageIndexes[i] = 0;
scale = random[i] * (maxScale - minScale) + minScale;
// starting positions
x = random[i + 1] * (maxX - minX) + minX;
y = minY;
// store in positions
positions[i * row + 0] = scale;
positions[i * row + 1] = x;
positions[i * row + 2] = y;
}
}
extern "C"
__global__ void calculate(float *positions, int *usageIndexes, int count, int iterations,
float wind, float angle, float gravity, float maxX, float minX, float maxY)
{
int id = blockIdx.x * blockDim.x + threadIdx.x, jump = gridDim.x * blockDim.x, i, j, usageIndex,
row = iterations * 2 + 1;
float x, y, windX, windY, scale, sin, cos, prevX, prevY;
// trigonometrics used for wind force
sincosf(angle, &sin, &cos);
windX = wind * sin;
windY = wind * cos;
// for each snowflake
for (i = id; i < count; i += jump)
{
scale = positions[i * row + 0];
usageIndex = usageIndexes[i];
prevX = positions[i * row + 1];
prevY = positions[i * row + 2];
// starting from index 2, as <0, 2> is to be prepared earlier
for (j = 3; j < iterations * 2 + 1; j += 2)
{
x = prevX + windX;
if (x < minX)
{
x = maxX + x - minX;
}
else if (x > maxX)
{
x = minX + x - maxX;
}
y = prevY + gravity * scale + windY;
if (y > maxY && usageIndex == 0)
{
usageIndex = j;
usageIndexes[i] = usageIndex;
}
positions[i * row + j] = x;
positions[i * row + j + 1] = y;
prevX = x;
prevY = y;
}
}
}
|
6,714 | #include <cmath>
#include <cstdlib>
#include <cstdio>
#include <fstream>
using namespace std;
#define nx 41
#define ny 41
#define nt 500
#define nit 50
#define BS 8
__device__ void bulid_up_b(double *b, double rho, double dt, double *u, double *v, double dx, double dy, int i, int j) {
if (i>0 && i<nx-1 && j>0 && j<ny-1) {
b[j*nx+i] = rho * (1/dt * (
(u[j*nx+i+1] - u[j*nx+i-1]) / (2*dx) +
(v[(j+1)*nx+i] - v[(j-1)*nx+i]) / (2*dy)) -
powf((u[j*nx+i+1] - u[j*nx+i-1]) / (2*dx), 2.0) -
2*((u[(j+1)*nx+i] - u[(j-1)*nx+i]) / (2*dy) *
(v[j*nx+i+1] - v[j*nx+i-1]) / (2*dx)) -
powf((v[(j+1)*nx+i] - v[(j-1)*nx+i]) / (2*dy), 2.0));
}
}
__device__ void pressure_poisson(double *p, double *po, double dx, double dy, double *b, int i, int j) {
for (int t=0; t<nit; t++) {
po[j*nx+i] = p[j*nx+i];
__syncthreads();
if (i>0 && i<nx-1 && j>0 && j<ny-1) {
p[j*nx+i] = ((po[j*nx+i+1] + po[j*nx+i-1]) * powf(dy, 2.0) +
(po[(j+1)*nx+i] + po[(j-1)*nx+i]) * powf(dx, 2.0)) / (2*(powf(dx, 2.0) + powf(dy, 2.0))) -
powf(dx, 2.0) * powf(dy, 2.0) / (2*(powf(dx, 2.0) + powf(dy, 2.0))) * b[j*nx+i];
}
__syncthreads();
if (i == 0) {
p[j*nx+nx-1] = p[j*nx+nx-2];
p[j*nx] = p[j*nx+1];
}
__syncthreads();
if (j == 0) {
p[i] = p[nx+i];
p[(ny-1)*nx+i] = 0;
}
__syncthreads();
}
}
__global__ void cavity_flow(double *u, double *v, double *p, double *uo, double *vo, double *po, double *b, double rho, double nu, double dt, double dx, double dy) {
int j = blockIdx.y * blockDim.y + threadIdx.y;
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= nx || j >= ny) return;
for (int n=0; n<nt; n++) {
uo[j*nx+i] = u[j*nx+i];
vo[j*nx+i] = v[j*nx+i];
bulid_up_b(b, rho, dt, u, v, dx, dy, i, j);
__syncthreads();
pressure_poisson(p, po, dx, dy, b, i, j);
__syncthreads();
if (i>0 && i<nx-1 && j>0 && j<ny-1) {
u[j*nx+i] = uo[j*nx+i] - uo[j*nx+i] * dt/dx * (uo[j*nx+i] - uo[j*nx+i-1]) -
vo[j*nx+i] * dt/dy * (uo[j*nx+i] - uo[(j-1)*nx+i]) -
dt/(2*rho*dx) * (p[j*nx+i+1] - p[j*nx+i-1]) +
nu * (dt/powf(dx, 2.0) * (uo[j*nx+i+1] - 2*uo[j*nx+i] + uo[j*nx+i-1]) +
dt/powf(dy, 2.0) * (uo[(j+1)*nx+i] - 2*uo[j*nx+i] + uo[(j-1)*nx+i]));
v[j*nx+i] = vo[j*nx+i] - uo[j*nx+i] * dt/dx * (vo[j*nx+i] - vo[j*nx+i-1]) -
vo[j*nx+i] * dt/dy * (vo[j*nx+i] - vo[(j-1)*nx+i]) -
dt/(2*rho*dy) * (p[(j+1)*nx+i] - p[(j-1)*nx+i]) +
nu * (dt/powf(dx, 2.0) * (vo[j*nx+i+1] - 2*vo[j*nx+i] + vo[j*nx+i-1]) +
dt/powf(dy, 2.0) * (vo[(j+1)*nx+i] - 2*vo[j*nx+i] + vo[(j-1)*nx+i]));
}
__syncthreads();
if (i == 0) {
u[j*nx] = 0;
u[j*nx+nx-1] = 0;
v[j*nx] = 0;
v[j*nx+nx-1] = 0;
}
if (j == 0) {
u[i] = 0;
v[i] = 0;
v[(ny-1)*nx+i] = 0;
__syncthreads();
u[(ny-1)*nx+i] = 1;
}
__syncthreads();
}
}
int main() {
double dx = 2.0 / ((double)nx - 1.0);
double dy = 2.0 / ((double)ny - 1.0);
double rho = 1.0;
double nu = 0.1;
double dt = 0.001;
double *u, *v, *p, *uo, *vo, *po, *b;
int size = ny * nx * sizeof(double);
cudaMallocManaged(&u, size);
cudaMallocManaged(&v, size);
cudaMallocManaged(&p, size);
cudaMallocManaged(&uo, size);
cudaMallocManaged(&vo, size);
cudaMallocManaged(&po, size);
cudaMallocManaged(&b, size);
for (int j=0; j<ny; j++) {
for (int i=0; i<nx; i++) {
u[j*nx+i] = 0;
v[j*nx+i] = 0;
p[j*nx+i] = 0;
po[j*nx+i] = 0;
b[j*nx+i] = 0;
}
}
dim3 grid = dim3((nx+BS-1)/BS, (ny+BS-1)/BS, 1);
dim3 block = dim3(BS, BS, 1);
cavity_flow<<<grid, block>>>(u, v, p, uo, vo, po, b, rho, nu, dt, dx, dy);
cudaDeviceSynchronize();
ofstream file("uvp.txt");
for(int j=0; j<ny; j++) {
for (int i=0; i<nx; i++)
file << u[j*nx+i] << " ";
file << "\n";
}
for(int j=0; j<ny; j++) {
for (int i=0; i<nx; i++)
file << v[j*nx+i] << " ";
file << "\n";
}
for(int j=0; j<ny; j++) {
for (int i=0; i<nx; i++)
file << p[j*nx+i] << " ";
file << "\n";
}
file.close();
cudaFree(u);
cudaFree(v);
cudaFree(p);
cudaFree(uo);
cudaFree(vo);
cudaFree(po);
cudaFree(b);
}
|
6,715 | // NOTE: The conversion formulas I used are those from the task text.
// I can notice that identical formulas are found on wikipedia as well.
// However, after using these formulas in the kernel and then converting using the OpenCV cvrColor function,
// I noticed that my resulting BGR image is a good size, however very blue-ish.
// It seemed to me that I had read one of the BGR components incorrectly.
// Then I noticed that by replacing the B and R channels in the kernel I get the correct BGR image after using cvrColor.
// I think the conversion formulas are correct, and what I assume could be a problem is the OpenCV cvrColor conversion function.
// Finally, I checked my version of OpenCV, and it seems to have some issues:
// https://github.com/opencv/opencv/issues/4946
__device__ unsigned char y_component (unsigned char b, unsigned char g, unsigned char r) {
unsigned char y = 0.299 * r + 0.587 * g + 0.114 * b;
return y;
}
__device__ unsigned char u_component (unsigned char y, unsigned char r, unsigned char delta) {
unsigned char cr = (r - y) * 0.713 + delta;
return cr;
}
__device__ unsigned char v_component (unsigned char y, unsigned char b, unsigned char delta) {
unsigned char cb = (b - y) * 0.564 + delta;
return cb;
}
__device__ unsigned char r_component (unsigned char y, unsigned char u, unsigned char delta) {
unsigned char r = y + 1.403 * (u - delta);
return r;
}
__device__ unsigned char g_component (unsigned char y, unsigned char u, unsigned char v, unsigned char delta) {
unsigned char g = y - 0.414 * (u - delta) - 0.344 * (v - delta);
return g;
}
__device__ unsigned char b_component (unsigned char y, unsigned char v, unsigned char delta) {
unsigned char b = y + 1.773 * (v - delta);
return b;
}
// Output byte order: Y0U0V0 Y1U1V1 Y2U2V2 Y3U3V3 etc.
__global__ void bgr2yuv444 (uchar3 *d_in, unsigned char *d_out, unsigned int imgwidth, unsigned int imgheight) {
int column_idx = blockIdx.x*blockDim.x+threadIdx.x;
int row_idx = blockIdx.y*blockDim.y+threadIdx.y;
if ((row_idx > imgheight) || (column_idx > imgwidth)) {
return;
}
int global_idx = row_idx * imgwidth + column_idx;
unsigned char b, g, r = 0;
// Note: Here is the swapping of B and R component I mentioned at the top of this file
b = d_in[global_idx].z;
g = d_in[global_idx].y;
r = d_in[global_idx].x;
unsigned char delta = 128;
// In YUV444 every pixel has each of 3 components
// Each pixel (global idx) reproduces 3 components on output and they are interspersed with each other
unsigned char y = y_component(b, g, r);
d_out[3 * global_idx] = y;
d_out[3 * global_idx + 1] = u_component(y, r, delta);
d_out[3 * global_idx + 2] = v_component(y, b, delta);
}
// Output byte order: U0Y0V0 Y1 U2Y2V2 Y3 etc.
__global__ void yuv444toyuv422 (unsigned char *d_in, unsigned char *d_out, unsigned int img_size) {
int global_idx = blockIdx.x*blockDim.x+threadIdx.x;
if (global_idx > img_size) {
return;
}
int y_in_idx, y_out_idx;
// Even threads (even global idx indices i.e. even pixels) consist of all 3 YUV components
if (global_idx % 2 == 0) {
y_in_idx = 3 * global_idx;
int u_in_idx = 3 * global_idx + 1;
int v_in_idx = 3 * global_idx + 2;
y_out_idx = 2 * global_idx + 1;
int u_out_idx = 2 * global_idx;
int v_out_idx = 2 * global_idx + 2;
d_out[u_out_idx] = d_in[u_in_idx];
d_out[y_out_idx] = d_in[y_in_idx];
d_out[v_out_idx] = d_in[v_in_idx];
} else {
// Odd threads (odd global idx indices i.e. odd pixels) consist of only Y component
y_in_idx = 3 * global_idx;
y_out_idx = y_in_idx - (global_idx - 1);
d_out[y_out_idx] = d_in[y_in_idx];
}
}
__global__ void extract_channels (unsigned char *d_in, unsigned char *y_out, unsigned char *u_out, unsigned char *v_out,
unsigned int img_size) {
int global_idx = blockIdx.x*blockDim.x+threadIdx.x;
if (global_idx > img_size) {
return;
}
int y_in_idx;
if (global_idx % 2 == 0) {
y_in_idx = 2 * global_idx + 1;
int u_in_idx = 2 * global_idx;
int v_in_idx = 2 * global_idx + 2;
int uv_out_idx = u_in_idx / 4;
u_out[uv_out_idx] = d_in[u_in_idx];
v_out[uv_out_idx] = d_in[v_in_idx];
} else {
y_in_idx = 3 * global_idx - (global_idx - 1);
}
y_out[global_idx] = d_in[y_in_idx];
}
__global__ void yuv422tobgr (unsigned char *d_in, uchar3 *d_out_bgr, unsigned int img_size) {
int global_idx = blockIdx.x*blockDim.x+threadIdx.x;
if (global_idx > img_size) {
return;
}
unsigned char delta = 128;
int y_in_idx;
int y, u, v = 0;
if (global_idx % 2 == 0) {
y_in_idx = 2 * global_idx + 1;
int u_in_idx = 2 * global_idx;
int v_in_idx = 2 * global_idx + 2;
y = d_in[y_in_idx];
u = d_in[u_in_idx];
v = d_in[v_in_idx];
} else {
y_in_idx = 3 * global_idx - (global_idx - 1);
y = d_in[y_in_idx];
u = d_in[y_in_idx - 3];
v = d_in[y_in_idx - 1];
}
d_out_bgr[global_idx].z = b_component(y, v, delta);
d_out_bgr[global_idx].y = g_component(y, u, v, delta);
d_out_bgr[global_idx].x = r_component(y, u, delta);
} |
6,716 | #include "includes.h"
__global__ void simple_add(float* a, float* b, int n)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if(i < n) {
a[i] = a[i] + b[i];
}
} |
6,717 | #include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#include <math.h>
#include <sys/types.h>
#include <sys/times.h>
#include <sys/time.h>
#include <time.h>
#define MAXN 6000 /* Matrix size */
int numThreads; //number of threads
int numBlocks; //number of blocks
int N; //Matrix Size
int row, col;
/* Matrices */
float A[MAXN*MAXN], B[MAXN*MAXN];
//int *ptrA;
float *ptrA = A;
float *ptrB = B;
size_t sizeOfMatrix = sizeof(float) * N *N;
/* returns a seed for srand based on the time */
unsigned int time_seed() {
struct timeval t;
struct timezone tzdummy;
gettimeofday(&t, &tzdummy);
return (unsigned int)(t.tv_usec);
}
/* Set the program parameters from the command-line arguments */
void parameters(int argc, char **argv) {
int seed = 0; /* Random seed */
// char uid[32]; /*User name */
/* Read command-line arguments */
srand(time_seed()); /* Randomize */
//changed count to 5 so that user can insert number of grid blocks
if (argc == 5) {
seed = atoi(argv[2]);
srand(seed);
numThreads=atoi(argv[3]);
numBlocks=atoi(argv[4]); //insert argument for number of blocks
printf("Random seed = %i\n", seed);
}
if (argc >= 2) {
N = atoi(argv[1]);
// if (N < 1 || N > MAXN) {
// printf("N = %i is out of range.\n", N);
// exit(0);
// }
}
else {
printf("Usage: %s <matrix_dimension> [random seed] [number of threads] [number of blocks]\n",
argv[0]);
exit(0);
}
/* Print parameters */
printf("\nMatrix dimension N = %i.\n", N);
printf("\nNumber of blocks = %i.\n", numBlocks);
printf("\nNumber of threads = %i.\n", numThreads);
}
/* Initialize A and B*/
void initialize_inputs() {
int row, col;
srand((unsigned)time(NULL));
for (row = 0; row < N; row++) {
for (col = 0; col < N; col++) {
A[col*N+row] = (float)rand() / 32768.0;
B[col*N+row] = 0.0;
}
}
}
/* Print input matrices */
void print_inputs() {
int row, col;
if (N < 10) {
printf("\nA =\n\t");
for (row = 0; row < N; row++) {
for (col = 0; col < N; col++) {
printf("%5.2f%s", A[col*N+row], (col < N-1) ? ", " : ";\n\t");
}
printf("\nB = [");
for (col = 0; col < N; col++) {
printf("%5.2f%s", B[col], (col < N-1) ? "; " : "]\n");
}
}
}
}
//using reduction algorithm to calculate first two steps
__global__ void matrixNorm(float *f_A, float *f_B, int matSize){
float mu, sigma;
//declare the dimensions x-axis and y-axis (row and col)
int row = blockDim.y * blockIdx.y + threadIdx.y;
int col = blockDim.x * blockIdx.x + threadIdx.x;
// int index = col + row * n;
//we want to check to make sure we don't have an excess number of threads
if(row<matSize && col<matSize){
for(col = 0; col < matSize; col++){
mu = 0.0;
for(row = 0; row < matSize; row++)
mu+= f_A[matSize * col + row];
mu /= (float) matSize;
//you cannot calculate sigma without the mean, so we need some synchronization heree
//to make sure threads have calculated the mean before getting to this step
__syncthreads();
sigma = 0.0;
for(row = 0; row < matSize; row++){
sigma += powf(f_A[matSize*col+row] - mu, 2.0);
sigma /= (float) matSize;
sigma = sqrt(sigma);
//again, we need to make sure that sigma has been calculated in order to result the normalized matrix
__syncthreads();
for (row = 0; row < matSize; row++){
if(sigma == 0.0)
f_B[matSize*col+row] = 0.0;
else
f_B[matSize*col+row] = (f_A[matSize*col+row] - mu) / sigma;
}
}
}
}
}
int main(int argc, char **argv) {
/* Timing variables */
struct timeval etstart, etstop; /* Elapsed times using gettimeofday() */
struct timezone tzdummy;
//unsigned long long runtime;
unsigned long long usecstart, usecstop;
struct tms cputstart, cputstop;
/* Process program parameters */
parameters(argc, argv);
/* Initialize A and B */
initialize_inputs();
/* Print input matrices */
print_inputs();
/* Start Clock */
printf("\n---------------------------------------------\n");
printf("Matrix size N = %d", N);
printf("\nStarting clock.\n\n");
gettimeofday(&etstart, &tzdummy);
times(&cputstart);
/* declare arrays */
/* Allocate memory of the matrix in device and copy the matrix from host to device to do work */
//first, declare size of the matrix
sizeOfMatrix = N * N * sizeof(float);
//create pointer to matrix
float *f_A, *f_B;
//cuda malloc the matrix (make room for it in memory
cudaMalloc((void**)&f_A, sizeOfMatrix);
cudaMalloc((void**)&f_B, sizeOfMatrix);
//initialize start and stop
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
//copy data from host to device
cudaMemcpy(f_A, &ptrA, sizeOfMatrix, cudaMemcpyHostToDevice);
//*m_A = &A[N][N];
cudaMemcpy(f_B, &ptrB, sizeOfMatrix, cudaMemcpyHostToDevice);
//use cuda checks to make sure the allocation was successful
cudaEventRecord(start);
/* Matrix Normalization */
matrixNorm<<<numBlocks, numThreads>>>(f_A, f_B, N);
cudaMemcpy(&ptrB, f_B, sizeOfMatrix, cudaMemcpyDeviceToHost);
cudaError_t err = cudaGetLastError();
if(err != cudaSuccess){
printf("CUDA Error: %s\n", cudaGetErrorString(err));
exit(-1);
}
cudaEventRecord(stop);
/* Free Cuda */
cudaFree(f_A);
cudaFree(f_B);
/* Stop Clock */
//gettimeofday(&stop, &tzdummy);
// runtime = (unsigned long long)(stop.tv_sec - start.tv_sec) * 1000000 + (stop.tv_usec - start.tv_usec);
/* Stop Clock CPU Times */
gettimeofday(&etstop, &tzdummy);
times(&cputstop);
printf("Stopped clock.\n");
usecstart = (unsigned long long)etstart.tv_sec * 1000000 + etstart.tv_usec;
usecstop = (unsigned long long)etstop.tv_sec * 1000000 + etstop.tv_usec;
/* Display timing results */
//printf("Runtime = %g ms.\n", (float)runtime/(float)1000);
printf("\nStopped clock.");
printf("\n---------------------------------------------\n");
/* Display other timing results */
printf("\nElapsed time = %g ms.\n",
(float)(usecstop - usecstart)/(float)1000);
printf("(CPU times are accurate to the nearest %g ms)\n",
1.0/(float)CLOCKS_PER_SEC * 1000.0);
printf("My total CPU time for parent = %g ms.\n",
(float)( (cputstop.tms_utime + cputstop.tms_stime) -
(cputstart.tms_utime + cputstart.tms_stime) ) /
(float)CLOCKS_PER_SEC * 1000);
printf("My system CPU time for parent = %g ms.\n",
(float)(cputstop.tms_stime - cputstart.tms_stime) /
(float)CLOCKS_PER_SEC * 1000);
printf("My total CPU time for child processes = %g ms.\n",
(float)( (cputstop.tms_cutime + cputstop.tms_cstime) -
(cputstart.tms_cutime + cputstart.tms_cstime) ) /
(float)CLOCKS_PER_SEC * 1000);
/* Contrary to the man pages, this appears not to include the parent */
printf("--------------------------------------------\n");
exit(0);
}
|
6,718 | // file: raceCondition.cu
__global__ void raceCondition(int *A) {
__shared__ int Shared[64];
Shared[threadIdx.x] = A[threadIdx.x];
// no synchronization barrier!
A[threadIdx.x] = Shared[63 - threadIdx.x]; // line 9 - faulting load
}
int main() {
int *validPtr = 0;
cudaMalloc((void **)&validPtr, sizeof(int)*64);
raceCondition<<< dim3(1,1), dim3(64, 1) >>>( validPtr );
return 0;
}
|
6,719 | __global__ void gpu_Actualizar(float *layer, int posicion, float energia,int layer_size) {
float umbral = 0.001;
int gid = (blockIdx.x + gridDim.x * blockIdx.y) * (blockDim.x * blockDim.y) + (threadIdx.x + blockDim.x * threadIdx.y);
if(gid < layer_size){
int distancia = posicion - gid;
if ( distancia < 0 ) distancia = - distancia;
distancia = distancia + 1;
float atenuacion = sqrtf( (float)distancia );
float energia_k = energia / atenuacion;
if ( energia_k >= umbral || energia_k <= -umbral ) layer[gid] = layer[gid] + energia_k;
}
}
__global__ void gpu_Copiar(float *layer, float *layer_copy,int layer_size) {
int gid = (blockIdx.x + gridDim.x * blockIdx.y) * (blockDim.x * blockDim.y) + (threadIdx.x + blockDim.x * threadIdx.y);
if(gid < layer_size) layer_copy[gid]=layer[gid];
}
__global__ void gpu_Relajacion(float *layer, float *layer_copy, int layer_size) {
int gid = (blockIdx.x + gridDim.x * blockIdx.y) * (blockDim.x * blockDim.y) + (threadIdx.x + blockDim.x * threadIdx.y);
if(gid>0 && gid < layer_size-1) layer[gid] = ( layer_copy[gid-1] + layer_copy[gid] + layer_copy[gid+1] ) / 3;
}
|
6,720 | #include <stdio.h>
#include <stdlib.h>
__global__ void global_reduction_kernel(float *data_out, float *data_in, int stride, int size) {
int idx_x = threadIdx.x + blockIdx.x * blockDim.x;
if(idx_x + stride < size) {
data_out[idx_x] += data_in[idx_x + stride]; //same at every step only stride differs
}
}
void global_reduction(float *d_out, float *d_in, int n_threads, int size) {
int blocks_n = (size + n_threads - 1) /n_threads;
for(int stride = 1; stride < size; stride *= 2) {
global_reduction_kernel<<<blocks_n,n_threads>>>(d_out,d_in,stride,size);
}
}
|
6,721 | #include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
#include <math.h>
__global__ void
jacobikernel( float* a, float* newa, float* lchange, int n, int m, float w0, float w1, float w2, int sz )
{
int ti = threadIdx.x;
int tj = threadIdx.y;
int i = blockIdx.x * blockDim.x + ti + 1;
int j = blockIdx.y * blockDim.y + tj + 1;
__shared__ float mychange[18*18];
float mnewa, molda;
mychange[tj*18+ti] = a[(j-1)*m+(i-1)];
if( ti < 2 ) mychange[tj*18+ti+16] = a[(j-1)*m+i+15];
if( tj < 2 ) mychange[(tj+16)*18+ti] = a[(j+15)*m+i-1];
if( tj < 2 && ti < 2 ) mychange[(tj+16)*18+ti+16] = a[(j+15)*m+i+15];
__syncthreads();
molda = mychange[(tj+1)*18+(ti+1)];
mnewa = w0*molda +
w1 * (mychange[(tj+1)*18+(ti )] + mychange[(tj )*18+(ti+1)] +
mychange[(tj+1)*18+(ti+2)] + mychange[(tj+2)*18+(ti+1)]) +
w2 * (mychange[(tj )*18+(ti )] + mychange[(tj+2)*18+(ti )] +
mychange[(tj )*18+(ti+2)] + mychange[(tj+2)*18+(ti+2)]);
newa[j*m+i] = mnewa;
__syncthreads();
int ii = ti+blockDim.x*tj;
mychange[ii] = fabsf( mnewa - molda );
__syncthreads();
int nn = blockDim.x * blockDim.y;
while( (nn>>=1) > 0 ){
if( ii < nn )
mychange[ii] = fmaxf( mychange[ii], mychange[ii+nn] );
__syncthreads();
}
if( ii == 0 )
lchange[blockIdx.x + gridDim.x*blockIdx.y] = mychange[0];
__syncthreads();
float mych = 0.0f;
int ni = ti+blockDim.x*tj;
if( ni < sz ) mych = lchange[ni];
int mm = 256;
while( mm <= sz ){
mych = fmaxf( mych, lchange[ni+mm] );
mm += 256;
}
mychange[ni] = mych;
__syncthreads();
nn = blockDim.x*blockDim.x;
while( (nn>>=1) > 0 ){
if( ni < nn )
mychange[ni] = fmaxf(mychange[ni], mychange[ni+nn]);
__syncthreads();
}
if( ni == 0 )
lchange[0] = mychange[0];
}
static float sumtime;
void JacobiGPU( float* a, int n, int m, float w0, float w1, float w2, float tol )
{
float change=0.0;
int iters;
size_t memsize;
int bx, by, gx, gy;
float *da, *dnewa, *lchange;
cudaEvent_t e1, e2;
bx = 16;
by = 16;
gx = (n-2)/bx + ((n-2)%bx == 0?0:1);
gy = (m-2)/by + ((m-2)%by == 0?0:1);
sumtime = 0.0f;
memsize = sizeof(float) * n * m;
cudaMalloc( &da, memsize );
cudaMalloc( &dnewa, memsize );
cudaMalloc( &lchange, gx * gy * sizeof(float) );
cudaEventCreate( &e1 );
cudaEventCreate( &e2 );
dim3 block( bx, by );
dim3 grid( gx, gy );
iters = 0;
cudaMemcpy( da, a, memsize, cudaMemcpyHostToDevice );
cudaMemcpy( dnewa, a, memsize, cudaMemcpyHostToDevice );
do{
float msec;
++iters;
cudaEventRecord( e1 );
jacobikernel<<< grid, block >>>( da, dnewa, lchange, n, m, w0, w1, w2, gx*gy );
cudaEventRecord( e2 );
cudaMemcpy( &change, lchange, sizeof(float), cudaMemcpyDeviceToHost );
cudaEventElapsedTime( &msec, e1, e2 );
sumtime += msec;
float *ta;
ta = da;
da = dnewa;
dnewa = ta;
}while( change > tol );
double time = sumtime/1000.0f;
double dNumOps = 15 * iters * n * m;
double gflops = dNumOps/time/1e9;
printf( "JacobiGPU converged in %d iterations to residual %f\n", iters, change );
printf( "JacobiGPU used %.5f seconds total\n", sumtime/1000.0f );
printf( "Size(Number of Operations) = %.0f Ops/sec \n", dNumOps );
printf( "Throughtput = %.4f GFlops/sec \n",gflops );
cudaMemcpy( a, dnewa, memsize, cudaMemcpyDeviceToHost );
cudaFree( da );
cudaFree( dnewa );
cudaFree( lchange );
cudaEventDestroy( e1 );
cudaEventDestroy( e2 );
}
static void init( float* a, int n, int m )
{
int i, j;
memset( a, 0, sizeof(float) * n * m );
/* boundary conditions */
for( j = 0; j < n; ++j ){
a[j*m+n-1] = j;
}
for( i = 0; i < m; ++i ){
a[(n-1)*m+i] = i;
}
a[(n-1)*m+m-1] = m+n;
}
static void init1( float* a, int n, int m )
{
int i, j;
memset( a, 0, sizeof(float) * n * m );
/* boundary conditions */
for (i=0; i<m; i++)
for (j=0; j<n; j++)
a[i*n+j] = i;
}
int main( int argc, char* argv[] )
{
int n, m;
float *a;
struct timeval tt1, tt2;
int ms;
float fms;
if( argc <= 1 ){
fprintf( stderr, "%s sizen [sizem]\n", argv[0] );
return 1;
}
n = atoi( argv[1] );
if( n <= 0 ) n = 100;
m = n;
if( argc > 2 ){
m = atoi( argv[2] );
if( m <= 0 ) m = 100;
}
printf( "Jacobi %d x %d\n", n, m );
a = (float*)malloc( sizeof(float) * n * m );
init( a, n, m );
gettimeofday( &tt1, NULL );
JacobiGPU( a, n, m, .2, .1, .1, .1 );
gettimeofday( &tt2, NULL );
ms = (tt2.tv_sec - tt1.tv_sec);
ms = ms * 1000000 + (tt2.tv_usec - tt1.tv_usec);
fms = (float)ms / 1000000.0f;
printf( "time(gpu ) = %f seconds\n", fms );
}
|
6,722 | #include <cstdlib>
int main() {
// Allocate 3 arrays on CPU
int nr_rows_A, nr_cols_A, nr_rows_B, nr_cols_B, nr_rows_C, nr_cols_C;
// for simplicity we are going to use square arrays
nr_rows_A = nr_cols_A = nr_rows_B = nr_cols_B = nr_rows_C = nr_cols_C = 3;
float *h_A = (float *)malloc(nr_rows_A * nr_cols_A * sizeof(float));
float *h_B = (float *)malloc(nr_rows_B * nr_cols_B * sizeof(float));
float *h_C = (float *)malloc(nr_rows_C * nr_cols_C * sizeof(float));
// Allocate 3 arrays on GPU
float *d_A, *d_B, *d_C;
cudaMalloc(&d_A,nr_rows_A * nr_cols_A * sizeof(float));
cudaMalloc(&d_B,nr_rows_B * nr_cols_B * sizeof(float));
cudaMalloc(&d_C,nr_rows_C * nr_cols_C * sizeof(float));
// ....
//Free GPU memory
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
// Free CPU memory
free(h_A);
free(h_B);
free(h_C);
return 0;
}
|
6,723 | #include <stdio.h>
__global__ void firstParallel()
{
printf("This should be running in parallel.\n");
}
int main()
{
// run firstParallel in 4 thread blocks each containing 5 threads
firstParallel<<<4, 5>>>();
// wait until gpu operations are complete
cudaDeviceSynchronize();
}
|
6,724 | #include "includes.h"
__global__ void mult_add_into_kernel(int n, float *a, float *b, float *c)
{
int i = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
if(i < n){
c[i] += a[i]*b[i];
}
} |
6,725 | // have fun :-) |
6,726 | #include "includes.h"
__global__ void stencil_no_sync(int *in, int *out)
{
__shared__ int temp[BLOCK_SIZE + 2 * RADIUS];
int gindex = threadIdx.x + blockIdx.x * blockDim.x;
int lindex = threadIdx.x + RADIUS;
// Read input elements into shared memory
temp[lindex] = in[gindex+RADIUS];
if (threadIdx.x < RADIUS) {
temp[lindex - RADIUS] = in[gindex];
temp[lindex + BLOCK_SIZE] = in[gindex + BLOCK_SIZE + RADIUS];
}
////////////////////////////// missing sync thread ////////////////////////
// Apply the stencil
int result = 0;
for (int offset = -RADIUS ; offset <= RADIUS ; offset++)
result += temp[lindex + offset];
// Store the result
out[gindex] = result;
} |
6,727 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <sys/time.h>
#include <cuda.h>
#define epsilon 0.00001
#define maxRep 40
__global__ void meanShift(double *x,size_t pitchx,double *y, size_t pitchy,double *ynew,size_t pitchynew,int N,int d,double sigma);
__device__ double calcDist(double *y,size_t pitchy,double *x,size_t pitchx,int d);
__device__ double gausK(double x,double sigma);
double froNorm(double *a,size_t pitcha,double *b,size_t pitchb,int N,int d);
void test(double *y,size_t pitchy,char *testfile,int N,int d);
int main(int argc,char **argv){
if(argc!=4){
printf("Usage: %s (dataset) (test) (sigma) where (dataset) ",argv[0]);
printf("is the name of the dataset .txt file, (test) is the name of the ");
printf(".txt test file and (sigma) is the value of sigma for the current dataset\n");
exit(1);
}
struct timeval startwtime, endwtime;
double time;
//turn (sigma) input from string to double
double sigma=atof(argv[3]);
int i,j;
//argv[1] is the (dataset) file
FILE *file = fopen(argv[1], "r");
if(file==NULL){
printf("Couldn't open %s\n",argv[1]);
exit(1);
}
//count the number of points and dimensions of (dataset)
int d=0,N=0;
char ch;
/**dimension and number of points counting found in
*https://www.opentechguides.com/how-to/article/c/72/c-file-counts.html
*/
while ((ch=getc(file)) != EOF) {
if ((ch == ' ' || ch == '\n') && N==0) { ++d; }
if (ch == '\n') { ++N; }
}
//1 dimension host memory allocation to fit cudaMemcpy2D
double *y;
size_t pitchy = sizeof(double) * d;
y = (double*)malloc(sizeof(double) * N * d);
double *ynew;
size_t pitchynew = sizeof(double) * d;
ynew = (double*)malloc(sizeof(double) * N * d);
double *x;
size_t pitchx = sizeof(double) * d;
x = (double*)malloc(sizeof(double) * N * d);
double *row_x,*row_y;
//return file pointer to the beggining of the file
fseek(file, 0, SEEK_SET);
for (i=0;i<N;i++){
row_x = (double*)((char*)x + i * pitchx );
row_y = (double*)((char*)y + i * pitchy );
for (j=0;j<d;j++){
fscanf(file,"%lf",&row_x[j]);
row_y[j]=row_x[j];
}
}
fclose(file);
//allocate 2d arrays for device memory
double *d_x;
double *d_y;
double *d_ynew;
size_t d_pitchx,d_pitchy,d_pitchynew;
cudaMallocPitch((void**)&d_x, &d_pitchx, d * sizeof(double), N);
cudaMallocPitch((void**)&d_y, &d_pitchy, d * sizeof(double), N);
cudaMallocPitch((void**)&d_ynew, &d_pitchynew, d * sizeof(double), N);
//copy data from host to device memory
cudaMemcpy2D(d_x,d_pitchx,x,pitchx, d * sizeof(double), N, cudaMemcpyHostToDevice);
cudaMemcpy2D(d_y,d_pitchy,y,pitchy, d * sizeof(double), N, cudaMemcpyHostToDevice);
int repeats=0;
double norm;
double *row_ynew;
gettimeofday (&startwtime, NULL);
do{
meanShift<<<N,d,d*sizeof(double)>>>(d_x,d_pitchx,d_y,d_pitchy,d_ynew,d_pitchynew,N,d,sigma);
cudaMemcpy2D(y, sizeof(double)*d, d_y, d_pitchy, sizeof(double) * d, N, cudaMemcpyDeviceToHost);
//calculate norm of (ynew-y)
norm = froNorm(y,pitchy,ynew,pitchynew,N,d);
//update ynew after a meanshift iteration
for (i=0;i<N ;i++){
row_ynew = (double*)((char*)ynew + i * pitchynew);
row_y = (double*)((char*)y +i * pitchy);
for (j=0;j<d;j++){
row_ynew[j] = row_y[j];
}
}
repeats++;
}while(norm>epsilon && repeats<maxRep);
gettimeofday (&endwtime, NULL);
time = (double)((endwtime.tv_usec - startwtime.tv_usec)/1.0e6
+ endwtime.tv_sec - startwtime.tv_sec);
printf("Wall clock time: %f \n", time);
//argv[2] is the (testfile) name
test(y,pitchy,argv[2],N,d);
return 0;
}
__global__ void meanShift(double *x,size_t pitchx,double *y, size_t pitchy,double *ynew,size_t pitchynew,int N,int d,double sigma){
int index=blockDim.x*blockIdx.x+threadIdx.x;
int id=threadIdx.x;
extern __shared__ double sy[];
if (index<N){
double sum=0,res=0;
int j,k;
double* row_y=(double*)((char*)y+index*pitchy);
double* row_ynew=(double*)((char*)ynew+index*pitchynew);
//copy y to shared memory
for(k=0;k<d;k++)
sy[id*d+k]=row_y[k];
__syncthreads();
//initialize ynew
for(k=0;k<d;k++)
row_ynew[k]=0;
for(j=0;j<N;j++){
double* row_x=(double*)((char*)x+j*pitchx);
if(calcDist(sy,pitchy,row_x,pitchx,d)<sigma*sigma){
double temp=0;
for(k=0;k<d;k++){
temp=(sy[k]-row_x[k])*(sy[k]-row_x[k])+temp;
//temp is the square of norm2(y_i-x_j)
}
res=gausK(temp,sigma);
for(k=0;k<d;k++){
row_ynew[k]=row_ynew[k]+row_x[k]*res;
}
sum=sum+res;
//calculating denominator of ynew_i
}
}
for(k=0;k<d;k++){
row_ynew[k]=row_ynew[k]/sum;
}
//update y from all treads
for(k=0;k<d;k++){
row_y[k]=row_ynew[k];
}
}
}
//calculate distance between x and y
__device__ double calcDist(double *y,size_t pitchy,double *x,size_t pitchx,int d){
double sum = 0;
int l;
for (l=0;l<d;l++){
sum = sum + (y[l]-x[l])*(y[l]-x[l]);
}
return sqrt(sum);
}
__device__ double gausK(double x,double sigma){
double f;
f = exp(-x/(2*(sigma*sigma)));
return f;
}
//calculate frobenius norm of (a-b)
double froNorm(double *a,size_t pitcha,double *b,size_t pitchb,int N,int d){
int i,j;
double sum=0;
double *row_b,*row_a;
for (i=0;i<N;i++){
row_a = (double*)((char*)a + i * pitcha);
row_b = (double*)((char*)b + i * pitchb);
for (j=0;j<d;j++){
sum = sum + (row_a[j]-row_b[j])*(row_a[j]-row_b[j]);
}
}
return sqrt(sum);
}
void test(double *y,size_t pitchy,char *testfile,int N,int d){
int i,j;
double **test;
//memory allocation for test input
test =(double **) malloc(sizeof(double*)*N);
for (i=0;i<N;i++){
test[i] = (double *)malloc(sizeof(double)*d);
}
FILE *file = fopen(testfile, "r");
if(file==NULL){
printf("Couldn't open %s\n",testfile);
exit(1);
}
for (i=0;i<N;i++){
for (j=0;j<d;j++){
fscanf(file,"%lf",&test[i][j]);
}
}
//compare the arrays
int failed=0;
for (i=0;i<N;i++){
double* row_y=(double*)((char*)y+i*pitchy);
for (j=0;j<d;j++){
//check if relative error to matlab output is small
if (fabs(row_y[j]-(double)test[i][j])/fabs((double)test[i][j]) > 0.1)
failed++;
}
}
//check if a small percentage of the result is wrong
if((double)(d*N-failed)/(double)(d*N)*100<90.0)
printf("Test failed!\n");
else
printf("Test passed!\n");
fclose(file);
}
|
6,728 | #include <stdio.h>
#define CHECK(call) \
{ \
const cudaError_t error = call; \
if (error != cudaSuccess) { \
fprintf(stderr, "Error: %s:%d, ", __FILE__, __LINE__); \
fprintf(stderr, "code: %d, reason: %s\n", error, \
cudaGetErrorString(error)); \
} \
}
/**
* CUDA Kernel: vector addition
*/
__global__ void vectorAdd(const float *A, const float *B, float *C,
int numElements) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < numElements)
C[i] = A[i] + B[i];
}
/**
* MAIN
*/
int main(void) {
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
// Print the vector length to be used, and compute its size
int N = 50000;
size_t size = N * sizeof(float);
printf("[Vector addition of %d elements]\n", N);
// Allocate the host input vector A,B,C
float *h_A = (float *) malloc(size);
float *h_B = (float *) malloc(size);
float *h_C = (float *) malloc(size);
// Initialize the host input vectors
for (int i = 0; i < N; ++i) {
h_A[i] = rand() % 10;
h_B[i] = rand() % 10;
}
// Allocate the device input vector A,B,C
float *d_A = NULL;
CHECK(cudaMalloc((void **) &d_A, size));
float *d_B = NULL;
CHECK(cudaMalloc((void **) &d_B, size));
float *d_C = NULL;
CHECK(cudaMalloc((void **) &d_C, size));
// Copy the host input vectors A and B in device memory
printf("Copy input data from the host memory to the CUDA device\n");
CHECK(cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice));
CHECK(cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice));
// Launch the Vector Add CUDA Kernel
int threadsPerBlock = 256;
int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock);
vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, N);
CHECK(cudaGetLastError());
if (err != cudaSuccess) {
fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n",
cudaGetErrorString(err));
exit (EXIT_FAILURE);
}
// Copy the device result vector in host memory
printf("Copy output data from the CUDA device to the host memory\n");
CHECK(cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost));
// Verify that the result vector is correct
for (int i = 0; i < N; ++i) {
if (fabs(h_A[i] + h_B[i] - h_C[i]) > 1e-5) {
fprintf(stderr, "Result verification failed at element %d!\n", i);
exit (EXIT_FAILURE);
}
}
printf("Test PASSED\n");
// Free device global memory
CHECK(cudaFree(d_A));
CHECK(cudaFree(d_B));
CHECK(cudaFree(d_C));
// Free host memory
free(h_A);
free(h_B);
free(h_C);
printf("Done\n");
return 0;
}
|
6,729 | float h_A[]= {
0.963419567694692, 0.6819365724864848, 0.8279674302112109, 0.6722984086761803, 0.9767496333590933, 0.5289006409712409, 0.507854825291526, 0.8481614848649393, 0.8606815376043206, 0.9290418102455709, 0.5721569787809533, 0.7657011881409136, 0.8914492867542556, 0.7360608252067679, 0.6756081183907992, 0.5585329458600858, 0.6111235539396483, 0.9467345523598518, 0.5859073049582266, 0.5222976012281664, 0.9352928817473389, 0.8889730192247458, 0.7084723950762546, 0.581257943793022, 0.9276482825583362, 0.826679731415185, 0.6805730323016784, 0.5570598328462253, 0.5556944479377812, 0.6061134633819512, 0.8520992664006874, 0.9770739993825746, 0.8636967322557468, 0.8955012743618453, 0.811733694050352, 0.8031921162746145, 0.7205812494805708, 0.894424669622666, 0.5920428638603392, 0.9646966592033, 0.9502568917748139, 0.9155705120242208, 0.6355832923567531, 0.995183972198376, 0.9831012377345868, 0.5754564307566201, 0.5654808239918432, 0.5415148629598396, 0.7739762391503662, 0.8349920844280948, 0.9713797409314122, 0.9541398599173818, 0.6489768014245281, 0.6763410886201523, 0.5089958259443729, 0.788569052501698, 0.7834813375804807, 0.5452196812879455, 0.639404669529382, 0.6482072989199653, 0.6729598849094998, 0.6341182006129562, 0.6589799928644335, 0.9927543136449772, 0.5448882237808619, 0.5801261942813287, 0.8487814065870636, 0.7383100380147991, 0.8846787212774759, 0.5881739839769626, 0.7147634578605045, 0.8812064006699079, 0.9917712296390168, 0.5215495353854249, 0.7537996849078, 0.7827933404767955, 0.6897525872863863, 0.9299549641632905, 0.5068512721309402, 0.6387795852794564, 0.9193554675092146, 0.7566635243362723, 0.6374329343745434, 0.5836013208951053, 0.8405002675103772, 0.6943513303978048, 0.5993495527343571, 0.729232683856542, 0.6327335193156391, 0.7140749088748655, 0.642667583495846, 0.9402091557094012, 0.9162458391656698, 0.8261497201483025, 0.9293304996901937, 0.9938557754156023, 0.7021900157433337, 0.8226020949608861, 0.8812763790926793, 0.6318149272586517, 0.651463370344282, 0.7885709700368977, 0.7061811317232819, 0.7256734071915764, 0.9999687125693829, 0.9087150507683108, 0.8512873543246066, 0.5722462058141224, 0.6384615678419776, 0.7496420235268568, 0.9588857261832529, 0.5111708872801295, 0.7116931279168028, 0.8624930310173089, 0.6093551336813856, 0.8392626741449862, 0.8503789194009597, 0.7113848519684937, 0.7441951025592723, 0.9869537106006385, 0.845348089481424, 0.9459992532753003, 0.5302662765598236, 0.9700083994767706, 0.8789912085782072, 0.5261865506868642, 0.8747442281462736, 0.6709652326865407, 0.6839461606142588, 0.87352536130499, 0.5964374235075868, 0.9294319597223859, 0.9634502750853196, 0.7678000769668609, 0.7566377503263957, 0.510126380569115, 0.7761701798647596, 0.9041338286090179, 0.6319466024867987, 0.6492435213450907, 0.8479559432188268, 0.8425962222554364, 0.7847667519403569, 0.6712590379451001, 0.8773926300740253, 0.6718933711671337, 0.7507309454669129, 0.8648784028857492, 0.8340041469353945, 0.8715156264037003, 0.9785809243174575, 0.5075640272513908, 0.7473109090047554, 0.589862361552977, 0.9952261808177023, 0.970678229606166, 0.8176295347732043, 0.9060188458618799, 0.8472206937264282, 0.7294675064015819, 0.5070467477570821, 0.8417535214513272, 0.6003335903463689, 0.6932260386358411, 0.7803246793246075, 0.6296068453935509, 0.8653015195105961, 0.5882580582526558, 0.8351490979489595, 0.9072843489092386, 0.7201252390373449, 0.9055250383946276, 0.5284499913684677, 0.7056338577433919, 0.6013837783087109, 0.5014326242302161, 0.9437114571410701, 0.727704771921549, 0.9664791310254011, 0.8245717024437281, 0.7374939312178528, 0.6332807404598848, 0.5531471966972572, 0.8932061632294043, 0.5349116070601893, 0.5519238228155133, 0.5012540807613324, 0.7865974782367575, 0.8974583848423507, 0.6637908246917021, 0.5733883236451984, 0.8956249317550333, 0.8320394224309628, 0.9481950376264519, 0.7210630110701035, 0.7967144976551144, 0.752482725694912, 0.5293802483895701, 0.5182263299556811, 0.5436425192561318, 0.6734059729927129, 0.9344442772771023, 0.7608064371053682, 0.8671543486766689, 0.6922127795750845, 0.705890821077005, 0.8427489894075533, 0.8776791811233812, 0.5826338153607296, 0.8578935097401321, 0.9613064644611691, 0.6569080711259415, 0.5964504845738735, 0.7238438716274622, 0.6152379585681582, 0.7028706225725743, 0.6286646502808991, 0.9454954059204039, 0.9657145435570724, 0.8887223697274484, 0.8319528336531563, 0.5521051136203532, 0.5272277803844592, 0.9798660204149985, 0.7409746453470806, 0.9220562212169872, 0.681539969684193, 0.9501918616884968, 0.5938937119165719, 0.9994507608101767, 0.641809567044199, 0.7904095104503841, 0.5819177662102673, 0.8623193264701006, 0.7939908676654184, 0.5475019513938757, 0.6068950738873848, 0.7694507980252558, 0.7832654012248532, 0.603625480750009, 0.6452419986881544, 0.7308955961591845, 0.827159130611637, 0.7345029813083852, 0.9886183967776535, 0.9210262466393436, 0.7052460318717644, 0.5925307076755095, 0.9252606405768147, 0.5275002187284666, 0.7348148869704649, 0.6022646689303777, 0.7270367878998142, 0.7310164852112656, 0.7912824881617857, 0.7796260478583426, 0.6339027365168979, 0.9159699089585278, 0.659648696749719, 0.6336178667283684, 0.6051983840082632, 0.5051881461295196, 0.9896664280870868, 0.7015517395654675, 0.8590421590093558, 0.5886282076351065, 0.7910048090087949, 0.6364362353783037, 0.9710008983382105, 0.8916159702636682, 0.9410253587449284, 0.9005166727251048, 0.638564253093431, 0.616468630349156, 0.6184881673329479, 0.6175126602325035, 0.6604745178858522, 0.6092522728678342, 0.801741752402612, 0.9634278232236331, 0.7387709109394963, 0.7630759983921123, 0.7283041442610403, 0.6203402288103971, 0.8015778980253627, 0.8391223135642112, 0.7944712359746922, 0.8838275439851018, 0.7433354392449414, 0.6911894149940389, 0.9803788811461798, 0.8007158577005433, 0.6930648631761736, 0.9001309176792125, 0.6707390794306789, 0.8354056627663782, 0.8435734782060744, 0.5797599553803378, 0.9066216632713424, 0.7991612386404454, 0.7955803970296196, 0.7632861822662925, 0.7405266378791044, 0.6582053623330482, 0.9155054096197781, 0.9593829726387233, 0.9041379435378043, 0.6681644228624846, 0.9454461944075463, 0.7193133927117152, 0.9213215577224918, 0.7874729952839149, 0.7519321048443814, 0.5908356416760131, 0.6939398188472583, 0.7355917033051953, 0.6783597620197914, 0.9517087080328119, 0.7880259326351198, 0.6027250072494603, 0.6676603363292982, 0.948351792369831, 0.819469877273101, 0.7727969871274771, 0.8111484198806831, 0.5231819217026783, 0.7600870039439256, 0.5480933345757237, 0.5805589710334431, 0.5349654796962973, 0.6581738675187683, 0.9097396226722196, 0.9020830173739374, 0.9647745324263013, 0.9003959327615022, 0.9040570025290546, 0.7499502939781535, 0.7206211077672091, 0.8940154224109221, 0.5719209149422212, 0.8351534922291851, 0.8984134750437912, 0.5418893047500222, 0.7112692912147542, 0.859747827607126, 0.6200663939331894, 0.6930071967052758, 0.7935547788347256, 0.7904121943880922, 0.6242237320807535, 0.8364913372096974, 0.7347587624060761, 0.9167447913610558, 0.7778631436795674, 0.6273263120673311, 0.6041810625675293, 0.8902934068976789, 0.6204001543117776, 0.915229280316318, 0.6679750868405399, 0.9481182544015814, 0.8232313260941868, 0.5026372403517755, 0.7295348910278137, 0.9044182073135916, 0.7474498919366019, 0.8884895888513868, 0.8839262991515248, 0.5050117891976109, 0.5082836948125902, 0.789327833436654, 0.7291028960151271, 0.7075227860366105, 0.52233864175372, 0.7599666055546077, 0.7793358994267771, 0.6844106054233956, 0.7372427138912789, 0.7400929189684142, 0.7062838327664647, 0.5342957040259997, 0.6598235411576279, 0.863189228409526, 0.5012407379118442, 0.559402512743872, 0.5239311647392197, 0.7875590528519905, 0.7423484508063435, 0.888310229063817, 0.9771385988335997, 0.9502194936518114, 0.8129801515150162, 0.639776228675315, 0.6675060941035695, 0.7545721348826149, 0.6075904968272774, 0.551418344866885, 0.7540086428301818, 0.742780165314501, 0.7942598880002558, 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0.977938295744087, 0.6105114162714617, 0.7167427905688974, 0.8000515708524792, 0.5482297872185238, 0.7508074742205322, 0.7322797572573767, 0.6753400699044635, 0.9137990365840745, 0.998461389548744, 0.690980308032658, 0.818938832750804, 0.8498909143114282, 0.8669922235357677, 0.6846412412530498, 0.8349251484402631, 0.5504895162639045, 0.5029441160403558, 0.6078325272361362, 0.941364753549758, 0.8684283802596386, 0.9698256690426523, 0.7623792872002471, 0.5866266273577398, 0.8744555023623695, 0.8656659329864247, 0.694749608089043, 0.8503419649932191, 0.6707528992301601, 0.7281683987937544, 0.5794004744607484, 0.5548483853536688, 0.8173238280606725, 0.5662136516014398, 0.9472877964692307, 0.9662189226521687, 0.8809637213507551, 0.9484995112031505, 0.603855762037673, 0.6190301073401612, 0.7759714495300014, 0.6812495525990976, 0.5581964664636048, 0.5157322660839125, 0.5327983328287655, 0.676662943038574, 0.7195561853912291, 0.8496203370603688, 0.7031654476690136, 0.7628927344364747, 0.6401246192637926, 0.873901303328094, 0.6067291219653064, 0.8856405857836454, 0.7458579983485729, 0.8572487689788263, 0.6966789430228922, 0.8573441996627968, 0.7684935262878682, 0.5300696236111822, 0.7004364722553948, 0.8654466450184333, 0.8642480326825016, 0.5858374292511832, 0.7030922001531389, 0.8106238250133202, 0.7399968819688154, 0.5460148858544501, 0.7250321467608289, 0.5571962870567713, 0.5078629528650159, 0.6982346922741237, 0.7857471842946786, 0.8877327587918169, 0.6585581540107309, 0.8116598151800688, 0.747715365437782, 0.9336380967506361, 0.694073960002382, 0.8951917397392773, 0.7473542664322421, 0.6449097761747643, 0.9853859172669315, 0.9758614060823007, 0.9692490868635653, 0.6304902155256678, 0.8154217582227172, 0.6007202366660322, 0.8991014004095202, 0.9836053210116185, 0.6248699484971304, 0.5022811790664113, 0.6354653434510809, 0.8972449525882675, 0.7867764329796896, 0.8009997506135567, 0.9827142501988879, 0.8701007566219271, 0.6934090181937895, 0.5490554555208669, 0.5162894533117021, 0.860909419753632, 0.6378925293508746, 0.6306613781804762, 0.9184258452672378, 0.514463406352677, 0.9892271741227459, 0.6857371488113535, 0.8408485081717467, 0.7228965240652016, 0.5475211271328937, 0.8716330917477402, 0.9383137586345289, 0.97716431034524, 0.5529319955635186, 0.548062024151347, 0.531981195644849, 0.5618950650886896, 0.966796630221276, 0.6477394566161587, 0.6304865288116894, 0.8253320893868286, 0.9931543046342433, 0.9313965444674197, 0.9673591687962413, 0.849836539230633, 0.8989959207840466, 0.9479778784016969, 0.8049732021072659, 0.812284747542995, 0.6862930676350383, 0.7603531638804137, 0.6129126429093278, 0.8344746364144594, 0.6014754767538051, 0.7984350438898118, 0.6559993813190429, 0.6039787169972943, 0.5907231701469875, 0.7852371706240101, 0.5503192155169732, 0.7823362813870136, 0.5703770154055332, 0.6731913810220241, 0.5327363273976906, 0.8483620282392144, 0.7272551163534895, 0.7701622268742793, 0.5294944389268883, 0.9105675026955353, 0.6523681802130985, 0.5637059999943499, 0.7303592812507731, 0.8683296121116015, 0.5467226447225682, 0.6260419165034325, 0.7846440267000485, 0.6562418084838257, 0.8505897190502905, 0.7806010737822862, 0.8491743586908472, 0.8098015578686022, 0.9694431575598912, 0.6220646076132311, 0.915680932398385, 0.8005596734144543, 0.9008880573911291, 0.6087675230327513, 0.6346070743110772, 0.6568114621957369, 0.9738223160703656, 0.8064588568312203, 0.5486314296534287, 0.6897433862581877, 0.5845634550179004, 0.7238529793749673, 0.6353308783903032, 0.7390482931498845, 0.6829113295876104, 0.6845041103925811, 0.582460320323684, 0.9495364423303935, 0.9484337159848175, 0.7333434873129251, 0.9723579624805812, 0.711285640064631, 0.8046510091630226, 0.6531471344160089, 0.766454764936972, 0.9177686988371256, 0.6420298878678479, 0.653299254541838, 0.7367180249609127, 0.5910997309327363, 0.7981958948823482, 0.9018233398662102, 0.8266027388131537, 0.6150723031046844, 0.6284683770685207, 0.7637957588027069, 0.5788283759007278, 0.8088043223471528, 0.9039664692019467, 0.841938698381181, 0.9770885772706168, 0.696092308566556, 0.9586040608182607, 0.5373125871423583, 0.5721428773423383, 0.9925011850621739, 0.7337317126837003, 0.8942757807023138, 0.8086787169708278, 0.7008989672113916, 0.9424709422742046, 0.7499338087656275, 0.9639437339944612, 0.5097418162510243, 0.7154324633630031, 0.8895186834833613, 0.6539072431865689, 0.5900085586863933, 0.8430707662220129, 0.5624373358110046, 0.6751407128785298, 0.9192115845720081, 0.8524322593131068, 0.642443894922206, 0.565520912481932, 0.8078023775651542, 0.5935456572782212, 0.9205581825304199, 0.679259053053731, 0.599735898918182, 0.6665303600592962, 0.8562310545165932, 0.7853349629036164, 0.5553921545989677, 0.655374490956194, 0.94573719258375, 0.9923587167343892, 0.6287070171260707, 0.6847474186650587, 0.6570987940130842, 0.5094437530616389, 0.584284424406281, 0.9048297127787109, 0.8935351387943827, 0.595393663077988, 0.8398012432198452, 0.6386059341960504, 0.728926246665449, 0.5211332927226959, 0.6775099231228755, 0.8965758096987755, 0.7814415356032653, 0.6107543589220623, 0.8011420980171877, 0.7590544136106354, 0.9524706877505695, 0.9006280759795233, 0.7072411358714381, 0.8986418604509009, 0.9615346072503841, 0.8164319799046598, 0.9925191841100148, 0.8229398579931427, 0.9682674746423386, 0.5790949429988343, 0.747555440790654, 0.9081853035343015, 0.6853664321738542, 0.538620774117532, 0.7349790559676209, 0.6954938322047198, 0.8290401738709626, 0.9903611598943909, 0.9751986807659287, 0.670294130148869, 0.5485967060177881, 0.9726268435487226, 0.9587362979261294, 0.8997325115033346, 0.6083545327380955, 0.632454959314743, 0.6431943710617111, 0.8477121493492338, 0.7242338948494179, 0.5749651974390352, 0.5807458792283178, 0.5693252905422597, 0.7407049099678038, 0.6384632796418149, 0.7213377251091105, 0.585651493494634, 0.9443771951131228, 0.7448448727725523, 0.9289553477980012, 0.9477075925242289, 0.6379967905457331, 0.9538756134247544, 0.8387210897104669, 0.8007358593580916, 0.6379788743053108, 0.7335730200287223, 0.7325820177151723, 0.8306758536374792, 0.8742811598251952, 0.7353022190337289, 0.9847328878976684, 0.6204890654114827, 0.7724372596252826, 0.7897003702683575, 0.9615601665161841, 0.9299312270054225, 0.6878132238912323, 0.5602821220570198, 0.599875395507881, 0.9493444853873728, 0.8396508687678221, 0.748368780464191, 0.9647760372388096, 0.5344289107790465, 0.8050947989594814, 0.7420251677608669, 0.7202878774043799, 0.8079162453838787, 0.7218634290658259, 0.842248542474948, 0.9229326397883748, 0.546194439434837, 0.616789308495397, 0.8036281120963695, 0.8062202591659293, 0.803168512170283, 0.617988721475243, 0.6610202640345997, 0.561382265937759, 0.8438922837134241, 0.5760125899865269, 0.6425909073008362, 0.9418073008453198, 0.5118833453577587, 0.6177494544357958, 0.8861530194932181, 0.9265611361376198, 0.5593228826821032, 0.8380689461639621, 0.695264005667601, 0.8509739125068339, 0.6071211620390118, 0.9471442544050959, 0.6542812043749224, 0.7275174642201909, 0.6386508851862411, 0.9227080948784381, 0.9758410404453209, 0.5726926987858678, 0.9777414639462099, 0.5771473755676657, 0.7998545761420697, 0.8936879841370415, 0.5743336340620835, 0.5932048949374049, 0.748567954419218, 0.7959043333315472, 0.7981366666044267, 0.7962483926033948, 0.5233442495237348, 0.8117279362206595, 0.551527582056685, 0.891937241776999, 0.7911948334772749, 0.7533472429321433, 0.6254067452095589, 0.7625851856668571, 0.812458019367909, 0.6646052753996774, 0.8890456820207715, 0.9109012796738831, 0.7852647726164101, 0.9867979542454555, 0.9873583126161198, 0.5596962711678218, 0.8267224843242118, 0.845461335367959, 0.5946669807609315, 0.9887610600945934, 0.7154899633596712, 0.5273869545113574, 0.717712938457719, 0.6419174851177298, 0.7602863515120313, 0.6041331462893915, 0.9785831659119211, 0.8501502821404858, 0.9596455086259299, 0.9957524534361913, 0.7520531109015071, 0.9980584309973553, 0.6450049943056506, 0.5788240579933717, 0.9436976194157136, 0.6220104681020796, 0.5615918858925122, 0.8565685017341333, 0.9353275210332923, 0.8060381179477629, 0.531416075110954, 0.5435732755672448, 0.6724472214975437, 0.5478298166891002, 0.5426993739270594, 0.8445072983840487, 0.8202044711246744, 0.9145993976098343, 0.6797513525758622, 0.5552941491920609, 0.814784046013868, 0.5421888066283573, 0.7335644449205668, 0.765140205048806, 0.6590523564086812, 0.7565760575486362, 0.8051338874920899, 0.7759567080545862, 0.7958513185238357, 0.791808094970754, 0.9042114218947648, 0.565609435020997, 0.7232223022120855, 0.5479899789865841, 0.5127404153569955, 0.5934477490885127, 0.556524603647209, 0.884150144266608, 0.7060844686303376, 0.642415117582579, 0.8821703668782432, 0.6365302933412622, 0.7948024864849565, 0.7936885590369943, 0.6799560039958294, 0.6276668843510438, 0.5694515361953587, 0.7416845766151062, 0.7089366080251205, 0.6304360980360443, 0.6432591700458898, 0.89638212597805, 0.8125794154176706, 0.6609319026078528, 0.8493981647854778, 0.9536562952727601, 0.6506227170115748, 0.5275771539924708, 0.7613086306889316, 0.8860108589243636, 0.6109878751501965, 0.5550534357761026, 0.5920700530162378, 0.8071229245902156, 0.9798226842911595, 0.6181063647335241, 0.7402002411929811, 0.6793917424051981, 0.8997227727698098, 0.8235587047372266, 0.5111811695063888, 0.9629648336334321, 0.5630050692613272, 0.8651796610136326, 0.831713505738368, 0.6205920883568838, 0.7982440797311188, 0.8115326761010195, 0.8058944038587942, 0.9007435608904053, 0.9254185208591595, 0.6695026512085047, 0.8173377956315773, 0.8154831736803348, 0.5049891927593384, 0.8641057611881313, 0.7385168430565561, 0.5855031879764652, 0.715707261330733, 0.746076308145571, 0.5451059749169278, 0.5937803849480555, 0.8868973958955277, 0.7445755222260202, 0.8374664087424166, 0.544983193953678, 0.9301726640954764, 0.8968943705055423, 0.8437591323977593, 0.5549532498252554, 0.9376322086997624, 0.9744589914066247, 0.7814045283366112, 0.7545293886217106, 0.675237812699901, 0.8019603583638506, 0.8617431861977017, 0.7208959815721632, 0.705362973338242, 0.6283293146722263, 0.748457581721768, 0.751593863578792, 0.5493798027644704, 0.7390545435391915, 0.5917331986302512, 0.8590997005159536, 0.94241241524125, 0.9008990209801245, 0.7501332063154458, 0.6077087504479122, 0.7940422491954733, 0.6933470938413862, 0.8601777004993398, 0.8824741700649763, 0.8412253720810614, 0.7864225040849245, 0.5670143023605417, 0.8896157494612131, 0.7695807911005296, 0.8187373216379833, 0.7322927679827669, 0.8255874206692386, 0.9053020401754817, 0.6522249262647131, 0.8448626844599094, 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, 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, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 47, 49, 51, 53, 56, 58, 60, 62, 64, 66, 68, 70, 73, 75, 77, 79, 81, 83, 85, 87, 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, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 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, 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, 475, 477, 479, 481, 483, 486, 488, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 512, 514, 516, 518, 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, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 682, 684, 686, 688, 691, 693, 695, 697, 699, 701, 703, 705, 707, 709, 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, 767, 769, 771, 773, 775, 777, 779, 781, 783, 785, 787, 789, 791, 793, 795, 797, 799, 801, 803, 805, 807, 809, 811, 813, 817, 819, 821, 823, 825, 827, 829, 831, 833, 835, 837, 839, 841, 843, 845, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 867, 869, 871, 873, 875, 877, 879, 881, 883, 885, 887, 889, 891, 893, 895, 897, 899, 901, 903, 905, 907, 909, 911, 913, 915, 917, 919, 921, 923, 925, 927, 929, 931, 933, 935, 937, 939, 941, 943, 947, 949, 951, 953, 955, 957, 959, 961, 963, 965, 967, 969, 972, 974, 976, 978, 980, 982, 984, 986, 988, 990, 992, 994, 996, 998, 1000, 1002, 1004, 1006, 1008, 1010, 1018, 1020, 1022, 1024, 1026, 1028, 1030, 1032, 1034, 1036, 1038, 1040, 1045, 1047, 1050, 1052, 1054, 1056, 1059, 1061, 1064, 1066, 1068, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1090, 1092, 1094, 1096, 1098, 1100, 1102, 1104, 1106, 1108, 1110, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1132, 1134, 1137, 1139, 1141, 1143, 1145, 1147, 1149, 1151, 1153, 1155, 1158, 1160, 1163, 1165, 1167, 1169, 1172, 1174, 1176, 1178, 1180, 1182, 1184, 1186, 1188, 1190, 1192, 1194, 1196, 1198, 1200, 1202, 1205, 1207, 1210, 1212, 1214, 1216, 1219, 1221, 1223, 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19633, 18854, 6464, 19021, 6663, 6629, 6624, 6676, 6686, 6681, 6696, 6691, 6706, 6701, 6721, 6716, 6731, 6726, 19021, 6663, 19024, 6676, 6686, 6681, 6696, 6691, 6706, 6701, 6721, 6716, 6731, 6726, 6701, 6706, 6646, 6795, 6790, 6800, 6785, 18868, 6764, 18870, 6858, 18872, 6871, 18875, 6764, 6858, 18877, 6871, 6083, 6078, 6093, 6088, 18882, 6106, 6040, 6035, 6119, 6133, 6138, 19649, 6062, 6057, 18891, 6083, 6078, 6093, 6088, 18882, 6106, 6040, 6035, 6119, 6133, 6138, 19651, 6062, 6057, 18891, 6083, 6078, 6093, 6088, 18897, 6106, 18900, 6119, 6138, 6133, 19655, 6138, 6133, 19657, 18907, 6154, 6212, 6217, 6222, 6227, 6237, 6232, 6242, 6252, 6247, 19659, 6265, 6260, 6270, 6252, 6247, 19661, 6265, 6260, 6275, 6217, 6212, 6227, 6222, 6237, 6232, 6242, 6252, 6247, 19663, 6265, 6260, 6275, 6270, 6285, 6280, 6320, 6315, 19665, 6328, 19667, 6333, 18942, 6320, 6315, 19669, 6333, 6328, 6362, 6357, 6362, 6357, 6362, 6357, 6367, 6377, 6372, 6387, 6382, 6397, 6392, 18961, 6428, 6423, 6418, 6428, 6423, 6433, 18969, 6451, 6446, 18973, 6464, 18976, 6477, 6721, 6716, 6731, 6726, 18983, 6764, 6795, 6790, 6785, 6795, 6790, 6800, 6823, 6818, 18993, 6795, 6790, 6785, 6795, 6790, 6800, 6823, 6818, 19002, 6833, 6838, 6843, 6853, 6848, 6853, 6848, 19011, 6871, 6629, 6624, 6663, 19024, 6676, 6686, 6681, 6696, 6691, 6701, 6706, 6646, 6721, 6716, 6731, 6726, 19021, 6663, 19024, 6676, 6686, 6681, 6696, 6691, 6706, 6701, 6711, 6721, 6716, 6731, 6726, 6795, 6790, 6800, 6785, 6823, 6818, 19055, 6823, 6818, 19058, 19043, 6764, 19046, 6858, 19066, 6871, 6795, 6790, 6785, 6795, 6790, 6800, 6823, 6818, 19055, 6823, 6818, 19058, 6838, 6833, 6843, 6853, 6848, 6858, 19066, 6871, 19242, 19241, 19244, 19243, 19244, 19243, 19244, 19243, 9709, 9704, 9719, 9714, 19699, 9709, 9704, 9719, 9714, 19702, 19705, 19242, 19241, 19244, 19243, 19709, 9726, 9724, 19598, 9677, 9675, 9696, 9691, 19711, 9709, 9704, 9719, 9714, 19638, 9677, 9675, 9696, 9691, 19713, 9709, 9704, 9719, 9714, 19715, 9726, 9724, 9577, 9577, 9696, 9691, 9709, 9704, 9719, 9714, 19717, 19719, 9674, 19721, 9674, 19723, 9696, 9691, 19725, 9696, 9691, 19727, 9709, 9704, 9719, 9714, 19729, 19731, 19733, 19735, 19695, 19694, 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, 19744, 19746, 19747, 19748, 19749, 19750, 19755, 19756, 19757, 19758, 19759, 19760, 19761, 19762, 19763, 19764, 19766, 19767, 19770, 19771, 19772, 19774, 19775, 19776, 19777, 19778, 19779, 19780, 19781, 19782, 19783, 19784, 19785, 19786, 19787, 19788, 19789, 19790, 19791, 19792, 19793, 19794, 19795, 19796, 19797, 19798, 19799, 19800, 19801, 19802, 19803, 19804, 19805, 19806, 19807, 19808, 19809, 19817, 19818, 19819, 19820, 19821, 19822, 19823, 19824, 19825, 19826, 19827, 19828, 19829, 19830, 19831, 19832, 19833, 19835, 19836, 19837, 19838, 19839, 19840, 19841, 19842, 19843, 19844, 19845, 19846, 19847, 19848, 19849, 19850, 19851, 19852, 19853, 19854, 19855, 19856, 19858, 19859, 19860, 19861, 19862, 19863, 19864, 19865, 19866, 19867, 19868, 19869, 19870, 19871, 19872, 19873, 19874, 19875, 19877, 19878, 19879, 19880, 19881, 19882, 19883, 19884, 19885, 19886, 19887, 19888, 19889, 19891, 19892, 19893, 19894, 19895, 19896, 19897, 19898, 19899, 19901, 19902, 19903, 19904, 19905, 19907, 19908, 19910, 19911, 19912, 19913, 19914, 19915, 19916, 19917, 19918, 19927, 19928, 19930, 19931, 19933, 19934, 19936, 19937, 19939, 19940, 19941, 19942, 19943, 19944, 19946, 19947, 19949, 19950, 19951, 19952, 19953, 19954, 19955, 19956, 19957, 19958, 19959, 19960, 19961, 19968, 19969, 19970, 19971, 19973, 19974, 19978, 19979, 19982, 19983, 19984, 19985, 19986, 19987, 19988, 19989, 19990, 19991, 19992, 19993, 19994, 19995, 19996, 19998, 19999, 20000, 20001, 20006, 20007, 20008, 20009, 20010, 20011, 20012, 20013, 20014, 20015, 20016, 20017, 20018, 20019, 20020, 20021, 20022, 20023, 20024, 20025, 20026, 20027, 20028, 20029, 20030, 20031, 20033, 20034, 20036, 20037, 20038, 20039, 20040, 20041, 20042, 20043, 20044, 20045, 20047, 20048, 20050, 20051, 20052, 20053, 20054, 20055, 20056, 20057, 20058, 20059, 20060, 20061, 20062, 20063, 20064, 20065, 20066, 20067, 20068, 20069, 20070, 20071, 20072, 20073, 20074, 20075, 20076, 20077, 20078, 20079, 20080, 20081, 20082, 20083, 20084, 20086, 20087, 20088, 20089, 20090, 20091, 20092, 20093, 20095, 20096, 20097, 20099, 20100, 20101, 20103, 20105, 20106, 20107, 20108, 20111, 20112, 20113, 20114, 20115, 20116, 20117, 20118, 20119, 20120, 20121, 20122, 20123, 20124, 20125, 20126, 20127, 20128, 20129, 20130, 20133, 20134, 20135, 20136, 20137, 20138, 20139, 20140, 20141, 20142, 20143, 20144, 20145, 20146, 20147, 20148, 20149, 20150, 20151, 20152, 20153, 20154, 20155, 20156, 20157, 20158, 20159, 20160, 20161, 20162, 20164, 20165, 20167, 20168, 20169, 20170, 20171, 20173, 20174, 20176, 20177, 20178, 20179, 20180, 20181, 20182, 20183, 20184, 20185, 20186, 20187, 20188, 20190, 20191, 20192, 20193, 20194, 20195, 20196, 20197, 20199, 20200, 20201, 20202, 20203, 20204, 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20376, 20377, 20378, 20379, 20380, 20381, 20383, 20384, 20385, 20386, 20387, 20388, 20389, 20390, 20391, 20392, 20393, 20394, 20395, 20396, 20397, 20398, 20399, 20400, 20401, 20402, 20403, 20404, 20405, 20406, 20407, 20408, 20409, 20410, 20411, 20412, 20414, 20415, 20417, 20418, 20419, 20420, 20421, 20422, 20424, 20425, 20427, 20428, 20429, 20430, 20431, 20432, 20433, 20434, 20435, 20436, 20437, 20438, 20439, 20440, 20441, 20442, 20443, 20444, 20445, 20446, 20447, 20449, 20450, 20452, 20453, 20454, 20455, 20456, 20457, 20458, 20459, 20460, 20461, 20462, 20463, 20464, 20465, 20466, 20467, 20468, 20469, 20470, 20471, 20472, 20473, 20474, 20475, 20476, 20477, 20478, 20479, 20480, 20481, 20482, 20483, 20484, 20485, 20486, 20487, 20488, 20489, 20490, 20491, 20492, 20493, 20494, 20495, 20496, 20497, 20498, 20499, 20500, 20501, 20502, 20503, 20504, 20505, 20506, 20507, 20508, 20509, 20510, 20511, 20512, 20513, 20514, 20515, 20516, 20517, 20518, 20519, 20520, 20521, 20522, 20523, 20524, 20525, 20526, 20527, 20528, 20529, 20530, 20531, 20532, 20533, 20534, 20535, 20536, 20537, 20538, 20539, 20540, 20541, 20542, 20543, 20544, 20545, 20546, 20547, 20548, 20549, 20550, 20551, 20552, 20553, 20554, 20555, 20556, 20557, 20558, 20559, 20560, 20561, 20562, 20563, 20564, 20565, 20566, 20567, 20568, 20569, 20570, 20571, 20572, 20573, 20574, 20575, 20576, 20577, 20578, 20579, 20580, 20581, 20582, 20583, 20584, 20585, 20586, 20587, 20588, 20589, 20590, 20591, 20592, 20593, 20594, 20595, 20596, 20597, 20598, 20599, 20600, 20601, 20602, 20603, 20604, 20605, 20606, 20607, 20608, 20609, 20610, 20611, 20612, 20613, 20615, 20616, 20618, 20619, 20621, 20622, 20623, 20624, 20625, 20626, 20627, 20628, 20629, 20630, 20631, 20632, 20633, 20635, 20636, 20637, 20638, 20639, 20640, 20642, 20643, 20645, 20646, 20648, 20649, 20650, 20651, 20652, 20653, 20654, 20655, 20656, 20658, 20659, 20661, 20662, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20674, 20676, 20677, 20679, 20680, 20683, 20684, 20685, 20686, 20687, 20688, 20689, 20690, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 20698, 20699, 20700, 20702, 20703, 20704, 20705, 20706, 20707, 20708, 20709, 20710, 20711, 20712, 20713, 20715, 20716, 20717, 20718, 20720, 20721, 20722, 20723, 20724, 20725, 20726, 20727, 20728, 20729, 20730, 20732, 20733, 20734, 20735, 20736, 20737, 20738, 20739, 20740, 20741, 20742, 20743, 20744, 20745, 20746, 20747, 20748, 20749, 20750, 20751, 20752, 20753, 20754, 20755, 20756, 20757, 20758, 20759, 20760, 20761, 20762, 20763, 20764, 20765, 20766, 20767, 20769, 20770, 20771, 20772, 20773, 20774, 20775, 20776, 20777, 20778, 20779, 20780, 20781, 20782, 20783, 20785, 20786, 20787, 20788, 20789, 20790, 20791, 20792, 20793, 20794, 20795, 20796, 20797, 20798, 20800, 20801, 20803, 20804, 20805, 20806, 20807, 20808, 20809, 20810, 20811, 20812, 20813, 20814, 20815, 20816, 20817, 20818, 20819, 20820, 20821, 20822, 20823, 20824, 20825, 20826, 20827, 20828, 20829, 20830, 20831, 20832, 20834, 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31279, 31331, 31337, 31341, 31861, 31863, 31865, 31867, 31869, 31872, 31874, 31876, 31878, 31880, 31884, 31885, 31383, 31889, 31890, 31388, 31894, 31895, 31897, 31898, 31395, 31397, 31904, 31905, 31403, 31909, 31405, 31407, 31912, 31410, 31915, 31918, 31417, 31922, 31923, 31420, 31925, 31927, 31930, 31932, 31935, 31434, 31939, 31940, 31437, 31942, 31944, 31946, 31948, 31951, 31952, 31953, 31955, 31956, 31957, 31959, 31961, 31963, 31966, 31968, 31970, 31973, 31975, 31978, 31980, 31983, 31985, 31987, 31990, 31991, 31478, 31994, 31481, 31997, 31999, 32002, 32003, 31492, 32006, 31495, 32009, 32011, 32013, 32015, 32016, 31507, 32019, 31510, 32022, 32026, 32024, 32029, 32030, 32032, 32033, 32036, 32034, 32039, 32040, 32042, 32043, 32044, 32048, 32046, 32051, 32052, 32054, 30747, 30753, 32055, 30762, 30758, 32056, 30762, 32058, 32059, 32061, 32062, 31536, 32066, 32067, 32069, 32070, 31543, 31847, 31848, 31849, 32073, 32076, 32078, 32079, 32081, 32083, 32086, 32088, 32089, 32091, 32094, 32096, 32097, 31850, 32101, 32104, 32106, 32107, 32109, 32111, 32113, 32118, 32120, 32122, 32124, 32126, 32129, 30180, 31852, 31853, 32133, 32134, 32135, 32137, 32138, 32139, 32141, 32143, 32145, 32146, 32147, 32148, 32151, 32154, 32157, 32160, 32162, 32164, 32166, 32168, 32172, 32174, 32176, 32178, 32181, 32184, 32185, 32187, 32189, 32191, 32193, 32196, 32197, 32200, 32204, 32202, 30972, 32206, 32208, 32209, 32213, 32211, 30972, 32215, 32217, 32218, 32221, 31684, 31688, 31693, 30972, 32230, 32232, 31701, 32235, 32237, 32240, 32245, 32243, 30972, 32247, 32249, 32250, 32252, 32253, 32258, 32256, 32260, 30972, 32262, 32263, 32265, 32269, 32267, 30433, 32274, 32272, 30453, 32278, 32279, 32282, 32283, 32285, 32286, 31746, 32289, 32291, 32292, 32293, 32294, 32296, 32298, 32300, 32301, 32302, 32304, 32306, 32308, 32309, 32310, 32312, 32314, 32317, 32318, 31783, 32321, 31786, 32324, 31860, 31871, 31882, 32128, 32132, 32199, 32116, 32195, 32201, 32128, 32132, 32195, 32201, 32199, 32116, 32192, 32128, 32132, 32220, 32223, 32225, 32229, 32255, 31729, 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, 32358, 32361, 32363, 32366, 31883, 31390, 31896, 31903, 32386, 31921, 31938, 32423, 32425, 32429, 32432, 32436, 32439, 32444, 32448, 32028, 32451, 30714, 32454, 32038, 32457, 30732, 31524, 32461, 32050, 32465, 32466, 32468, 32469, 32471, 30768, 32057, 31534, 32065, 31541, 32482, 32483, 32484, 32498, 32506, 32509, 32512, 32511, 32513, 32514, 32518, 32523, 32544, 32546, 32550, 32551, 30978, 32207, 32556, 32557, 30978, 32216, 32565, 31699, 32573, 32574, 30978, 32248, 32581, 32583, 30978, 32261, 32588, 32589, 31000, 32591, 32592, 31016, 32277, 32281, 31744, 32603, 32609, 32614, 32616, 32620, 32623, 32624, 32625, 32370, 32373, 32379, 32378, 32384, 32382, 32394, 31917, 31919, 32394, 31929, 31931, 31934, 31936, 32403, 32476, 32481, 32183, 31950, 31576, 32408, 32171, 32103, 32411, 32410, 31962, 31960, 31958, 32416, 32171, 31965, 31969, 32171, 31977, 32156, 31972, 31981, 31979, 32476, 32424, 31992, 32431, 32004, 32438, 32017, 32446, 32476, 32307, 31770, 31769, 32290, 31751, 31750, 32476, 32481, 32626, 32627, 31605, 31604, 32142, 32140, 32487, 32171, 32075, 32082, 32080, 32492, 32171, 32085, 32092, 32090, 32183, 32180, 31576, 32503, 32505, 32496, 32495, 32628, 32629, 32630, 32631, 32192, 32632, 32633, 31605, 31604, 32142, 32140, 32501, 32171, 32103, 32108, 31576, 32503, 32505, 32504, 32634, 32635, 32636, 32637, 32638, 32639, 32640, 31605, 31604, 32142, 32140, 32171, 32150, 32153, 32156, 32159, 32167, 32165, 32163, 32161, 32536, 32171, 32170, 32177, 32183, 32180, 31644, 31643, 32543, 32542, 32194, 32195, 32190, 32201, 32599, 32641, 32562, 32642, 32562, 32643, 32563, 32644, 32564, 32568, 32236, 31750, 31751, 32238, 32239, 31770, 31769, 32242, 32319, 32622, 32645, 32579, 32646, 31727, 32599, 32290, 31751, 31750, 32299, 31761, 31760, 32307, 31770, 31769, 32319, 32622, 31, 32672, 32674, 32681, 32682, 32691, 32693, 32695, 32697, 32700, 32464, 32467, 32470, 32706, 32715, 32718, 32722, 32724, 32726, 32727, 32730, 32731, 32733, 32736, 32737, 32582, 32741, 32744, 32745, 32747, 32748, 32369, 32677, 32760, 32372, 32677, 32761, 32377, 32762, 32763, 32381, 32764, 32765, 32680, 32680, 32766, 32767, 32768, 32769, 32770, 32771, 32772, 32773, 32774, 32027, 32037, 32473, 32775, 32708, 32776, 32710, 32777, 32778, 32779, 32780, 32781, 32782, 32783, 32784, 32785, 32786, 32787, 32788, 32789, 32790, 32791, 32792, 32793, 32794, 32795, 32796, 32797, 32027, 32037, 32473, 32698, 32798, 32683, 32799, 32426, 32800, 32685, 32801, 32433, 32802, 32687, 32803, 32441, 32804, 32689, 32805, 32027, 32037, 32473, 32806, 32698, 32807, 32808, 32809, 32615, 32810, 32811, 32812, 32605, 32049, 32473, 32813, 32708, 32478, 32814, 32710, 31545, 31546, 32817, 32818, 31547, 32721, 32819, 32820, 32821, 32822, 32823, 32824, 32825, 32826, 32827, 32828, 32829, 32830, 32831, 32832, 32833, 32834, 32835, 32836, 32837, 32842, 32838, 32840, 32100, 32845, 32846, 32721, 32847, 32848, 32849, 32850, 32851, 32852, 32853, 32854, 32855, 32856, 32857, 32859, 32864, 32865, 31603, 31602, 32721, 32866, 32867, 32868, 32869, 32870, 32871, 32872, 32873, 32874, 32875, 32876, 32877, 32878, 32879, 32880, 32881, 32882, 32883, 32884, 32885, 32886, 32887, 32888, 32889, 32192, 32890, 32205, 32554, 32891, 32751, 32214, 32560, 32893, 32561, 32895, 32897, 32899, 32900, 32734, 32901, 32902, 32903, 32605, 32904, 32905, 32906, 32907, 32615, 32908, 32909, 32756, 32910, 32246, 32577, 32912, 32259, 32585, 32914, 32270, 32275, 32596, 32594, 32915, 32751, 32916, 32917, 32918, 32605, 32919, 32920, 32921, 32610, 32922, 32923, 32924, 32615, 32617, 32925, 32756, 32926, 25, 26, 27, 28, 29, 30, 31, 32673, 32675, 32958, 32959, 32961, 32962, 32964, 32965, 32967, 32968, 32970, 32971, 32930, 32931, 32031, 32981, 32933, 32041, 32982, 32935, 32983, 32985, 32987, 32988, 32990, 32992, 32994, 32996, 32999, 33003, 33005, 33007, 32031, 33009, 32933, 32041, 33010, 32935, 33011, 33012, 33014, 33016, 33018, 33020, 33022, 33024, 33026, 32031, 33028, 32933, 32041, 33029, 32935, 33030, 33032, 33034, 33036, 33038, 33040, 32053, 33041, 32940, 32704, 32702, 33042, 33044, 33045, 33047, 32716, 32131, 33048, 33049, 33052, 33050, 33053, 33054, 32943, 33056, 33059, 33061, 33064, 33066, 33068, 33070, 33074, 32716, 32131, 33076, 33079, 33080, 32943, 33082, 33086, 33088, 33090, 32716, 32131, 33094, 33095, 33092, 33096, 33097, 32943, 33099, 33101, 33104, 33106, 33108, 33112, 33114, 33116, 32199, 33121, 33119, 33123, 32946, 33124, 33126, 33127, 32948, 33128, 32892, 33130, 32894, 32896, 32898, 32953, 33135, 33137, 33139, 33142, 33144, 33147, 33149, 32951, 33150, 32911, 33152, 32953, 33153, 32913, 33155, 32955, 33156, 32957, 33157, 33158, 33160, 33162, 33164, 33166, 33168, 33170, 33172, 33173, 33175, 32327, 32335, 32979, 32974, 32329, 32979, 32977, 32331, 32343, 32333, 32335, 32337, 32339, 33146, 32341, 33146, 32341, 32343, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33184, 33185, 33187, 33189, 33196, 33197, 33198, 33200, 33201, 33203, 32984, 32986, 33209, 33211, 33212, 33213, 33216, 33218, 33219, 33221, 33223, 33231, 33233, 33234, 33236, 33031, 33243, 33245, 33246, 33247, 33043, 33046, 33252, 33253, 33254, 33260, 33261, 33263, 33267, 33268, 33269, 33270, 33271, 33274, 33275, 33278, 33279, 33280, 33281, 33286, 33287, 33289, 33291, 33295, 33296, 33299, 33125, 33303, 33310, 33134, 33318, 33322, 33326, 33328, 33329, 33159, 33208, 33293, 33193, 33191, 33312, 31806, 33340, 32326, 33341, 32334, 33314, 31807, 33342, 33343, 33344, 33345, 33346, 33347, 33332, 31810, 33336, 31812, 33174, 33348, 32342, 33208, 33293, 33349, 32332, 33017, 33350, 32334, 33021, 33351, 32336, 33025, 33352, 32338, 33239, 31804, 33241, 31805, 33312, 31806, 33314, 31807, 33353, 33354, 32340, 33266, 33276, 33293, 33305, 33307, 33308, 33309, 33312, 31806, 33334, 33314, 31807, 33355, 33356, 32340, 33320, 33324, 33332, 31810, 33334, 31811, 33336, 31812, 33174, 33357, 32342, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33382, 33384, 33392, 33394, 33397, 33399, 33402, 33403, 33410, 33418, 33283, 33426, 33429, 32816, 32757, 33411, 33389, 33442, 33415, 32863, 32759, 32758, 33425, 33443, 33215, 33002, 33444, 33445, 33379, 33378, 33446, 33447, 33449, 33451, 33452, 33453, 33454, 32328, 33457, 32330, 33386, 33387, 33460, 33461, 33462, 33463, 33464, 33466, 32816, 32815, 33411, 33389, 33467, 33415, 32863, 32862, 33425, 33468, 33215, 33002, 33396, 33470, 33471, 33473, 33474, 33476, 33477, 33479, 33401, 33480, 33481, 33482, 33483, 33406, 33407, 33484, 33485, 33486, 33487, 33490, 32816, 32815, 33411, 33491, 33264, 33262, 33415, 32844, 32843, 33419, 33492, 33085, 33421, 32863, 32862, 33425, 33493, 33111, 33431, 33432, 33433, 33494, 33495, 33496, 33497, 33434, 33435, 33498, 33499, 33500, 33501, 33502, 33505, 33436, 33506, 33437, 33507, 33439, 33438, 33441, 33508, 33509, 33510, 33511, 33512, 33513, 33514, 33516, 33543, 33544, 33548, 33549, 33550, 33551, 33552, 33554, 33555, 33556, 33557, 33558, 33284, 33427, 33560, 33561, 33564, 33565, 33562, 33448, 33450, 33573, 33575, 33385, 33383, 33576, 33577, 33465, 33584, 33585, 33586, 33587, 33589, 33590, 33591, 33592, 33284, 33427, 33594, 33595, 33395, 33393, 33596, 33469, 33472, 33475, 33478, 33400, 33398, 33604, 33609, 33610, 33489, 33616, 33617, 33618, 33620, 33621, 33622, 33623, 33624, 33625, 33272, 33627, 33628, 33629, 33630, 33631, 33284, 33633, 33427, 33634, 33635, 33636, 33641, 33642, 33504, 33649, 33651, 33653, 33654, 33655, 33515, 33659, 33567, 33571, 33581, 33659, 33579, 33659, 33608, 33606, 33614, 33659, 33612, 33647, 33659, 33644, 33661, 33659, 33657, 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, 33666, 33667, 33258, 33670, 33672, 33676, 33677, 33678, 33664, 33680, 33456, 33459, 33687, 33688, 33692, 33258, 33695, 33697, 33700, 33701, 33702, 33704, 33705, 33711, 33712, 33664, 33717, 33258, 33619, 33723, 33726, 33626, 33729, 33732, 33734, 33632, 33743, 33690, 33747, 33748, 33684, 33683, 33749, 33690, 33691, 33750, 33751, 33752, 33715, 33710, 33709, 33708, 33707, 33715, 33753, 33754, 33755, 33715, 33716, 33756, 33757, 33758, 33638, 33637, 33736, 33639, 33739, 33640, 33740, 33759, 33760, 33761, 33652, 33650, 33746, 33762, 33763, 33764, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33794, 33796, 33675, 33559, 33792, 33800, 33682, 33804, 33807, 33699, 33593, 33792, 33813, 33815, 33817, 33819, 33820, 33725, 33731, 33827, 33792, 33671, 33829, 33802, 33832, 33833, 33803, 33835, 33836, 33838, 33696, 33840, 33841, 33842, 33843, 33844, 33845, 33846, 33849, 33850, 33852, 33728, 33854, 33855, 33856, 33857, 33858, 33859, 33860, 33862, 33745, 33864, 33865, 33866, 33868, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33669, 33891, 33892, 33694, 33898, 33899, 33719, 33908, 33909, 33890, 33894, 33911, 33914, 33831, 33689, 33916, 33918, 33897, 33706, 33920, 33922, 33713, 33925, 33714, 33927, 33722, 33905, 33929, 33906, 33930, 33932, 33934, 33936, 33938, 33941, 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, 33952, 33961, 33954, 33962, 33963, 33913, 33966, 33967, 33955, 33969, 33957, 33970, 33971, 33973, 33975, 33976, 33958, 33977, 33978, 33980, 33959, 33981, 33984, 33985, 33986, 25, 26, 27, 28, 29, 30, 31, 34016, 34018, 33910, 34020, 34021, 33915, 34024, 34026, 33919, 33924, 33926, 34032, 34036, 34037, 34039, 33979, 33979, 33979, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34051, 34049, 34063, 33960, 34023, 34055, 34064, 33968, 34031, 33974, 34028, 34060, 34065, 34033, 34040, 34038, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34081, 34083, 34084, 34080, 34085, 34087, 34088, 34089, 34090, 34091, 34093, 34094, 34095, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34112, 34115, 34116, 34118, 34121, 34123, 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, 34144, 34114, 34146, 34147, 34148, 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, 34149, 34179, 34177, 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, 34208, 34209, 34210, 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, 34240, 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, 34272, 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[]= {
1, 3, 5, 7, 9, 11, 13, 15, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 48, 50, 52, 54, 57, 59, 61, 63, 65, 67, 69, 71, 74, 76, 78, 80, 82, 84, 86, 88, 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, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 171, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 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, 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, 476, 478, 480, 482, 484, 487, 489, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 513, 515, 517, 519, 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, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 641, 643, 645, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 683, 685, 687, 689, 692, 694, 696, 698, 700, 702, 704, 706, 708, 710, 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, 768, 770, 772, 774, 776, 778, 780, 782, 784, 786, 788, 790, 792, 794, 796, 798, 800, 802, 804, 806, 808, 810, 812, 814, 818, 820, 822, 824, 826, 828, 830, 832, 834, 836, 838, 840, 842, 844, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 892, 894, 896, 898, 900, 902, 904, 906, 908, 910, 912, 914, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 948, 950, 952, 954, 956, 958, 960, 962, 964, 966, 968, 970, 973, 975, 977, 979, 981, 983, 985, 987, 989, 991, 993, 995, 997, 999, 1001, 1003, 1005, 1007, 1009, 1011, 1019, 1021, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1039, 1041, 1046, 1048, 1051, 1053, 1055, 1057, 1060, 1062, 1065, 1067, 1069, 1071, 1073, 1075, 1077, 1079, 1081, 1083, 1085, 1087, 1089, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1111, 1113, 1115, 1117, 1119, 1121, 1123, 1125, 1127, 1129, 1131, 1133, 1135, 1138, 1140, 1142, 1144, 1146, 1148, 1150, 1152, 1154, 1156, 1159, 1161, 1164, 1166, 1168, 1170, 1173, 1175, 1177, 1179, 1181, 1183, 1185, 1187, 1189, 1191, 1193, 1195, 1197, 1199, 1201, 1203, 1206, 1208, 1211, 1213, 1215, 1217, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1245, 1247, 1249, 1251, 1254, 1256, 1259, 1261, 1264, 1266, 1269, 1271, 1274, 1276, 1278, 1280, 1282, 1284, 1287, 1289, 1292, 1294, 1297, 1299, 1302, 1304, 1307, 1309, 1312, 1314, 1317, 1319, 1322, 1324, 1327, 1329, 1332, 1334, 1336, 1338, 1340, 1342, 1344, 1346, 1349, 1351, 1354, 1356, 1362, 1364, 1366, 1368, 1370, 1372, 1375, 1377, 1379, 1381, 1383, 1385, 1388, 1390, 1392, 1394, 1397, 1399, 1402, 1404, 1410, 1412, 1414, 1416, 1418, 1420, 1422, 1424, 1426, 1428, 1431, 1433, 1436, 1438, 1440, 1442, 1444, 1446, 1449, 1451, 1454, 1456, 1458, 1460, 1462, 1464, 1467, 1469, 1472, 1474, 1477, 1479, 1482, 1484, 1487, 1489, 1492, 1494, 1497, 1499, 1502, 1504, 1507, 1509, 1512, 1514, 1517, 1519, 1521, 1523, 1526, 1528, 1531, 1533, 1539, 1541, 1543, 1545, 1547, 1549, 1552, 1554, 1557, 1559, 1562, 1564, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 1581, 1583, 1585, 1587, 1589, 1591, 1593, 1595, 1597, 1599, 1601, 1603, 1605, 1607, 1609, 1611, 1613, 1615, 1617, 1619, 1621, 1623, 1625, 1627, 1629, 1631, 1633, 1635, 1637, 1640, 1642, 1644, 1646, 1649, 1651, 1653, 1655, 1657, 1659, 1661, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1679, 1681, 1683, 1685, 1687, 1689, 1691, 1693, 1695, 1697, 1699, 1701, 1703, 1705, 1707, 1709, 1711, 1713, 1715, 1717, 1719, 1721, 1723, 1725, 1727, 1729, 1731, 1733, 1735, 1737, 1739, 1741, 1743, 1745, 1747, 1749, 1751, 1753, 1755, 1757, 1759, 1761, 1763, 1765, 1767, 1769, 1771, 1773, 1775, 1777, 1779, 1781, 1783, 1785, 1787, 1789, 1791, 1793, 1795, 1797, 1799, 1801, 1803, 1805, 1807, 1810, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1826, 1828, 1830, 1832, 1834, 1836, 1840, 1842, 1844, 1846, 1848, 1850, 1852, 1854, 1856, 1858, 1860, 1862, 1864, 1866, 1868, 1870, 1872, 1874, 1876, 1878, 1881, 1883, 1885, 1887, 1889, 1891, 1894, 1896, 1899, 1901, 1904, 1906, 1909, 1911, 1913, 1915, 1918, 1920, 1922, 1924, 1927, 1929, 1933, 1935, 1937, 1939, 1941, 1943, 1945, 1947, 1949, 1951, 1953, 1955, 1957, 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13217, 32939, 32938, 32937, 13221, 13223, 13224, 13226, 32941, 32942, 13231, 13232, 13235, 33051, 13236, 33055, 32522, 33057, 33060, 33062, 33065, 33067, 33069, 33071, 33075, 32941, 32942, 13266, 13269, 33081, 32522, 33083, 33087, 33089, 33091, 32941, 32942, 13292, 13293, 33093, 13294, 33098, 32522, 33100, 33102, 33105, 33107, 33109, 33113, 33115, 33117, 32944, 13321, 33120, 13323, 32945, 13325, 13327, 13328, 32947, 13330, 33129, 13333, 33131, 33132, 33133, 32949, 13342, 33138, 13346, 33143, 13352, 13356, 13360, 32950, 13362, 33151, 13365, 32952, 13367, 33154, 13370, 32954, 13372, 32956, 13374, 13375, 13377, 33163, 13381, 33167, 13386, 33171, 13391, 13393, 13395, 32975, 32972, 32978, 32973, 32975, 32978, 32976, 32980, 33176, 33015, 33019, 33023, 33027, 33145, 33148, 33145, 33148, 33176, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 13099, 13101, 32960, 32963, 13122, 13130, 13134, 13136, 13137, 13139, 33205, 33206, 33210, 32998, 33001, 33214, 13166, 13168, 13169, 13171, 33013, 13197, 13199, 13200, 13202, 33238, 13216, 13218, 13219, 13220, 33249, 33251, 13227, 13229, 33255, 13239, 33058, 33063, 33072, 33073, 13262, 13264, 33077, 13272, 33084, 32861, 13286, 13288, 33282, 13297, 33288, 33290, 33110, 13317, 33122, 13324, 33301, 13329, 13340, 33311, 13361, 13366, 13371, 13373, 33330, 33331, 33265, 33292, 33192, 33190, 33136, 33313, 13423, 33194, 13425, 33195, 33141, 33315, 13429, 13430, 13431, 13433, 13434, 13435, 33161, 33333, 33169, 33337, 33338, 13446, 33339, 33207, 33292, 13467, 33224, 33225, 13470, 33226, 33227, 13473, 33228, 33229, 13476, 33230, 33033, 33240, 33037, 33242, 33136, 33313, 33141, 33315, 13492, 13493, 33316, 33265, 33292, 33292, 33304, 33306, 33323, 33323, 33136, 33313, 33140, 33141, 33315, 13532, 13533, 33316, 33319, 33323, 33161, 33333, 33165, 33335, 33169, 33337, 33338, 13549, 33339, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33199, 33202, 33217, 33220, 33232, 33235, 33244, 33404, 33257, 33078, 33424, 33103, 33118, 33409, 33408, 33259, 33388, 13404, 33414, 33423, 33377, 33376, 33285, 13411, 33391, 33390, 13417, 13418, 33188, 33186, 13421, 13422, 13424, 13426, 13427, 13428, 33455, 33380, 33458, 33381, 33204, 33250, 13441, 13442, 13443, 13444, 13445, 13447, 33409, 33408, 33259, 33388, 13453, 33414, 33423, 33422, 33285, 13459, 33391, 33390, 33222, 13468, 13469, 13471, 13472, 13474, 13475, 13477, 33237, 13481, 13482, 13483, 13484, 33248, 33250, 13488, 13489, 13490, 13491, 13494, 33409, 33408, 33259, 13499, 33413, 33412, 33414, 33417, 33416, 33273, 13507, 33420, 33277, 33423, 33422, 33285, 13514, 33428, 33298, 33300, 33302, 13521, 13522, 13523, 13524, 33321, 33323, 13527, 13528, 13529, 13530, 13531, 13534, 33317, 13536, 33321, 13538, 33327, 33325, 33440, 13542, 13543, 13544, 13545, 13546, 13547, 13548, 13550, 33405, 33256, 33297, 13399, 13400, 13401, 13403, 13405, 13406, 13407, 13408, 13409, 33546, 33547, 13413, 13414, 13419, 13420, 33563, 33568, 33569, 13432, 13436, 33537, 33536, 13439, 13440, 33583, 13448, 13449, 13450, 13452, 13454, 13455, 13456, 13457, 33546, 33547, 13461, 13462, 33539, 33538, 13466, 33597, 33599, 33601, 33603, 33541, 33540, 13480, 13486, 13487, 33615, 13495, 13496, 13497, 13500, 13501, 13502, 13503, 13504, 13505, 33545, 13508, 13509, 13510, 13511, 13512, 33546, 13515, 33547, 13518, 13519, 13520, 13525, 13526, 33648, 13535, 13537, 13539, 13540, 13541, 33663, 33645, 33566, 33570, 33580, 33658, 33578, 33658, 33607, 33605, 33613, 33645, 33611, 33646, 33645, 33643, 33660, 33658, 33656, 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, 33430, 33668, 33665, 33553, 33673, 13410, 13412, 33679, 33542, 33681, 33685, 33686, 13437, 13438, 33693, 33665, 33588, 33698, 13458, 13460, 33703, 13464, 13465, 13478, 13479, 33542, 33718, 33665, 33720, 33724, 13506, 33727, 33730, 13513, 13516, 33733, 33744, 33742, 13558, 13559, 33572, 33572, 13563, 33742, 33582, 13567, 13568, 13569, 33742, 33602, 33600, 33598, 33598, 33742, 13582, 13583, 13584, 33742, 33488, 13588, 13589, 13590, 33738, 33737, 33735, 33738, 33738, 33738, 33503, 13604, 13605, 13606, 33742, 33741, 33662, 13611, 13612, 13613, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 13402, 33674, 33797, 33798, 33294, 13416, 33801, 33805, 13451, 33810, 33811, 33294, 33814, 33816, 13485, 13498, 33721, 33822, 33825, 33826, 33294, 33795, 13555, 33572, 13560, 13561, 33574, 13564, 13566, 33839, 33808, 13574, 13576, 13577, 13578, 13579, 13580, 33847, 13585, 13587, 33853, 33823, 13597, 13598, 13599, 13600, 13601, 13602, 13603, 33863, 33828, 13608, 13609, 13610, 33869, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33888, 33799, 13415, 33896, 33812, 13463, 33903, 13517, 13552, 33889, 33893, 13557, 13562, 33912, 33895, 33837, 13571, 33809, 33900, 33921, 33923, 33901, 33848, 33902, 33851, 33904, 33821, 13594, 33824, 33931, 33933, 33935, 33861, 13607, 33867, 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, 33793, 13553, 33953, 13556, 33830, 33964, 13565, 33917, 33806, 13572, 33956, 13575, 33972, 13581, 13586, 33928, 33818, 13592, 13593, 13595, 33907, 33982, 33937, 33939, 33942, 25, 26, 27, 28, 29, 30, 31, 13551, 13554, 34019, 33965, 33834, 34022, 13570, 13573, 34027, 34029, 34030, 13591, 13596, 33983, 33940, 34034, 34034, 34034, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34052, 34017, 13615, 34048, 34053, 34025, 13620, 34054, 34058, 34057, 34056, 34035, 13626, 34059, 34062, 34061, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 13614, 13616, 13617, 34050, 13619, 13621, 13622, 13623, 13624, 13625, 13627, 13628, 13629, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 34082, 13618, 34086, 34119, 34092, 34124, 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, 34113, 34145, 34117, 34120, 34122, 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, 34180, 34178, 34176, 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, 13630, 13631, 13632, 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, 34241, 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, 34242, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 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, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 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, 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, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 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, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 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, 0, 1, 0, 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, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 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, 0, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 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, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 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, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 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, 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, 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, 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, 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, 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, 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 13664
#define SIZE_OF_AC 20672
__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[1073*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];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 427*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 + 428*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 + 429*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 + 430*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 + 431*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 + 432*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 + 433*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 + 434*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 + 435*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 + 436*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 + 437*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 + 438*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 + 439*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 + 440*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 + 441*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 + 442*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 + 443*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 + 444*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 + 445*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 + 446*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 + 447*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 + 448*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 + 449*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 + 450*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 + 451*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 + 452*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 + 453*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 + 454*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 + 455*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 + 456*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 + 457*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 + 458*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 + 459*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 + 460*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 + 461*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 + 462*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 + 463*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 + 464*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 + 465*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 + 466*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 + 467*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 + 468*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 + 469*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 + 470*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 + 471*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 + 472*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 + 473*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 + 474*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 + 475*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 + 476*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 + 477*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 + 478*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 + 479*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 + 480*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 + 481*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 + 482*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 + 483*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 + 484*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 + 485*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 + 486*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 + 487*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 + 488*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 + 489*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 + 490*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 + 491*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 + 492*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 + 493*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 + 494*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 + 495*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 + 496*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 + 497*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 + 498*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 + 499*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 + 500*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 + 501*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 + 502*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 + 503*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 + 504*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 + 505*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 + 506*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 + 507*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 + 508*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 + 509*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 + 510*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 + 511*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 + 512*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 + 513*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 + 514*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 + 515*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 + 516*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 + 517*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 + 518*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 + 519*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 + 520*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 + 521*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 + 522*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 + 523*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 + 524*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 + 525*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 + 526*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 + 527*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 + 528*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 + 529*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 + 530*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 + 531*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 + 532*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 + 533*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 + 534*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 + 535*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 + 536*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 + 537*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 + 538*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 + 539*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 + 540*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 + 541*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 + 542*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 + 543*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 + 544*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 + 545*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 + 546*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]];
__syncthreads();
R[i + 547*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 + 548*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 + 549*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 + 550*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 + 551*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 + 552*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 + 553*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 + 554*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 + 555*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 + 556*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 + 557*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 + 558*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 + 559*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 + 560*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 + 561*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 + 562*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 + 563*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 + 564*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 + 565*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 + 566*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 + 567*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 + 568*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 + 569*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 + 570*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 + 571*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 + 572*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 + 573*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 + 574*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 + 575*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 + 576*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 + 577*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 + 578*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 + 579*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 + 580*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 + 581*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 + 582*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 + 583*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 + 584*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 + 585*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 + 586*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 + 587*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 + 588*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 + 589*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 + 590*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 + 591*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 + 592*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 + 593*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]];
R[i + 594*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 + 595*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 + 596*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 + 597*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 + 598*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 + 599*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 + 600*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 + 601*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 + 602*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 + 603*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 + 604*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 + 605*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 + 606*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 + 607*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 + 608*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 + 609*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 + 610*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 + 611*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 + 612*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 + 613*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 + 614*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 + 615*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 + 616*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]];
__syncthreads();
R[i + 617*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 + 618*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 + 619*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 + 620*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 + 621*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 + 622*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 + 623*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 + 624*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 + 625*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 + 626*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 + 627*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 + 628*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 + 629*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 + 630*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 + 631*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 + 632*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 + 633*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 + 634*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 + 635*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 + 636*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 + 637*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 + 638*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 + 639*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 + 640*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 + 641*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 + 642*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 + 643*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 + 644*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 + 645*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 + 646*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 + 647*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 + 648*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 + 649*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 + 650*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 + 651*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 + 652*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 + 653*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 + 654*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 + 655*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 + 656*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 + 657*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 + 658*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 + 659*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 + 660*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 + 661*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 + 662*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 + 663*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 + 664*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 + 665*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 + 666*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 + 667*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 + 668*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 + 669*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 + 670*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 + 671*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 + 672*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 + 673*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 + 674*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 + 675*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 + 676*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 + 677*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 + 678*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 + 679*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 + 680*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 + 681*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 + 682*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 + 683*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 + 684*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 + 685*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 + 686*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]];
R[i + 687*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 + 688*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 + 689*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 + 690*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 + 691*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 + 692*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]];
__syncthreads();
R[i + 693*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 + 694*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 + 695*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 + 696*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 + 697*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 + 698*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 + 699*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 + 700*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 + 701*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 + 702*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 + 703*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 + 704*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 + 705*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 + 706*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 + 707*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 + 708*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 + 709*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 + 710*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 + 711*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 + 712*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 + 713*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 + 714*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 + 715*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 + 716*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 + 717*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 + 718*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 + 719*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 + 720*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 + 721*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 + 722*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 + 723*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 + 724*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 + 725*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 + 726*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 + 727*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 + 728*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 + 729*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 + 730*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 + 731*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 + 732*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 + 733*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 + 734*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 + 735*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 + 736*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 + 737*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 + 738*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 + 739*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 + 740*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 + 741*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 + 742*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 + 743*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 + 744*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 + 745*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 + 746*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 + 747*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 + 748*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 + 749*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 + 750*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 + 751*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 + 752*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 + 753*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 + 754*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 + 755*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 + 756*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 + 757*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 + 758*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 + 759*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 + 760*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 + 761*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 + 762*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]];
__syncthreads();
R[i + 763*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 + 764*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 + 765*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 + 766*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 + 767*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 + 768*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 + 769*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 + 770*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 + 771*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 + 772*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 + 773*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 + 774*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 + 775*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 + 776*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 + 777*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 + 778*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 + 779*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 + 780*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 + 781*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]];
R[i + 782*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 + 783*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 + 784*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 + 785*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 + 786*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 + 787*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 + 788*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 + 789*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 + 790*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 + 791*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 + 792*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 + 793*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 + 794*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 + 795*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 + 796*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 + 797*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 + 798*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 + 799*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 + 800*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 + 801*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 + 802*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]];
__syncthreads();
R[i + 803*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 + 804*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 + 805*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 + 806*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 + 807*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 + 808*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 + 809*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 + 810*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 + 811*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 + 812*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 + 813*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 + 814*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 + 815*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 + 816*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 + 817*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 + 818*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 + 819*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 + 820*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 + 821*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 + 822*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 + 823*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 + 824*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 + 825*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 + 826*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 + 827*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 + 828*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 + 829*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 + 830*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 + 831*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 + 832*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 + 833*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 + 834*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 + 835*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 + 836*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 + 837*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 + 838*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 + 839*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 + 840*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 + 841*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 + 842*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 + 843*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 + 844*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 + 845*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 + 846*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 + 847*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 + 848*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 + 849*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 + 850*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 + 851*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 + 852*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 + 853*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 + 854*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 + 855*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 + 856*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]];
__syncthreads();
R[i + 857*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 + 858*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 + 859*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 + 860*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 + 861*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 + 862*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 + 863*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 + 864*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 + 865*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 + 866*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 + 867*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 + 868*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 + 869*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]];
R[i + 870*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 + 871*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 + 872*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 + 873*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 + 874*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 + 875*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 + 876*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 + 877*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 + 878*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 + 879*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 + 880*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 + 881*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 + 882*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 + 883*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 + 884*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 + 885*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 + 886*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 + 887*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 + 888*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 + 889*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 + 890*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 + 891*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 + 892*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 + 893*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 + 894*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 + 895*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 + 896*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 + 897*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 + 898*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 + 899*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 + 900*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]];
__syncthreads();
R[i + 901*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 + 902*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 + 903*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 + 904*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 + 905*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 + 906*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 + 907*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 + 908*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 + 909*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 + 910*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]];
R[i + 911*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 + 912*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 + 913*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 + 914*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 + 915*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 + 916*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 + 917*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 + 918*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 + 919*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 + 920*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 + 921*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 + 922*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 + 923*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 + 924*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 + 925*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 + 926*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 + 927*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]];
__syncthreads();
R[i + 928*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 + 929*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 + 930*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 + 931*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 + 932*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 + 933*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 + 934*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 + 935*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 + 936*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 + 937*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 + 938*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 + 939*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 + 940*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 + 941*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 + 942*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 + 943*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 + 944*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 + 945*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 + 946*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 + 947*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 + 948*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 + 949*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 + 950*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 + 951*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]];
R[i + 952*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 + 953*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 + 954*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 + 955*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]];
__syncthreads();
R[i + 956*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 + 957*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 + 958*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 + 959*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 + 960*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 + 961*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 + 962*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 + 963*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 + 964*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 + 965*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 + 966*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 + 967*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 + 968*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 + 969*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 + 970*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 + 971*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 + 972*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 + 973*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 + 974*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 + 975*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]];
__syncthreads();
R[i + 976*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 + 977*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 + 978*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 + 979*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 + 980*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 + 981*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 + 982*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 + 983*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 + 984*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 + 985*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 + 986*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 + 987*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]];
R[i + 988*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 + 989*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 + 990*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 + 991*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 + 992*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 + 993*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 + 994*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]];
__syncthreads();
R[i + 995*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 + 996*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 + 997*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 + 998*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 + 999*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 + 1000*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 + 1001*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 + 1002*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 + 1003*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 + 1004*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 + 1005*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]];
R[i + 1006*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 + 1007*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 + 1008*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 + 1009*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 + 1010*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]];
__syncthreads();
R[i + 1011*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 + 1012*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 + 1013*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 + 1014*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 + 1015*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 + 1016*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 + 1017*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 + 1018*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 + 1019*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 + 1020*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]];
__syncthreads();
R[i + 1021*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 + 1022*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]];
R[i + 1023*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 + 1024*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 + 1025*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 + 1026*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 + 1027*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 + 1028*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]];
__syncthreads();
R[i + 1029*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 + 1030*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 + 1031*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 + 1032*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 + 1033*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 + 1034*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]];
R[i + 1035*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 + 1036*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]];
__syncthreads();
R[i + 1037*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 + 1038*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 + 1039*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 + 1040*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 + 1041*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 + 1042*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]];
__syncthreads();
R[i + 1043*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 + 1044*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]];
R[i + 1045*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 + 1046*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 + 1047*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]];
__syncthreads();
R[i + 1048*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 + 1049*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 + 1050*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 + 1051*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]];
__syncthreads();
R[i + 1052*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 + 1053*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 + 1054*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]];
R[i + 1055*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]];
__syncthreads();
R[i + 1056*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 + 1057*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 + 1058*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]];
__syncthreads();
R[i + 1059*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 + 1060*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]];
__syncthreads();
R[i + 1061*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]];
R[i + 1062*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]];
__syncthreads();
R[i + 1063*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]];
__syncthreads();
R[i + 1064*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]];
__syncthreads();
R[i + 1065*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]];
__syncthreads();
R[i + 1066*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]];
__syncthreads();
R[i + 1067*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]];
__syncthreads();
R[i + 1068*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]];
__syncthreads();
R[i + 1069*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]];
__syncthreads();
R[i + 1070*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 + 1071*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]];
__syncthreads();
R[i + 1072*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]];
if (i==0) { final += R[1072*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
6,730 | /**
* Copyright 1993-2012 NVIDIA Corporation. All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*/
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
/**
* This macro checks return value of the CUDA runtime call and exits
* the application if the call failed.
*/
#define CUDA_CHECK_RETURN(value) { \
cudaError_t _m_cudaStat = value; \
if (_m_cudaStat != cudaSuccess) { \
fprintf(stderr, "Error %s at line %d in file %s\n", \
cudaGetErrorString(_m_cudaStat), __LINE__, __FILE__); \
exit(1); \
} \
}
float scan_seq(float *, int, int);
int calculate_grid_size(int, int);
float scan(float*, int, int, char*, int);
void fprint_mat(FILE *, float *, int);
float* init(int);
__global__ void scan_kernel(float *, int);
__global__ void scan_kernel_coalesc(float *, int);
int block_size;
int main(int argc, char **argv) {
if(argc < 4) {
printf("usage: %s <tamanho_vetor> <algoritmo> <tamanho_bloco> <debug opcional>\n\
algoritmo:\n\ts (Sequencial)\n\tc (Cuda nao coalescente)\n\tcc (Cuda coalescente)\n", argv[0]);
exit(-1);
}
FILE *input = fopen(argv[1], "r");
int debug = (argc > 4 ? strcmp("debug", argv[4]) == 0 : 0);
int size = atoi(argv[1]);
float *vector = init(size);
float size_of = size * sizeof(float);
float mega_byte = 1024 * 1024;
block_size = atoi(argv[3]);
printf("%f MBytes\n", (size_of / mega_byte));
if(strncmp(argv[2], "s", 1) == 0) {
float sum = scan_seq(vector, size, debug);
printf("Sum: %f\n", sum);
} else {
float sum = scan(vector, block_size, size, argv[2], debug);
printf("Sum: %f\n", sum);
}
return 0;
}
int calculate_grid_size(int tile_size, int size) {
int div = size / tile_size;
int remainder = size - (tile_size * div);
if(remainder > 0) {
div++;
}
return div;
}
float scan(float *input, int tile_size, int size, char *algorithm, int debug) {
int dim_grid = calculate_grid_size(tile_size, size);
float *device_input;
FILE *f = NULL;
size_t size_bytes = size * sizeof(float);
size_t block_size_bytes = tile_size * sizeof(float);
CUDA_CHECK_RETURN(cudaMalloc((void **) &device_input, size_bytes));
CUDA_CHECK_RETURN(cudaMemcpy(device_input, input, size_bytes, cudaMemcpyHostToDevice));
if(strlen(algorithm) == 1 && strncmp(algorithm, "c", 1) == 0) {
scan_kernel <<<dim_grid, tile_size>>> (device_input, size);
if(debug) {
f = fopen("saida_c.txt", "w");
}
} else {
scan_kernel_coalesc <<<dim_grid, tile_size, block_size_bytes>>> (device_input, size);
if(debug) {
f = fopen("saida_cc.txt", "w");
}
}
CUDA_CHECK_RETURN(cudaGetLastError());
CUDA_CHECK_RETURN(cudaMemcpy(input, device_input, size_bytes, cudaMemcpyDeviceToHost));
CUDA_CHECK_RETURN(cudaFree(device_input));
// Percorro as bordas dos blocos para pegar o menor
int index = 0;
float sum = 0;
float sum1 = 0.0, sum2 = 0.0;
int half_dim_grid = dim_grid / 2;
if(debug) {
fprint_mat(f, input, size);
fclose(f);
}
// Soma as bordas, soma a metade para evitar de perder precisao
for(int i = 1; i < half_dim_grid; ++i) {
index = (tile_size * i) - 1;
sum1 += input[index];
}
for(int i = half_dim_grid; i < dim_grid; ++i) {
index = (tile_size * i) - 1;
sum2 += input[index];
}
sum = sum1;
if((size) != index) {
sum += input[size - 1];
}
sum += sum2;
return sum;
}
float scan_seq(float *input, int size, int debug) {
float found = 0;
for(int i = 0; i < size; ++i) {
found += input[i];
}
if(debug) {
FILE *f = fopen("saida_s.txt", "w");
fprint_mat(f, input, size);
fclose(f);
}
return found;
}
void fprint_mat(FILE *f, float *v, int n) {
for(int i = 0; i < n-1; i++) {
fprintf(f, "%f ", v[i]);
}
fprintf(f, "%f", v[n-1]);
}
float* init(int n) {
float *v = (float*) malloc(sizeof(float) * n);
for(int i = 0; i < n; ++i) {
v[i] = (float) i;
}
return v;
}
////////////////////////////////////////////////////////////////////////////////
/***
* Esse Scan é feito apenas no interior do bloco, logo quem chamou ele precisa verificar
* os limites dos blocos para pegar o menor valor
*/
__global__
void scan_kernel(float *input, int size) {
int local_block_dim = blockDim.x;
int bi = local_block_dim * blockIdx.x;
int ti = bi + threadIdx.x;
int ti_bi = ti - bi; // ti - bi evita que a thread saia do limite do bloco.
int local_size = size;
float aux = 0;
int offset = 1;
// Algoritmo do Scan em cima das posições e threads relativas ao bloco
for(offset = 1; offset < local_block_dim; offset *= 2) {
if(ti_bi >= offset && ti < local_size) {
aux = input[ti - offset];
}
__syncthreads();
if(ti_bi >= offset && ti < local_size) {
input[ti] = aux + input[ti];
}
__syncthreads();
}
}
__global__
void scan_kernel_coalesc(float *input, int size) {
int local_block_dim = blockDim.x;
int thread_id = threadIdx.x;
int bi = local_block_dim * blockIdx.x;
int ti = bi + threadIdx.x;
int local_size = size;
float aux = 0;
int offset = 1;
extern __shared__ float local[];
// Carrega o valor relativo a thread para a memória local.
if(ti < local_size) {
local[thread_id] = input[ti];
}
__syncthreads();
// Aplica o Scan em cima da memória local
for(offset = 1; offset < local_block_dim; offset *= 2) {
if(thread_id >= offset && thread_id < local_block_dim) {
aux = local[thread_id - offset];
}
__syncthreads();
if(thread_id >= offset && thread_id < local_block_dim) {
local[thread_id] = aux + local[thread_id];
}
__syncthreads();
}
// Volta com o valor local na memória global
input[ti] = local[thread_id];
}
|
6,731 | #include "includes.h"
__global__ void ReduceInitKernel(float *dst, int length)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
if (x < length) {
dst[x] = 0;
}
} |
6,732 | #include<iostream>
#include<cuda.h>
using namespace std;
__global__
void square(unsigned int* a,int n){
int i=threadIdx.x;
a[i]=a[i]*a[i];
}
int main(int argc,char ** argv){
int N=1<<20;
unsigned int A[N];
//cpu initialization
for(int i=0;i<N;i++){
A[i]=i;
}
int arraysize=N*sizeof(unsigned int);
//cuda memory
unsigned int * d_in;
cudaMalloc(&d_in,arraysize);
//copy data
cudaMemcpy(d_in,A,arraysize,cudaMemcpyHostToDevice);
//lanch kernel
square<<<1,N>>>(d_in,N);
//copy back
cudaMemcpy(A,d_in,arraysize,cudaMemcpyDeviceToHost);
/*for(int i=0;i<N;i++){
cout<<i<<":"<<A[i]<<endl;
}*/
int a;
cin>>a;
cudaFree(d_in);
}
|
6,733 | /*#########################################
## 姓名：17341137 宋震鹏
## 文件说明：Hello World on Cuda
#########################################*/
#include "cuda_HelloWorld.cuh"
using namespace std;
int main()
{
CheckDevice();
SayHello();
return 0;
} |
6,734 | #include "includes.h"
__device__ float function_a_appli(float x);
__global__ void mandelbrot ( int nb_ligne, int nb_col, float seuil, float x_min, float x_max, float y_min, float y_max, float* res) {
int max_ITER=10000;
int iter=0;
int index_col=threadIdx.x+blockDim.x*blockIdx.x;
int index_ligne=threadIdx.y+blockDim.y*blockIdx.y;
int global_index;
float x,y,xtemp,x0,y0;
if ((index_col >= nb_col) || (index_ligne>=nb_ligne) ) return;
global_index=index_ligne*nb_col+index_col;
x0=((float)index_col/(float)nb_col)*(x_max-x_min)+x_min;
y0=((float)(nb_ligne-index_ligne)/(float)nb_ligne)*(y_max-y_min)+y_min;
x=0;y=0;
while((x*x+y*y <= seuil) && (iter < max_ITER))
{ xtemp = x*x-y*y+x0;
y = 2*x*y+y0;
x = xtemp;
iter++;
}
res[global_index]=((float) iter/(float)max_ITER);
} |
6,735 | //fail
//--blockDim=32 --gridDim=64 --no-inline
#include <cuda.h>
#include <stdio.h>
#include <assert.h>
#define N 2//32
__device__ void f(float *odata, int* ai) {
int thid = threadIdx.x;
*ai = thid;
odata[*ai] = 2*threadIdx.x;
}
__global__ void k(float *g_odata) {
int ai;
f(g_odata,&ai);
}
|
6,736 | #include "includes.h"
__global__ void gpuAdd(int d_a, int d_b, int *d_c) {
*d_c = d_a + d_b;
} |
6,737 | #include <iostream>
#include <math.h>
#include <time.h>
/* the length of testing vector */
#define T 100000
/* the number of levels (number fo subproblems) */
#define LEVELS 50
/* if the lenght of vector is large, set this to zero */
#define PRINT_VECTOR_CONTENT 0
/* number of performed tests */
#define NMB_OF_TEST_CPU 10
#define NMB_OF_TEST_GPU 1000000
/* which CUDA calls to test? */
#define CALL_NAIVE 1
#define CALL_OPTIMAL 1
#define CALL_TEST 0
/* for measuring time */
double getUnixTime(void){
struct timespec tv;
if(clock_gettime(CLOCK_REALTIME, &tv) != 0) return 0;
return (((double) tv.tv_sec) + (double) (tv.tv_nsec / 1000000000.0));
}
#ifdef USE_CUDA
/* CUDA stuff: */
#include <stdio.h>
#include "cuda.h"
/* cuda error check */
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"\n\x1B[31mCUDA error:\x1B[0m %s %s \x1B[33m%d\x1B[0m\n\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
/* kernel called in this example */
__global__ void mykernel(double *x_arr, int mysize){
int i = blockIdx.x*blockDim.x + threadIdx.x; /* compute my id */
if(i<mysize){ /* maybe we call more than mysize kernels */
x_arr[i] = i;
}
/* i >= mysize then relax and do nothing */
}
/* kernel called in this example */
__global__ void mykernel_block(double *x_arr, int mysize, int blocksize){
int block_i = blockIdx.x*blockDim.x + threadIdx.x; /* compute my id */
for(int i=block_i*blocksize;i<(block_i)*blocksize;i++){
if(i < mysize){
x_arr[i] = i;
}
}
/* i >= mysize then relax and do nothing */
}
/* print kernel, call only one (!) */
/* in practical applications, this way is a real bottleneck, array should be transfered to CPU and printed there */
/* anyway, in this sample I want to print only small arrays, so who cares.. */
__global__ void printkernel(double *x_arr, int mysize){
printf(" [");
for(int i=0;i<mysize;i++){
printf(" %f", x_arr[i]);
if(i < mysize-1) printf(",");
}
printf(" ]\n");
}
/* end of CUDA stuff */
#endif
int main( int argc, char *argv[] )
{
#ifdef USE_CUDA
gpuErrchk( cudaDeviceReset() );
#endif
/* print problem info */
std::cout << "Benchmark started" << std::endl;
std::cout << " T = " << T << std::endl;
std::cout << " LEVELS = " << LEVELS << std::endl;
std::cout << std::endl;
double timer;
double times1[LEVELS];
double times2[LEVELS];
double times3[LEVELS];
double times4[LEVELS];
double *x_arr; /* my array on GPU */
int mysize; /* the lenght of alocated vector (array) */
for(int level=0; level < LEVELS; level++){
/* compute the size of testing array on this level */
mysize = ceil(T/(double)(level+1));
std::cout << "(" << level+1 << ".): problem of size = " << mysize << std::endl;
#ifdef USE_CUDA
/* ------- CUDA version ------- */
int minGridSize, blockSize, gridSize;
/* allocate array */
timer = getUnixTime(); /* start to measure time */
gpuErrchk( cudaMalloc(&x_arr, sizeof(double)*mysize) );
std::cout << " - allocation: " << getUnixTime() - timer << "s" << std::endl;
/* fill array */
if(CALL_NAIVE){
/* the easiest call */
timer = getUnixTime();
for(int k=0;k<NMB_OF_TEST_GPU;k++){
mykernel<<<1, mysize>>>(x_arr,mysize);
gpuErrchk( cudaDeviceSynchronize() ); /* synchronize threads after computation */
}
times1[level] = getUnixTime() - timer;
std::cout << " - call naive: " << times1[level] << "s" << std::endl;
}
if(CALL_OPTIMAL){
gpuErrchk( cudaOccupancyMaxPotentialBlockSize( &minGridSize, &blockSize,mykernel, 0, 0) );
gridSize = (mysize + blockSize - 1)/ blockSize;
timer = getUnixTime();
for(int k=0;k<NMB_OF_TEST_GPU;k++){
mykernel<<<blockSize, gridSize>>>(x_arr, mysize);
gpuErrchk( cudaDeviceSynchronize() );
}
times2[level] = getUnixTime() - timer;
std::cout << " - call optimal: " << times2[level] << "s" << std::endl;
std::cout << " ( gridSize = " << gridSize << ", blockSize = " << blockSize << " )" << std::endl;
}
if(CALL_TEST){
timer = getUnixTime();
// int thread_blocksize = 1;
// int nmb_block = ceil(mysize/(double)thread_blocksize);
// gpuErrchk( cudaOccupancyMaxPotentialBlockSize( &minGridSize, &blockSize,mykernel_block, 0, 0) );
// gridSize = (nmb_block + blockSize - 1)/ blockSize;
for(int k=0;k<NMB_OF_TEST_GPU;k++){
mykernel<<<gridSize,blockSize>>>(x_arr,mysize);
gpuErrchk( cudaDeviceSynchronize() ); /* synchronize threads after computation */
}
times4[level] = getUnixTime() - timer;
std::cout << " - call test: " << times4[level] << "s" << std::endl;
}
/* print array */
if(PRINT_VECTOR_CONTENT){
timer = getUnixTime();
printkernel<<<1,1>>>(x_arr,mysize);
gpuErrchk( cudaDeviceSynchronize() );
std::cout << " - printed in: " << getUnixTime() - timer << "s" << std::endl;
}
/* destroy array */
timer = getUnixTime();
gpuErrchk( cudaFree(x_arr) );
std::cout << " - destruction: " << getUnixTime() - timer << "s" << std::endl;
#else
/* ------- SEQUENTIAL version ------- */
/* allocate array */
timer = getUnixTime();
x_arr = new double[mysize];
std::cout << " - allocation: " << getUnixTime() - timer << "s" << std::endl;
/* fill array */
timer = getUnixTime();
for(int k=0;k<NMB_OF_TEST_CPU;k++){
for(int i=0;i<mysize;i++){
x_arr[i] = i;
}
}
times3[level] = getUnixTime() - timer;
std::cout << " - call sequential: " << times3[level] << "s" << std::endl;
/* print array */
if(PRINT_VECTOR_CONTENT){
timer = getUnixTime();
std::cout << " [";
for(int i=0;i<mysize;i++){
std::cout << " " << x_arr[i];
if(i < mysize-1) std::cout << ",";
}
std::cout << " ]" << std::endl;
std::cout << " - printed in: " << getUnixTime() - timer << "s" << std::endl;
}
/* destroy array */
timer = getUnixTime();
delete [] x_arr;
std::cout << " - destruction: " << getUnixTime() - timer << "s" << std::endl;
#endif
}
/* final print of timers */
std::cout << std::endl;
std::cout << "---- TIMERS ----" << std::endl;
#ifdef USE_CUDA
if(CALL_NAIVE){
std::cout << " GPU naive = [";
for(int i=0;i<LEVELS;i++){
std::cout << " " << times1[i];
if(i < LEVELS-1) std::cout << ",";
}
std::cout << " ]" << std::endl;
}
if(CALL_OPTIMAL){
std::cout << " GPU optimal = [";
for(int i=0;i<LEVELS;i++){
std::cout << " " << times2[i];
if(i < LEVELS-1) std::cout << ",";
}
std::cout << " ]" << std::endl;
}
if(CALL_TEST){
std::cout << " GPU test = [";
for(int i=0;i<LEVELS;i++){
std::cout << " " << times4[i];
if(i < LEVELS-1) std::cout << ",";
}
std::cout << " ]" << std::endl;
}
#else
std::cout << " CPU seq = [";
for(int i=0;i<LEVELS;i++){
std::cout << " " << times3[i];
if(i < LEVELS-1) std::cout << ",";
}
std::cout << " ]" << std::endl;
#endif
std::cout << std::endl;
return 0;
}
|
6,738 | #include "includes.h"
__device__ inline float stableSigmoid(float x) {
if(x >= 0) {
float z = expf(-x);
return 1.0 / (1.0 + z);
} else {
float z = expf(x);
return z / (1.0 + z);
}
}
__global__ void gHighwayForward(float* out, const float* in1, const float* in2, const float* t, size_t length) {
for(int bid = 0; bid < length; bid += blockDim.x * gridDim.x) {
int index = bid + blockDim.x * blockIdx.x + threadIdx.x;
if(index < length) {
float sigma = stableSigmoid(t[index]);
out[index] = in1[index] * sigma + in2[index] * (1.f - sigma);
}
}
} |
6,739 | #define _NTHREAD 512
#define _NBLOCK 65535
#include<cuda.h>
__global__ void _AFFINE_KERNEL(int* ,int ,int ,int* ,int ,int ,int ,int ,int ,int ,int ,int ,int ,int );
#include<stdio.h>
#include<stdlib.h>
int main()
{
int x[20][20];
int y[20][20];
int a[20],i,j,k;
int n=20;
for(i=0;i<20;i++)
for(j=0;j<20;j++)
{
x[i][j]=i+j;
y[i][j]=2*i+j;
a[i]=2*i;
}
int _SZ_y_2 = 20;
int _SZ_y_1 = 20;
int _SZ_x_2 = 20;
int _SZ_x_1 = 20;
int *_DEV_y;
cudaMalloc((void**) &_DEV_y, sizeof(int)*_SZ_y_2*_SZ_y_1);
cudaMemcpy(_DEV_y, y, sizeof(int)*_SZ_y_2*_SZ_y_1, cudaMemcpyHostToDevice);
int *_DEV_x;
cudaMalloc((void**) &_DEV_x, sizeof(int)*_SZ_x_2*_SZ_x_1);
cudaMemcpy(_DEV_x, x, sizeof(int)*_SZ_x_2*_SZ_x_1, cudaMemcpyHostToDevice);
float _NUM_THREADS = 400,_NUM_BLOCKS=1;
int _NUM_TILE=1;
dim3 _THREADS(512);
dim3 _BLOCKS(1);
if(_NUM_THREADS < _NTHREAD)
{
_THREADS.x=20;
_THREADS.y=20;
}
else {
_NUM_BLOCKS=_NUM_THREADS/256;
_BLOCKS.x=_BLOCKS.y=ceil(sqrt(_NUM_BLOCKS));
_THREADS.x=_THREADS.y=ceil(sqrt(400.0/(_BLOCKS.x*_BLOCKS.y)));
int temp=_NUM_BLOCKS;
if(_NUM_BLOCKS>_NBLOCK)
_NUM_TILE=(temp % _NBLOCK == 0)?(_NUM_BLOCKS/_NBLOCK):((_NUM_BLOCKS/_NBLOCK)+1);
}
int _CUDA_TILE;
for(i=0;i<=20;i+=3)
for(j=0;j<=15;j+=2)
for(_CUDA_TILE=0;_CUDA_TILE<_NUM_TILE;_CUDA_TILE++)
_AFFINE_KERNEL<<<_BLOCKS,_THREADS>>>(_DEV_y, _SZ_y_2, _SZ_y_1, _DEV_x, _SZ_x_2, _SZ_x_1, 2, i, j, 0, 20, 0, 15, _CUDA_TILE);
cudaDeviceSynchronize();
cudaMemcpy(y, _DEV_y, sizeof(int)*_SZ_y_2*_SZ_y_1, cudaMemcpyDeviceToHost);
cudaMemcpy(x, _DEV_x, sizeof(int)*_SZ_x_2*_SZ_x_1, cudaMemcpyDeviceToHost);
for(i=0;i<20;i++)
for(j=0;j<20;j++)
printf("%d\t%d\n",x[i][j],y[i][j]);
return 0;
}
__global__ void _AFFINE_KERNEL(int* y,int _SZ_y_2,int _SZ_y_1,int* x,int _SZ_x_2,int _SZ_x_1,int phi_count, int CUDA_i, int CUDA_j, int CUDA_L_i,int CUDA_U_i, int CUDA_L_j,int CUDA_U_j, int _CUDA_TILE)
{
int i = gridDim.x*blockDim.x*_CUDA_TILE + blockDim.x*blockIdx.x + threadIdx.x;
int j = gridDim.y*blockDim.y*_CUDA_TILE + blockDim.y*blockIdx.y + threadIdx.y;
if((CUDA_i<=i)&&(i<(CUDA_i+3))&&(i<=CUDA_U_i)){
if((CUDA_j<=j)&&(j<(CUDA_j+2))&&(j<=CUDA_U_j)){
x[(3+i+5)*_SZ_x_1+5+j+1]=y[(3+i-1)*_SZ_y_1+5+j-2]+x[(3+i)*_SZ_x_1+5+j-1];
y[(3+i+2)*_SZ_y_1+5+j+2]=x[(3+i+2)*_SZ_x_1+5+j-1];
}}}
|
6,740 |
#include <string>
#include <cuda.h>
#include <iostream>
#ifdef _WIN32
#define EXPORT __declspec(dllexport)
#else
#define EXPORT
#endif
int dynamic_base_func(int);
EXPORT int __host__ cuda_dynamic_host_func(int x)
{
return dynamic_base_func(x);
}
static
__global__
void DetermineIfValidCudaDevice()
{
}
EXPORT void cuda_dynamic_lib_func()
{
DetermineIfValidCudaDevice <<<1,1>>> ();
cudaError_t err = cudaGetLastError();
if(err == cudaSuccess)
{
std::cerr << cudaGetErrorString(err) << std::endl;
}
}
|
6,741 | #include "includes.h"
__global__ void sobel(unsigned char *output, unsigned char *input, int width, int height)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
if (y >= height || x >= width)
return;
const int BLOCK_SIZE = 16;
// Where does our data start
int blockStartIndexX = blockIdx.x * blockDim.x - 1;
int blockStartIndexY = blockIdx.y * blockDim.y - 1;
// Clamp to edge
if (blockStartIndexX < 0)
blockStartIndexX = 0;
if (blockStartIndexX >= width)
blockStartIndexX = blockDim.x - 1;
if (blockStartIndexY < 0)
blockStartIndexY = 0;
if (blockStartIndexY >= height)
blockStartIndexY = blockDim.y - 1;
// Shared Data
__shared__ unsigned char pixels[BLOCK_SIZE + 2][BLOCK_SIZE + 2];
// Where is our data
unsigned char* cacheInput = input + (blockStartIndexX + blockStartIndexY * width);
// Linear index (16x16 -> 0..255)
int threadIndex = threadIdx.x + threadIdx.y * blockDim.x;
int maxLoadSizeBytes = (BLOCK_SIZE + 2) * (BLOCK_SIZE + 2); // 18x18 Block -> 324 Bytes
int maxIndexBytes = maxLoadSizeBytes / sizeof(short); // 18x18 Block -> Index 162
if (threadIndex < maxIndexBytes)
{
// Calculate offset
int offsetInBytes = threadIndex * sizeof(short);
int block_half = (BLOCK_SIZE + 2) / 2;
int byteRow = offsetInBytes / (BLOCK_SIZE + 2);
int byteCol = threadIndex % block_half * 2;
int offset = byteCol + byteRow * width;
//int offsetBuffer = byteCol + byteRow * (BLOCK_SIZE + 2);
// Copy Data
unsigned char* toLoad = cacheInput + offset;
/**(&pixels[0][0] + offsetBuffer) = *toLoad;
*(&pixels[0][0] + offsetBuffer + 1) = *(toLoad + 1);*/
pixels[byteRow][byteCol] = *toLoad;
pixels[byteRow][byteCol + 1] = *(toLoad + 1);
}
__syncthreads();
// Sobel weights
float weightsX[9] = { -1, -2, -1,
0, 0, 0,
1, 2, 1 };
float weightsY[9] = { -1, 0, 1,
-2, 0, 2,
-1, 0, 1 };
int offsetY[9] = { -1, -1, -1,
0, 0, 0,
1, 1, 1 };
int offsetX[9] = { -1, 0, 1,
-1, 0, 1,
-1, 0, 1 };
float pointX = 0.f;
float pointY = 0.f;
#pragma unroll
for (int i = 0; i < 9; i++)
{
int indexX = threadIdx.x + 1 + offsetX[i];
int indexY = threadIdx.y + 1 + offsetY[i];
unsigned char pixel = pixels[indexY][indexX];
pointX += pixel * weightsX[i];
pointY += pixel * weightsY[i];
}
// Do Sobel here!
int index = x + y * width;
unsigned char * outputData = output + index;
outputData[0] = sqrtf(pointX * pointX + pointY * pointY);
} |
6,742 | #include <stdio.h>
#include <sys/time.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
#include <driver_types.h>
#define M 1024
__global__
void Calcu(float *address, size_t pitch)
{
//pitchの使い道を理解した。勝利。
int i=blockIdx.x * blockDim.x + threadIdx.x;
int j=blockIdx.y * blockDim.y + threadIdx.y;
float up, down, left, right;
float r=0.2;
for(int count=0; count<100; count++){
if(0<i && i<M && 0<j && j<M){
float* row=(float*)((char*)address+j*pitch);
float* up_row=(float*)((char*)address+(j-1)*pitch);
float* down_row=(float*)((char*)address+(j+1)*pitch);
up=up_row[i];
down=down_row[i];
left=row[i-1];
right=row[i+1];
__syncthreads();
row[i]=(1-4*r)*row[i]+r*up+r*down+r*left+r*right;
}
__syncthreads();
}
}
int main(void)
{
struct timeval t0,t1;
int i,j;
float dx=1/M;
float r=0.2;
float array[M+1][M+1];
float time;
size_t pitch;
float *address;
for(i=0;i<M+1;i++)
for(j=0;j<M+1;j++)
array[i][j]=1;
for(i=0;i<M+1;i++){
array[0][i]=0;
array[M][i]=0;
array[i][0]=0;
array[i][M]=0;
}
cudaMallocPitch(&address, &pitch, (M+1)*sizeof(float), M+1);
cudaMemcpy2D(address ,pitch ,array ,(M+1)*sizeof(float), (M+1)*sizeof(float), M+1, cudaMemcpyHostToDevice);
dim3 threadPerBlock(32,32);
dim3 numBlock(M/threadPerBlock.x, M/threadPerBlock.y);
gettimeofday(&t0, NULL);
Calcu<<<numBlock, threadPerBlock>>>(address, pitch);
cudaDeviceSynchronize();
cudaMemcpy2D(array ,(M+1)*sizeof(float) ,address ,pitch, (M+1)*sizeof(float), M+1, cudaMemcpyDeviceToHost);
cudaFree(address);
gettimeofday(&t1, NULL);
time = t1.tv_sec-t0.tv_sec + (t1.tv_usec - t0.tv_usec)*1.0e-6;
printf("Elapsed time = %lf\n", time);
printf("FLOPS = %lf\n", (M-1)*(M-1)*100*6/time);
for(j=10;j>=0;j--){
for(i=0;i<11;i++)
printf("%f ",array[i][j]);
printf("\n");
}
return 0;
}
|
6,743 | #define CUDA_BLOCK_X 128
#define CUDA_BLOCK_Y 1
#define CUDA_BLOCK_Z 1
__global__ void _auto_kernel_0(int a[100][2])
{
int thread_x_id;thread_x_id = blockIdx.x * blockDim.x + threadIdx.x;
int thread_y_id;thread_y_id = blockIdx.y * blockDim.y + threadIdx.y;
if (thread_x_id && thread_y_id)
if (thread_x_id <= 100 && thread_y_id <= 2) {
a[2 * thread_x_id + -2][2 * thread_y_id + -2] = a[2 * thread_x_id + -1][2 * thread_y_id + -1];
}
}
int main()
{
int a[100][2];
int i;
int j;
i = 0;
{
{
/* Auto-generated code for call to _auto_kernel_0 */
typedef int _narray_a[2];
_narray_a *d_a;
cudaMalloc((void **) &d_a, sizeof(int ) * 100 * 2);
cudaMemcpy(d_a, a, sizeof(int ) * 100 * 2, cudaMemcpyHostToDevice);
int CUDA_GRID_X;
CUDA_GRID_X = (100 + CUDA_BLOCK_X - 1)/CUDA_BLOCK_X;
int CUDA_GRID_Y;
CUDA_GRID_Y = (2 + CUDA_BLOCK_Y - 1)/CUDA_BLOCK_Y;
int CUDA_GRID_Z;
CUDA_GRID_Z = (1 + CUDA_BLOCK_Z - 1)/CUDA_BLOCK_Z;
const dim3 CUDA_blockSize(CUDA_BLOCK_X, CUDA_BLOCK_Y, CUDA_BLOCK_Z);
const dim3 CUDA_gridSize(CUDA_GRID_X, CUDA_GRID_Y, CUDA_GRID_Z);
_auto_kernel_0<<<CUDA_gridSize,CUDA_blockSize>>>(d_a);
cudaMemcpy(a, d_a, sizeof(int ) * 100 * 2, cudaMemcpyDeviceToHost);
}
}
return 2;
}
|
6,744 | #include "includes.h"
__global__ void cuda_kernel_texture_2d(unsigned char *surface, int width, int height, size_t pitch, float t) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
// in the case where, due to quantization into grids, we have
// more threads than pixels, skip the threads which don't
// correspond to valid pixels
if (x >= width || y >= height) {
return;
}
// get a pointer to the pixel at (x,y)
float *pixel = (float *)(surface + y * pitch) + 4 * x;
// populate it
float value_x = 0.5f + 0.5f * cos(t + 10.0f * ((2.0f * x) / width - 1.0f));
float value_y = 0.5f + 0.5f * cos(t + 10.0f * ((2.0f * y) / height - 1.0f));
pixel[0] = 0.5 * pixel[0] + 0.5 * pow(value_x, 3.0f); // red
pixel[1] = 0.5 * pixel[1] + 0.5 * pow(value_y, 3.0f); // green
pixel[2] = 0.5f + 0.5f * cos(t); // blue
pixel[3] = 1.0f; // alpha
} |
6,745 | #include "includes.h"
/*Performs separable convolution on 3d cube*/
__global__ void convolution_sep(float *output, const float *input, const float *kernel, const int kernel_size, const dim3 imsize, int dir)
{
size_t ix, iy, iz;
if (dir == X_DIR)
{
ix = blockDim.x*blockIdx.x + threadIdx.x;
iy = blockDim.y*blockIdx.y + threadIdx.y;
iz = blockIdx.z;
}
else if (dir == Y_DIR)
{
iy = blockDim.x*blockIdx.x + threadIdx.x;
ix = blockDim.y*blockIdx.y + threadIdx.y;
iz = blockIdx.z;
}
else if (dir == EPS_DIR)
{
iz = blockDim.x*blockIdx.x + threadIdx.x;
ix = blockDim.y*blockIdx.y + threadIdx.y;
iy = blockIdx.z;
}
const bool valid = ix < imsize.x && iy < imsize.y && iz < imsize.z;
const size_t cube_idx = ix + iy*imsize.x + iz*imsize.x*imsize.y;
const size_t radius_size = kernel_size / 2;
extern __shared__ float s_image[]; //size is on kernel call
const size_t s_dim_x = blockDim.x + 2 * radius_size;
const size_t s_ix = radius_size + threadIdx.x;
const size_t s_iy = threadIdx.y;
float result = 0.0;
if (threadIdx.x < radius_size) //is on the left part of the shared memory
{
s_image[s_ix - radius_size + s_iy*s_dim_x] = 0.0f;
}
if (threadIdx.x >= (blockDim.x - radius_size)) //is on the right part
{
s_image[s_ix + radius_size + s_iy*s_dim_x] = 0.0f;
}
s_image[s_ix + s_iy*s_dim_x] = (valid) ? input[cube_idx] : 0.0f;
__syncthreads();
#pragma unroll
for (int i = 0; i < kernel_size; i++)
{
result += kernel[i] * s_image[s_ix - i + radius_size + s_iy*s_dim_x];
}
if (valid)
{
output[cube_idx] = result;
}
} |
6,746 | __global__ void initmem( int Ntot, float *a ) {
int idx = blockIdx.x*blockDim.x + threadIdx.x;
if ( idx < Ntot ) a[idx] = 0;
}
__global__ void diff( int Nx, int Ny, float dx, float *a, float *da ) {
int idx = blockIdx.x*blockDim.x + threadIdx.x;
if ( idx < (Nx-1)*(Ny-1) ) da[idx] = (1/dx)*( a[idx+Ny+1] - a[idx] );
}
|
6,747 | #include <stdio.h>
#include <thrust/scan.h>
#include <thrust/execution_policy.h>
#define BLOCK_DIM 16
#define UPDIV(n, d) (((n)+(d)-1)/(d))
/*
* horizontal_exclusive_scan: exclusive scans a 2d array horizontally
* REQUIRES: threads per block == width/2
* REQUIRES: num blocks == height
* REQUIRES: input, output have size height x width
* REQUIRES: 2 * threads_per_block * sizeof(float) shared memory
*
* TODO: Currently, this function assigns one row per block which may
* be inefficient. Also this has a max width of 2048 based on 1024 max
* on threads per block. Odd number width may also be accomodated.
*
* Code adapted from MIT paper
*/
__global__ void horizontal_exclusive_scan(float* input, float* output,
int height, int width)
{
extern __shared__ float temp[];
int tdx = threadIdx.x;
int bdx = blockIdx.x;
int offset = 1;
temp[2*tdx] = input[bdx*width + 2*tdx];
temp[2*tdx+1] = input[bdx*width + 2*tdx+1];
for(int d = width>>1; d > 0; d >>= 1)
{
__syncthreads();
if(tdx < d)
{
int ai = offset*(2*tdx+1)-1;
int bi = offset*(2*tdx+2)-1;
temp[bi] += temp[ai];
}
offset *= 2;
}
if(tdx == 0) temp[width - 1] = 0;
for(int d = 1; d < width; d *= 2)
{
offset >>= 1;
__syncthreads();
if(tdx < d)
{
int ai = offset*(2*tdx+1)-1;
int bi = offset*(2*tdx+2)-1;
float t = temp[ai];
temp[ai] = temp[bi];
temp[bi] += t;
}
__syncthreads();
}
output[bdx*width + 2*tdx] = temp[2*tdx];
output[bdx*width + 2*tdx+1] = temp[2*tdx+1];
}
/*
transpone: transposes a 2d matrix
* REQUIRES: block dimensions that cover the input matrix
*
* Code borrowed from MIT paper
*/
__global__ void transpose(float* input, float* output, int height, int width)
{
int y = blockIdx.y * BLOCK_DIM + threadIdx.y;
int x = blockIdx.x * BLOCK_DIM + threadIdx.x;
if(y < height && x < width)
output[x * height + y] = input[y * width + x];
}
/*
* integral_image: computes the exclusive integral image of input
* REQUIRES: input, output on device memory and have size height x width
* REQUIRES: width % 2 == 0
*
* IMPORTANT: destructive on input
*
* TODO: Currently uses a block for each row, but this may be improved for
* performance.
* TODO: May need to change input, output to be on host memory
*/
void integral_image(float* input, float* output, int height, int width)
{
int size1 = width * sizeof(float);
int size2 = height * sizeof(float);
horizontal_exclusive_scan<<<height, width/2, size1>>>(input, input, height, width);
dim3 dimBlock(BLOCK_DIM, BLOCK_DIM);
dim3 dimGrid1(UPDIV(width, BLOCK_DIM), UPDIV(height, BLOCK_DIM));
transpose<<<dimGrid1, dimBlock>>>(input, output, height, width);
horizontal_exclusive_scan<<<width, height/2, size2>>>(output, input, width, height);
dim3 dimGrid2(UPDIV(height, BLOCK_DIM), UPDIV(width, BLOCK_DIM));
transpose<<<dimGrid2, dimBlock>>>(input, output, width, height);
}
/*
* integral_image_loc: computes the exclusive integral image of input
* REQUIRES: input, output on host memory and have size height x width
* REQUIRES: width % 2 == 0
*/
void integral_image_host(float* input, float* output, int height, int width)
{
int num = height * width;
int size = num * sizeof(float);
float* cudaInput;
float* cudaOutput;
cudaMalloc(&cudaInput, size);
cudaMalloc(&cudaOutput, size);
cudaMemcpy(cudaInput, input, size, cudaMemcpyHostToDevice);
integral_image(cudaInput, cudaOutput, height, width);
cudaMemcpy(output, cudaOutput, size, cudaMemcpyDeviceToHost);
}
|
6,748 | #include "includes.h"
extern "C"
{
}
__global__ void xsigny_update(const int n, const double *a, double *b, double *c)
{
int i = threadIdx.x + blockIdx.x * blockDim.x;
if (i<n)
{
if (b[i]>0)
{c[i]+=a[i];}
else
{if (b[i]<0)
{c[i]-=a[i];}
}
}
} |
6,749 | extern "C" {
// kernel function to blur a single color channel (R || G || B)
__global__ void convolute(float *ch, float *res, int height, int width,int ksize) {
int x = blockIdx.x * blockDim.x + threadIdx.x; // width index
int y = blockIdx.y * blockDim.y + threadIdx.y; // height index
//int radius = 8;
float PI = atanf(1) * 4;
if ((x < width) && (y < height)) {
float sum = 0;
float val = 0;
int idx = x * width + y; // current pixel index
int radius = ksize / 2;
for (int i = y - radius; i < y + radius + 1; i++) {
for (int j = x - radius; j < x + radius + 1; j++) {
if ((y-radius) > 0 && (y+radius) < height && (x - radius) > 0 && (x+radius)<width){
int h = fminf(height - 1, fmaxf(0, i));
int w = fminf(width - 1, fmaxf(0, j));
int dsq = (j - x) * (j - x) + (i - y) * (i - y);
float wght = expf(-dsq / (2 * radius * radius)) / (PI * 2 * radius * radius);
val += ch[w * width + h] * wght;
sum += wght;
}
}
}
res[idx] = round(val / sum);
}
}
} |
6,750 | #include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <assert.h>
#include <iostream>
//8 X 8 mask
#define MASK_LEN 8
#define MASK_OFFSET (MASK_LEN / 2)
/*as mask is never changing we can define a constant memory on the device side so that
we do not have to copu again and again and loading from const cache is much much faster that
loading from d-ram.
*/
__constant__ int mask[MASK_LEN * MASK_LEN];
__global__ void conv_2d(int* Mat, int* res, int n) {
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int start_r = row - MASK_OFFSET;
int start_c = col - MASK_OFFSET;
int temp = 0;
for (int i = 0; i < MASK_LEN; i++)
{
for (int j = 0; j < MASK_LEN; j++)
{
if ((start_r + i >= 0) && (start_r + i < n))
{
if ((start_c + j >= 0) && (start_c + j < n))
{
temp += Mat[(start_r + i) * n + (start_c + j)] * mask[i * MASK_LEN + j];
}
}
}
}
res[row * n + col] = temp;
}
// Initialize
void Array_init(int* a, int n, int div) {
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
a[i*n + j] = rand() % div;
}
}
}
void check_answer(int* mat, int* mask, int* res, int n) {
int temp;
int offset_r;
int offset_c;
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
temp = 0;
for (int k = 0; k < MASK_LEN; k++)
{
offset_r = i - MASK_OFFSET + k;
for (int l = 0; l < MASK_LEN; l++)
{
offset_c = j - MASK_OFFSET + l;
if (offset_r >= 0 && offset_r < n)
{
if (offset_c >= 0 && offset_c < n)
{
temp += mat[offset_r * n + offset_c] * mask[k * MASK_LEN + l];
}
}
}
}
assert(res[i * n + j] == temp);
}
}
}
int main() {
// number of elements in result array
int n = 1 << 12;
int n_bytes = n * n * sizeof(int);
int m_bytes = MASK_LEN * MASK_LEN * sizeof(int);
//allocate the array
int* h_Mat = new int[n * n];
Array_init(h_Mat, n, 100);
//allocate the mask and intialize it
int* h_mask = new int[MASK_LEN * MASK_LEN];
Array_init(h_mask, MASK_LEN, 10);
//allocate space for result
int* h_result = new int[n * n];
//allocate space on device memory
int* d_Mat, * d_res;
cudaMalloc(&d_Mat, n_bytes);
cudaMalloc(&d_res, n_bytes);
cudaMemcpy(d_Mat, h_Mat, n_bytes, cudaMemcpyHostToDevice);
//special function to copy to a symbol
cudaMemcpyToSymbol(mask, h_mask, m_bytes);
int threads = 16;
int grid = (n + threads - 1) / threads;
dim3 block_dim(threads, threads);
dim3 grid_dim(grid, grid);
conv_2d <<<grid_dim, block_dim >>> (d_Mat, d_res, n);
cudaMemcpy(h_result, d_res, n_bytes, cudaMemcpyDeviceToHost);
check_answer(h_Mat, h_mask, h_result, n);
free(h_result);
free(h_mask);
free(h_Mat);
cudaFree(d_Mat);
cudaFree(d_res);
printf("COMPLETED SUCCESFULLY\n");
return 0;
} |
6,751 | #include <cstdio>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "curand.h"
/**
* Отладочные сообщения
*/
//#define DEBUG
/*
* Количество нитей на блок. Для Pascal оптимальное значение 128/256
*/
constexpr const int maxThreadsPerBlock = 256;
/*
* Количество CUDA ядер на один SM. Для Pascal - 128
*/
constexpr const unsigned int blocksPerSm = 8;
constexpr const size_t MATRIX_DIM = 2000;
constexpr const size_t MATRIX_SIZE = MATRIX_DIM*MATRIX_DIM;
using matrixType = float;
enum PriorityModes {
MAX_BLOCKS_WITH_OPT_NUM_TH, ALL_BLOCKS_WITH_MAX_THREADS
};
class optimalParams {
public:
dim3 blocksDim;
dim3 threadsDim;
unsigned int sharedMem = 0;
optimalParams(dim3 b, dim3 t) : blocksDim{b}, threadsDim{t} {}
optimalParams(dim3 b, dim3 t, unsigned int s) : blocksDim{b}, threadsDim{t}, sharedMem{s} {}
};
__global__ void helloworld(void) {
printf("Hello, World from GPU! %d, %d\n", blockIdx.x,
threadIdx.x);
}
__global__ void multiply(matrixType *matrixA, matrixType *matrixB, matrixType *matrixC, int threadNum) {
/**
* blockIdx.x * threadNum + threadIdx.x
*/
#ifdef DEBUG
if (blockIdx.x + threadIdx.x == 0) {
printf("A0, B0, C0: %f, %f, %f\n", matrixA[0], matrixB[0], matrixC[0]);
}
if (threadIdx.x == 0) {
printf("Block %d\n", blockIdx.x);
}
#endif
size_t n = blockIdx.x * threadNum + threadIdx.x;
if (n < MATRIX_SIZE) {
size_t x = n/MATRIX_DIM;
size_t y = n%MATRIX_DIM;
for(size_t i = 0; i < MATRIX_DIM; ++i) {
matrixC[n] += (matrixA[x*MATRIX_DIM + i] * matrixB[i*MATRIX_DIM + y]);
}
#ifdef DEBUG
printf("x: %llu y: %llu %f \n",x,y, matrixC[n]);
#endif
}
}
__global__ void multiplyShared(matrixType *matrixA, matrixType *matrixB, matrixType *matrixC, int threadNum) {
/**
* blockIdx.x * threadNum + threadIdx.x
*/
#ifdef DEBUG
if (blockIdx.x + threadIdx.x == 0) {
printf("A0, B0, C0: %f, %f, %f\n", matrixA[0], matrixB[0], matrixC[0]);
}
if (threadIdx.x == 0) {
printf("Block %d\n", blockIdx.x);
}
#endif
size_t n = blockIdx.x * threadNum + threadIdx.x;
if (n < MATRIX_SIZE) {
size_t x = n/MATRIX_DIM;
size_t y = n%MATRIX_DIM;
for(size_t i = 0; i < MATRIX_DIM; ++i) {
matrixC[n] += (matrixA[x*MATRIX_DIM + i] * matrixB[i*MATRIX_DIM + y]);
}
#ifdef DEBUG
printf("x: %llu y: %llu %f \n",x,y, matrixC[n]);
#endif
}
}
__global__ void printMatrix(matrixType* A) {
for(size_t i = 0; i<MATRIX_SIZE; ++i){
printf("%f ", A[i]);
if ((i+1)%MATRIX_DIM == 0) printf("\n");
}
printf("\n");
}
/*__global__ void add(int a, int b, int *const c) {
*c = a + b;
}*/
unsigned next_power_of_two(unsigned int x) {
--x;
x |= (x >> 1);
x |= (x >> 2);
x |= (x >> 4);
x |= (x >> 8);
return x + 1;
}
cudaDeviceProp showProperties() {
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, 0);//определение параметров GPU с номером 0
#ifdef DEBUG
printf("Device name : %s\n", deviceProp.name);
printf("Total global memory : %llu MB\n",
deviceProp.totalGlobalMem / 1024 / 1024);
printf("Shared memory per block : %zu\n",
deviceProp.sharedMemPerBlock);
printf("Registers per block : %d\n",
deviceProp.regsPerBlock);
printf("Warp size : %d\n", deviceProp.warpSize);
printf("Memory pitch : %zu\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: %zu\n",
deviceProp.totalConstMem);
printf("Compute capability: %d.%d\n",
deviceProp.major, deviceProp.minor);
printf("Texture alignment: %zu\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");
printf("Can map host memory: %s\n",
deviceProp.canMapHostMemory ? "true" :
"false");
printf("Device has Compute Capability %d.%d\n",
deviceProp.major, deviceProp.minor);
#endif
return deviceProp;
}
optimalParams detectOptimalBlockAndGridSize(cudaDeviceProp *prop, size_t matrixSize, PriorityModes mode) {
switch (mode) {
case MAX_BLOCKS_WITH_OPT_NUM_TH: {
unsigned int blocks = (matrixSize / maxThreadsPerBlock) + 1;
return optimalParams{blocks, maxThreadsPerBlock};
}
default: {
return optimalParams{blocksPerSm, maxThreadsPerBlock};
}
}
}
size_t getMatrixSize() {
return MATRIX_SIZE * sizeof(matrixType);
}
int main() {
curandGenerator_t gen;
matrixType *A, *B, *C;
auto size = getMatrixSize();
/**
* Выделяем память на GPU
*/
cudaMalloc((void **) &A, size);
cudaMalloc((void **) &B, size);
cudaMalloc((void **) &C, size);
/**
* Создаём ГПСЧ для заполнения матриц и заполняем их
*/
curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_MTGP32);
curandSetPseudoRandomGeneratorSeed(gen, 2281489ULL);
curandGenerateNormal(gen, A, MATRIX_SIZE, 5, 2);
curandGenerateNormal(gen, B, MATRIX_SIZE, 9, 3);
curandGenerateNormal(gen, C, MATRIX_SIZE, 0, 0);
//printMatrix<<<1,1>>>(A);
//printMatrix<<<1,1>>>(B);
auto prop = showProperties();
auto optimal = detectOptimalBlockAndGridSize(&prop, MATRIX_SIZE, MAX_BLOCKS_WITH_OPT_NUM_TH);
printf("Optimal: %d, %d\n", optimal.blocksDim.x, optimal.threadsDim.x);
// инициализируем события
cudaEvent_t start, stop;
float elapsedTime;
// создаем события
cudaEventCreate(&start);
cudaEventCreate(&stop);
// запись события
cudaEventRecord(start, 0);
// вызов ядра
multiply<<<optimal.blocksDim, optimal.threadsDim>>>(A, B, C, optimal.threadsDim.x);
cudaEventRecord(stop, 0);
// ожидание завершения работы ядра
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsedTime, start, stop);
// вывод информации
printf("Time spent executing by the GPU: %.2f millseconds\n", elapsedTime);
// уничтожение события
cudaEventDestroy(start);
cudaEventDestroy(stop);
//printMatrix<<<1,1>>>(C);
/*int res;
int* res_ptr;
cudaMalloc((void**)&res_ptr, sizeof(int));
add<<<1,1>>>(2, 7, res_ptr);
cudaMemcpy(&res, res_ptr, sizeof(int), cudaMemcpyDeviceToHost);
printf("Result: %d\n", res);
cudaFree((void*)res_ptr);*/
//helloworld<<<2, 5>>>();
//printf("Hello, World from CPU!\n");
/**
* Очищаем память
*/
curandDestroyGenerator(gen);
cudaFree(A);
cudaFree(B);
cudaFree(C);
return 0;
}
|
6,752 | // Test File read.cpp : Defines the entry point for the console application.
//
#include <stdio.h>
#include <iostream>
#include <fstream>
#include <math.h>
#include <ctime>
using namespace std;
int main (int argc, char *argv[])
{
ifstream in_stream;
in_stream.open(argv[1]);
int a;
int b;
in_stream >> a;
in_stream >> b;
in_stream.close();
int c;
c = a * b;
ofstream fs(argv[2]);
if(!fs)
{
cerr<<"Cannot open the output file."<<endl;
return 1;
}
fs<<c;
fs.close();
return 0;
}
|
6,753 | #include <iostream>
#include <cuda.h>
#include <chrono>
#define SIZE 1000
void checkCudaError(cudaError_t msg, int x)
{
if (msg != cudaSuccess) {
fprintf(stderr, "line: %d %s\n", x, cudaGetErrorString(msg));
exit(1);
}
return;
}
int main()
{
float *s, *dev_s;
int i;
std::chrono::time_point<std::chrono::system_clock> start, end;
double time;
s = (float *)malloc(sizeof(float)*SIZE);
for (i = 0; i < SIZE; i++) {
s[i] = i;
}
checkCudaError(cudaMalloc((void**)&dev_s, sizeof(float)*SIZE), __LINE__);
start = std::chrono::system_clock::now();
checkCudaError(cudaMemcpy(dev_s, s, sizeof(float)*SIZE, cudaMemcpyHostToDevice), __LINE__);
checkCudaError(cudaThreadSynchronize(), __LINE__);
end = std::chrono::system_clock::now();
time = std::chrono::duration_cast<std::chrono::microseconds>(end - start).count();
free(s);
checkCudaError(cudaFree(dev_s), __LINE__);
std::cout << time << "usec." << std::endl;
return 0;
} |
6,754 | #include "includes.h"
__global__ void kernel_histo_per_vertex_shared( unsigned int *ct, unsigned int *histo){
// get unique id for each thread in each block
unsigned int tid_x = threadIdx.x + blockDim.x*blockIdx.x;
unsigned int tid_y = threadIdx.y + blockDim.y*blockIdx.y;
if( tid_x >= constant_n_test_vertices ) return;
unsigned int vertex_offset = tid_x*constant_n_hits;
unsigned int bin;
unsigned int stride = blockDim.y*gridDim.y;
unsigned int stride_block = blockDim.y;
unsigned int ihit = vertex_offset + tid_y;
unsigned int time_offset = tid_x*constant_n_time_bins;
unsigned int local_ihit = threadIdx.y;
extern __shared__ unsigned int temp[];
while( local_ihit<constant_n_time_bins ){
temp[local_ihit] = 0;
local_ihit += stride_block;
}
__syncthreads();
while( ihit<vertex_offset+constant_n_hits){
bin = ct[ihit];
atomicAdd(&temp[bin - time_offset],1);
ihit += stride;
}
__syncthreads();
local_ihit = threadIdx.y;
while( local_ihit<constant_n_time_bins ){
atomicAdd( &histo[local_ihit+time_offset], temp[local_ihit]);
local_ihit += stride_block;
}
} |
6,755 | #include <stdio.h>
// Macro for checking errors in CUDA API calls
#define cudaErrorCheck(call) \
do{ \
cudaError_t cuErr = call; \
if(cudaSuccess != cuErr){ \
printf("CUDA Error - %s:%d: '%s'\n", __FILE__, __LINE__, cudaGetErrorString(cuErr));\
exit(0); \
} \
}while(0)
// Size of array
#define N 1048576
// Kernel
__global__ void add_vectors(int *a, int *b, int *c)
{
int id = blockDim.x * blockIdx.x + threadIdx.x;
if(id < N) c[id] = a[id] + b[id];
}
// Main program
int main()
{
// Number of bytes to allocate for N integers
size_t bytes = N*sizeof(int);
// Allocate memory for arrays A, B, and C on host
int *A = (int*)malloc(bytes);
int *B = (int*)malloc(bytes);
int *C = (int*)malloc(bytes);
// Allocate memory for arrays d_A, d_B, and d_C on device
int *d_A, *d_B, *d_C;
cudaErrorCheck( cudaMalloc(&d_A, bytes) );
cudaErrorCheck( cudaMalloc(&d_B, bytes) );
cudaErrorCheck( cudaMalloc(&d_C, bytes) );
// Fill host arrays A and B
for(int i=0; i<N; i++)
{
A[i] = 1;
B[i] = 2;
}
// Copy data from host arrays A and B to device arrays d_A and d_B
cudaErrorCheck( cudaMemcpy(d_A, A, bytes, cudaMemcpyHostToDevice) );
cudaErrorCheck( cudaMemcpy(d_B, B, bytes, cudaMemcpyHostToDevice) );
// Set execution configuration parameters
// thr_per_blk: number of CUDA threads per grid block
// blk_in_grid: number of blocks in grid
int thr_per_blk = 256;
int blk_in_grid = ceil( float(N) / thr_per_blk );
// Launch kernel
add_vectors<<< blk_in_grid, thr_per_blk >>>(d_A, d_B, d_C);
// Check for errors in kernel launch (e.g. invalid execution configuration paramters)
cudaError_t cuErrSync = cudaGetLastError();
// Check for errors on the GPU after control is returned to CPU
cudaError_t cuErrAsync = cudaDeviceSynchronize();
if (cuErrSync != cudaSuccess) { printf("CUDA Error - %s:%d: '%s'\n", __FILE__, __LINE__, cudaGetErrorString(cuErrSync)); exit(0); }
if (cuErrAsync != cudaSuccess) { printf("CUDA Error - %s:%d: '%s'\n", __FILE__, __LINE__, cudaGetErrorString(cuErrAsync)); exit(0); }
// Copy data from device array d_C to host array C
cudaErrorCheck( cudaMemcpy(C, d_C, bytes, cudaMemcpyDeviceToHost) );
// Verify results
for(int i=0; i<N; i++)
{
if(C[i] != 3)
{
printf("Error: value of C[%d] = %d instead of 3\n", i, C[i]);
exit(-1);
}
}
// Free CPU memory
free(A);
free(B);
free(C);
// Free GPU memory
cudaErrorCheck( cudaFree(d_A) );
cudaErrorCheck( cudaFree(d_B) );
cudaErrorCheck( cudaFree(d_C) );
printf("\n---------------------------\n");
printf("__SUCCESS__\n");
printf("---------------------------\n");
printf("N = %d\n", N);
printf("Threads Per Block = %d\n", thr_per_blk);
printf("Blocks In Grid = %d\n", blk_in_grid);
printf("---------------------------\n\n");
return 0;
}
|
6,756 | __global__
/**
* This is the kernel that convolutes the two given float arrays.
* The result is saved in the third array.
*/
void cudaConvolve(float *a, float *b, float *res, int aLen, int bLen)
{
// Calculate the length of the result
int abMax = max(aLen, bLen);
int convLen = aLen + bLen - 1;
// Find the starting point and the step of the loop
int index = blockIdx.x * blockDim.x + threadIdx.x;
int step = blockDim.x * gridDim.x;
for (int n = index; n < convLen; n += step)
{
float prod = 0;
// Find the minimum amount of iterations needed
int kMax = min(abMax, n);
for (int k = 0; k <= kMax; ++k)
{
// Make sure we're in bounds for both arrays,
// otherwise there's no overlap between the two.
if (k < aLen && n - k < bLen)
{
prod += a[k] * b[n - k];
}
}
res[n] = prod;
}
}
/**
* This method calls the CUDA kernel for the convolution, after
* calculating the proper amount of blocks and threads needed.
*/
void myConvolve(float *a, float *b, float *res, int aLen, int bLen)
{
int blockSize = 256;
int numBlocks = ((aLen + bLen - 1) + blockSize - 1) / blockSize;
cudaConvolve<<<numBlocks, blockSize>>>(a, b, res, aLen, bLen);
cudaDeviceSynchronize(); // Wait for all CUDA cores to finish
}
|
6,757 | #include <thrust/device_vector.h>
#include <iostream>
using namespace std;
const int N = 100;
__global__
void calculate (float *px, float *py, float *pz) {
float ctr = 0;
for (int tid = threadIdx.x; tid < N; tid+= blockDim.x) {
py[tid] = sqrt(px[tid]);
if (tid%2==0) __syncthreads();
pz[tid] = ctr++;
__syncthreads();
}
}
int main () {
thrust::device_vector<float> xs(N), ys(N), zs(N);
for (int i=0; i<N; i++) {
xs[i] = i;
}
calculate<<<1, 32>>>
(thrust::raw_pointer_cast(&*xs.begin()),
thrust::raw_pointer_cast(&*ys.begin()),
thrust::raw_pointer_cast(&*zs.begin())
);
for (int i = 0; i < N; i++) {
cout<<"sqrt "<<xs[i]<<" is "<<ys[i] << " updated at " << zs[i]<<endl;
}
}
|
6,758 | #include "includes.h"
__global__ void vectorAdd(int numElements, float *x, float *y)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < numElements)
{
y[i] = x[i] + y[i];
}
} |
6,759 |
/* This is a automatically generated test. Do not modify */
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
__global__
void compute(float comp, int var_1,int var_2,float var_3,float var_4,float var_5,float var_6,float var_7,float var_8,float var_9,float var_10,float var_11,float var_12,float var_13,float var_14,float var_15,float var_16,float var_17,float var_18,float var_19,float var_20,float var_21,float var_22,float var_23,float var_24,float var_25,float var_26,float var_27,float var_28,float* var_29,float* var_30,float* var_31,float var_32,float var_33,float var_34,float var_35,float var_36,float var_37,float var_38,float var_39) {
if (comp > +1.5965E-6f * +0.0f - sinf(acosf(sinf((var_3 * var_4 / (+0.0f - -1.7945E-17f)))))) {
float tmp_1 = var_5 * -1.5286E-43f / var_6;
comp = tmp_1 - var_7 - fmodf(-1.6283E-35f, acosf((+0.0f / (var_8 * (+1.5672E35f / var_9)))));
for (int i=0; i < var_1; ++i) {
float tmp_2 = -1.0614E36f;
comp = tmp_2 - var_10 / (+0.0f - -0.0f * (var_11 + var_12));
}
if (comp == +1.2665E4f - (var_13 - (-0.0f * sqrtf(var_14 / var_15 - expf(var_16 * +1.5636E-42f / -0.0f - var_17))))) {
comp += -0.0f * (-1.0037E-35f + -0.0f / (var_18 - var_19));
comp = powf((-0.0f - -1.0754E-42f), fmodf((-1.0412E34f - var_20 + atan2f((-1.4733E-21f * +1.9789E36f + -1.8577E-27f / (var_21 + var_22 / var_23)), var_24 * ldexpf(asinf((var_25 + var_26)), 2))), (-1.4438E12f - (var_27 + var_28 - +0.0f))));
comp += asinf(-1.4041E-14f);
}
for (int i=0; i < var_2; ++i) {
var_29[i] = +1.6733E-28f / (+1.5325E36f - +1.4437E34f / atanf(+1.4076E17f / (-1.5764E-41f / ceilf((+1.9181E-43f * var_32 + (var_33 * +0.0f))))));
var_30[i] = atan2f(-1.9927E-42f, var_34 + tanhf(var_35 * (var_36 / +1.2159E-12f / +1.5273E-37f)));
var_31[i] = -0.0f;
comp += var_31[i] + var_30[i] * var_29[i] - var_37 * var_38 / var_39;
}
}
printf("%.17g\n", comp);
}
float* initPointer(float v) {
float *ret = (float*) malloc(sizeof(float)*10);
for(int i=0; i < 10; ++i)
ret[i] = v;
return ret;
}
int main(int argc, char** argv) {
/* Program variables */
float tmp_1 = atof(argv[1]);
int tmp_2 = atoi(argv[2]);
int tmp_3 = atoi(argv[3]);
float tmp_4 = atof(argv[4]);
float tmp_5 = atof(argv[5]);
float tmp_6 = atof(argv[6]);
float tmp_7 = atof(argv[7]);
float tmp_8 = atof(argv[8]);
float tmp_9 = atof(argv[9]);
float tmp_10 = atof(argv[10]);
float tmp_11 = atof(argv[11]);
float tmp_12 = atof(argv[12]);
float tmp_13 = atof(argv[13]);
float tmp_14 = atof(argv[14]);
float tmp_15 = atof(argv[15]);
float tmp_16 = atof(argv[16]);
float tmp_17 = atof(argv[17]);
float tmp_18 = atof(argv[18]);
float tmp_19 = atof(argv[19]);
float tmp_20 = atof(argv[20]);
float tmp_21 = atof(argv[21]);
float tmp_22 = atof(argv[22]);
float tmp_23 = atof(argv[23]);
float tmp_24 = atof(argv[24]);
float tmp_25 = atof(argv[25]);
float tmp_26 = atof(argv[26]);
float tmp_27 = atof(argv[27]);
float tmp_28 = atof(argv[28]);
float tmp_29 = atof(argv[29]);
float* tmp_30 = initPointer( atof(argv[30]) );
float* tmp_31 = initPointer( atof(argv[31]) );
float* tmp_32 = initPointer( atof(argv[32]) );
float tmp_33 = atof(argv[33]);
float tmp_34 = atof(argv[34]);
float tmp_35 = atof(argv[35]);
float tmp_36 = atof(argv[36]);
float tmp_37 = atof(argv[37]);
float tmp_38 = atof(argv[38]);
float tmp_39 = atof(argv[39]);
float tmp_40 = atof(argv[40]);
compute<<<1,1>>>(tmp_1,tmp_2,tmp_3,tmp_4,tmp_5,tmp_6,tmp_7,tmp_8,tmp_9,tmp_10,tmp_11,tmp_12,tmp_13,tmp_14,tmp_15,tmp_16,tmp_17,tmp_18,tmp_19,tmp_20,tmp_21,tmp_22,tmp_23,tmp_24,tmp_25,tmp_26,tmp_27,tmp_28,tmp_29,tmp_30,tmp_31,tmp_32,tmp_33,tmp_34,tmp_35,tmp_36,tmp_37,tmp_38,tmp_39,tmp_40);
cudaDeviceSynchronize();
return 0;
}
|
6,760 |
#include <cuda_runtime.h>
#include <vector>
#include <iterator>
#include <iostream>
#include <algorithm>
#include <stdio.h>
static void print_device_info(int device_id){
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, device_id);
printf(" Device ID: %d\n", device_id);
printf(" Device name: %s\n", prop.name);
printf(" Device architecture: %d.%d\n", prop.major, prop.minor);
printf(" Device global memory: %d\n", prop.totalGlobalMem);
printf(" Device clockrate: %d\n", prop.clockRate);
}
static std::vector<int> print_devices(){
int devices;
cudaGetDeviceCount(&devices);
printf("CUDA Capable Devices with architecture greater than equal to 3.5\n\n");
//int usable_devices = 0;
std::vector<int> usable_devices;
cudaDeviceProp prop;
for(int device = 0; device < devices; device++){
cudaGetDeviceProperties(&prop, device);
if(prop.major > 3){
usable_devices.push_back(device);
printf(" Device ID: %d\n", device);
printf(" Device name: %s\n", prop.name);
printf(" Device architecture: %d.%d\n", prop.major, prop.minor);
}else if((prop.major == 3) && (prop.minor >= 5)){
printf(" Device ID: %d\n", device);
printf(" Device name: %s\n", prop.name);
printf(" Device architecture: %d.%d\n", prop.major, prop.minor);
usable_devices.push_back(device);
}
}
printf("\n");
return usable_devices;
}
extern "C" std::vector<int> select_devices(){
bool devices_selected = false;
bool get_input = true;
std::vector<int> usable_devices = print_devices();
std::vector<int> selected_devices;
if(usable_devices.size() == 0)
return selected_devices;
std::string device_selected;
printf("\n");
do{
printf("Select devices:\n");
for(int iter = 0 ; iter < usable_devices.size(); iter++){
print_device_info(usable_devices[iter]);
get_input = true;
do{
try{
printf("y/n? ");
std::cin >> device_selected;
printf("\n");
std::transform(device_selected.begin(), device_selected.end(),device_selected.begin(), ::toupper);
if(device_selected == "Y" || device_selected == "YES"){
selected_devices.push_back(usable_devices[iter]);
}else if (device_selected != "N" && device_selected != "NO"){
throw;
}
get_input = false;
}catch(...){
printf("Error in selection\n");
}
}while(get_input);
}
get_input = true;
printf("Devices selected:\n");
do{
for(int iter = 0; iter < selected_devices.size(); iter++){
print_device_info(selected_devices[iter]);
}
try{
printf("Is this right (y/n)? ");
std::cin >> device_selected;
printf("\n");
std::transform(device_selected.begin(), device_selected.end(),device_selected.begin(), ::toupper);
if(device_selected == "Y" || device_selected == "YES"){
devices_selected = true;
}else if (device_selected != "N" && device_selected != "NO"){
throw;
}
get_input = false;
}catch(...){
printf("Error in selection\n");
}
}while(get_input);
if(devices_selected == false)
selected_devices.clear();
}while(devices_selected == false);
return selected_devices;
}
|
6,761 | #include <cuda.h>
#include <cuda_runtime.h>
// read: single value-0 (50 % access)
// write: single value-1 (50 %access)
__global__
void op1(float *p, size_t N) {
size_t idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx < N / 2) {
p[idx] = p[idx] + 1;
}
}
// zero value (25% access)
__global__
void op2(float *p, size_t N) {
size_t idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx < N / 4) {
p[idx] = 0.0;
}
}
// approximate
__global__
void op3(float *p, size_t N) {
size_t idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx < N / 2) {
p[idx] = 1.0 + idx / 10000.0;
}
}
// dense value (50% access)
__global__
void op4(int *p, size_t N) {
size_t idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx < N / 2) {
p[idx] = 3;
}
}
// structured value (50% access)
__global__
void op5(int *p, size_t N) {
size_t idx = blockDim.x * blockIdx.x + threadIdx.x;
if (idx % 2 && idx < N) {
p[idx] = idx;
}
}
static const size_t N = 1000;
static const int THREADS = 128;
int main() {
float *p1;
cudaMalloc(&p1, N * sizeof(float));
cudaMemset(&p1, 0, N * sizeof(float));
auto blocks = (N - 1) / THREADS + 1;
op1<<<blocks, THREADS>>>(p1, N);
op2<<<blocks, THREADS>>>(p1, N);
op3<<<blocks, THREADS>>>(p1, N);
int *p2;
cudaMalloc(&p2, N * sizeof(int));
op4<<<blocks, THREADS>>>(p2, N);
op5<<<blocks, THREADS>>>(p2, N);
cudaFree(p1);
cudaFree(p2);
return 0;
}
|
6,762 | #include <stdlib.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/device_ptr.h>
#include <sys/time.h>
#include "ftvp.cuh"
#include "memory.cuh"
#include "kernels/kernels-2d.cuh"
// Handle errors raised by the GPU
// From http://stackoverflow.com/questions/14038589/what-is-the-canonical-way-to-check-for-errors-using-the-cuda-runtime-api
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline 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);
}
}
// Return the exec time in ms.
inline double bench_time(timeval start, timeval end) {
return (end.tv_sec*1000.0 + (end.tv_usec / 1000.0)) - (start.tv_sec*1000.0 + (start.tv_usec / 1000.0));
}
extern
void prox_tv_2d_noalloc(struct memory_cuda *mem, struct res_cuda *res,
int sx, int sy, double lambda, double epsilon,
int Nit, int block_size, int steps, int gapiter, double gap_factor, int use_newton, int upd_strat) {
int it=0; // current iteration
double t=1; // relaxation parameter
double tt;
double gamma, gammap;
double theta=0; // relaxation parameter
double gap=0;
timeval start, end;
int sxy = sx * sy;
double w = 2 * lambda * lambda;
double ws = M_SQRT2*lambda; // sqrt(w)
epsilon /= ws; // |Du|_C threshold
double we2 = w*epsilon*epsilon;
double tau = 0.25;
double q;
if (upd_strat == UPDATE_VARYING) {
gamma = epsilon * tau;
gammap = gamma + gamma/(1+gamma);
q = gammap/(1+gamma);
} else {
q = tau*epsilon/(1+tau*epsilon);
}
int Ke = sx/2, Le=sy/2;
int Ko = (sx-2)/2, Lo = (sy-2)/2;
dim3 blocks_odd((Ko+block_size-1)/block_size, (Lo+block_size-1)/block_size);
dim3 blocks_even((Ke+block_size-1)/block_size, (Le+block_size-1)/block_size);
dim3 threads(block_size, block_size);
gettimeofday(&start, NULL);
do {
// Over-relaxation
if (upd_strat == UPDATE_CONSTANT) {
theta = 1/(1+tau*sqrt(epsilon));
theta *= theta;
} else if (upd_strat == UPDATE_VARYING) {
tt = t;
t = .5*((1-q*t*t)+sqrt((1-q*t*t)*(1-q*t*t)+4*(1+gammap)/(1+gamma)*t*t));
theta = (tt-1)/t * (1-(t-1)*gammap);
} else {
tt = t;
t = .5*((1.-q*t*t)+sqrt((1-q*t*t)*(1-q*t*t)+4*t*t));
theta = (tt-1)/t * (1-(t-1)*tau*epsilon);
}
if (theta > 0) {
over_relax_eps_2d<<<blocks_odd,threads>>>(sx, mem->dev_xio, mem->dev_xiobar, mem->dev_u, theta, Lo, Ko, 0);
// Swap xio and xiobar
mem->dev_xioswp = mem->dev_xio;
mem->dev_xio = mem->dev_xiobar;
mem->dev_xiobar = mem->dev_xioswp;
}
// Minimization
opt_eps_split<<<blocks_even,threads>>>(sx, mem->dev_xie, mem->dev_u, epsilon, w, ws, we2, Le, Ke, steps, use_newton, 1);
opt_eps_split<<<blocks_odd,threads>>>(sx, mem->dev_xiobar, mem->dev_u, epsilon, w, ws, we2, Lo, Ko, steps, use_newton, 0);
// Gap
if (it % gapiter == 0) {
gap_arr_eps_2d<<<blocks_odd,threads>>>(sx, mem->dev_glo, mem->dev_xio, mem->dev_u, epsilon, w, ws, we2, Lo, Ko, 0);
thrust::device_ptr<double> D = thrust::device_pointer_cast(mem->dev_glo);
gap = thrust::reduce(D, D+Ko*Lo, (double) 0, thrust::plus<double>());
gap_arr_eps_2d<<<blocks_even,threads>>>(sx, mem->dev_gle, mem->dev_xie, mem->dev_u, epsilon, w, ws, we2, Le, Ke, 1);
D = thrust::device_pointer_cast(mem->dev_gle);
gap += thrust::reduce(D, D+Ke*Le, (double) 0, thrust::plus<double>());
}
it++;
} while (it < Nit && gap > sxy * gap_factor);
gettimeofday(&end, NULL);
double msec = bench_time(start, end);
res->it = it;
res->msec = msec;
res->gap = gap;
res->rmse = sqrt(gap/(sx*sy));
return;
}
extern
res_cuda* prox_tv(int sx, int sy, int sz,
double *u, double lambda, double epsilon,
int Nit, int block_size, int steps, int gapiter, double gap_factor,
OptimizationMethod opt_meth, int upd_strat) {
struct res_cuda *res = (struct res_cuda *) malloc(sizeof(struct res_cuda));
struct memory_cuda *mem = (struct memory_cuda *) malloc(sizeof(struct memory_cuda));
int use_newton = opt_meth == OE_SPLIT_NEWTON ? 1 : 0;
init_memory(mem, res, sx, sy, sz, u);
if (sz > 1) {
exit(1);
} else {
prox_tv_2d_noalloc(mem, res, sx, sy, lambda, epsilon, Nit, block_size, steps, gapiter, gap_factor, use_newton, upd_strat);
}
// Memory management
gpuErrchk( cudaMemcpy(u, mem->dev_u, sx*sy*sz*sizeof(double), cudaMemcpyDeviceToHost) );
free_memory(mem);
return res;
}
|
6,763 | #include <iostream>
#include <cstdlib>
#include <iomanip>
#include <cstring>
#include <cuda_runtime.h>
#include <cstdio>
//C++ timers
#include <chrono>
#define BLOCK_SIZE 16
#define CUDA_WARN(XXX) \
do { if (XXX != cudaSuccess) std::cerr << "CUDA Error: " << \
cudaGetErrorString(XXX) << ", at line " << __LINE__ \
<< std::endl; cudaDeviceSynchronize(); } while (0)
typedef std::chrono::high_resolution_clock Clock;
typedef std::chrono::nanoseconds nanoseconds;
typedef struct {
//Dimensions of A
int M;
int N;
//Scalars
double alpha;
double beta;
//First dimension of A
int lda;
//Increments for X and Y
int incX;
int incY;
//Array A and vectors X, Y;
double *A, *X, *Y;
} dgemv_data;
// BASIC DGEMM METHOD
void dgemm(int N, double alpha,
double *X, double *Y,
double beta, double *A) {
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
double tmp = 0;
for (int k = 0; k < N; ++k) {
tmp += X[i * N + k] * Y[k * N + j];
}
A[i * N + j] = alpha * tmp + beta * A[i * N + j];
}
}
}
// CUDA DGEMM W/O SHARED MEMORY
__global__ void dgemm_cuda (int N, double alpha,
double *X, double *Y,
double beta, double *A) {
int i = blockDim.y * blockIdx.y + threadIdx.y;
int j = blockDim.x * blockIdx.x + threadIdx.x;
if (i < N && j < N) {
double temp = 0;
for (int k = 0; k < N; ++k) {
temp += X[i * N + k] * Y[k * N + j];
}
A[i * N + j] = alpha * temp + beta * A[i * N + j];
}
}
// CUDA DGEMM W/O SHARED MEMORY
__global__ void dgemm_cuda_shared (int N, double alpha,
double *X, double *Y,
double beta, double *A) {
// Create Shared Memory Arrays
__shared__ double Xshared[BLOCK_SIZE][BLOCK_SIZE];
__shared__ double Yshared[BLOCK_SIZE][BLOCK_SIZE];
//Setup i and j
int i = blockIdx.y * BLOCK_SIZE + threadIdx.y;
int j = blockIdx.x * BLOCK_SIZE + threadIdx.x;
double temp = 0;
for (int s = 0; s < gridDim.x; ++s) {
int index = i * N + s * BLOCK_SIZE + threadIdx.x;
if(index >= N*N)
Xshared[threadIdx.y][threadIdx.x] = 0;
else
Xshared[threadIdx.y][threadIdx.x] = X[index];
index = (s * BLOCK_SIZE + threadIdx.y) * N + j;
if(index >= N*N)
Yshared[threadIdx.y][threadIdx.x] = 0;
else
Yshared[threadIdx.y][threadIdx.x] = Y[index];
__syncthreads();
for (int k = 0; k < BLOCK_SIZE; ++k)
temp += Xshared[threadIdx.y][k] * Yshared[k][threadIdx.x];
__syncthreads();
}
if(i < N && j < N) {
A[i * N + j] = temp * alpha + beta * A[i * N + j];
}
}
double* createMatrix(int M, int N) {
double *A = new double[M*N];
int i;
#pragma omp parallel for private(i)
for(i = 0; i < M*N; i++) {
A[i] = ((double) rand() / (RAND_MAX)); //Random number from 0 to 1
}
return A;
}
double* createVector(int len) {
double *A = new double[len];
int i;
#pragma omp parallel for private(i)
for(i = 0; i < len; i++) {
A[i] = ((double) rand() / (RAND_MAX));
}
return A;
}
dgemv_data* generateRandomData(int M, int N) {
dgemv_data *data = new dgemv_data();
data->M = M;
data->N = N;
data->A = new double[M*N];
data->alpha = (double) (rand() % 10) + ((double) rand() / (RAND_MAX));
data->beta = (double) (rand() % 10) + ((double) rand() / (RAND_MAX));
data->lda = M; //Not used for dgemm
data->X = createMatrix(N,N);
data->Y = createMatrix(N,N);
data->incX = 1; //Not used for dgemm
data->incY = 1; //not used for dgemm
return data;
}
dgemv_data* copyData(dgemv_data* data) {
dgemv_data* copy = new dgemv_data();
copy->M = data->N;
copy->N = data->N;
copy->alpha = data->alpha;
copy->beta = data->beta;
copy->lda = data->lda;
copy->incX = data->incX;
copy->incY = data->incY;
copy->X = new double[data->N * data->N];
copy->Y = new double[data->N * data->N];
copy->A = new double[data->N * data->N];
memcpy (copy->X, data->X, data->N * data->N * sizeof(double));
memcpy (copy->Y, data->Y, data->N * data->N * sizeof(double));
memcpy (copy->A, data->A, data->N * data->N * sizeof(double));
return copy;
}
bool compareMatrices(double *A, double *B, int length) {
int i;
for(i = 0; i < length; i++) {
//To account for doubleing pt error, check if greater than some epsilon
if(abs(A[i] - B[i]) > 0.000001) {
std::cout <<"i: " << i << " A[i]: " << A[i] << " B[i]: " << B[i] << std::endl;
return false;
}
}
return true;
}
void freeDataStruct(dgemv_data* data) {
delete[](data->A);
delete[](data->X);
delete[](data->Y);
}
void testOutput(dgemv_data *data, dgemv_data* test_data) {
dgemm(test_data->N, test_data->alpha,
test_data->X, test_data->Y,
test_data->beta,test_data->A);
if(compareMatrices(data->A, test_data->A, data->N * data->N)) {
std::cout << "Output: PASSED" << std::endl;
} else {
std::cout << "Output: FAILED" << std::endl;
}
}
void printTimeTaken(unsigned long ns) {
std::cout << std::fixed;
std::cout << std::setprecision(10)
<< "Time taken: "
<< ns
<< " ns or "
<< (double) ns/1000000000.0
<< " s\n"
<< std::endl;
}
int main(int argc, char **argv) {
//Clock for C++
if (argc != 3) {
std::cout << "Invalid set of arguments.\n"
<< "Usage: ./dgemv [Testing Off/On(0/1)] [size N]"
<< std::endl;
exit(-1);
}
//Get user arguments
int M,N;
bool test;
//Square matrix
if(argc == 3) {
M = atoi(argv[2]);
N = M;
}
test = (atoi(argv[1]) > 0) ? true : false;
//Feed random seed
srand(time(NULL));
//Generate the data
dgemv_data *unModifiedData = generateRandomData(M,N); //DO not run functions on this
/************************************************************************/
//Run DGEMVT Serial Version
//Get data sets to run with and test with (Since the arrays are modified)
dgemv_data *data = copyData(unModifiedData);
dgemv_data *serialTestData = copyData(unModifiedData);
std::cout << "-----------------------------------------------" << std::endl;
std::cout << "Running Serial Version of DGEMVT" << std::endl;
auto start = Clock::now();
dgemm(data->N, data->alpha, data->X,
data->Y, data->beta, data->A);
auto end = Clock::now();
unsigned long ns = (unsigned long) std::chrono::duration_cast<nanoseconds>(end-start).count();
printTimeTaken(ns);
//Test the output
if(test) {
testOutput(data, serialTestData);
}
//Congifure CUDA blocksize and dim grid/block
size_t gridR = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
size_t gridC = (N + BLOCK_SIZE - 1) / BLOCK_SIZE;
dim3 dimGrid(gridC,gridR);
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
/************************************************************************/
//Run DGEMVT CUDA Version
//Get data sets to run with and test with (Since the arrays are modified)
dgemv_data *cuData = copyData(unModifiedData);
dgemv_data *cuTestData = copyData(unModifiedData);
//Double arrays for the GPU
double *cuA, *cuX, *cuY;
//Allocate space and copy data into GPU allocated arrays
CUDA_WARN(cudaMalloc(&cuA, N * N * sizeof(double)));
CUDA_WARN(cudaMalloc(&cuX, N * N * sizeof(double)));
CUDA_WARN(cudaMalloc(&cuY, N * N * sizeof(double)));
CUDA_WARN(cudaMemcpy(cuA, cuData->A, N*N*sizeof(double), cudaMemcpyHostToDevice));
CUDA_WARN(cudaMemcpy(cuX, cuData->X, N*N*sizeof(double), cudaMemcpyHostToDevice));
CUDA_WARN(cudaMemcpy(cuY, cuData->Y, N*N*sizeof(double), cudaMemcpyHostToDevice));
std::cout << "-----------------------------------------------" << std::endl;
std::cout << "Running CUDA Version of DGEMVT" << std::endl;
start = Clock::now();
dgemm_cuda<<<dimGrid, dimBlock>>>
(N,cuData->alpha, cuX,
cuY, cuData->beta, cuA);
CUDA_WARN(cudaThreadSynchronize());
end = Clock::now();
ns = (unsigned long) std::chrono::duration_cast<nanoseconds>(end-start).count();
CUDA_WARN(cudaGetLastError());
//Copy the cude result back
cudaMemcpy(cuData->A, cuA, N*N*sizeof(double), cudaMemcpyDeviceToHost);
//Print the time taken
printTimeTaken(ns);
//Test the output
if(test) {
testOutput(cuData, cuTestData);
}
/************************************************************************/
//Run DGEMVT CUDA Shared Memory Version
//Get data sets to run with and test with (Since the arrays are modified)
dgemv_data *cuDataShared = copyData(unModifiedData);
dgemv_data *cuTestDataShared = copyData(unModifiedData);
//Double arrays for the GPU
double *scuA, *scuX, *scuY;
//Allocate space and copy data into GPU allocated arrays
CUDA_WARN(cudaMalloc(&scuA, N * N * sizeof(double)));
CUDA_WARN(cudaMalloc(&scuX, N * N * sizeof(double)));
CUDA_WARN(cudaMalloc(&scuY, N * N * sizeof(double)));
CUDA_WARN(cudaMemcpy(scuA, cuDataShared->A, N*N*sizeof(double), cudaMemcpyHostToDevice));
CUDA_WARN(cudaMemcpy(scuX, cuDataShared->X, N*N*sizeof(double), cudaMemcpyHostToDevice));
CUDA_WARN(cudaMemcpy(scuY, cuDataShared->Y, N*N*sizeof(double), cudaMemcpyHostToDevice));
std::cout << "-----------------------------------------------" << std::endl;
std::cout << "Running CUDA Shared Memory Version of DGEMVT" << std::endl;
start = Clock::now();
dgemm_cuda_shared<<<dimGrid, dimBlock>>>
(N,cuDataShared->alpha, scuX,
scuY, cuDataShared->beta, scuA);
CUDA_WARN(cudaThreadSynchronize());
end = Clock::now();
ns = (unsigned long) std::chrono::duration_cast<nanoseconds>(end-start).count();
CUDA_WARN(cudaGetLastError());
//Copy the cude result back
cudaMemcpy(cuDataShared->A, scuA, N*N*sizeof(double), cudaMemcpyDeviceToHost);
//Print the time taken
printTimeTaken(ns);
//Test the output
if(test) {
testOutput(cuDataShared, cuTestDataShared);
}
return 0;
}
|
6,764 | #include "includes.h"
//==========================================================================================================
// A small snippet of code to solve equation of types Ax=B using Gaussian Elimniation
// Author - Anmol Gupta, Naved Ansari
// Course - EC513 - Introduction to Computer Architecture
// Boston University
//==========================================================================================================
//==========================================================================================================
// Command to compile the code
//nvcc -o GaussianElimination GaussianElimination.cu
//==========================================================================================================
// Assertion to check for errors
__global__ void gauss_elimination_cuda_new(float *a_d, float *b_d ,int size) {
int i, j;
int idy = threadIdx.x;
__shared__ float temp[MAXSIZE+10][MAXSIZE+10];
//copy to share
for(i=0; i<size+1; i++){
temp[idy][i] = a_d[(idy * (size+1)) + i];
//cuPrintf("T idy=%d, num = %d, temp=%f\n", idy, i, temp[idy][i]);
}
__syncthreads();
//loop through every row, calculate every column in parallel
for(i=1; i<size; i++){
//cuPrintf("\nthread %d(idy) going to loop %d(i)\n", idy, i);
if(idy >= i){
float t[MAXSIZE+10];
//perform calculation
for(j=0; j<size+1; j++){
if(j >= i-1){
t[j] = temp[i-1][j] - (temp[i-1][i-1] / temp[idy][i-1]) * temp[idy][j];
//cuPrintf("calculate No %d, answer %f\n", j, t);
}
}
__syncthreads();
//store data
for(j=0; j<size+1; j++){
if(j >= i-1){
temp[idy][j] = t[j];
}
}
}
__syncthreads();
}
//copy to host
for(i=0; i<size+1; i++){
b_d[idy * (size+1) + i] = temp[idy][i];
}
} |
6,765 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <cuda.h>
#include <device_functions.h>
#include <cuda_runtime_api.h>
#include <stdio.h> /* printf, scanf, puts, NULL */
#include <stdlib.h> /* srand, rand */
#include <time.h>
#include <device_functions.h>
#define SIZE 32
#define TSIZE 1024
using namespace std;
__global__ void addKernel(int *A, int *B)
{
__shared__ int auxMatrix[SIZE][SIZE];
int col = threadIdx.x + blockIdx.x * blockDim.x;
int row = threadIdx.y + blockIdx.y * blockDim.y;
int pos = col + row * SIZE;
//if 0,0
if (row == 0 && col == 0)
auxMatrix[row][col] = A[pos] + A[pos + 1] + A[pos + SIZE];
//if 0,31
else if (row == 0 && col == 31)
auxMatrix[row][col] = A[pos] + A[pos - 1] + A[pos + SIZE];
//if 31,0
else if (row == 31 && col == 0)
auxMatrix[row][col] = A[pos] + A[pos + 1] + A[pos - SIZE];
//if 31,31
else if (row == 31 && col == 31)
auxMatrix[row][col] = A[pos] + A[pos - 1] + A[pos - SIZE];
//if row 0 only
else if (row == 0)
auxMatrix[row][col] = A[pos] + A[pos + 1] + A[pos - 1] + A[pos + SIZE];
//if row 31 only
else if (row == 31)
auxMatrix[row][col] = A[pos] + A[pos + 1] + A[pos - 1] + A[pos - SIZE];
//if col 0 only
else if (col == 0)
auxMatrix[row][col] = A[pos] + A[pos + 1] + A[pos - SIZE] + A[pos + SIZE];
//if col 1 only
else if (col == 31)
auxMatrix[row][col] = A[pos] + A[pos - 1] + A[pos - SIZE] + A[pos + SIZE];
//if central position in the matrix
else if (row != 0 && col != 0 && row != 31 && col != 31)
auxMatrix[row][col] = A[pos] + A[pos + 1] + A[pos - 1] + A[pos + SIZE] + A[pos - SIZE];
__syncthreads();
//printf("\nA[%d]=%d", pos, A[pos]);
//printf("\nA[%d]=%d\nauxMatrix[%d][%d]=%d", pos, A[pos], row, col, auxMatrix[row][col]);
//printf("\nCol: %d , threadIdx.x: %d, blockIdx.x: %d, blockDim.x: %d", col, threadIdx.x, blockIdx.x, blockDim.x);
//printf("\nRow: %d , threadIdx.y: %d, blockIdx.y: %d, blockDim.y: %d", row, threadIdx.y, blockIdx.y, blockDim.y);
B[pos] = auxMatrix[row][col];
//printf("\nB[%d]=%d", pos, B[pos]);
}
int main()
{
int A[SIZE][SIZE], B[SIZE][SIZE];
srand(time(NULL));
for (int i = 0; i < SIZE; i++)
{
for (int k = 0; k < SIZE; k++)
{
A[i][k] = rand() % 99;
}
}
// Add vectors in parallel.
int *dev_A, *dev_B;
size_t sharedMem = 64;
dim3 dimGrid(2, 2);
dim3 dimBlock(16, 16);
cudaMalloc((void**)&dev_A, SIZE * SIZE *sizeof(int));
cudaMalloc((void**)&dev_B, SIZE * SIZE *sizeof(int));
cudaMemcpy(dev_A, A, SIZE *SIZE *sizeof(int), cudaMemcpyHostToDevice);
addKernel <<<dimGrid, dimBlock, sharedMem>>> (dev_A, dev_B);
cudaDeviceSynchronize();
cudaMemcpy(B, dev_B, 32 * 32 * sizeof(int), cudaMemcpyDeviceToHost);
printf("\n\n--A--");
for (int i = 0; i < SIZE; i++)
{
printf("\n[");
for (int j = 0; j < SIZE; j++)
{
if (j != 31)
if (A[i][j] / 10 == 0)
printf("00%d-", A[i][j]);
else if (A[i][j] / 100 == 0)
printf("0%d-", A[i][j]);
else
printf("%d-", A[i][j]);
else
if (A[i][j] / 10 == 0)
printf("00%d", A[i][j]);
else if (A[i][j] / 100 == 0)
printf("0%d", A[i][j]);
else
printf("%d", A[i][j]);
}
printf("]");
}
printf("\n\n--B--");
for (int i = 0; i < SIZE; i++)
{
printf("\n[");
for (int j = 0; j < SIZE; j++)
{
if(j!=31)
if (B[i][j] / 10 == 0)
printf("00%d-", B[i][j]);
else if(B[i][j]/100 == 0)
printf("0%d-", B[i][j]);
else
printf("%d-", B[i][j]);
else
if (B[i][j] / 10 == 0)
printf("00%d", B[i][j]);
else if (B[i][j] / 100 == 0)
printf("0%d", B[i][j]);
else
printf("%d", B[i][j]);
}
printf("]");
}
// cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);
cudaFree(dev_A);
cudaFree(dev_B);
return 0;
}
|
6,766 | #include <cuda.h>
#include <stdio.h>
#include <stdlib.h>
__device__ int diverge_gpu(float c_re, float c_im, int max) {
float z_re = c_re, z_im = c_im;
int i;
for (i = 0; i < max; ++i) {
if (z_re * z_re + z_im * z_im > 4.f)
break;
float new_re = z_re * z_re - z_im * z_im;
float new_im = 2.f * z_re * z_im;
z_re = c_re + new_re;
z_im = c_im + new_im;
}
return i;
}
__global__ void mandelKernel(int *c, float lowerX, float lowerY, float stepX, float stepY, int resX, int resY, int maxIterations) {
// To avoid error caused by the floating number, use the following pseudo code
//
// float x = lowerX + thisX * stepX;
// float y = lowerY + thisY * stepY;
/*
int pix_per_thread = resX * resY / (gridDim.x * blockDim.x);
int tId = blockDim.x * blockIdx.x + threadIdx.x;
int offset = pix_per_thread * tId;
int i;
for (i = offset; i < offset + pix_per_thread; i++){
int x = i % resX;
int y = i / resX;
float cr = lowerX + x * stepX;
float ci = lowerY + y * stepY;
c[y * resX + x] = diverge_gpu(cr, ci, maxIterations);
}
if (gridDim.x * blockDim.x * pix_per_thread < resX * resY && tId < (resX * resY) - (blockDim.x * gridDim.x)){
int i = blockDim.x * gridDim.x * pix_per_thread + tId;
int x = i % resX;
int y = i / resX;
float cr = lowerX + x * stepX;
float ci = lowerY + y * stepY;
c[y * resY + x] = diverge_gpu(cr, ci, maxIterations);
}
*/
int index = threadIdx.x + blockIdx.x * blockDim.x;
int thisX = index % resX;
int thisY = index / resX;
float x = lowerX + thisX * stepX;
float y = lowerY + thisY * stepY;
c[index] = diverge_gpu(x, y, maxIterations);
}
// Host front-end function that allocates the memory and launches the GPU kernel
void hostFE (float upperX, float upperY, float lowerX, float lowerY, int* img, int resX, int resY, int maxIterations)
{
float stepX = (upperX - lowerX) / resX;
float stepY = (upperY - lowerY) / resY;
int size = resX * resY;
int *h_c;
int *d_c;
h_c = (int *)malloc(size * sizeof(int));
cudaMalloc(&d_c, size * sizeof(int));
int block_size = 1024;
int grid_size = 1;
//dim3 dimBlock(block_size, block_size);
//dim3 dimGrid(grid_size, grid_size);
mandelKernel<<<size/block_size, block_size>>>(d_c, lowerX, lowerY, stepX, stepY, resX, resY, maxIterations);
cudaMemcpy(h_c, d_c, size * sizeof(int), cudaMemcpyDeviceToHost);
memcpy(img, h_c, size * sizeof(int));
cudaFree(d_c);
free(h_c);
}
|
6,767 |
// GPU kernel
__global__ void summation_kernel(int data_size, float * data_out)
{
extern __shared__ float sdata[];
unsigned int tid = threadIdx.x;
/* Version de calcul par blocs continus (en descendant) */
int i = blockIdx.x * blockDim.x + threadIdx.x;
int k;
float res=0;
float tmp=1.0;
int debut = i*data_size;
int fin = debut + data_size;
for (k=fin-1; k>=debut; k--){
if ((k%2) == 1) {tmp = -1.0;}
else tmp = 1.0;
res += tmp / (k + 1);
}
sdata[tid] = res;
__syncthreads();
for(unsigned int s = 1; s < blockDim.x; s*=2){
int index = 2*s*tid;
if(index < blockDim.x) {
sdata[index] += sdata[index + s];
}
__syncthreads();
}
if(tid == 0){
data_out[blockIdx.x] = sdata[0];
}
/* Version de calcul par rang (en descendant) */
/*
int i = blockIdx.x * blockDim.x + threadIdx.x;
int k;
float res=0.0;
float tmp=1.0;
int debut = i;
int fin = debut+data_size*blockDim.x;
for (k=fin-blockDim.x; k>=debut; k-=blockDim.x){
if ((k%2) == 1) {tmp = -1.0;}
else tmp = 1.0;
res += tmp / (k + 1);
}
data_out[i] = res;
*/
}
// GPU kernel
__global__ void second_summation_kernel(float *data, float *res)
{
unsigned int i;
float temp = 0.0;
for(i=0; i<blockDim.x; i++){
temp += data[i];
}
*res = temp;
}
|
6,768 | #include <stdio.h>
// So, a __global__ to tell NVCC this is a device function for calling from the host
// and a pointer to some memory to write the result to
__global__ void addTwoNumbers(int x, int y, int *result) {
*result = x + y;
}
int main(int argc, char** args)
{
// This is in system memory
int cpuVisibleResult;
// This is just an unitialized pointer
int *gpuVisibleResult;
// This sets that pointer to point at a memory location on device memory
cudaMalloc( (void**)&gpuVisibleResult, sizeof(int) );
// Call the method, ignore 1,1 for now
addTwoNumbers<<<1,1>>>(2,7, gpuVisibleResult);
// Download the result from the device to the host
cudaMemcpy( &cpuVisibleResult, gpuVisibleResult, sizeof(int), cudaMemcpyDeviceToHost );
// Print the results
printf( " 2 + 7 = %d\n", cpuVisibleResult);
// Free up that memory on the device
cudaFree( gpuVisibleResult );
return 1;
}
|
6,769 | #define N 16
#include <stdio.h>
__global__ void matrixMult (int *a, int *b, int *c, int width);
int main() {
int a[N][N], b[N][N], c[N][N];
int *dev_a, *dev_b, *dev_c;
// initialize matrices a and b with appropriate values
for (int i = 0; i < N; ++i)
{
for (int j = 0; j < N; ++j)
{
a[i][j] = rand() % 10;
b[i][j] = rand() % 10;
}
}
int size = N * N * sizeof(int);
cudaMalloc((void **) &dev_a, size);
cudaMalloc((void **) &dev_b, size);
cudaMalloc((void **) &dev_c, size);
cudaMemcpy(dev_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, size, cudaMemcpyHostToDevice);
dim3 dimGrid(1, 1);
dim3 dimBlock(N, N);
matrixMult<<<dimGrid, dimBlock>>>(dev_a, dev_b, dev_c, N);
cudaMemcpy(c, dev_c, size, cudaMemcpyDeviceToHost);
cudaFree(dev_a); cudaFree(dev_b); cudaFree(dev_c);
int i, j;
printf("a {\n");
for (i = 0; i < N; i++)
{
for (j = 0; j < N; ++j)
{
printf("\"[%2d][%2d]\" : %3d, ", i, j, a[i][j]);
}
}
printf("}\n");
printf("b {\n");
for (i = 0; i < N; i++)
{
for (j = 0; j < N; ++j)
{
printf("\"[%2d][%2d]\" : %3d, ", i, j, b[i][j]);
}
}
printf("}\n");
printf("c {\n");
for (i = 0; i < N; i++)
{
for (j = 0; j < N; ++j)
{
printf("\"[%2d][%2d]\" : %3d, ", i, j, c[i][j]);
}
}
printf("}\n");
}
__global__ void matrixMult (int *a, int *b, int *c, int width) {
int k, sum = 0;
int col = threadIdx.x + blockDim.x * blockIdx.x;
int row = threadIdx.y + blockDim.y * blockIdx.y;
if(col < width && row < width) {
for (k = 0; k < width; k++) {
sum += a[row * width + k] * b[k * width + col];
c[row * width + col] = sum;
}
}
} |
6,770 | #include <stdio.h>
extern "C"
int example(int a, int b){
return a + b;
}
extern "C"
void example2(int input){
printf("%s%d\n", "hello ", input);
}
extern "C"
void gpuTest(void){
int n = 10;
int a[n], b[n], c[n];
int *dev_a, *dev_b, *dev_c;
cudaMalloc( (void**)&dev_a, n * sizeof(int) );
cudaMalloc( (void**)&dev_b, n * sizeof(int) );
cudaMalloc( (void**)&dev_c, n * sizeof(int) );
for (int i=0; i<n; i++) {
a[i] = -i;
b[i] = i * i;
}
cudaMemcpy( dev_a, a, n * sizeof(int), cudaMemcpyHostToDevice );
cudaMemcpy( dev_b, b, n * sizeof(int), cudaMemcpyHostToDevice );
//add<<<n,1>>>( dev_a, dev_b, dev_c );
// copy the array 'c' back from the GPU to the CPU
cudaMemcpy( c, dev_c, n * sizeof(int), cudaMemcpyDeviceToHost );
// display the results
for (int i=0; i<n; i++) {
printf( "%d + %d = %d\n", a[i], b[i], c[i] );
}
// free the memory allocated on the GPU
cudaFree( dev_a );
cudaFree( dev_b );
cudaFree( dev_c );
}
/*
int main(){
gpuTest();
printf("hello");
}
*/ |
6,771 | #define LENGTH_V 1024*1024
#define LENGTH_SHOW 10
#define BLOCK_SIZE 1024
#include <stdio.h>
#include <stdlib.h>
#include <limits.h>
#include <time.h>
// Kernel: naive scan
__global__ void scan_incl(int *g_odata, int *g_idata, int ndata) {
int thid = threadIdx.x;
extern __shared__ int temp[];
// Double buffer dinamically reserved
int pout = 0, pin = 1;
temp[pout * ndata + thid] = g_idata[thid];
__syncthreads();
for (int offset = 1; offset < ndata; offset *= 2) {
pout = 1 - pout; // swap double buffer indices
pin = 1 - pout;
temp[pout * ndata + thid] = temp[pin * ndata + thid];
if (thid >= offset) {
temp[pout * ndata + thid] += temp[pin * ndata + thid - offset];
}
__syncthreads();
}
g_odata[thid] = temp[pout * ndata + thid]; // write output
}
// Kernel: block scan
__global__ void scan_incl_blocks(int *g_odata, int *g_idata, int blockSize, int ndata) {
int block = blockIdx.x;
int thid = threadIdx.x;
int posArray = block * blockSize + thid;
int numElemts;
if (block == (ceil( (double) ndata / (double) blockSize) - 1) && ndata % blockSize != 0) {
numElemts = ndata % blockSize;
} else {
numElemts = blockSize;
}
extern __shared__ int temp[];
// Double buffer dinamically reserved
int pout = 0, pin = 1;
temp[pout * numElemts + thid] = g_idata[posArray];
__syncthreads();
for (int offset = 1; offset < numElemts; offset *= 2) {
pout = 1 - pout; // swap double buffer indices
pin = 1 - pout;
temp[pout * numElemts + thid] = temp[pin * numElemts + thid];
if (thid >= offset) {
temp[pout * numElemts + thid] += temp[pin * numElemts + thid - offset];
}
__syncthreads();
}
g_odata[posArray] = temp[pout * numElemts + thid];
}
__global__ void scan_getBlocksAux(int *g_block, int *g_idata, int blockSize, int ndata) {
int block = blockIdx.x;
int thid = threadIdx.x;
int posArray = block * blockSize + thid;
int numElemts;
if (block == (ceil( (double) ndata / (double) blockSize) - 1) && ndata % blockSize != 0) {
numElemts = ndata % blockSize;
} else {
numElemts = blockSize;
}
if (thid == (numElemts - 1)) {
g_block[block] = g_idata[posArray];
}
}
__global__ void addToBlocks(int *g_odata, int *g_block, int blockSize) {
int block = blockIdx.x;
int thid = threadIdx.x;
int posArray = block * blockSize + thid;
if (block != 0) {
g_odata[posArray] += g_block[block - 1];
}
}
void show_vector(char *myString, int lengthMyString, int *vector, int length) {
int j;
printf("\n%s\n",myString);
for (j = 0; j < lengthMyString; j++)
printf("-");
printf("\n");
if (LENGTH_SHOW * 2 < length) {
for (j = 0; j < LENGTH_SHOW; j++)
printf(" %d", vector[j]);
printf(" ...");
for (j = length-LENGTH_SHOW; j < length; j++)
printf(" %d", vector[j]);
printf("\n");
} else {
for (j=0 ; j<length; j++)
printf(" %d", vector[j]);
printf("\n");
}
}
int main(void) {
int h_VectorOriginal[LENGTH_V];
int h_VectorScanGPU[LENGTH_V];
int *d_VectorOriginal, *d_VectorScanGPUBlocks, *d_VectorScanGPUBlocksAux, *d_VectorScanGPUBlocks2;
int j;
clock_t start, end;
double time_used;
// -----------------------
srand(time(NULL));
for (j = 0; j < LENGTH_V; j++) {
h_VectorOriginal[j] = (int)(rand() % 100) - 50;
}
char msg1[] = "Vector original";
show_vector(msg1, strlen(msg1), h_VectorOriginal, LENGTH_V);
// -----------------------
// Vector scan blocks (GPU)
// -----------------------
cudaMalloc((void **)&d_VectorOriginal, sizeof(int) * LENGTH_V);
cudaMalloc((void **)&d_VectorScanGPUBlocks, sizeof(int) * LENGTH_V);
cudaMemcpy(d_VectorOriginal, h_VectorOriginal, sizeof(int) * LENGTH_V, cudaMemcpyHostToDevice);
int numBlocks = ceil( (double) LENGTH_V / (double) BLOCK_SIZE);
printf("\nNumero de bloques: %d\n", numBlocks);
int numThreads = BLOCK_SIZE;
printf("Numero de threads: %d\n", numThreads);
cudaMalloc((void **)&d_VectorScanGPUBlocksAux, sizeof(int) * numBlocks);
cudaMalloc((void **)&d_VectorScanGPUBlocks2, sizeof(int) * numBlocks);
start = clock();
scan_incl_blocks <<< numBlocks, numThreads, BLOCK_SIZE * 2 * sizeof(int) >>>(
d_VectorScanGPUBlocks,
d_VectorOriginal,
BLOCK_SIZE,
LENGTH_V);
scan_getBlocksAux <<< numBlocks, numThreads >>>(
d_VectorScanGPUBlocksAux,
d_VectorScanGPUBlocks,
BLOCK_SIZE,
LENGTH_V
);
scan_incl <<< 1, numBlocks, numBlocks * 2 * sizeof(int) >>>(
d_VectorScanGPUBlocks2,
d_VectorScanGPUBlocksAux,
numBlocks
);
addToBlocks <<< numBlocks, numThreads >>>(
d_VectorScanGPUBlocks,
d_VectorScanGPUBlocks2,
BLOCK_SIZE
);
end = clock();
cudaMemcpy(h_VectorScanGPU, d_VectorScanGPUBlocks, sizeof(int) * LENGTH_V, cudaMemcpyDeviceToHost);
// -----------------------
char msg3[] = "Vector scan blocks (GPU)";
show_vector(msg3, strlen(msg3), h_VectorScanGPU, LENGTH_V);
time_used = 1000.0 * ((double)(end - start)) / CLOCKS_PER_SEC;
printf("GPU scan kernel processing time: %f millisec. (nº elements %d)\n",time_used, LENGTH_V);
cudaFree(d_VectorOriginal);
cudaFree(d_VectorScanGPUBlocks);
cudaFree(d_VectorScanGPUBlocksAux);
} |
6,772 | /* ================================================================
*
* PyCA Project
*
* Copyright (c) J. Samuel Preston, Linh K. Ha, Sarang C. Joshi. All
* rights reserved. See Copyright.txt or for details.
*
* This software is distributed WITHOUT ANY WARRANTY; without even the
* implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
* PURPOSE. See the above copyright notice for more information.
*
* ================================================================ */
#define MAX_NUMBER_REDUCE_STREAMS 4
#define MAX_NUMBER_REDUCE_THREADS 128
#define MAX_NUMBER_REDUCE_BLOCKS 128
namespace PyCA {
template<typename T, typename traits, uint blockSize>
__inline__ __device__ void reduce_shared(T& mySum, volatile T* sdata, uint tid)
{
// do reduction in shared mem
if (blockSize >= 512) {
if (tid < 256) { sdata[tid] = mySum = traits::op(mySum, sdata[tid + 256]); } __syncthreads();
}
if (blockSize >= 256) {
if (tid < 128) { sdata[tid] = mySum = traits::op(mySum, sdata[tid + 128]); } __syncthreads();
}
if (blockSize >= 128) {
if (tid < 64) { sdata[tid] = mySum = traits::op(mySum, sdata[tid + 64]); } __syncthreads();
}
if (tid < 32)
{
if (blockSize >= 64) sdata[tid] = mySum = traits::op(mySum, sdata[tid + 32]);
if (blockSize >= 32) sdata[tid] = mySum = traits::op(mySum, sdata[tid + 16]);
if (blockSize >= 16) sdata[tid] = mySum = traits::op(mySum, sdata[tid + 8]);
if (blockSize >= 8) sdata[tid] = mySum = traits::op(mySum, sdata[tid + 4]);
if (blockSize >= 4) sdata[tid] = mySum = traits::op(mySum, sdata[tid + 2]);
if (blockSize >= 2) sdata[tid] = mySum = traits::op(mySum, sdata[tid + 1]);
}
}
/**
* @brief Perform cuda kernel parallel reductin
* s = a1 + a2 + a3 + .... + an
* @param[in] T Input data type (currently int, float)
* traits Binary operation (+, max, min)
* blockSize Size of block (related to optimize problem)
* d_i Input data
* n Size of the input
* @param[out] array of output redution perform for each block
*
*/
template <typename T, typename traits, uint blockSize>
__global__ void reduce_kernel(const T *d_i, T *d_o, uint n)
{
volatile __shared__ T sdata[MAX_NUMBER_REDUCE_THREADS];
// reading from global memory, writing to shared memory
uint tid = threadIdx.x;
uint i = blockIdx.x*(blockSize*2) + tid;
uint gridSize = blockSize * 2 * gridDim.x;
T mySum = traits::identity();;
while (i + blockSize < n )
{
traits::iop(mySum,traits::op(d_i[i],d_i[i+blockSize]));
i += gridSize;
}
if ( i < n) traits::iop(mySum, d_i[i]);
sdata[tid] = mySum;
__syncthreads();
reduce_shared<T, traits, blockSize>(mySum, sdata, tid);
// write result for this block to global mem
if (tid == 0) {
d_o[blockIdx.x] = mySum;
}
}
/**
* @brief Perform cuda kernel parallel reductin
* s = a1 + a2 + a3 + .... + an
* @param[in] T Input data type (currently int, float)
* traits Binary operation (+, max, min)
* traits1 Self data function (square, cude, sqrt, abs)
* blockSize Size of block (related to optimize problem)
* d_i Input data
* n Size of the input
* @param[out] array of output redution perform for each block
*
*/
// Correct version of reduce function
template <typename T, typename traits, typename traits1, uint blockSize>
__global__ void reduce_kernel(const T *d_i, T *d_o, uint n)
{
volatile __shared__ T sdata[MAX_NUMBER_REDUCE_THREADS];
// perform first level of reduction,
// reading from global memory, writing to shared memory
uint tid = threadIdx.x;
uint i = blockIdx.x*(blockSize*2) + tid;
uint gridSize = blockSize*2*gridDim.x;
T mySum = traits::identity();
// we reduce multiple elements per thread. The number is determined by the
// number of active thread blocks (via gridSize). More blocks will result
// in a larger gridSize and therefore fewer elements per thread
while (i + blockSize < n ) {
traits::iop(mySum,traits::op(traits1::op(d_i[i]),traits1::op(d_i[i+blockSize])));
i += gridSize;
}
if ( i < n)
traits::iop(mySum, traits1::op(d_i[i]));
sdata[tid] = mySum;
__syncthreads();
reduce_shared<T, traits, blockSize>(mySum, sdata, tid);
// write result for this block to global mem
if (tid == 0)
d_o[blockIdx.x] = mySum;
}
////////////////////////////////////////////////////////////////////////////////
//
////////////////////////////////////////////////////////////////////////////////
template <typename T, typename oper, typename oper1, uint blockSize>
__global__ void biReduce_kernel(T *d_o, T *d_o1, const T *d_i, uint n)
{
volatile __shared__ T s0[MAX_NUMBER_REDUCE_THREADS];
volatile __shared__ T s1[MAX_NUMBER_REDUCE_THREADS];
// perform first level of reduction,
// reading from global memory, writing to shared memory
uint tid = threadIdx.x;
uint i = blockIdx.x*(blockSize*2) + tid;
uint gridSize = blockSize * 2 * gridDim.x;
T mySum = oper::identity();
T mySum1 = oper1::identity();
while (i + blockSize < n )
{
oper::iop(mySum, oper::op(d_i[i],d_i[i+blockSize]));
oper1::iop(mySum1 ,oper1::op(d_i[i],d_i[i+blockSize]));
i += gridSize;
}
if ( i < n){
oper::iop(mySum, d_i[i]);
oper1::iop(mySum1, d_i[i]);
}
s0[tid] = mySum;
s1[tid] = mySum1;
__syncthreads();
// do reduction in shared mem
if (blockSize >= 512) {
if (tid < 256) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 256]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 256]);
}
__syncthreads();
}
if (blockSize >= 256) {
if (tid < 128) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 128]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 128]);
}
__syncthreads();
}
if (blockSize >= 128) {
if (tid < 64) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 64]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 64]);
}
__syncthreads();
}
if (tid < 32)
{
if (blockSize >= 64) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 32]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 32]);
}
if (blockSize >= 32) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 16]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 16]);
}
if (blockSize >= 16) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 8]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 8]);
}
if (blockSize >= 8) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 4]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 4]);
}
if (blockSize >= 4) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 2]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 2]);
}
if (blockSize >= 2) {
s0[tid] = mySum = oper::op(mySum, s0[tid + 1]);
s1[tid] = mySum1 = oper1::op(mySum1, s1[tid + 1]);
}
}
// write result for this block to global mem
if (tid == 0){
d_o[blockIdx.x] = mySum;
d_o1[blockIdx.x] = mySum1;
}
}
////////////////////////////////////////////////////////////////////////////////
//
////////////////////////////////////////////////////////////////////////////////
template <typename T, typename opers, typename opers1, uint blockSize>
__global__ void reduceProduct_kernel(T *d_o, const T*d_i, const T*d_i1, size_t n)
{
volatile __shared__ T sdata[MAX_NUMBER_REDUCE_THREADS];
// perform first level of reduction,
// reading from global memory, writing to shared memory
uint blockId = blockIdx.x;
uint tid = threadIdx.x;
uint i = blockId * (blockSize * 2) + tid;
uint gridSize = (blockSize * 2) * gridDim.x;
T mySum = opers::identity();
// we reduce multiple elements per thread. The number is determined by the
// number of active thread blocks (via gridSize). More blocks will result
// in a larger gridSize and therefore fewer elements per thread
while (i + blockSize < n ) {
T t1 = opers1::op(d_i[i], d_i1[i]);
T t2 = opers1::op(d_i[i + blockSize], d_i1[i + blockSize]);
opers::iop(mySum,opers::op(t1, t2));
i += gridSize;
}
if ( i < n) {
T t1 = opers1::op(d_i[i], d_i1[i]);
opers::iop(mySum,t1);
}
sdata[tid] = mySum;
__syncthreads();
// do reduction in shared mem
reduce_shared<T, opers, blockSize>(mySum, sdata, tid);
// write result for this block to global mem
if (tid == 0)
d_o[blockIdx.x] = sdata[0];
}
} // end namespace PyCA
|
6,773 | #include "utils.cuh"
__host__
void readMatrixFromFile(char* input_filename,
// First row of file
int* rows, int* columns, int* num_of_non_zero_entries,
// Return variables
int** row_ptr_array, int** row_ptr_array_init,
int** col_ind_array,
double** values_array) {
FILE *fptr;
fptr = fopen(input_filename, "r");
if (fptr == NULL) {
printf("Error reading file");
return;
} else {
int index = 0;
int row = 0, column = 0, tmp = 0;
double non_zero_val = 0.0;
// Read first row from matrix file
fscanf(fptr, "%d %d %d", &row, &column, &tmp);
*rows = row;
*columns = column;
*num_of_non_zero_entries = tmp;
int* elem_ptr_arr = (int*)malloc(sizeof(int) * (*rows + 1));
*row_ptr_array = (int*)malloc(sizeof(int) * (*rows + 1));
*row_ptr_array_init = (int*)malloc(sizeof(int) * *num_of_non_zero_entries);
*col_ind_array = (int*)malloc(sizeof(int) * *num_of_non_zero_entries);
*values_array = (double*)malloc(sizeof(double) * *num_of_non_zero_entries);
// read lines into 3 variables line by line
while (index < *num_of_non_zero_entries)
{
fscanf(fptr, "%d", &row);
fscanf(fptr, "%d", &column);
fscanf(fptr, "%lf", &non_zero_val);
// -1 to make indices start from 0
elem_ptr_arr[row - 1]++;
// (*row_ptr_array)[index] = row - 1;
(*row_ptr_array_init)[index] = row - 1;
(*col_ind_array)[index] = column - 1;
(*values_array)[index] = non_zero_val;
index++;
}
// Prefix sum
for (int k = 1; k < *rows; k++)
{
elem_ptr_arr[k] += elem_ptr_arr[k - 1];
(*row_ptr_array)[k] = elem_ptr_arr[k];
}
// for (int k = *rows; k > 0; k--)
// (*row_ptr_array)[k] = *num_of_non_zero_entries - elem_ptr_arr[k - 1];
(*row_ptr_array)[0] = 0;
free(elem_ptr_arr);
}
}
__host__
void printMatrix(int rows, int columns, int num_of_non_zero_entries,
int* row_ptr_array, int* col_ind_array,
double * values_array) {
printf("%d\t%d\t%d\n", rows, columns, num_of_non_zero_entries);
// For each row
for (int i = 0; i < num_of_non_zero_entries; i++)
{
printf("%d\t%d\t%lf\n", row_ptr_array[i] + 1,
col_ind_array[i] + 1, values_array[i]);
}
}
__host__
void printVector(int rows, double* x_array)
{
for (int i = 0; i < rows; i++)
{
printf("%lf\n", x_array[i]);
}
printf("\n");
}
__host__
void CUDAErrorCheck(const char* msg) {
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err) {
fprintf(stderr, "Cuda error: %s: %d: %s.\n", msg, (int)err, cudaGetErrorName(err));
// exit(EXIT_FAILURE);
}
}
__host__
void mmult_serial(// First row of file
int rows, int columns, int num_of_non_zero_entries,
int num_repetitions,
// Return variables
int* row_ptr_array, int* col_ind_array,
double* values_array, double** x_array, double** x_array_old)
{
for ( int row = 0; row < rows; row++)
{
double tmp_product = 0;
int row_start = row_ptr_array[row];
int row_end = row_ptr_array[row + 1];
// Iterate over the sparse row
for (int j = row_start; j < row_end; j++)
tmp_product += values_array[j] * (*x_array_old)[col_ind_array[j]];
(*x_array)[row] = tmp_product;
}
}
/*__host__
void init_vector_to_1(// First row of file
int rows, int columns, int num_of_non_zero_entries,
int num_repetitions,
// Return variables
int* row_ptr_array, int* col_ind_array,
double* values_array, double** x_array)
{
for ( int row = 0; row < rows; row++)
{
int row_start = row_ptr_array[row];
int row_end = row_ptr_array[row + 1];
// Iterate over the sparse row
for (int j = row_start; j < row_end; j++)
(*x_array)[col_ind_array[j]] = 1.0f;
}
}*/
|
6,774 | #include <cuda_runtime.h>
#include <stdio.h>
__global__ void checkIndex()
{
printf("ThreadIdx:(%d %d %d) blockIdx:(%d %d %d) blockDim:(%d %d %d) "
"gridDim:(%d %d %d)\n", threadIdx.x, threadIdx.y, threadIdx.z,
blockIdx.x, blockIdx.y, blockIdx.z, blockDim.x, blockDim.y,
blockDim.z, gridDim.x, gridDim.y, gridDim.z);
}
int main(int argc, char **argv)
{
// define total data element
int nElem = 6;
// define grid and block structure
dim3 block(3);
dim3 grid((nElem+block.x-1) / block.x);
// check grid and block dimension from host side
printf("grid.x %d grid.y %d grid.z %d\n", grid.x, grid.y, grid.z);
printf("block.x %d block.y %d block.z %d\n", block.x, block.y, block.z);
// check grid and block dimension from device side
checkIndex <<<grid, block>>> ();
// reset device before leaving
cudaDeviceReset();
return 0;
}
|
6,775 | #include <stdio.h>
#include <stdlib.h>
void initialize(int* input, int size) {
for (int index = 0; index < size; index++) {
input[index] = index;
}
}
int cpu_reduction(int* input, int size) {
int result = 0;
for (int index = 0; index < size; index++) {
result += index;
}
return result;
}
void showResult(int cpu_result, int gpu_result) {
printf("cpu_result: %d, gpu_result: %d\n", cpu_result, gpu_result);
}
//// 1. reduction neighbored pairs kernel
__global__ void redunction_v1(int* input, int* output, int size) {
int tid = threadIdx.x;
int gid = blockDim.x * blockIdx.x + threadIdx.x;
if (gid >= size) return;
for (int offset = 1; offset <= blockDim.x / 2; offset *= 2) {
if (tid % (2 * offset) == 0) {
input[gid] += input[gid + offset];
}
__syncthreads();
}
if (tid == 0) {
output[blockIdx.x] = input[gid];
}
}
//// 2. warp_divergence_improved of #1 reduction_v1
__global__ void reduction_v1_improved(int* input, int* output, int size) {
int tid = threadIdx.x;
int gid = blockDim.x * blockIdx.x + threadIdx.x;
// local data block pointer
int* i_data = input + blockDim.x * blockIdx.x;
if (gid >= size) return;
for (int offset = 1; offset <= blockDim.x / 2; offset *= 2) {
int index = 2 * offset * tid;
if (index < blockDim.x) {
i_data[index] += i_data[index + offset];
}
__syncthreads();
}
if (tid == 0) {
output[blockIdx.x] = input[gid];
}
}
int main(int argc, char** argv) {
// int size = 1 << 27; // 128 Mb of data
int size = 512;
dim3 block(128);
dim3 grid(size / block.x);
int* h_ref = (int*)malloc(grid.x * sizeof(int));
int gpu_result;
//// input
int* h_input;
h_input = (int*)malloc(size * sizeof(int));
initialize(h_input, size);
//// cpu redunction
int cpu_result = cpu_reduction(h_input, size);
//// gpu redunction
int *d_input, *d_output;
cudaMalloc((void**)&d_input, size * sizeof(int));
cudaMalloc((void**)&d_output, grid.x * sizeof(int));
//// #1
cudaMemcpy(d_input, h_input, size * sizeof(int), cudaMemcpyHostToDevice);
cudaMemset(d_output, 0, grid.x * sizeof(int));
redunction_v1<<<grid, block>>>(d_input, d_output, size);
cudaDeviceSynchronize();
memset(h_ref, 0, grid.x * sizeof(int));
cudaMemcpy(h_ref, d_output, grid.x * sizeof(int), cudaMemcpyDeviceToHost);
gpu_result = 0;
for (int i = 0; i < grid.x; i++) {
gpu_result += h_ref[i];
}
showResult(cpu_result, gpu_result);
//// #2
cudaMemcpy(d_input, h_input, size * sizeof(int), cudaMemcpyHostToDevice);
cudaMemset(d_output, 0, grid.x * sizeof(int));
reduction_v1_improved<<<grid, block>>>(d_input, d_output, size);
cudaDeviceSynchronize();
memset(h_ref, 0, grid.x * sizeof(int));
cudaMemcpy(h_ref, d_output, grid.x * sizeof(int), cudaMemcpyDeviceToHost);
gpu_result = 0;
for (int i = 0; i < grid.x; i++) {
gpu_result += h_ref[i];
}
showResult(cpu_result, gpu_result);
cudaFree(d_output);
cudaFree(d_input);
free(h_ref);
free(h_input);
cudaDeviceReset();
return 0;
}
|
6,776 | // includes, system
#include <stdio.h>
#include <assert.h>
#include <stdlib.h>
__global__ void fillArray(int *data, int N)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < N)
data[idx] = idx;
}
/////////////////////////////////////////////////////////////////////
// Program main
/////////////////////////////////////////////////////////////////////
int main( int argc, char** argv)
{
int *data_h;
int *data_d;
int i;
const int N = 100;
// allocate host and device memory
data_h = ( int* ) malloc(N * sizeof(int));
cudaMalloc(&data_d, N * sizeof(int));
//Fill the array
fillArray<<<N,1>>>( data_d, N );
//Make sure the device has finished
cudaThreadSynchronize();
//Copy the results to the host
cudaMemcpy(data_h, data_d, N*sizeof(int), cudaMemcpyDeviceToHost);
// verify the data is correct
for (i = 0; i < N; i++)
{
assert(data_h[i] == i );
}
// If the program makes it this far, then the results are
// correct and there are no run-time errors. Good work!
printf("Correct!\n");
free(data_h);
cudaFree(data_d);
return 0;
}
|
6,777 | #include "includes.h"
__global__ void init(double* out, int n){
int gid = threadIdx.x + blockIdx.x * blockDim.x;
if(gid >= n) return ;
out[gid] = 0.0;
} |
6,778 | // Simple CUDA example
#include <stdio.h>
const int N = 16;
const int blocksize = 16;
__global__ void hello(char *a, int *b)
{
a[threadIdx.x] += b[threadIdx.x];
}
// Kernel definition Vector Add
__global__ void VecAdd(float* A, float* B, float* C)
{
int i = threadIdx.x;
C[i] = A[i] + B[i];
}
int main()
{
float a[N], b[N], c[N];
// Initial values
for (int i = 0; i < N; ++i)
{
a[i] = 0.1f*i*i;
b[i] = 0.1f*i;
}
float *ad, *bd, *cd;
const int size = N*sizeof(float);
// print a, b, c before
printf("a\tb\tc\n");
for (int i = 0; i < N; ++i)
{
printf("%.2f\t%.2f\t%.2f\n", a[i], b[i], c[i]);
}
cudaMalloc( (void**)&ad, size );
cudaMalloc( (void**)&bd, size );
cudaMalloc( (void**)&cd, size );
cudaMemcpy( ad, a, size, cudaMemcpyHostToDevice );
cudaMemcpy( bd, b, size, cudaMemcpyHostToDevice );
dim3 dimBlock( blocksize, 1 );
dim3 dimGrid( 1, 1 );
VecAdd<<<dimGrid, dimBlock>>>(ad, bd, cd);
cudaMemcpy( c, cd, size, cudaMemcpyDeviceToHost );
cudaFree( ad );
cudaFree( bd );
cudaFree( cd );
// print results
printf("a\tb\tc\n");
for (int i = 0; i < N; ++i)
{
printf("%.2f\t%.2f\t%.2f\n", a[i], b[i], c[i]);
}
return EXIT_SUCCESS;
}
|
6,779 | #include <stdio.h>
#include <cuda.h>
#define Width 1920
#define Height 2520
#define iterations 100
__global__ void convolution_kernel(unsigned char* in_device_buffer,unsigned char* out_device_buffer);
void swap_images(unsigned char **in_image,unsigned char **out_image);
int main (){
unsigned char *in_image ;
unsigned char *in_device_buffer;
unsigned char *out_image ;
unsigned char *out_device_buffer ;
int grid, i;
grid = Width* Height;
in_image =(unsigned char*)malloc(grid);
out_image =(unsigned char*)malloc(grid);
FILE *fp;
fp = fopen("images/grey_X1.raw","rb");
if (fp == NULL){
printf("Empty file ... Exiting");
exit(1);
}else {
fread(in_image,grid,1,fp);
fclose(fp);
}
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
dim3 threadNum(32,32);
dim3 blockNum(Width/((threadNum.x)-2),Height/((threadNum.y)-2));
cudaMalloc(&in_device_buffer,grid);
cudaMalloc(&out_device_buffer,grid);
cudaMemcpy(in_device_buffer,in_image,grid,cudaMemcpyHostToDevice);
cudaEventRecord(start, 0);
for (i = 0 ; i < iterations; i++){
convolution_kernel <<<blockNum,threadNum>>> (in_device_buffer, out_device_buffer);
swap_images(&in_device_buffer,&out_device_buffer);
}
cudaEventRecord(stop, 0);
cudaMemcpy(out_image, out_device_buffer, grid, cudaMemcpyDeviceToHost);
cudaFree(out_device_buffer);
cudaFree(in_device_buffer);
cudaEventSynchronize(stop);
fp = fopen("out.raw","w+");
fwrite(out_image,grid,1,fp);
fclose(fp);
return 0;
}
void swap_images(unsigned char **in_image,unsigned char **out_image)
{
unsigned char* temp = *in_image;
*in_image = *out_image;
*out_image = temp;
}
__global__ void convolution_kernel(unsigned char* A, unsigned char* B)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
int x = i-2*blockIdx.x-1;
int y = j-2*blockIdx.y-1;
__shared__ unsigned char As[32][32];
//Copy from global memory to shared memory
if (x<0) {
x=0;
} else if (x==Width) {
x=Width-1;
}
if (y<0) {
y=0;
} else if (y == Height) {
y = Height-1;
}
As[threadIdx.x][threadIdx.y] = A[Width*y + x];
__syncthreads();
// Computations
if (threadIdx.x!=0 && threadIdx.x!=31 && threadIdx.y!=0 && threadIdx.y!=31) {
B[Width*y + x] = (As[threadIdx.x-1][threadIdx.y-1] +
As[threadIdx.x ][threadIdx.y-1] * 2 +
As[threadIdx.x+1][threadIdx.y-1] +
As[threadIdx.x-1][threadIdx.y ] *2 +
As[threadIdx.x ][threadIdx.y ] *4 +
As[threadIdx.x+1][threadIdx.y ] * 2 +
As[threadIdx.x-1][threadIdx.y+1] * 1 +
As[threadIdx.x ][threadIdx.y+1] * 2 +
As[threadIdx.x+1][threadIdx.y+1] * 1)/16;
}
}
|
6,780 | #include "includes.h"
__global__ void update_vb(float *d_verts_ptr, int vertex_count, float timeElapsed)
{
const unsigned long long int threadId = blockIdx.x * blockDim.x + threadIdx.x;
if (threadId < vertex_count * 4)
{
float valx = d_verts_ptr[threadId * 4 + 0];
float valy = d_verts_ptr[threadId * 4 + 1];
float valz = d_verts_ptr[threadId * 4 + 2];
d_verts_ptr[threadId * 4 + 0] = valx * timeElapsed;
d_verts_ptr[threadId * 4 + 1] = valy * timeElapsed;
d_verts_ptr[threadId * 4 + 2] = valz * timeElapsed;
}
} |
6,781 | /*#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <iostream>
#define threads 1024
#define blocks 1024
#define size blocks*threads
//Do not interchange datatypes this ain't python...
//an example performing reduction operationn
__global__ void kernel(unsigned int* d_in, unsigned int* d_0)
{
unsigned int myId = threadIdx.x + blockIdx.x*blockDim.x;
int tid = threadIdx.x;
extern __shared__ unsigned int sData[];
sData[tid] = d_in[myId];//cop all data to shared memory... faster than global
__syncthreads();
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s)
sData[tid] += sData[tid+s];
__syncthreads();
}
unsigned int count = 0;
if (tid==0) {
d_0[blockIdx.x]=sData[tid];//data back to globbal, this is a partial sum yet to be calculated at
}
__syncthreads();
}
void main()
{
unsigned int* d_arr;
unsigned int* d_0;
unsigned int* h_arr = (unsigned int*)malloc(sizeof(unsigned int)*size);
unsigned int* h_0 = (unsigned int*)malloc(sizeof(unsigned int)*threads);
unsigned int count = 0;
for (unsigned int i = 0; i < size; i++)
{
h_arr[i] = rand()%246;
count += h_arr[i];
}
//std::cout << count;
cudaMalloc(&d_arr, size*sizeof(unsigned int));
cudaMalloc(&d_0, threads*sizeof(unsigned int));
cudaMemcpy(d_arr,h_arr,sizeof(unsigned int)*size,cudaMemcpyHostToDevice);
kernel << <blocks, threads,sizeof(unsigned int)*threads >> >(d_arr,d_0);
cudaMemcpy(h_0,d_0,sizeof(unsigned int)*threads,cudaMemcpyDeviceToHost);
unsigned int newCount = 0;
for (unsigned int i = 0; i < threads; i++)
{
newCount+= h_0[i];
}
if (newCount == count)
std::cout << "True\n";
else
std::cout << "false\n";
cudaFree(d_arr);
cudaFree(d_0);
free(h_arr);
free(h_0);
system("pause");
}
*/ |
6,782 | #include <stdio.h>
#include <cuda.h>
#include "cuComplex.h"
#define WARP_SIZE 32
// Kernel that executes on the CUDA device
template <int N>
__global__ void test(float *in, float *mul, float *out)
{
int lane_id = threadIdx.x % WARP_SIZE;
int warp_id = threadIdx.x / WARP_SIZE;
__shared__ float smem [WARP_SIZE*2];
volatile float* my_smem = &smem[WARP_SIZE*warp_id];
__shared__ float smul[N];
my_smem[lane_id] = in[lane_id];
if (lane_id < N)
smul[lane_id] = mul[lane_id];
my_smem[lane_id] = smul[lane_id%N] * my_smem[lane_id];
out[lane_id] = my_smem[lane_id];
}
// main routine that executes on the host
int main(void)
{
const int N = 8; // Number of elements in arrays
size_t elems = 256;
size_t size = elems * sizeof(float);
float* in_h = (float*)calloc(elems, sizeof(float)); // Allocate array on host
float* mul_h = (float*)calloc(N, sizeof(float));
float* out_h = (float*)calloc(elems, sizeof(float)); // Allocate array on host
for (int i = 0; i < elems; i++)
{
in_h[i] = (float)i;
}
for (int i = 0; i < N; i++)
{
mul_h[i] = 1.f;
}
float *in_d, *out_d, *mul_d;
cudaMalloc((void **) &in_d, size); // Allocate array on device
cudaMalloc((void **) &out_d, size); // Allocate array on device
cudaMalloc((void **) &mul_d, N*sizeof(float)); // Allocate array on device
cudaMemcpy(in_d, in_h, size, cudaMemcpyDefault);
cudaMemcpy(out_d, out_h, size, cudaMemcpyDefault);
cudaMemcpy(mul_d, mul_h, N*sizeof(float), cudaMemcpyDefault);
cudaDeviceSynchronize();
{
test<N> <<< 1, WARP_SIZE >>> (in_d, mul_d, out_d);
cudaDeviceSynchronize();
cudaMemcpy(out_h, out_d, WARP_SIZE*sizeof(float), cudaMemcpyDefault);
cudaDeviceSynchronize();
float expected = 0.0, got = 0.0, test = 0.0;
for (int j = 0; j < 32; j++)
{
expected += in_h[j]*mul_h[j%N];
got += out_h[j];
}
// Print results
printf("sz: %d, Expected: %f, Got: %f\n", 32, expected, got);
}
// Cleanup
free(in_h);
free(mul_h);
free(out_h);
cudaFree(in_d);
cudaFree(out_d);
cudaFree(mul_d);
return 0;
}
|
6,783 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <math.h>
#define N 1024
#define blockCount 1
//http://cacs.usc.edu/education/cs596/src/cuda/pi.cu
__global__ void calculatePi(float *out)
{
//Shared memeory for sum, only works with a blockCount of 1
__shared__ float cache[N];
int threadID = blockIdx.x * blockDim.x + threadIdx.x;
int stepSize = blockDim.x * gridDim.x;
// y = root(1 - x^2)
// calculate y from x = threadID/N (giving an over estimate)
//sum of all y*x*4 = solution
float x = float(1) /(blockCount*N);
float thisX = float(threadID) / (blockCount*N);
float y;
float area = 0.0f;
//Increase the accuracy of the estimation by further splitting each thread rectangle into 3
int max = 3;
for (int i = 0; i < max; i++)
{
thisX += i * (x/ max);
y = sqrt(1 - thisX * thisX);
area += y * (x / max) * 4;
}
cache[threadID] = area;
__syncthreads();
if (threadIdx.x == 0)
{
float sum = 0.0;
for (int i = 0; i < N; i++)
{
sum += cache[i];
}
*out = sum;
}
}
int main()
{
float out, *d_out;
cudaMalloc((void**)&d_out, sizeof(float));
cudaMemcpy(d_out, &out, sizeof(float), cudaMemcpyHostToDevice);
//Parallel pi calculation
calculatePi<<<blockCount, N >>>(d_out);
cudaMemcpy(&out, d_out, sizeof(float), cudaMemcpyDeviceToHost);
printf("%f\n", out);
cudaFree(d_out);
return 0;
} |
6,784 | #include "Matrix.cuh"
class HostMatrixDeleter {
public:
void operator()(float* ptr) const {
if (ptr) {
cudaFreeHost(ptr);
}
}
};
HostMatrix::HostMatrix(unsigned int height, unsigned int width): Matrix(height, width, false) {
float* rawElements = 0;
cudaMallocHost(&rawElements, height * width * sizeof(float));
elements = std::shared_ptr<float>(rawElements, HostMatrixDeleter());
}
float HostMatrix::getElement(unsigned int i, unsigned int j) const {
if (i >= height || j >= width) {
throw std::invalid_argument("Invalid argument.");
} else {
return (elements.get())[i * width + j];
}
}
void HostMatrix::setElement(unsigned int i, unsigned int j, float value) {
if (i >= height || j >= width) {
throw std::invalid_argument("Invalid argument.");
} else {
(elements.get())[i * width + j] = value;
}
}
class DeviceMatrixDeleter {
public:
void operator()(float* ptr) const {
if (ptr) {
cudaFree(ptr);
}
}
};
DeviceMatrix::DeviceMatrix(unsigned int height, unsigned int width): Matrix(height, width, true) {
float* rawElements = 0;
cudaMalloc(&rawElements, height * width * sizeof(float));
elements = std::shared_ptr<float>(rawElements, DeviceMatrixDeleter());
}
float DeviceMatrix::getElement(unsigned int i, unsigned int j) const {
if (i >= height || j >= width) {
throw std::invalid_argument("Invalid argument.");
} else {
float value = 0.0f;
cudaMemcpy(&value, elements.get() + i * width + j, sizeof(float), cudaMemcpyDeviceToHost);
return value;
}
}
void DeviceMatrix::setElement(unsigned int i, unsigned int j, float value) {
if (i >= height || j >= width) {
throw std::invalid_argument("Invalid argument.");
} else {
cudaMemcpy(elements.get() + i * width + j, &value, sizeof(float), cudaMemcpyHostToDevice);
}
} |
6,785 |
#include <stdio.h>
#include <stdlib.h>
#include <stdbool.h>
#include <math.h>
// Number of threads
#define BLOCK_SIZE 16
#define GRID_SIZE 256
#define CUDA_CALL(x) {if((x) != cudaSuccess){ \
printf("CUDA error at %s:%d\n",__FILE__,__LINE__); \
printf(" %s\n", cudaGetErrorString(cudaGetLastError())); \
exit(EXIT_FAILURE);}}
// nCentroids and size on device
__constant__ int dev_nCentroids;
__constant__ int dev_size;
// global variables
int CLUSTER_BYTES = 0; // nCentroids * sizeof(int)
int IMAGE_BYTES = 0; // width * height * sizeof(int)
//R,G,B Centroid's triple on device
__constant__ int dev_RedCentroid[20];
__constant__ int dev_GreenCentroid[20];
__constant__ int dev_BlueCentroid[20];
void initialise_centroids(int nCentroids, int* redCentroid, int* greenCentroid, int* blueCentroid, int r[], int g[], int b[], int size) {
int i;
for(i=0;i<nCentroids;++i)
{
int index = rand()%size;
redCentroid[i] = r[index];
blueCentroid[i] = b[index];
greenCentroid[i] = g[index];
}
}
bool loadRawImage(char* filename, int* r, int* g, int* b, int size) {
FILE *imageFile;
imageFile = fopen(filename, "r");
if (imageFile == NULL) {
return false;
} else {
for (int i = 0; i < size; i++) {
r[i] = fgetc(imageFile);
g[i] = fgetc(imageFile);
b[i] = fgetc(imageFile);
}
fclose(imageFile);
/*for(int j = 0; j < h * w; j++) {
printf("%d, %d, %d ", r[j], g[j], b[j]);
}*/
return true;
}
}
bool writeRawImage(char* filename, int* labelArray, int* redCentroid, int* greenCentroid, int* blueCentroid, int size){
FILE *imageFile;
imageFile = fopen(filename, "wb");
if(imageFile == NULL) {
return false;
} else {
for (int i = 0; i < size; i++) {
fputc((char) redCentroid[labelArray[i]], imageFile);
fputc((char) greenCentroid[labelArray[i]], imageFile);
fputc((char) blueCentroid[labelArray[i]], imageFile);
}
fclose(imageFile);
return true;
}
}
//Clears arrays before each kernel getClusterLabel iteration
__global__ void clearArrays(int *dev_sumRed,int *dev_sumGreen,int *dev_sumBlue, int* dev_pixelClusterCounter, int* dev_tempRedCentroid, int* dev_tempGreenCentroid, int* dev_tempBlueCentroid ) {
// 1 block, 16x16 threads
int threadID = threadIdx.x + threadIdx.y * blockDim.x;
if(threadID < dev_nCentroids) {
// nCentroids long
dev_sumRed[threadID] = 0;
dev_sumGreen[threadID] = 0;
dev_sumBlue[threadID] = 0;
dev_pixelClusterCounter[threadID] = 0;
dev_tempRedCentroid[threadID] = 0;
dev_tempGreenCentroid[threadID] = 0;
dev_tempBlueCentroid[threadID] = 0;
}
}// end clearArrays
/*
* Clear label array before each kernel getClusterLabel iteration
*/
__global__ void clearLabelArray(int *dev_labelArray){
// Global thread index
int threadID = (threadIdx.x + blockIdx.x * blockDim.x) + (threadIdx.y + blockIdx.y * blockDim.y) * blockDim.x * gridDim.x;
//int threadID = (blockIdx.y*gridDim.x + blockIdx.x)*(blockDim.x*blockDim.y) + threadIdx.y*blockDim.x + threadIdx.x;
// labelArray is "size" long
if(threadID < dev_size) {
dev_labelArray[threadID] = 0;
}
}// end clearLabelArray
__global__ void getClusterLabel(int *dev_Red,int *dev_Green,int *dev_Blue,int *dev_labelArray) {
// Global thread index
int threadID = (threadIdx.x + blockIdx.x * blockDim.x) + (threadIdx.y + blockIdx.y * blockDim.y) * blockDim.x * gridDim.x;
//int threadID = (blockIdx.y*gridDim.x + blockIdx.x)*(blockDim.x*blockDim.y) + threadIdx.y*blockDim.x + threadIdx.x;
//default min value of distance
float min = 500.0, value;
//will be label
int index = 0;
if(threadID < dev_size) {
// Finding the nearest centroid to current triple identified by threadID thread
for(int i = 0; i < dev_nCentroids; i++) {
// Performing Euclidean distance, Saving current value
value = sqrtf(powf((dev_Red[threadID]-dev_RedCentroid[i]),2.0) + powf((dev_Green[threadID]-dev_GreenCentroid[i]),2.0) + powf((dev_Blue[threadID]-dev_BlueCentroid[i]),2.0));
if(value < min){
// saving new nearest centroid
min = value;
// Updating his index
index = i;
}
}// end for
// Writing to global memory the index of the nearest centroid
// for dev_Red[threadID], dev_Green[threadID], dev_Blue[threadID] pixel triple
dev_labelArray[threadID] = index;
}// end if
}// end getClusterLabel
/*
* Summing Red, Green, Blue values per cluster
* Counting how many pixels there are in each cluster
*
*/
__global__ void sumCluster(int *dev_Red,int *dev_Green,int *dev_Blue,int *dev_sumRed,int *dev_sumGreen,int *dev_sumBlue,int *dev_labelArray,int *dev_pixelClusterCounter) {
// Global thread index
int threadID = (threadIdx.x + blockIdx.x * blockDim.x) + (threadIdx.y + blockIdx.y * blockDim.y) * blockDim.x * gridDim.x;
//int threadID = (blockIdx.y*gridDim.x + blockIdx.x)*(blockDim.x*blockDim.y) + threadIdx.y*blockDim.x + threadIdx.x;
if(threadID < dev_size) {
int currentLabelArray = dev_labelArray[threadID];
int currentRed = dev_Red[threadID];
int currentGreen = dev_Green[threadID];
int currentBlue = dev_Blue[threadID];
// Writing to global memory needs a serialization. Many threads are writing into the same few locations
atomicAdd(&dev_sumRed[currentLabelArray], currentRed);
atomicAdd(&dev_sumGreen[currentLabelArray], currentGreen);
atomicAdd(&dev_sumBlue[currentLabelArray], currentBlue);
atomicAdd(&dev_pixelClusterCounter[currentLabelArray], 1);
}
}// end sumCluster
/*
* Calculates the new R,G,B values of the centroids dividing the sum of color (for each channel) by the number of pixels in that cluster
* New values are stored in global memory since the current R,G,B values of the centroids are in read-only constant memory.
*/
__global__ void updateCentroids(int *dev_tempRedCentroid, int *dev_tempGreenCentroid, int *dev_tempBlueCentroid,int* dev_sumRed, int *dev_sumGreen,int *dev_sumBlue, int* dev_pixelClusterCounter,int *dev_flag) {
// 1 block , 16*16 threads
int threadID = threadIdx.x + threadIdx.y * blockDim.x;
if(threadID < dev_nCentroids) {
int currentPixelCounter = dev_pixelClusterCounter[threadID];
int sumRed = dev_sumRed[threadID];
int sumGreen = dev_sumGreen[threadID];
int sumBlue = dev_sumBlue[threadID];
//new RGB Centroids' values written in global memory
dev_tempRedCentroid[threadID] = (int)(sumRed/currentPixelCounter);
dev_tempGreenCentroid[threadID] = (int)(sumGreen/currentPixelCounter);
dev_tempBlueCentroid[threadID] = (int)(sumBlue/currentPixelCounter);
if(dev_tempGreenCentroid[threadID]!=dev_GreenCentroid[threadID] || dev_tempRedCentroid[threadID]!=dev_RedCentroid[threadID] || dev_tempBlueCentroid[threadID]!=dev_BlueCentroid[threadID])
*dev_flag=1;
}
}// end updateCentroids
/*******************************************************************/
int main(int argc, char *argv[]) {
cudaSetDevice(0);
cudaDeviceSynchronize();
cudaThreadSynchronize();
char *inputFile, *outputFile;
int *r, *g, *b, *redCentroid, *greenCentroid, *blueCentroid;
int *dev_Red, *dev_Green, *dev_Blue, *dev_tempRedCentroid, *dev_tempGreenCentroid, *dev_tempBlueCentroid;
int *labelArray, *dev_labelArray;
int width, height, nCentroids, nIterations,size;
//int IMAGE_BYTES, CLUSTER_BYTES;
int *pixelClusterCounter, *dev_pixelClusterCounter;
int *sumRed, *sumGreen, *sumBlue;
int flag = 0;
int *dev_sumRed, *dev_sumGreen, *dev_sumBlue;
int *dev_flag;
inputFile = argv[1];
outputFile = argv[2];
width = atoi(argv[3]);
height = atoi(argv[4]);
nCentroids = atoi(argv[5]);
nIterations = atoi(argv[6]);
// Setting image size in bytes
IMAGE_BYTES = width * height * sizeof(int);
CLUSTER_BYTES = nCentroids * sizeof(int);
size = width * height;
printf("Image: %s\n",inputFile);
printf("Width: %d, Height: %d\n", width, height);
printf("#Clusters: %d, #Iterations: %d\n", nCentroids, nIterations);
r = (int*)(malloc(IMAGE_BYTES));
g = (int*)(malloc(IMAGE_BYTES));
b = (int*)(malloc(IMAGE_BYTES));
redCentroid = (int*)(malloc(CLUSTER_BYTES));
greenCentroid = (int*)(malloc(CLUSTER_BYTES));
blueCentroid = (int*)(malloc(CLUSTER_BYTES));
labelArray = (int*)(malloc(IMAGE_BYTES)); //stores the cluster number for each pixel
sumRed = (int*)(malloc(CLUSTER_BYTES));
sumGreen = (int*)(malloc(CLUSTER_BYTES));
sumBlue = (int*)(malloc(CLUSTER_BYTES));
pixelClusterCounter = (int*)(malloc(CLUSTER_BYTES));
// Loading image in r, g, b arrays
printf("Image loading...\n");
if (loadRawImage(inputFile, r, g, b, size)) {
printf("Image loaded!\n");
} else {
printf("NOT loaded!\n");
return -1;
}
cudaEvent_t start;
cudaEvent_t stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
// Setting initial centroids
printf("Initial Centroids: \n");
initialise_centroids(nCentroids, redCentroid, greenCentroid, blueCentroid,r,g,b,size);
printf("\n");
if(IMAGE_BYTES == 0 || CLUSTER_BYTES == 0) {
return -1;
}
// allocate memory on GPU
CUDA_CALL(cudaMalloc((void**) &dev_Red, IMAGE_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_Green, IMAGE_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_Blue, IMAGE_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_tempRedCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_tempGreenCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_tempBlueCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_labelArray, IMAGE_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_sumRed, CLUSTER_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_sumGreen, CLUSTER_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_sumBlue, CLUSTER_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_pixelClusterCounter, CLUSTER_BYTES));
CUDA_CALL(cudaMalloc((void**) &dev_flag, sizeof(int)));
// copy host CPU memory to GPU
CUDA_CALL(cudaMemcpy(dev_Red, r, IMAGE_BYTES, cudaMemcpyHostToDevice));
CUDA_CALL(cudaMemcpy(dev_Green, g, IMAGE_BYTES, cudaMemcpyHostToDevice));
CUDA_CALL(cudaMemcpy(dev_Blue, b, IMAGE_BYTES, cudaMemcpyHostToDevice));
CUDA_CALL(cudaMemcpy(dev_tempRedCentroid, redCentroid,CLUSTER_BYTES,cudaMemcpyHostToDevice ));
CUDA_CALL(cudaMemcpy(dev_tempGreenCentroid, greenCentroid,CLUSTER_BYTES,cudaMemcpyHostToDevice ));
CUDA_CALL(cudaMemcpy(dev_tempBlueCentroid, blueCentroid,CLUSTER_BYTES,cudaMemcpyHostToDevice ));
CUDA_CALL(cudaMemcpy(dev_labelArray, labelArray, IMAGE_BYTES, cudaMemcpyHostToDevice));
CUDA_CALL(cudaMemcpy(dev_flag,&flag,sizeof(int),cudaMemcpyHostToDevice));
//CUDA_CALL(cudaMemcpy(dev_sumRed, sumRed, CLUSTER_BYTES, cudaMemcpyHostToDevice));
//CUDA_CALL(cudaMemcpy(dev_sumGreen, sumGreen, CLUSTER_BYTES, cudaMemcpyHostToDevice));
//CUDA_CALL(cudaMemcpy(dev_sumBlue, sumBlue, CLUSTER_BYTES, cudaMemcpyHostToDevice));
CUDA_CALL(cudaMemcpy(dev_pixelClusterCounter, pixelClusterCounter, CLUSTER_BYTES, cudaMemcpyHostToDevice));
CUDA_CALL(cudaMemcpyToSymbol(dev_RedCentroid, redCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMemcpyToSymbol(dev_GreenCentroid, greenCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMemcpyToSymbol(dev_BlueCentroid, blueCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMemcpyToSymbol(dev_nCentroids,&nCentroids, sizeof(int)));
CUDA_CALL(cudaMemcpyToSymbol(dev_size, &size, sizeof(int)));
// Clearing centroids on host
for(int i = 0; i < nCentroids; i++) {
redCentroid[i] = 0;
greenCentroid[i] = 0;
blueCentroid[i] = 0;
}
// Defining grid size
int BLOCK_X, BLOCK_Y;
BLOCK_X = ceil(width/BLOCK_SIZE);
BLOCK_Y = ceil(height/BLOCK_SIZE);
if(BLOCK_X > GRID_SIZE)
BLOCK_X = GRID_SIZE;
if(BLOCK_Y > GRID_SIZE)
BLOCK_Y = GRID_SIZE;
dim3 dimGRID(BLOCK_X,BLOCK_Y);
dim3 dimBLOCK(BLOCK_SIZE,BLOCK_SIZE);
//Starting timer
cudaEventRecord(start, 0);
printf("Launching K-Means Kernels.. \n");
//Iteration of kmeans algorithm
int num_iterations;
for(int i = 0; i < nIterations; i++) {
num_iterations = i;
flag=0;
CUDA_CALL(cudaMemcpy(dev_flag,&flag,sizeof(int),cudaMemcpyHostToDevice));
clearArrays<<<1, dimBLOCK>>>(dev_sumRed, dev_sumGreen, dev_sumBlue, dev_pixelClusterCounter, dev_tempRedCentroid, dev_tempGreenCentroid, dev_tempBlueCentroid);
clearLabelArray<<<dimGRID, dimBLOCK>>>(dev_labelArray);
getClusterLabel<<< dimGRID, dimBLOCK >>> (dev_Red, dev_Green, dev_Blue,dev_labelArray);
sumCluster<<<dimGRID, dimBLOCK>>> (dev_Red, dev_Green, dev_Blue, dev_sumRed, dev_sumGreen, dev_sumBlue, dev_labelArray,dev_pixelClusterCounter);
updateCentroids<<<1,dimBLOCK >>>(dev_tempRedCentroid, dev_tempGreenCentroid, dev_tempBlueCentroid, dev_sumRed, dev_sumGreen, dev_sumBlue, dev_pixelClusterCounter,dev_flag);
CUDA_CALL(cudaMemcpy(redCentroid, dev_tempRedCentroid, CLUSTER_BYTES,cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpy(greenCentroid, dev_tempGreenCentroid, CLUSTER_BYTES,cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpy(blueCentroid, dev_tempBlueCentroid, CLUSTER_BYTES,cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpy(&flag, dev_flag,sizeof(int),cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpyToSymbol(dev_RedCentroid, redCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMemcpyToSymbol(dev_GreenCentroid, greenCentroid, CLUSTER_BYTES));
CUDA_CALL(cudaMemcpyToSymbol(dev_BlueCentroid, blueCentroid, CLUSTER_BYTES));
if(flag==0)
break;
}
cudaEventRecord(stop, 0);
float elapsed;
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed, start, stop);
CUDA_CALL(cudaMemcpy(labelArray, dev_labelArray, IMAGE_BYTES, cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpy(sumRed, dev_sumRed, CLUSTER_BYTES, cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpy(sumGreen, dev_sumGreen, CLUSTER_BYTES, cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpy(sumBlue, dev_sumBlue, CLUSTER_BYTES, cudaMemcpyDeviceToHost));
CUDA_CALL(cudaMemcpy(pixelClusterCounter, dev_pixelClusterCounter, CLUSTER_BYTES, cudaMemcpyDeviceToHost));
printf("Kmeans code ran in: %f secs.\n", elapsed/1000.0);
printf("Converged in %d iterations.\n",num_iterations);
printf("\n");
// labelArray DEBUG
int counter = 0;
printf("Label Array:\n");
for(int i = 0; i < (size); i++) {
//printf("%d\n", labelArray[i]);
counter++;
}
printf("printing counter %d\n", counter);
counter = 0;
printf("Sum Arrays:\n");
for(int j = 0; j < nCentroids; j++) {
printf("r: %u g: %u b: %u \n", sumRed[j], sumGreen[j], sumBlue[j]);
counter++;
}
printf("\n");
printf("Pixels per centroids:\n");
for(int k = 0; k < nCentroids; k++){
printf("%d centroid: %d pixels\n", k, pixelClusterCounter[k]);
}
printf("\n");
printf("New centroids:\n");
for(int i = 0; i < nCentroids; i++) {
printf("%d, %d, %d \n", redCentroid[i], greenCentroid[i], blueCentroid[i]);
}
// writing...
printf("Image writing...\n");
if (writeRawImage(outputFile,labelArray, redCentroid, greenCentroid, blueCentroid, size)) {
printf("Image written!\n");
} else {
printf("NOT written!\n");
return -1;
}
free(r);
free(g);
free(b);
free(redCentroid);
free(greenCentroid);
free(blueCentroid);
free(labelArray);
free(sumRed);
free(sumGreen);
free(sumBlue);
free(pixelClusterCounter);
CUDA_CALL(cudaFree(dev_Red));
CUDA_CALL(cudaFree(dev_Green));
CUDA_CALL(cudaFree(dev_Blue));
CUDA_CALL(cudaFree(dev_tempRedCentroid));
CUDA_CALL(cudaFree(dev_tempGreenCentroid));
CUDA_CALL(cudaFree(dev_tempBlueCentroid));
CUDA_CALL(cudaFree(dev_labelArray));
CUDA_CALL(cudaFree(dev_sumRed));
CUDA_CALL(cudaFree(dev_sumGreen));
CUDA_CALL(cudaFree(dev_sumBlue));
CUDA_CALL(cudaFree(dev_pixelClusterCounter));
printf("That's the end.\n");
return 0;
}
|
6,786 | #include <stdio.h>
#include <stdlib.h>
#define PROMOTER_LEN 51594
#define SCORE_LEN 1963
#define PNVEC_MAX 18000
#define Nchunk 5000
#define MAX_LINE_LEN 24000
#define DELIM "\t"
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline 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 tfbs_kernel(int N, int pvec_max,
char *pvec, int *pvec_len,
float *pwm, int pwm_len,
float tf_threshold,
float *bg,
float *output)
{
int id = blockIdx.x * blockDim.x + threadIdx.x;
int ind1, ind2;
float wt_score1, wt_score2;
float wt_score31, wt_score41;
float wt_score32, wt_score42;
float wt_score33, wt_score43;
float wt_score34, wt_score44;
int ioffset = 29;
if (id < N ) {
int ncol = 4;
/* loop through positions */
for ( int ipos = ioffset; ipos < pvec_len[id] - ioffset; ipos++ ){
float fs1 = -99999.;
float fs2 = -99999.;
float fs31 = -99999.;
float fs41 = -99999.;
float fs32 = -99999.;
float fs42 = -99999.;
float fs33 = -99999.;
float fs43 = -99999.;
float fs34 = -99999.;
float fs44 = -99999.;
/* loop through all k-mers for that position */
for ( int ik = 0; ik < pwm_len; ik ++ ) {
wt_score1 = 0; wt_score2 = 0;
wt_score31 = 0; wt_score41=0;
wt_score32 = 0; wt_score42=0;
wt_score33 = 0; wt_score43=0;
wt_score34 = 0; wt_score44=0;
/* loop through the k-mer */
for ( int i = 0; i < pwm_len; i++){
ind1 = pvec[id * pvec_max + ipos + ik - pwm_len + 1 + i];
if ( ind1 < 1 ) { /* skip kmere if not A,C,T,G */
wt_score1 = 0; wt_score2 = 0;
wt_score31 = 0; wt_score41=0;
wt_score32 = 0; wt_score42=0;
wt_score33 = 0; wt_score43=0;
wt_score34 = 0; wt_score44=0;
break;
}
ind2 = 5 - pvec[id * pvec_max + ipos + ik - i ];
if ( ind2 > 4 ) { /* skip kmere if not A,C,T,G */
wt_score1 = 0; wt_score2 = 0;
wt_score31 = 0; wt_score41=0;
wt_score32 = 0; wt_score42=0;
wt_score33 = 0; wt_score43=0;
wt_score34 = 0; wt_score44=0;
break;
}
wt_score1 = wt_score1 + log(pwm[ (i * ncol ) + (ind1 -1) ] / bg[ ind1 - 1 ] );
wt_score2 = wt_score2 + log(pwm[ (i * ncol ) + (ind2 -1) ] / bg[ ind2 - 1 ] );
if ( i == (pwm_len -ik -1)) ind1 = 1;
if ( i == ik) ind2 = 5 - 1;
wt_score31 = wt_score31 + log(pwm[ (i * ncol ) + (ind1 -1) ] / bg[ ind1 - 1 ] );
wt_score41 = wt_score41 + log(pwm[ (i * ncol ) + (ind2 -1) ] / bg[ ind2 - 1 ] );
if ( i == (pwm_len -ik -1)) ind1 = 2;
if ( i == ik) ind2 = 5 - 2;
wt_score32 = wt_score32 + log(pwm[ (i * ncol ) + (ind1 -1) ] / bg[ ind1 - 1 ] );
wt_score42 = wt_score42 + log(pwm[ (i * ncol ) + (ind2 -1) ] / bg[ ind2 - 1 ] );
if ( i == (pwm_len -ik -1)) ind1 = 3;
if ( i == ik) ind2 = 5 - 3;
wt_score33 = wt_score33 + log(pwm[ (i * ncol ) + (ind1 -1) ] / bg[ ind1 - 1 ] );
wt_score43 = wt_score43 + log(pwm[ (i * ncol ) + (ind2 -1) ] / bg[ ind2 - 1 ] );
if ( i == (pwm_len -ik -1)) ind1 = 4;
if ( i == ik) ind2 = 5 - 4;
wt_score34 = wt_score34 + log(pwm[ (i * ncol ) + (ind1 -1) ] / bg[ ind1 - 1 ] );
wt_score44 = wt_score44 + log(pwm[ (i * ncol ) + (ind2 -1) ] / bg[ ind2 - 1 ] );
}
/* for all kmers for this position calculate maximum scores */
fs1 = ( fs1 < wt_score1 ) ? wt_score1 : fs1;
fs2 = ( fs2 < wt_score2 ) ? wt_score2 : fs2;
fs31 = ( fs31 < wt_score31 ) ? wt_score31 : fs31;
fs41 = ( fs41 < wt_score41 ) ? wt_score41 : fs41;
fs32 = ( fs32 < wt_score32 ) ? wt_score32 : fs32;
fs42 = ( fs42 < wt_score42 ) ? wt_score42 : fs42;
fs33 = ( fs33 < wt_score33 ) ? wt_score33 : fs33;
fs43 = ( fs43 < wt_score43 ) ? wt_score43 : fs43;
fs34 = ( fs34 < wt_score34 ) ? wt_score34 : fs34;
fs44 = ( fs44 < wt_score44 ) ? wt_score44 : fs44;
}
/* if all the scores are less than threshold do not store them */
int icol = id * (pvec_max - ioffset * 2 ) + (ipos - ioffset );
int ichunk = N * (pvec_max - ioffset * 2 );
output[ 1 * ichunk + icol ] = ipos + 1;
output[ 2 * ichunk + icol ] = fs1;
output[ 3 * ichunk + icol ] = fs2;
if ( (fs31 >= tf_threshold | fs41 >= tf_threshold ) & pvec[ id * pvec_max + ipos] != 1 ){
output[ icol ] = 1;
output[ 4 * ichunk + icol ] = fs31;
output[ 5 * ichunk + icol ] = fs41;
} else if( ( fs1 > tf_threshold | fs2 > tf_threshold) & pvec[ id * pvec_max + ipos] != 1 ){
output[ icol ] =0;
output[ 4 * ichunk + icol ] = fs31;
output[ 5 * ichunk + icol ] = fs41;
} else {
output[ icol ] = 0;
}
if ( ( fs32 >= tf_threshold | fs42 >= tf_threshold) & pvec[ id * pvec_max + ipos] != 2 ){
output[ icol ] = output[ icol ] + 10;
output[ 6 * ichunk + icol ] = fs32;
output[ 7 * ichunk + icol ] = fs42;
} else if( ( fs1 > tf_threshold | fs2 > tf_threshold) & pvec[ id * pvec_max + ipos] != 2 ){
output[ 6 * ichunk + icol ] = fs32;
output[ 7 * ichunk + icol ] = fs42;
}
if ( ( fs33 >= tf_threshold | fs43 >= tf_threshold) & pvec[ id * pvec_max + ipos] != 3 ){
output[ icol ] = output[ icol ] + 100;
output[ 8 * ichunk + icol ] = fs33;
output[ 9 * ichunk + icol ] = fs43;
} else if( ( fs1 > tf_threshold | fs2 > tf_threshold) & pvec[ id * pvec_max + ipos] != 3 ){
output[ 8 * ichunk + icol ] = fs33;
output[ 9 * ichunk + icol ] = fs43;
}
if ( ( fs34 >= tf_threshold | fs44 >= tf_threshold) & pvec[ id * pvec_max + ipos] != 4 ){
output[ icol ] = output[ icol ] + 1000;
output[ 10 * ichunk + icol ] = fs34;
output[ 11 * ichunk + icol ] = fs44;
} else if( ( fs1 > tf_threshold | fs2 > tf_threshold) & pvec[ id * pvec_max + ipos] != 4 ){
output[ 10 * ichunk + icol ] = fs34;
output[ 11 * ichunk + icol ] = fs44;
}
}
}
}
void tfbs_cuda( char *pvec, // promoter
int *pvec_len,
int pvec_max,
float *pwm,
int pwm_len,
float tf_threshold,
float *bg,
int N,
float *output) {// output matrix
char *d_pvec;
int *d_pvec_len;
float *d_output;
float *d_pwm;
float *d_bg;
//printf("Allocating GPU memory for pvec_len\n");
gpuErrchk( cudaMalloc( (void**)&d_pvec_len, N * sizeof( int)) );
//printf("Copying GPU memory for pvec_len\n");
gpuErrchk( cudaMemcpy( d_pvec_len, pvec_len, N * sizeof( int), cudaMemcpyHostToDevice ) );
//printf("Allocating GPU memory for pvec\n");
gpuErrchk( cudaMalloc( (void**) &d_pvec, N * pvec_max * sizeof( char)) );
//printf("Copying GPU memory for pvec\n");
gpuErrchk( cudaMemcpy( d_pvec, pvec, N * pvec_max * sizeof( char), cudaMemcpyHostToDevice ) );
//printf("Allocating GPU memory for pwm\n");
gpuErrchk( cudaMalloc( (void**)&d_pwm, 4 * pwm_len * sizeof(float)) );
//printf("Copying GPU memory for pwm\n");
gpuErrchk( cudaMemcpy( d_pwm, pwm, 4 * pwm_len * sizeof(float), cudaMemcpyHostToDevice ) );
//printf("Allocating GPU memory for bg\n");
gpuErrchk( cudaMalloc( (void**)&d_bg, 4 * sizeof(float)) );
//printf("Copying GPU memory for bg\n");
gpuErrchk( cudaMemcpy( d_bg, bg, 4 * sizeof(float), cudaMemcpyHostToDevice ) );
//printf("Allocating GPU memory for result\n");
gpuErrchk( cudaMalloc( (void**)&d_output, N * 12 * (pvec_max -58 ) * sizeof(float) ) );
//printf("Before kernel\n");
dim3 dimBlock( 32, 1 );
dim3 dimGrid( N/32 + 1, 1 );
//printf("Calling CUDA kernel\n");
tfbs_kernel<<<dimGrid,dimBlock>>>(N, pvec_max,
d_pvec, d_pvec_len,
d_pwm, pwm_len,
tf_threshold, d_bg,
d_output);
gpuErrchk( cudaPeekAtLastError() );
//cudaDeviceSynchronize();
//printf("After CUDA kernel\n");
gpuErrchk( cudaMemcpy( output, d_output, N * 12 * (pvec_max -58 ) * sizeof(float), cudaMemcpyDeviceToHost ) );
//printf("Free GPU memory \n");
cudaFree( d_pvec );
cudaFree( d_pvec_len );
cudaFree( d_output );
cudaFree( d_pwm );
cudaFree( d_bg );
return;
}
int main( int argc, char *argv[] ){
char filename[1024];
int taskID;
float bg[] = {0.25, 0.25, 0.25, 0.25};
FILE *ifp, *ofp;
char in_line[MAX_LINE_LEN];
char *token;
int chrom[PROMOTER_LEN];
int istart[PROMOTER_LEN];
int i, j, jj, i1, i2, j1, j2, icurr;
float score_threshold[SCORE_LEN];
char **score_files;
float pwm[30 * 4];
int pwm_length;
int any0;
float sum;
char *pnvec;
int *pnvec_len;
float *result;
int ires;
/* command line processing */
taskID = atoi(argv[1]);
printf("Input file name: %s\n", argv[2]);
printf("tf.info file name: %s\n", argv[3]);
printf("pwm directory: %s\n", argv[4]);
sprintf(filename,"./%s/%s_%03d.txt", argv[5], argv[6], taskID);
printf("Output file name: %s\n", filename);
/* allocate memory to hold char arrays */
//sequence = (char **) malloc( PROMOTER_LEN * sizeof(char *) );
score_files = (char **) malloc( SCORE_LEN * sizeof(char *) );
result = (float *) malloc( Nchunk * 12 * ( PNVEC_MAX - 58 ) * sizeof(float) );
pnvec_len = ( int * ) malloc ( PROMOTER_LEN * sizeof( int ) );
if ( pnvec_len == 0 ) {
fprintf( stderr, "ERROR allocating pnvec_len: Out of memory\n");
exit(1);
}
//printf("Allocating memory for pnvec\n");
pnvec = ( char * ) malloc ( PROMOTER_LEN * PNVEC_MAX * sizeof( char) );
if ( pnvec == 0 ) {
fprintf( stderr, "ERROR allocating pnvec: Out of memory\n");
exit(2);
}
printf("Reading Input files\n");
/* read input file line by line (only 1st, 2nd and 8th columns) */
ifp = fopen(argv[2], "r");
fgets(in_line, MAX_LINE_LEN, ifp); //skip header line
i=0;
while( fgets(in_line, MAX_LINE_LEN, ifp )!= NULL ){
token = strtok( in_line, DELIM);
sscanf( token,"chr%d", &(chrom[i]));
token = strtok( NULL, DELIM);
sscanf( token,"%d", istart + i );
token = strtok( NULL, DELIM);
token = strtok( NULL, DELIM);
token = strtok( NULL, DELIM);
token = strtok( NULL, DELIM);
token = strtok( NULL, DELIM);
token = strtok( NULL, DELIM);
//if(i > 14655) printf(" start processing letter\n");
pnvec_len[i] = strlen(token);
for (j = 0; j < pnvec_len[i] ; j++) {
//if (i == 14660)printf("%c",token[j]);
switch( token[j] ){
case 'A':
pnvec[ i * PNVEC_MAX + j ]=1;
break;
case 'a':
pnvec[ i * PNVEC_MAX + j ]=1;
break;
case 'C':
pnvec[ i * PNVEC_MAX + j ]=2;
break;
case 'c':
pnvec[ i * PNVEC_MAX + j ]=2;
break;
case 'G':
pnvec[ i * PNVEC_MAX + j ]=3;
break;
case 'g':
pnvec[ i * PNVEC_MAX + j ]=3;
break;
case 'T':
pnvec[ i * PNVEC_MAX + j ]=4;
break;
case 't':
pnvec[ i * PNVEC_MAX + j ]=4;
break;
default:
pnvec[ i * PNVEC_MAX + j ]=0;
break;
}
}
i++;
//if ( i > 14600) { printf("i=%d\n",i);}
}
fclose(ifp);
printf(" Read %d lines from the input file\n", i);
/* Read tf.info file */
ifp = fopen(argv[3], "r");
i=0;
while( fgets(in_line, MAX_LINE_LEN, ifp ) ){
token = strtok( in_line, DELIM);
score_files[i] = (char *) malloc ( (strlen(token) + 1 ) * sizeof(char ));
strcpy( score_files[i], token );
token = strtok( NULL, DELIM);
score_threshold[i] = atof(token);
i++;
}
fclose(ifp);
printf(" Read %d lines from %s file\n", i, argv[3]);
/* process chunks */
i1 = (taskID - 1) * 100 + 1; // was 10 originally
i2 = taskID * 100;
if ( i2 > SCORE_LEN ) i2 = SCORE_LEN;
/* open output file */
ofp = fopen(filename,"w");
if (ofp == NULL) {
fprintf( stderr, " Can't open output file\n");
exit(3);
}
for ( icurr = i1; icurr <= i2; icurr++){
printf(" icurr =%d\n", icurr);
sprintf( filename, "./%s/%s\0", argv[4], score_files[icurr-1] );
ifp = fopen( filename , "r");
fgets(in_line, MAX_LINE_LEN, ifp ); // skip first line
i = 0;
any0 = 0;
while( fgets(in_line, MAX_LINE_LEN, ifp ) ){
token = strtok( in_line, DELIM); //skip first value
token = strtok( NULL, DELIM);
pwm[i*4 + 0] = atof(token);
if ( !strcmp(token, "0.0") ) any0=1;
token = strtok( NULL, DELIM);
pwm[i*4 + 1] = atof(token);
if ( !strcmp(token, "0.0") ) any0=1;
token = strtok( NULL, DELIM);
pwm[i*4 + 2] = atof(token);
if ( !strcmp(token, "0.0") ) any0=1;
token = strtok( NULL, DELIM);
pwm[i*4 + 3] = atof(token);
if ( !strcmp(token, "0.0\n") ) any0=1;
i++;
}
fclose(ifp);
pwm_length = i;
printf(" Read %d lines from %s file\n", i, score_files[icurr-1]);
/* part of create_pwm function */
if ( any0 ) {
for ( j = 0; j < i; j++ ){
sum = pwm[ j*4 + 0] + pwm[ j*4 + 1] + pwm[ j*4 + 2] + pwm[ j*4 + 3] + 0.001 * 4;
pwm[ j*4 + 0] = (pwm[ j*4 + 0] + 0.001)/sum;
pwm[ j*4 + 1] = (pwm[ j*4 + 1] + 0.001)/sum;
pwm[ j*4 + 2] = (pwm[ j*4 + 2] + 0.001)/sum;
pwm[ j*4 + 3] = (pwm[ j*4 + 3] + 0.001)/sum;
}
}
/* inner loop in R*/
for ( j = 1; j < 12; j++ ){
j1 = (j - 1) * Nchunk + 1;
j2 = j * Nchunk;
if ( j2 > PROMOTER_LEN )j2 = PROMOTER_LEN;
int n = j2 - j1 + 1;
printf(" j = %d through %d; threshold = %f\n", j1, j2, score_threshold[icurr - 1]);
tfbs_cuda (pnvec + (j1 -1) * PNVEC_MAX,
pnvec_len + j1 -1,
PNVEC_MAX,
pwm,
pwm_length,
score_threshold[icurr - 1],
bg,
n,
result);
fflush(stdout);
/* save result in the output file */
for (i = 0; i < n * ( PNVEC_MAX - 58 ); i++){
ires = (int) result [ i ];
int in = i/( PNVEC_MAX - 58 );
//printf("%d ",i);
if (ires > 0 || (score_threshold[ icurr - 1] < result[ i + ( PNVEC_MAX - 58 ) * 2 * n] )|| (score_threshold[icurr - 1] < result[i + (PNVEC_MAX - 58 ) * 3 * n])) {
fprintf(ofp,"%d ", chrom [ in ]);
//unsigned int ipos = result [ i + ( PNVEC_MAX - 58 ) * n * 1] + istart[ j1 - 1 + i/( PNVEC_MAX - 58 ) ];
unsigned int ipos = 30 + i%( PNVEC_MAX - 58 ) + istart[ j1 - 1 + in ];
fprintf(ofp,"%d %d ", ipos, ipos + 1);
fprintf(ofp,"%d %d ", j1 + in, icurr);
fprintf(ofp,"%d ", ires );
// use %f.3 for printing results with 3 digits
for ( jj = 2; jj < 11; jj++ )fprintf(ofp,"%.3f ", result [ i + ( PNVEC_MAX - 58 ) * jj * n] );
fprintf(ofp,"%.3f\n", result [ i + ( PNVEC_MAX - 58 ) * 11 * n] );
}
}
} // end of j loop
} // end of icurr loop
fclose(ofp);
exit(0);
}
|
6,787 | #include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
#include <math.h>
// MASK SIZE
#define MASK_WIDTH 5
// MASK RADIO
#define MASK_R (MASK_WIDTH-1)/2
#define COMMENT "Histogram_GPU"
#define RGB_COMPONENT_COLOR 255
// SIZE OF TILE
#define TILE_WIDTH 32
// SIZE OF SHARE MATRIX
#define SHARED_SIZE (MASK_WIDTH-1 + TILE_WIDTH)
typedef struct {
unsigned char red, green, blue;
} PPMPixel;
typedef struct {
int x, y;
PPMPixel *data;
} PPMImage;
double rtclock()
{
struct timezone Tzp;
struct timeval Tp;
int stat;
stat = gettimeofday (&Tp, &Tzp);
if (stat != 0) printf("Error return from gettimeofday: %d",stat);
return(Tp.tv_sec + Tp.tv_usec*1.0e-6);
}
static PPMImage *readPPM(const char *filename) {
char buff[16];
PPMImage *img;
FILE *fp;
int c, rgb_comp_color;
fp = fopen(filename, "rb");
if (!fp) {
fprintf(stderr, "Unable to open file '%s'\n", filename);
exit(1);
}
if (!fgets(buff, sizeof(buff), fp)) {
perror(filename);
exit(1);
}
if (buff[0] != 'P' || buff[1] != '6') {
fprintf(stderr, "Invalid image format (must be 'P6')\n");
exit(1);
}
img = (PPMImage *) malloc(sizeof(PPMImage));
if (!img) {
fprintf(stderr, "Unable to allocate memory\n");
exit(1);
}
c = getc(fp);
while (c == '#') {
while (getc(fp) != '\n')
;
c = getc(fp);
}
ungetc(c, fp);
if (fscanf(fp, "%d %d", &img->x, &img->y) != 2) {
fprintf(stderr, "Invalid image size (error loading '%s')\n", filename);
exit(1);
}
if (fscanf(fp, "%d", &rgb_comp_color) != 1) {
fprintf(stderr, "Invalid rgb component (error loading '%s')\n",
filename);
exit(1);
}
if (rgb_comp_color != RGB_COMPONENT_COLOR) {
fprintf(stderr, "'%s' does not have 8-bits components\n", filename);
exit(1);
}
while (fgetc(fp) != '\n')
;
img->data = (PPMPixel*) malloc(img->x * img->y * sizeof(PPMPixel));
if (!img) {
fprintf(stderr, "Unable to allocate memory\n");
exit(1);
}
if (fread(img->data, 3 * img->x, img->y, fp) != img->y) {
fprintf(stderr, "Error loading image '%s'\n", filename);
exit(1);
}
fclose(fp);
return img;
}
void writePPM(PPMImage *img) {
fprintf(stdout, "P6\n");
fprintf(stdout, "# %s\n", COMMENT);
fprintf(stdout, "%d %d\n", img->x, img->y);
fprintf(stdout, "%d\n", RGB_COMPONENT_COLOR);
fwrite(img->data, 3 * img->x, img->y, stdout);
fclose(stdout);
}
__global__ void smoothing_kernel(PPMImage *d_image, PPMImage *d_image_copy) {
// Creating variables
int i, j, row, col;
int total_red = 0, total_blue = 0, total_green = 0;
int index_dst_y, index_dst_x, index_src_y, index_src_x;
// Get Row and COl
row = blockIdx.y * TILE_WIDTH + threadIdx.y;
col = blockIdx.x * TILE_WIDTH + threadIdx.x;
// Create Shared block of data
__shared__ PPMPixel shared_image_data[SHARED_SIZE*SHARED_SIZE];
// Filling the shared variable with a two-step for.
// first step fill the data with index inside the blocks dim
// -----------------
// |x|x|x|x|x|x|x| |
// |x|x|x|x|x|x|x| |
// |x|x|x|x|x|x|x| |
// |x|x|x|x|x|x|x| |
// |x|x|x|x|x|x|x| |
// |x|x|x|x|x|x|x| |
// |x|x|x|x|x|x|x| |
// | | | | | | | | |
// -----------------
// Second step fill the data with index outside the blocks dim
// -----------------
// | | | | | | | |x|
// | | | | | | | |x|
// | | | | | | | |x|
// | | | | | | | |x|
// | | | | | | | |x|
// | | | | | | | |x|
// | | | | | | | |x|
// |x|x|x|x|x|x|x|x|
// -----------------
for (i = 0; i <= TILE_WIDTH * TILE_WIDTH; i = i + TILE_WIDTH * TILE_WIDTH)
{
// Get indexs of dst matrix
index_dst_y = (threadIdx.y * TILE_WIDTH + threadIdx.x + i) / SHARED_SIZE;
index_dst_x = (threadIdx.y * TILE_WIDTH + threadIdx.x + i) % SHARED_SIZE;
// Get indexs of destination matrix
index_src_y = (blockIdx.y * TILE_WIDTH) + index_dst_y - MASK_R;
index_src_x = (blockIdx.x * TILE_WIDTH) + index_dst_x - MASK_R;
//Work only if dst geral index stay into shared matrix size
if (index_dst_y * SHARED_SIZE + index_dst_x < (SHARED_SIZE*SHARED_SIZE)) {
// if src index stay into image save images values else save 0
if (index_src_y >= 0 && index_src_y < d_image->y && index_src_x >= 0 && index_src_x < d_image->x){
shared_image_data[index_dst_y * SHARED_SIZE + index_dst_x].red = d_image_copy->data[(index_src_y * d_image->x) + index_src_x].red;
shared_image_data[index_dst_y * SHARED_SIZE + index_dst_x].blue = d_image_copy->data[(index_src_y * d_image->x) + index_src_x].blue;
shared_image_data[index_dst_y * SHARED_SIZE + index_dst_x].green = d_image_copy->data[(index_src_y * d_image->x) + index_src_x].green;
}
else{
shared_image_data[index_dst_y * SHARED_SIZE + index_dst_x].red = 0;
shared_image_data[index_dst_y * SHARED_SIZE + index_dst_x].blue = 0;
shared_image_data[index_dst_y * SHARED_SIZE + index_dst_x].green = 0;
}
}
}
// sync threads
__syncthreads();
// if row and col stay into image proceed with convolution
if (row < d_image->y && col < d_image->x){
for (i = 0; i < MASK_WIDTH; i++){
for (j = 0; j < MASK_WIDTH; j++) {
total_red += shared_image_data[((threadIdx.y + j) * SHARED_SIZE) + (threadIdx.x + i)].red;
total_blue += shared_image_data[((threadIdx.y + j) * SHARED_SIZE) + (threadIdx.x + i)].blue;
total_green += shared_image_data[((threadIdx.y + j) * SHARED_SIZE) + (threadIdx.x + i)].green;
}
}
// Save data of convolution into devise image
d_image->data[(row * d_image->x) + col].red = total_red / (MASK_WIDTH*MASK_WIDTH);
d_image->data[(row * d_image->x) + col].blue = total_blue / (MASK_WIDTH*MASK_WIDTH);
d_image->data[(row * d_image->x) + col].green = total_green / (MASK_WIDTH*MASK_WIDTH);
}
}
void smoothing_GPU(PPMImage *image, PPMImage *image_copy) {
unsigned int rows, cols, img_size;
PPMImage *d_image, *d_image_copy;
PPMPixel *d_pixels, *d_pixels_copy, *new_pixels;
// Get data
cols = image->x;
rows = image->y;
img_size = cols * rows;
// Alloc structure to devise
cudaMalloc((void **)&d_image, sizeof(PPMImage));
cudaMalloc((void **)&d_image_copy, sizeof(PPMImage));
// Alloc image to devise
cudaMalloc((void **)&d_pixels, sizeof(PPMPixel) * img_size);
cudaMalloc((void **)&d_pixels_copy, sizeof(PPMPixel) * img_size);
// cpy stucture to devise
cudaMemcpy(d_image, image, sizeof(PPMImage), cudaMemcpyHostToDevice);
cudaMemcpy(d_pixels, image->data, sizeof(PPMPixel) * img_size, cudaMemcpyHostToDevice);
cudaMemcpy(&(d_image->data), &d_pixels, sizeof(PPMPixel *), cudaMemcpyHostToDevice);
cudaMemcpy(d_image_copy, image, sizeof(PPMImage), cudaMemcpyHostToDevice);
cudaMemcpy(d_pixels_copy, image->data, sizeof(PPMPixel) * img_size, cudaMemcpyHostToDevice);
cudaMemcpy(&(d_image_copy->data), &d_pixels_copy, sizeof(PPMPixel *), cudaMemcpyHostToDevice);
dim3 dimGrid(ceil((float)cols / TILE_WIDTH), ceil((float)rows / TILE_WIDTH), 1);
dim3 dimBlock(TILE_WIDTH, TILE_WIDTH, 1);
// Call function
smoothing_kernel<<<dimGrid, dimBlock>>>(d_image, d_image_copy);
new_pixels = (PPMPixel *) malloc(img_size * sizeof(PPMPixel));
// Copy result to local array
cudaMemcpy(image, d_image, sizeof(PPMImage), cudaMemcpyDeviceToHost);
cudaMemcpy(new_pixels, d_pixels, sizeof(PPMPixel) * img_size, cudaMemcpyDeviceToHost);
image->data = new_pixels;
//Free memory
cudaFree(d_image);
cudaFree(d_image_copy);
cudaFree(d_pixels);
cudaFree(d_pixels_copy);
}
int main(int argc, char *argv[]) {
if( argc != 2 ) {
printf("Too many or no one arguments supplied.\n");
}
//double t_start, t_end;
//int i;
char *filename = argv[1]; //Recebendo o arquivo!;
PPMImage *image = readPPM(filename);
PPMImage *image_output = readPPM(filename);
//t_start = rtclock();
smoothing_GPU(image_output, image);
//t_end = rtclock();
writePPM(image_output);
//fprintf(stdout, "\n%0.6lfs\n", t_end - t_start);
free(image);
free(image_output);
}
|
6,788 | #include <iostream>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/generate.h>
#include <thrust/reduce.h>
#include <thrust/random.h>
int my_rand()
{
static thrust::default_random_engine rng;
static thrust::uniform_int_distribution<int> dist(0, 999);
return dist(rng);
}
int main(int argc, char* argv[])
{
// Allocate a vector in host memory.
thrust::host_vector<int> h(1e+7);
// Generate random numbers on the host.
thrust::generate(h.begin(), h.end(), my_rand);
// Copy the vector from host memory to device memory.
thrust::device_vector<int> d = h;
// Compute the sum on the device.
int actual = thrust::reduce(d.begin(), d.end(), 0, thrust::plus<int>());
// Compute the sum on the host.
int expected = 0;
for (int i = 0; i < h.size(); ++i)
{
expected += h[i];
}
// Validate the result.
if (actual != expected)
{
std::cout << "actual = " << actual << ", expected = " << expected << std::endl;
}
}
|
6,789 | //MatAdd.cu
// author: Pan Yang
// date : 2015-7-3
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define M 4 // height of matrix
#define N 5 // width of matrix
#define SIZE (M*N)
#define BLOCK_SIZE 16
// Kernel definition
/*
__global__ void MatAdd_B(float A[m][m], float B[m][m], float C[m][m])
{
int i = threadIdx.x;
int j = threadIdx.y;
if (i < m*m && j < m*m)
C[i][j] = A[i][j] + B[i][j];
}
*/
// Kernel definition
__global__ void MatAdd(float *A, float *B, float *C)
{
int i = blockIdx.y * blockDim.y + threadIdx.y;
int j = blockIdx.x * blockDim.x + threadIdx.x;
if (i < M && j < N)
C[i*N + j] = A[i*N + j] + B[i*N + j];
}
int main()
{
// time for the whole process
clock_t start, finish;
float time;
start = clock();
//----------------------------------1D implementation for 2D array--------------------------------------------------------
// define a, b, c and alloc memory for them
float *a, *b, *c;
int i, j;
a = (float *)malloc(SIZE * sizeof(float));
b = (float *)malloc(SIZE * sizeof(float));
c = (float *)malloc(SIZE * sizeof(float));
for (i = 0; i < SIZE; ++i)
{
a[i] = rand() / (float)RAND_MAX;
b[i] = rand() / (float)RAND_MAX;
}
float *d_a, *d_b, *d_c;
cudaMalloc( (void**)&d_a, SIZE * sizeof(float));
cudaMalloc( (void**)&d_b, SIZE * sizeof(float));
cudaMalloc( (void**)&d_c, SIZE * sizeof(float));
// copy data from host memory to device memory
cudaMemcpy( d_a, a, SIZE * sizeof(int), cudaMemcpyHostToDevice );
cudaMemcpy( d_b, b, SIZE * sizeof(int), cudaMemcpyHostToDevice );
//cudaMemcpy( d_c, c, SIZE * sizeof(int), cudaMemcpyHostToDevice );
// Kernel invocation with m*16*16 threads
dim3 threadsPerBlock(BLOCK_SIZE, BLOCK_SIZE);
dim3 blocksPerGrid((N + threadsPerBlock.x -1) / threadsPerBlock.x, (M + threadsPerBlock.y -1) / threadsPerBlock.y);
MatAdd<<<blocksPerGrid, threadsPerBlock>>>(d_a, d_b, d_c);
// copy results form device memory to host memory
cudaMemcpy( c, d_c, SIZE * sizeof(int), cudaMemcpyDeviceToHost );
//--------------------------------end of 1D implementation for 2D array----------------------------------------------------
for (i = 0; i < M; ++i)
{
for (j = 0; j < N; ++j)
printf(" %9f ", a[i * N +j]);
printf("...");
printf("\n");
}
printf("--------------------------------------------------\n");
for (i = 0; i < M; ++i)
{
for (j = 0; j < N; ++j)
printf(" %9f ", b[i * N +j]);
printf("...");
printf("\n");
}
printf("--------------------------------------------------\n");
for (i = 0; i < M; ++i)
{
for (j = 0; j < N; ++j)
printf(" %9f ", c[i * N +j]);
printf("...");
printf("\n");
}
// free space
free(a);
free(b);
free(c);
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
finish = clock();
time = (float)(finish - start) / CLOCKS_PER_SEC;
printf("calculation time = %fms\n", time);
return 0;
}
|
6,790 | #include <cstdio>
#include <cstdlib>
#include <sys/time.h>
#include <cassert>
__global__ void threads2d() {
printf("threadIdx:(%d, %d, %d) blockIdx:(%d, %d, %d) blockDim:(%d, %d, %d) "
"gridDim:(%d, %d, %d)\n", threadIdx.x, threadIdx.y, threadIdx.z,
blockIdx.x, blockIdx.y, blockIdx.z, blockDim.x, blockDim.y, blockDim.z,
gridDim.x,gridDim.y,gridDim.z);
}
int main (int argc, char** argv) {
int nElem = 36;
dim3 block(3,3);
dim3 grid((nElem + block.x - 1) / block.x, (nElem + block.x - 1) / block.x);
fprintf(stdout,"grid.x %d grid.y %d grid.z %d\n", grid.x, grid.y, grid.z);
fprintf(stdout,"block.x %d block.y %d block.z %d\n", block.x, block.y, block.z);
threads2d<<<grid, block>>>();
cudaDeviceSynchronize();
return 0;
}
|
6,791 | #include <assert.h>
#include "shareconvtrial.cuh"
#include "real.h"
#include <iostream>
void trial(){
constexpr int asize=1000;
constexpr int bsize=11;
real A[asize];
for(int i=0; i< asize; i++){
A[i]=1;
}
real M[bsize];
for (int i=0; i<bsize; ++i){
M[i]=i;
}
real P[asize];
share_conv(A,M,P,bsize,asize);
//for (int i=0; i<asize; ++i)
// std::cout << "P[" << i << "]=" << P[i] <<std::endl;
//assert(P[20]==55);
}
int main(){
trial();
//std::cout << "success!!!!" <<std::endl;
}
|
6,792 | //#include "mdCuda.h"
__device__ void cudaReposition(float PP0, float PP1, float PP2, float PA0, float PA1, float PA2, float PL0, float PL1, float PL2, float &Xi, float &Yi, float &Zi, float NA)
{
float PAPL, H, B;
if(PP0 > 0){
PAPL = PA0 + PL0;
H = (Xi-PAPL) / PL0;
B = H - 2.0*int(H);
Xi = B*PL0 + PAPL;
}
if(PP1 > 0){
PAPL = PA1 + PL1;
H = (Yi-PAPL) / PL1;
B = H - 2.0*int(H);
Yi = B*PL1 + PAPL;
}
if(PP2 > 0){
PAPL = PA2 + PL2;
H = (Zi-PAPL) / PL2;
B = H - 2.0*int(H);
Zi = B*PL2 + PAPL;
}
}
__global__ void mdKernel(int NA, float* FFX, float* FFY, float* FFZ, float* EE, float* X, float* Y, float* Z, int IPBC,
float PP0, float PP1, float PP2, float AL1, float AL2, float A1, float A2, float RL1, float RL2, float D21, float D22,
float PA0, float PA1, float PA2, float PB0, float PB1, float PB2, float PL0, float PL1, float PL2)
{
float XIJ, YIJ, ZIJ, RIJ, RIJ2, EPP, FX2, FY2, FZ2;
float ARG1, ARG2, EXP1, EXP2, UIJ1, UIJ2, UIJ;
float FAC1, FAC2, FAC12, XRIJ, YRIJ, ZRIJ;
int i = blockIdx.x * blockDim.x + threadIdx.x;
float Xi = X[i];
float Yi = Y[i];
float Zi = Z[i];
cudaReposition(PP0, PP1, PP2, PA0, PA1, PA2, PL0, PL1, PL2, Xi, Yi, Zi, NA);
///////////////////////////////////////////////////
// FORCE
EPP = 0;
// Forces that effect atoms indexed with i in all three axes
FX2 = 0;
FY2 = 0;
FZ2 = 0;
for(int j=0; j<NA; j++)
{
if(i == j)
continue;
// Apply periodic boundaries and find distances between atom I and j. RIJ2 is square of RIJ
XIJ = X[i] - X[j];
YIJ = Y[i] - Y[j];
ZIJ = Z[i] - Z[j];
double DD, ID;
if(IPBC != 0){
if(PP0 > 0){
DD = XIJ / PP0;
ID = int(DD);
XIJ = XIJ - PP0*(ID+int(2.0*(DD-ID)));
}
if(PP1 > 0){
DD = YIJ / PP1;
ID = int(DD);
YIJ = YIJ - PP1*(ID+int(2.0*(DD-ID)));
}
if(PP2 > 0){
DD = ZIJ / PP2;
ID = int(DD);
ZIJ = ZIJ - PP2*(ID+int(2.0*(DD-ID)));
}
}
RIJ2 = XIJ*XIJ + YIJ*YIJ + ZIJ*ZIJ;
RIJ = sqrt(RIJ2);
// Calculate potential energy U(r)
ARG1 = AL1*RIJ2;
ARG2 = AL2*RIJ2;
EXP1 = exp(-ARG1);
EXP2 = exp(-ARG2);
UIJ1 = A1*EXP1/(pow(RIJ,RL1));
UIJ2 = A2*EXP2/(pow(RIJ,RL2));
UIJ = D21*UIJ1 + D22*UIJ2;
EPP = EPP+UIJ;
// Calculate forces
FAC1 = -(RL1/RIJ + 2.0*AL1*RIJ);
FAC2 = -(RL2/RIJ + 2.0*AL2*RIJ);
FAC12 = FAC1*D21*UIJ1 + FAC2*D22*UIJ2;
XRIJ = XIJ/RIJ;
YRIJ = YIJ/RIJ;
ZRIJ = ZIJ/RIJ;
FX2 += FAC12*XRIJ;
FY2 += FAC12*YRIJ;
FZ2 += FAC12*ZRIJ;
}
FFX[i] = -FX2;
FFY[i] = -FY2;
FFZ[i] = -FZ2;
EE[i] = EPP;
} |
6,793 | /***************************************************************************//**
* \file intermediateVelocity.cu
* \author Christopher Minar (minarc@oregonstate.edu)
* \brief kernels to generate the right hand side for the initial velocity solve
*/
#include "intermediateVelocity.h"
/**
* \namespace kernels
* \brief Contains all the custom-written CUDA kernels.
*/
namespace kernels
{
/*
* sums the components of the right hand side of the intermediate velocity equation
* param rhs right hand side fo the velocity equation
* param L explicit diffusion terms
* param N explicit advection terms
* param u u velocities
* param bc1 boundary condition terms
* param dt change in time
* param nx number of cells in x direction
* param ny number of cells in y direction
*/
__global__
void generateRHS(double *rhs, double *L, double *Nold, double *N, double *u, double *bc1, double dt, int nx, int ny)
{
if (threadIdx.x + (blockDim.x * blockIdx.x) >= (ny-1)*nx + (nx-1)*ny)
return;
int i = threadIdx.x + (blockDim.x * blockIdx.x);
rhs[i] = u[i] + dt*(0.5*Nold[i] - 1.5*N[i] + 0.5*L[i]) + bc1[i];
}
/*
* calculates boundary terms for u intermediate velocity
* param u u velocities
* param bc1 boundry terms
* param ym yminus boundary velocities
* param yp yplus boundary velocities
* param xm xminus boundary velocities
* param xp xplus boundary velocities
* param dx distance between nodes in the x direction (measured between node sides, where u velocites are stored)
* param dy distance between nodes in the y direction (measured between node top/bot, where v velocites are stored)
* param nu viscosity
* param dt change in time
* param nx number of cells in x direction
* param ny number of cells in y direction
*/
__global__
void bc1X(double *u, double *bc1, double *ym, double *yp, double *xm, double *xp, double *dx, double *dy, double nu, double dt, int nx, int ny)
{
if (threadIdx.x + blockDim.x * blockIdx.x >= (nx-1)*ny)
return;
int i = threadIdx.x + blockDim.x * blockIdx.x,
I = i % (nx-1),
J = i / (nx-1);
double temp = 0;
//East
if (I == nx-2)
{
temp += xp[J] * 0.5*dt*nu*(1/(dx[I+1]*(dx[I+1]+dx[I])*0.5));
}
//West
if (I == 0)
{
temp += xm[J] * 0.5*dt*nu * (1/(dx[I]*(dx[I+1]+dx[I])*0.5));
}
//North
if (J == ny-1)
{
temp += (2*yp[I] - u[i]) * nu*dt*0.5 / dy[J] / dy[J];
}
//South
if (J == 0)
{
temp += (2*ym[I] - u[i]) * nu*dt*0.5 / dy[J] / dy[J];
}
bc1[i] = temp;
}
/*
* calculates boundary terms for u intermediate velocity
* param u v velocities
* param bc1 boundry terms
* param ym yminus boundary velocities
* param yp yplus boundary velocities
* param xm xminus boundary velocities
* param xp xplus boundary velocities
* param dx distance between nodes in the x direction (measured between node sides, where u velocites are stored)
* param dy distance between nodes in the y direction (measured between node top/bot, where v velocites are stored)
* param nu viscosity
* param dt change in time
* param nx number of cells in x direction
* param ny number of cells in y direction
*/
__global__
void bc1Y(double *u, double *bc1, double *ym, double *yp, double *xm, double *xp, double *dx, double *dy, double nu, double dt, int nx, int ny)
{
if (threadIdx.x + blockDim.x * blockIdx.x >= nx*(ny-1))
return;
int ip = threadIdx.x + blockDim.x * blockIdx.x,
I = ip % nx,
J = ip / nx,
iv = ip + (nx-1)*ny;
double temp = 0;
//East
if (I == nx-1)
{
temp += (2*xp[ny+J] - u[iv]) * nu*dt*0.5 / dx[I] / dx[I];
}
//West
if (I == 0)
{
temp += (2*xm[ny + J] - u[iv]) * nu*dt*0.5 / dx[I] / dx[I];
}
//North
if (J == ny-2)
{
temp += yp[(nx-1) + I] * 0.5*dt*nu*(1/(dy[J+1]*(dy[J]+dy[J+1])*0.5));
}
//South
if (J == 0)
{
temp += ym[(nx-1) + I] * 0.5*dt*nu*(1/(dy[J]*(dy[J]+dy[J+1])*0.5));
}
bc1[iv] = temp;
}
} // end of namespace kernels
|
6,794 | /*
CS 4402 Distributed and Parallel Systems
Assignment 2 Question 1: N thread blocks and N threads per thread block
Sarah Whelan 250778849
TO RUN: nvcc q1_swhela2.cu -o q1_swhela2
./q1_swhela2
*/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
int modBy = 103; // common prime num used for modding coefficient values during generation, multiplication, and addition
void genPolynomials(int *polyA, int *polyB, int size);
void multPolynomialsSerial(int *polyA, int *polyB, int polySize, int *product, int productSize);
__global__ void multPolynomialsParallel(int *polyA, int *polyB, int *product, int polySize, int modBy);
__global__ void sumProductsParallel(int prodSize, int threadsPerBlock, int *summedProduct, int *products, int numBlocks, int modBy);
void checkCUDAError(const char* msg);
int main() {
srand(time(NULL));
int numTerms;
// get user desired input on length of polynomials
printf("Specify the number of terms in the polynomial by specifying the exponent on base 2, UP TO 10, e.g. enter '3' if you want 2^3 terms (AKA 8 terms) per polynomial: ");
scanf("%d", &numTerms);
printf("\nYou entered '%d'.\n", numTerms);
if (numTerms > 10) {
printf("Invalid entry. The maximum number of terms is 2^10. Please enter a term less than or equal to 10 next time.");
return 1;
}
// then bitshift by input value to determine actual value of numTerms
numTerms = 1 << numTerms;
printf("Number of terms per polynomial = %d, hence each polynomial will have degree = %d.\n\n", numTerms, numTerms-1);
// use numTerms as the number of blocks per thread and the number of blocks
int threadsPerBlock = numTerms;
int blocks = numTerms;
// instantiate and allocate host memory blocks to store each polynomial of size numTerms
int *host_polyA, *host_polyB;
host_polyA = (int *) malloc(numTerms * sizeof(int));
host_polyB = (int *) malloc(numTerms * sizeof(int));
// generate random polynomials of size numTerms
printf("Generating polynomials...\n\n");
genPolynomials(host_polyA, host_polyB, numTerms);
printf("polyA:\n");
for (int i = 0; i < numTerms; i++) {
printf("%dx^%d ", host_polyA[i], i);
if (i != numTerms-1) {
printf("+ ");
}
}
printf("\n\npolyB:\n");
for (int i = 0; i < numTerms; i++) {
printf("%dx^%d ", host_polyB[i], i);
if (i != numTerms-1) {
printf("+ ");
}
}
printf("\n\n");
// determine degree of product
int degreeOfProduct = (numTerms - 1) * 2; // e.g. degree(polyA, polyB) = 3 then x^3 * x^3 = x^6 and degree = numTerms - 1
// allocate blocks of memory on the host for storing the product with size degreeOfProduct + 1 (serial)
// and numTerms*numTerms for the intermediary parallel product, as well asthe final parallel product
// two different allocations in order to verify results at the end!
int *host_product_serial, *host_product_parallel, *host_final_product;
host_product_serial = (int *) malloc((degreeOfProduct+1) * sizeof(int)); // sum of products is intrinsic
host_product_parallel = (int *) malloc(numTerms * numTerms * sizeof(int)); // because of n threads in each n thread blocks
host_final_product = (int *) malloc((degreeOfProduct+1) * sizeof(int)); // final product from parallel version once summed
// ensure all vals in host_product_parallel are 0 (this is done within the serial version so we don't need to worry about that one)
for (int i = 0; i < numTerms*numTerms; i++) {
host_product_parallel[i] = 0;
}
// ensure all vals in host_final_product are 0
for (int i = 0; i < degreeOfProduct+1; i++) {
host_final_product[i] = 0;
}
// initialize and allocate memory on the devices for storing dev_polyA, dev_polyB, and dev_product
int *dev_polyA, *dev_polyB, *dev_product;
cudaMalloc( (void **) &dev_polyA, numTerms * sizeof(int));
cudaMalloc( (void **) &dev_polyB, numTerms * sizeof(int));
cudaMalloc( (void **) &dev_product, numTerms * numTerms * sizeof(int));
// copy polynomials: host -> device (dest, src, size, direction)
cudaMemcpy(dev_polyA, host_polyA, numTerms * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_polyB, host_polyB, numTerms * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_product, host_product_parallel, numTerms * numTerms * sizeof(int), cudaMemcpyHostToDevice);
// setup kernel params & launch
dim3 dimGrid(blocks);
dim3 dimBlock(threadsPerBlock);
multPolynomialsParallel<<<dimGrid, dimBlock>>>(dev_polyA, dev_polyB, dev_product, numTerms, modBy);
cudaThreadSynchronize(); // wait for ALL threads from all blocks to complete
checkCUDAError("kernel invocation");
// copy dev_product back into host_product_parallel (dest, src, size, direction)
cudaMemcpy(host_product_parallel, dev_product, numTerms * numTerms * sizeof(int), cudaMemcpyDeviceToHost);
/* ~~~ now we need to deal with the summation of intermediary products ~~~ */
// allocate device mem for final product
int *dev_final;
cudaMalloc( (void **) &dev_final, (degreeOfProduct+1) * sizeof(int));
// copy zero'd host_final_product to dev_final and host_product_parallel to dev_product
// (dest, src, size, direction)
cudaMemcpy(dev_final, host_final_product, (degreeOfProduct+1) * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_product, host_product_parallel, numTerms * numTerms * sizeof(int), cudaMemcpyHostToDevice);
// parameters are (int prodSize, int threadsPerBlock, int *summedProduct, int *products, int numBlocks, int modBy)
sumProductsParallel<<<dimGrid, dimBlock>>>(degreeOfProduct+1, threadsPerBlock, dev_final, dev_product, blocks, modBy);
cudaThreadSynchronize(); // wait for ALL threads from all blocks to complete
checkCUDAError("kernel invocation");
// copy summation of products back to host (dest, src, size, direction)
cudaMemcpy(host_final_product, dev_final, (degreeOfProduct+1) * sizeof(int), cudaMemcpyDeviceToHost);
// multiply polynomials in serial and write to host_product_serial for verification
multPolynomialsSerial(host_polyA, host_polyB, numTerms, host_product_serial, degreeOfProduct+1);
printf("Serial result:\n");
for (int i = 0; i < degreeOfProduct+1; i++) {
printf("%dx^%d ", host_product_serial[i], i);
if (i != degreeOfProduct) {
printf("+ ");
}
}
printf("\n\nParallel result:\n");
for (int i = 0; i < degreeOfProduct+1; i++) {
printf("%dx^%d ", host_final_product[i], i);
if (i != degreeOfProduct) {
printf("+ ");
}
}
printf("\n\n");
bool allRight = 1;
for (int i = 0; i < degreeOfProduct+1; i++) {
if (host_product_serial[i] == host_final_product[i]) {
continue;
} else {
printf("Coefficients at degree %d are not equivalent: serial!=parallel (%d!=%d)\n", i, host_product_serial[i], host_final_product[i]);
allRight = 0;
}
}
if (allRight) {
printf("Verification successful. The serial and parallel polynomial multiplications produced the same result!\n\n");
} else {
printf("Looks like there were some discrepancies. Verification failed.\n\n");
}
// free host and device memory
free(host_polyA);
free(host_polyB);
free(host_product_serial);
free(host_product_parallel);
free(host_final_product);
cudaFree(dev_polyA);
cudaFree(dev_polyB);
cudaFree(dev_product);
cudaFree(dev_final);
return 0;
}
// genPolynomials takes two polynomials and their size (number of terms per polynomial),
// and generates random coefficients for each term mod p
void genPolynomials(int *polyA, int *polyB, int size) {
// coefficient generation using rand mod p where p = 103
for (int i = 0; i < size; i++) {
polyA[i] = rand() % modBy;
if (polyA[i] == 0) { // we don't want any zeros!!!
polyA[i] = 1;
}
polyB[i] = rand() % modBy;
if (polyB[i] == 0) {
polyB[i] = 1;
}
}
}
// multPolynomialsSerial takes two polynomials and their size, in addition to a memory block to place
// the sum of products into, as well as the size of the product polynomial
void multPolynomialsSerial(int *polyA, int *polyB, int polySize, int *product, int productSize) {
int degreeOfTerms;
// ensure all coefficients of product are 0
for (int i = 0; i < productSize; i++) {
product[i] = 0;
}
// calculate sum of products
for (int a = 0; a < polySize; a++) { // iterate through terms in A
for (int b = 0; b < polySize; b++) { // for each term in A, iterate through all terms in B
// add degrees (indices) to determine which index this product belongs to in the product array block
degreeOfTerms = a + b;
// add product of terms to previous sum and mod by 103
product[degreeOfTerms] = (product[degreeOfTerms] + polyA[a] * polyB[b]) % modBy;
}
}
}
// multPolynomialsParallel determines the intermediary products of the polynomial multiplication problem
__global__ void multPolynomialsParallel(int *polyA, int *polyB, int *product, int polySize, int modBy) {
int a = blockIdx.x; // all threads in the same block will access the same polyA element
int b = threadIdx.x; // but all threads will access individual polyB elements
int myIndex = blockDim.x * blockIdx.x + threadIdx.x; // where to write this thread's product
product[myIndex] = (polyA[a] * polyB[b]) % modBy;
}
// sumProductsParallel uses prodSize threads, each thread corresponding to a degree, to sum common terms and determine the actual product of polyA and polyB
__global__ void sumProductsParallel(int prodSize, int threadsPerBlock, int *summedProduct, int *products, int numBlocks, int modBy) {
int responsibleFor = blockIdx.x * blockDim.x + threadIdx.x; // used to check which threads are going to be active during this step
if (responsibleFor < prodSize) { // e.g. 1 < 7 so thread 1 is going to be in charge of summing the x^1 terms, threads >= prodSize will be inactive for remainder
for (int blockNum = 0; blockNum < numBlocks; blockNum++) { // loop through blocks
for (int indexInBlock = 0; indexInBlock < threadsPerBlock; indexInBlock++) { // loop through each index per block
int degreeOfElement = blockNum + indexInBlock; // the degree related to the coefficient stored at each products[] index is equal to the block number + the relative index in the block
if (indexInBlock == 0 && degreeOfElement > responsibleFor) {
return; // this thread is done summing its common terms
}
else if (degreeOfElement == responsibleFor) { // if this thread is responsible for the degree we just calculated
int spotInProducts = blockNum * blockDim.x + indexInBlock; // get its actual index in products[]
summedProduct[responsibleFor] = (summedProduct[responsibleFor] + products[spotInProducts]) % modBy; // and write that value into the final summedProduct[our degree]
}
}
}
}
}
void checkCUDAError(const char *msg) {
cudaError_t err = cudaGetLastError();
if(cudaSuccess != err)
{
fprintf(stderr, "CUDA error: %s: %s.\n", msg, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}
|
6,795 | #include <cassert>
#include <stdio.h>
__global__ void MatAdd(int n, float *c, const float *a, const float *b, int pitch)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
int ij = i * pitch + j;
if(i < n && j < n)
c[ij] = a[ij] + b[ij];
}
int main()
{
const int n = 2;
float c[n][n], a[n][n], b[n][n];
for(int i=0; i < n; i++){
for(int j=0; j < n; j++){
a[i][j] = 1;
b[i][j] = 2;
c[i][j] = 3;
}
}
const int rowsize = n * sizeof(float);
float *dc, *da, *db;
size_t pitch;
cudaMallocPitch((void**)&da, &pitch, rowsize, n);
cudaMallocPitch((void**)&db, &pitch, rowsize, n);
cudaMallocPitch((void**)&dc, &pitch, rowsize, n);
// Copy over input from host to device
cudaMemcpy2D(da, pitch, a, rowsize, rowsize, n, cudaMemcpyHostToDevice);
cudaMemcpy2D(db, pitch, b, rowsize, rowsize, n, cudaMemcpyHostToDevice);
dim3 blocksize(16, 16);
dim3 gridsize((n + blocksize.x - 1) / blocksize.x, (n + blocksize.y - 1) / blocksize.y);
assert(pitch % sizeof(float) == 0);
const int ipitch = pitch / sizeof(float);
MatAdd<<<gridsize, blocksize>>>(n, dc, da, db, ipitch);
// Copy over output from device to host
cudaMemcpy2D(c, rowsize, dc, pitch, rowsize, n, cudaMemcpyDeviceToHost);
for(int i=0; i < n;i++){
for(int j=0; j < n;j++){
printf("%f ", c[i][j]);
}
printf("\n");
}
// Free device memory
cudaFree(da);
cudaFree(db);
cudaFree(dc);
}
|
6,796 | #include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <time.h>
typedef struct {
int *array;
size_t used;
size_t size;
} Array;
void initArray(Array *a, size_t initialSize) {
a->array = (int*) malloc(initialSize * sizeof(int));
a->used = 0;
a->size = initialSize;
}
void insertArray(Array *a, int element) {
if (a->used == a->size) {
a->size += 1;
a->array =(int*) realloc(a->array, a->size * sizeof(int));
}
a->array[a->used++] = element;
}
Array initArrayA(){
FILE *fp;
char str[50000];
Array a;
initArray(&a, 1);
/* opening file for reading */
fp = fopen("inp.txt" , "r");
if(fp == NULL) {
printf("%s","error");
return a;
}
while( fgets (str, 50000, fp)!=NULL ) {
/* writing content to stdout */
// printf("%s\n", str);
char* token;
char* rest = str;
while ((token = strtok_r(rest, " , ", &rest)))
insertArray(&a, atoi(token));
}
fclose(fp);
return a;
}
__global__ void global_reduce_kernel(int * d_out, int * d_in, int size)
{
int myId = threadIdx.x + blockDim.x * blockIdx.x;
int tid = threadIdx.x;
// do reduction in global mem
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s && tid+s < size && tid < size)
{
if ( d_in[myId] > d_in[myId + s]){
d_in[myId]= d_in[myId + s];
}
}
__syncthreads();
}
// only thread 0 writes result for this block back to global mem
if (tid == 0)
{
d_out[blockIdx.x] = d_in[myId];
}
}
__global__ void shmem_reduce_kernel(int * d_out, int * d_in, int size)
{
// sdata is allocated in the kernel call: 3rd arg to <<<b, t, shmem>>>
extern __shared__ float sdata[];
int myId = threadIdx.x + blockDim.x * blockIdx.x;
int tid = threadIdx.x;
// load shared mem from global mem
sdata[tid] = d_in[myId];
__syncthreads(); // make sure entire block is loaded!
// do reduction in shared mem
for (unsigned int s = blockDim.x / 2; s > 0; s >>= 1)
{
if (tid < s && tid+s < size && tid < size)
{
if ( sdata[tid] > sdata[tid + s]){
sdata[tid]= sdata[tid + s];
}
}
__syncthreads(); // make sure all adds at one stage are done!
}
// only thread 0 writes result for this block back to global mem
if (tid == 0)
{
d_out[blockIdx.x] = sdata[0];
}
}
void reduce(int * d_out, int * d_intermediate, int * d_in,
int size, bool usesSharedMemory)
{
// assumes that size is not greater than maxThreadsPerBlock^2
// and that size is a multiple of maxThreadsPerBlock
const int maxThreadsPerBlock = 512;
int threads = maxThreadsPerBlock;
int blocks = ceil(float(size) / float(maxThreadsPerBlock));
if (usesSharedMemory)
{
shmem_reduce_kernel<<<blocks, threads, threads * sizeof(int)>>>
(d_intermediate, d_in, size);
}
else
{
global_reduce_kernel<<<blocks, threads>>>
(d_intermediate, d_in,size);
}
// now we're down to one block left, so reduce it
threads = blocks; // launch one thread for each block in prev step
blocks = 1;
if (usesSharedMemory)
{
shmem_reduce_kernel<<<blocks, threads, threads * sizeof(int)>>>
(d_out, d_intermediate, size);
}
else
{
global_reduce_kernel<<<blocks, threads>>>
(d_out, d_intermediate,size);
}
}
__global__ void getlastdigit(int *d_out, int *d_in, int size) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
if (index < size)
d_out[index] = (d_in[index] % 10);
// int idx = threadIdx.x;
}
int main(int argc, char **argv)
{
FILE *q1a;
FILE *q1b;
q1a = fopen("q1a.txt", "w");
q1b = fopen("q1b.txt", "w");
int deviceCount;
cudaGetDeviceCount(&deviceCount);
if (deviceCount == 0) {
fprintf(q1a, "error: no devices supporting CUDA.\n");
exit(EXIT_FAILURE);
}
int dev = 0;
cudaSetDevice(dev);
cudaDeviceProp devProps;
if (cudaGetDeviceProperties(&devProps, dev) == 0)
{
fprintf(q1a,"Using device %d:\n", dev);
fprintf(q1a,"%s; global mem: %dB; compute v%d.%d; clock: %d kHz\n",
devProps.name, (int)devProps.totalGlobalMem,
(int)devProps.major, (int)devProps.minor,
(int)devProps.clockRate);
}
Array A = initArrayA();
int * h_in = A.array;
const int ARRAY_SIZE = A.size;
const int ARRAY_BYTES = A.size * sizeof(int);
// int min = 0;
fprintf(q1a,"array size is %d\n", ARRAY_SIZE);
// clock_t t;
// t = clock();
// fprintf(q1a,"\n\nRUNNING PROBLEM 1 PART A\n\n" );
// for(int i = 0; i < ARRAY_SIZE; i++) {
// if (min > h_in[i]){
// min = h_in[i];
// }
// }
// t = clock() - t;
// double time_taken = ((double)t)/(CLOCKS_PER_SEC/1000); // calculate the elapsed time
// fprintf("The host took %f ms to execute\n", time_taken);
// fprintf("min at host: %d\n", min);
// declare GPU memory pointers
int * d_in, * d_intermediate, * d_out;
// allocate GPU memory
cudaMalloc((void **) &d_in, ARRAY_BYTES);
cudaMalloc((void **) &d_intermediate, ARRAY_BYTES); // overallocated
cudaMalloc((void **) &d_out, sizeof(int));
// transfer the input array to the GPU
cudaMemcpy(d_in, h_in, ARRAY_BYTES, cudaMemcpyHostToDevice);
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEvent_t start2, stop2;
cudaEventCreate(&start2);
cudaEventCreate(&stop2);
fprintf(q1a,"Running global reduce for min\n");
cudaEventRecord(start, 0);
reduce(d_out, d_intermediate, d_in, ARRAY_SIZE, false);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
int h_out;
cudaMemcpy(&h_out, d_out, sizeof(int), cudaMemcpyDeviceToHost);
fprintf(q1a,"average time elapsed in ms: %f\n", elapsedTime);
fprintf(q1a,"min returned by device: %d\n", h_out);
fprintf(q1a,"Running reduce with shared mem\n");
cudaEventRecord(start2, 0);
reduce(d_out, d_intermediate, d_in, ARRAY_SIZE, true);
cudaEventRecord(stop2, 0);
cudaEventSynchronize(stop2);
float elapsedTime2;
cudaEventElapsedTime(&elapsedTime2, start2, stop2);
int h_out2;
cudaMemcpy(&h_out2, d_out, sizeof(int), cudaMemcpyDeviceToHost);
fprintf(q1a,"average time elapsed in ms: %f\n", elapsedTime2);
fprintf(q1a,"min returned by device: %d\n", h_out2);
// free GPU memory allocation
cudaFree(d_in);
cudaFree(d_intermediate);
cudaFree(d_out);
// fprintf("\n\nRUNNING PROBLEM 1 PART B\n\n" );
cudaGetDeviceCount(&deviceCount);
if (deviceCount == 0) {
fprintf(q1b, "error: no devices supporting CUDA.\n");
exit(EXIT_FAILURE);
}
dev = 0;
cudaSetDevice(dev);
// cudaDeviceProp devProps;
if (cudaGetDeviceProperties(&devProps, dev) == 0) {
fprintf(q1b,"Using device %d:\n", dev);
fprintf(q1b,"%s; global mem: %dB; compute v%d.%d; clock: %d kHz\n",
devProps.name, (int) devProps.totalGlobalMem,
(int) devProps.major, (int) devProps.minor,
(int) devProps.clockRate);
}
int h2_out[ARRAY_SIZE];
fprintf(q1b,"array size is %d\n", ARRAY_SIZE);
// fprintf(q1b,"%s\n", "From Host:");
//
// for (int i = 0; i < ARRAY_SIZE; i++) {
// fprintf(q1b,"%d, ", (h_in[i] % 10));
// }
fprintf(q1b,"\n%s\n\n", "From Device:");
int *d2_in;
int *d2_out;
// allocate GPU memory
cudaMalloc((void **) &d2_in, ARRAY_BYTES);
cudaMalloc((void **) &d2_out, ARRAY_BYTES);
// transfer the array to the GPU
cudaMemcpy(d2_in, h_in, ARRAY_BYTES, cudaMemcpyHostToDevice);
// launch the kernel
int M = 256;
getlastdigit<<<(ARRAY_SIZE + M-1) / M,M>>>(d2_out, d2_in,ARRAY_SIZE );
// copy back the result array to the CPU
cudaMemcpy(h2_out, d2_out, ARRAY_BYTES, cudaMemcpyDeviceToHost);
// print out the resulting array
for (int i = 0; i < ARRAY_SIZE; i++) {
if (i< ARRAY_SIZE) {
fprintf(q1b,"%d, ", h2_out[i]);
}
}
cudaFree(d2_in);
cudaFree(d2_out);
fprintf(q1b,"\n");
return 0;
} |
6,797 | const char* dgemm_desc = "Dlaiton, best way dgemm.";
#define THREADSPERBLOCK 256
__global__
void dlaitonComeback(int n,float *A,float *B,float *C){
int tid=threadIdx.x+(blockDim.x*blockIdx.x);
if(tid>=n*n) return;
int i=tid%n;
int j=tid/n;
for(int k=0;k<n;k++){
C[i+j*n]+=A[i+k*n]*B[k+j*n];
}
}
__host__
void square_dgemm(int n, double* A, double* B, double* C){
float *d_A,*d_B,*d_C;
int n2=n*n;
int size=n*sizeof(float);
cudaMalloc((void**)&d_A,size);
cudaMalloc((void**)&d_B,size);
cudaMalloc((void**)&d_C,size);
cudaMemcpy(d_A,A,size,cudaMemcpyHostToDevice);
cudaMemcpy(d_B,B,size,cudaMemcpyHostToDevice);
cudaMemcpy(d_C,C,size,cudaMemcpyHostToDevice);
dlaitonComeback<<<(n2+THREADSPERBLOCK-1)/THREADSPERBLOCK,THREADSPERBLOCK>>>(n,d_A,d_B,d_C);
cudaMemcpy(C,d_C,size,cudaMemcpyDeviceToHost);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
} |
6,798 | #include "includes.h"
__device__ inline unsigned int RM_Index(unsigned int row, unsigned int col, unsigned int width) {
return (row * width + col);
}
__global__ void GaussianNBSumKernel(const float *d_data, const int *d_labels, float *feature_means_, int *class_count_, unsigned int n_samples_, unsigned int n_classes_, unsigned int n_features_) {
// Each thread will take care of one feature for all training samples
unsigned int tidx = threadIdx.x;
unsigned int feat_col = tidx + (blockIdx.x * blockDim.x);
unsigned int i = 0, row = 0;
if (feat_col < n_features_) { /* End condition check */
for (i = 0; i < n_samples_; ++i) { /* For each training sample */
row = d_labels[i];
// No race condition since each thread deals with one feature only
feature_means_[RM_Index(row, feat_col, n_features_)] +=
d_data[RM_Index(i, feat_col, n_features_)];
// WARNING: thread divergence :/
if (feat_col == 0) {
class_count_[row] += 1;
}
}
}
return;
} |
6,799 | /*
*To find sum of two large matrices
*Compute the speed up obtained by GPU
*/
#include <stdio.h>
const int ROW_SIZE = 300, COL_SIZE = 400;
const int MATRIX_BYTES = ROW_SIZE * COL_SIZE * sizeof(int);
//kernal
__global__ void sum(int d_sum[][ROW_SIZE], int d_a[][ROW_SIZE], int d_b[][ROW_SIZE]) {
d_sum[blockIdx.x][threadIdx.x] = d_a[blockIdx.x][threadIdx.x] + d_b[blockIdx.x][threadIdx.x];
}
//to check the final result
int checkSum(int h_a[][ROW_SIZE], int h_b[][ROW_SIZE], int h_sum[][ROW_SIZE]) {
int flag = 1;
for(int i = 0; i < COL_SIZE; i++) {
for(int j = 0; j < ROW_SIZE; j++) {
if(h_sum[i][j] != h_a[i][j] + h_b[i][j]) {
flag = 0;
break;
}
}
}
return flag;
}
int main() {
int h_a[COL_SIZE][ROW_SIZE], h_b[COL_SIZE][ROW_SIZE], h_sum[COL_SIZE][ROW_SIZE];
for(int i = 0; i < COL_SIZE; i++) {
for(int j = 0; j < ROW_SIZE; j++) {
h_a[i][j] = ((int)rand())%1000;
h_b[i][j] = ((int)rand())%1000;
}
}
int (*d_a)[ROW_SIZE], (*d_b)[ROW_SIZE], (*d_sum)[ROW_SIZE];
cudaMalloc((void**) &d_a, MATRIX_BYTES);
cudaMalloc((void**) &d_b, MATRIX_BYTES);
cudaMalloc((void**) &d_sum, MATRIX_BYTES);
cudaMemcpy(d_a, h_a, MATRIX_BYTES, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, h_b, MATRIX_BYTES, cudaMemcpyHostToDevice);
cudaEvent_t start, stop;
float time;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
sum<<<COL_SIZE, ROW_SIZE>>>(d_sum, d_a, d_b);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaMemcpy(h_sum, d_sum, MATRIX_BYTES, cudaMemcpyDeviceToHost);
if(checkSum(h_a, h_b, h_sum)) {
printf("The result is computed successfully!\n");
cudaEventElapsedTime(&time, start, stop);
printf("Computation time taken by device: %f\n", time);
cudaEvent_t start, stop;
float time;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
for(int i = 0; i < COL_SIZE; i++) {
for(int j = 0; j < ROW_SIZE; j++) {
h_sum[i][j] = h_a[i][j] + h_b[i][j];
}
}
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time, start, stop);
printf("Computation time taken by host: %f\n", time);
}
else {
printf("The result is not computed correctly!");
}
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_sum);
return 0;
}
|
6,800 | #include <stdio.h>
#include <time.h>
#include <stdlib.h>
#include <math.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#define m(data,y,x,dim) data[y*dim+x]
#define MAX_THREADS 1024
struct cuComplex {
float r;
float i;
__device__ cuComplex() : r(0), i(0) {}
__device__ cuComplex( float a) : r(a), i(0) {}
__device__ cuComplex( float a, float b ) : r(a), i(b) {}
float real(void){return r;}
float imag(void){return i;}
__device__ float magnitude2( void ) {
return r * r + i * i;
}
__device__ cuComplex operator*(const cuComplex& a) {
return cuComplex(r*a.r - i*a.i, i*a.r + r*a.i);
}
__device__ cuComplex operator+(const cuComplex& a) {
return cuComplex(r+a.r, i+a.i);
}
__device__ cuComplex operator-(const cuComplex& a) {
return cuComplex(r-a.r, i-a.i);
}
};
struct myComplex {
float r;
float i;
myComplex() : r(0), i(0) {}
myComplex( float a) : r(a), i(0) {}
myComplex( float a, float b ) : r(a), i(b) {}
float real(void){return r;}
float imag(void){return i;}
float magnitude2( void ) {
return r * r + i * i;
}
myComplex operator*(const myComplex& a) {
return myComplex(r*a.r - i*a.i, i*a.r + r*a.i);
}
myComplex operator+(const myComplex& a) {
return myComplex(r+a.r, i+a.i);
}
myComplex operator-(const myComplex& a) {
return myComplex(r-a.r, i-a.i);
}
myComplex operator/(const cuComplex& a) {
return myComplex(r-a.r, i-a.i);
}
};
// ===========================> Functions Prototype <===============================
void fill(float* data, int size);
float maxi(float in1, float in2);
void estimX(myComplex v, myComplex wvar, myComplex* umean, myComplex* uvar);
double calc_mse(float* data1, float* data2, int size);
double calc_mse(myComplex* data1, cuComplex* data2, int size);
void cpuMul_yx(float* a, float* b, myComplex* c, int di2, int di3, int y, int x);
void cpuMul(float* a, float* b, myComplex* c, int di1, int di2, int di3);
void cpuKernel(float* a1, float* b1, myComplex* c1, float* a2, float* b2, myComplex* c2, int di1, int di2, int di3);
__device__ float cuMulReal(float* a, float* b, int row, int column, int di2, int di3);
__device__ void cuEstimX(cuComplex* out1, float* out2, float s1, float s2);
__global__ void kernelFunc(float* a1d, float* b1d, cuComplex* c1d, float* a2d, float* b2d, cuComplex* c2d, int di1, int di2, int di3);
void gpuKernel(float* a1, float* b1, cuComplex* c1, float* a2, float* b2, cuComplex* c2, int di1, int di2, int di3);
// =================================================================================
int main(int argc, char** argv) {
struct cudaDeviceProp p;
cudaGetDeviceProperties(&p, 0);
printf("Device Name: %s\n", p.name);
printf("shared mem size: %d\n", p.regsPerBlock);
// get parameter from command line to build Matrix dimension
const int di1 = 200;
const int di2 = 64;
const int di3 = 450;
// allocate memory in CPU for calculation
float* a1;
float* b1;
myComplex* c1_serial;
cuComplex* c1;
float* a2;
float* b2;
myComplex* c2_serial;
cuComplex* c2;
a1 = (float*)malloc(di1*di2 * sizeof(float));
b1 = (float*)malloc(di2*di3 * sizeof(float));
c1_serial = (myComplex*)malloc(di1*di3 * sizeof(myComplex));
c1 = (cuComplex*)malloc(di1*di3 * sizeof(cuComplex));
a2 = (float*)malloc(di1*di2 * sizeof(float));
b2 = (float*)malloc(di2*di3 * sizeof(float));
c2_serial = (myComplex*)malloc(di1*di3 * sizeof(myComplex));
c2 = (cuComplex*)malloc(di1*di3 * sizeof(cuComplex));
// fill a, b matrices with random values between -16.0f and 16.0f
srand(0);
fill(a1, di1*di2);
fill(b1, di2*di3);
fill(a2, di1*di2);
fill(b2, di2*di3);
// time measurement for CPU calculation
clock_t t0 = clock();
cpuKernel (a1, b1, c1_serial, a2, b2, c2_serial, di1, di2, di3);
clock_t t1 = clock();
// time measurement for GPU calculation
clock_t t2 = clock();
gpuKernel (a1, b1, c1, a2, b2, c2, di1, di2, di3);
clock_t t3 = clock();
// check correctness of calculation
float mse;
mse = calc_mse( c1_serial, c1, di1*di3 ) + calc_mse( c2_serial, c2, di1*di3 );
printf("dim1=%d dim2=%d dim3=%d\t CPU=%06ld ms GPU=%06ld ms mse=%f\n",di1, di2, di3, (t1-t0)/1000, (t3-t2)/1000, mse);
// free allocated memory for later use
free(a1);
free(b1);
free(c1_serial);
free(c1);
free(a2);
free(b2);
free(c2_serial);
free(c2);
return 0;
}
//-----------------------------------------------------------------------------
void fill(float* data, int size) {
for (int i=0; i<size; ++i)
data[i] = (float) (rand() % 33 - 16);
}
double calc_mse (myComplex* data1, cuComplex* data2, int size) {
double mse = 0.0;
int i; for (i=0; i<size; i++) {
myComplex diff = (data1[i]/data2[i]);
float e = diff.magnitude2();
// printf("1r=%.4f, 1i=%.2f, 2r=%.4f, 2i=%.2f",data1[i].real(), data1[i].imag(), data2[i].real(), data2[i].imag());
// printf(" diffR=%.2f, diffI=%.2f, e=%f\r\n",diff.real(), diff.imag(),e);
mse += e;
// printf("i=%d, mse=%f ",i,mse);
}
// i=1839;
// printf("1r=%.4f, 1i=%.2f, 2r=%.4f, 2i=%.2f\r\n",data1[i].real(), data1[i].imag(), data2[i].real(), data2[i].imag());
mse = mse / size;
return mse;
}
double calc_mse (float* data1, float* data2, int size) {
double mse = 0.0;
int i; for (i=0; i<size; i++) {
double e = data1[i]-data2[i];
e = e * e;
mse += e;
}
mse = mse / size;
return mse;
}
//-----------------------------------------------------------------------------
void cpuMul_yx(float* a, float* b, myComplex* c, int di2, int di3, int y, int x) { // one element
m(c,y,x,di3)=0;
for(int k=0; k<di2; k++) {
m(c,y,x,di3) = m(c,y,x,di3) + myComplex(m(a,y,k,di2) * m(b,k,x,di3));
}
}
void cpuMul(float* a, float* b, myComplex* c, int di1, int di2, int di3) { // entire matrix
for(int y=0; y<di1; y++)
for(int x=0; x<di3; x++)
{
cpuMul_yx(a,b,c,di2,di3,y,x);
}
}
void cpuKernel(float* a1, float* b1, myComplex* c1, float* a2, float* b2, myComplex* c2, int di1, int di2, int di3) { // entire matrix
int i=0;
// myComplex umean;
// float uvar;
cpuMul(a1,b1,c1,di1,di2,di3);
cpuMul(a2,b2,c2,di1,di2,di3);
for(i=0; i<di1*di3; i++)
estimX(c1[i], c2[i], c1+i, c2+i);
// printf("umean=%f, uvar=%f", creal(umean), creal(uvar));
}
float maxi(float in1, float in2) {
if (in1 > in2)
return in1;
else
return in2;
}
void estimX(myComplex v, myComplex wvar, myComplex* umean, myComplex* uvar) {
float wvar_inv;
float logpxr1, logpxr2, logpxr3, logpxr4, max_log;
float pxr1, pxr2, pxr3, pxr4, sum_pxr;
float uvar1, uvar2, uvar3, uvar4;
myComplex x0_1(-0.7071, 0.7071);
myComplex x0_2(-0.7071, -0.7071);
myComplex x0_3(0.7071, 0.7071);
myComplex x0_4(0.7071, -0.7071);
wvar_inv = 1;///(wvar.r);//imag=0 ast
logpxr1 = wvar_inv*((v - x0_1).magnitude2());
logpxr2 = wvar_inv*((v - x0_2).magnitude2());
logpxr3 = wvar_inv*((v - x0_3).magnitude2());
logpxr4 = wvar_inv*((v - x0_4).magnitude2());
max_log = maxi(maxi(logpxr1,logpxr2),maxi(logpxr3,logpxr4));
logpxr1 = logpxr1 - max_log;
logpxr2 = logpxr2 - max_log;
logpxr3 = logpxr3 - max_log;
logpxr4 = logpxr4 - max_log;
pxr1 = exp(logpxr1);
pxr2 = exp(logpxr2);
pxr3 = exp(logpxr3);
pxr4 = exp(logpxr4);
sum_pxr = pxr1 + pxr2 + pxr3 + pxr4;
pxr1 = pxr1;///sum_pxr;
pxr2 = pxr2;///sum_pxr;
pxr3 = pxr3;///sum_pxr;
pxr4 = pxr4;///sum_pxr;
*umean = myComplex(pxr1,0)*x0_1 + myComplex(pxr2,0)*x0_2 + myComplex(pxr3,0)*x0_3 + myComplex(pxr4,0)*x0_4;
// printf("umeanR=%.3f, I=%.3f\r\n", (*umean-x0_1).r, (*umean-x0_1).i);
uvar1 = pxr1*((*umean-x0_1).magnitude2());
uvar2 = pxr2*((*umean-x0_2).magnitude2());
uvar3 = pxr3*((*umean-x0_3).magnitude2());
uvar4 = pxr4*((*umean-x0_4).magnitude2());
*uvar = myComplex(uvar1 + uvar2 + uvar3 + uvar4);
}
__device__ void cuEstimX(cuComplex* out1, float* out2, float s1, float s2){
float s2_inv;
cuComplex s1_cpx;
float logpxr1, logpxr2, logpxr3, logpxr4, max_log;
float pxr1, pxr2, pxr3, pxr4, sum_pxr;
float uvar1, uvar2, uvar3, uvar4;
cuComplex x0_1(-0.7071, 0.7071);
cuComplex x0_2(-0.7071, -0.7071);
cuComplex x0_3(0.7071 , 0.7071);
cuComplex x0_4(0.7071 , -0.7071);
s2_inv = 1;///s2;
s1_cpx.r = s1;
logpxr1 = s2_inv*((s1_cpx - x0_1).magnitude2());
logpxr2 = s2_inv*((s1_cpx - x0_2).magnitude2());
logpxr3 = s2_inv*((s1_cpx - x0_3).magnitude2());
logpxr4 = s2_inv*((s1_cpx - x0_4).magnitude2());
max_log = logpxr1;
if(logpxr2>max_log)
max_log=logpxr2;
if(logpxr3>max_log)
max_log=logpxr3;
if(logpxr4>max_log)
max_log=logpxr4;
logpxr1 = logpxr1 - max_log;
logpxr2 = logpxr2 - max_log;
logpxr3 = logpxr3 - max_log;
logpxr4 = logpxr4 - max_log;
pxr1 = exp(logpxr1);
pxr2 = exp(logpxr2);
pxr3 = exp(logpxr3);
pxr4 = exp(logpxr4);
sum_pxr = pxr1 + pxr2 + pxr3 + pxr4;
pxr1 = pxr1;//sum_pxr;
pxr2 = pxr2;//sum_pxr;
pxr3 = pxr3;//sum_pxr;
pxr4 = pxr4;//sum_pxr;
*out1 = (cuComplex(pxr1,0)*x0_1 + cuComplex(pxr2,0)*x0_2 + cuComplex(pxr3,0)*x0_3 + cuComplex(pxr4,0)*x0_4);
uvar1 = pxr1*((*out1-x0_1).magnitude2());
uvar2 = pxr2*((*out1-x0_2).magnitude2());
uvar3 = pxr3*((*out1-x0_3).magnitude2());
uvar4 = pxr4*((*out1-x0_4).magnitude2());
*out2 = (uvar1 + uvar2 + uvar3 + uvar4);
}
__device__ float cuMulReal(float* a, float* b, int row, int column, int di2, int di3){
int k;
float s = 0.0f;
for (k=0; k<di2; k++){
s += m(a,row,k,di2) * m(b,k,column,di3);
}
return s;
}
__device__ cuComplex cuMulCpx(cuComplex* a, cuComplex* b, int row, int column, int di2, int di3){
int k;
cuComplex s(0.0f, 0.0f);
for (k=0; k<di2; k++){
s = s + (m(a,row,k,di2) * m(b,k,column,di3));
}
return s;
}
__global__ void kernelFunc(float* a1d, float* b1d, cuComplex* c1d, float* a2d, float* b2d, cuComplex* c2d, int di1, int di2, int di3) {
int tx = threadIdx.x;
int by = blockIdx.y;
int bx = blockIdx.x;
int row, column;
float s1 = 0.0f;
float s2 = 0.0f;
cuComplex tmp(0);
cuComplex* out1=&tmp;
float tmp2=0.0f;
float* out2=&tmp2;
row = bx;
column = (by)*(blockDim.x)+tx;
s1 = cuMulReal(a1d, b1d, row, column, di2, di3);
s2 = cuMulReal(a2d, b2d, row, column, di2, di3);
cuEstimX(out1, out2, s1, s2);
m(c1d,row,column,di3) = cuComplex(*out1);
m(c2d,row,column,di3) = cuComplex(*out2);
}
//-----------------------------------------------------------------------------
void gpuKernel(float* a1, float* b1, cuComplex* c1, float* a2, float* b2, cuComplex* c2, int di1, int di2, int di3) {
// allocate memory on GPU
// copy data to GPU
// call kernelFunc
// copy the results back to CPU
// free GPU memory
float *a1d, *b1d;
float *a2d, *b2d;
cuComplex *c1d, *c2d;
cudaMalloc((void**)&a1d, di1*di2*sizeof(float));
cudaMalloc((void**)&b1d, di2*di3*sizeof(float));
cudaMalloc((void**)&c1d, di1*di3*sizeof(cuComplex));
cudaMalloc((void**)&a2d, di1*di2*sizeof(float));
cudaMalloc((void**)&b2d, di2*di3*sizeof(float));
cudaMalloc((void**)&c2d, di1*di3*sizeof(cuComplex));
cudaMemcpy(a1d, a1, di1*di2*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(b1d, b1, di2*di3*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(a2d, a2, di1*di2*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(b2d, b2, di2*di3*sizeof(float), cudaMemcpyHostToDevice);
//kernelFunc <<<dim3(n/MAX_THREADS,n/MAX_THREADS,MAX_THREADS),MAX_THREADS>>> (ad,bd,cd,n);
kernelFunc<<< dim3(di1,1,1), di3 >>>(a1d, b1d, c1d, a2d, b2d, c2d, di1, di2, di3);
cudaMemcpy(c1, c1d, di1*di3*sizeof(cuComplex), cudaMemcpyDeviceToHost);
cudaMemcpy(c2, c2d, di1*di3*sizeof(cuComplex), cudaMemcpyDeviceToHost);
cudaFree(a1d);
cudaFree(b1d);
cudaFree(c1d);
cudaFree(a2d);
cudaFree(b2d);
cudaFree(c2d);
} |
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