serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
6,301 | #include <stdio.h>
#include <stdlib.h>
#include <iostream>
#define KERNEL_LOOP 4096 // Used to exacerbate runtime affects
#define NUM_ELEMENTS 4096
#define MAX_THREADS_PER_BLOCK 1024
__constant__ unsigned int const_data_gpu[NUM_ELEMENTS];
static unsigned int const_data_host[NUM_ELEMENTS];
__device__ void manipulate_shared_data(unsigned int * const data,
unsigned int * const s_data,
const unsigned int tid)
{
// Load data into shared memory while doing an operation at the same time
unsigned int i = gridDim.x*(blockDim.x)/2;
if(tid < i){
s_data[tid] = data[tid] + data[tid+i];
}
__syncthreads();
// Add up data through reduction
// Divides the probelm in half, but half the threads are also idle.
// I know this isn't the fastest way through, but it's a start
// Then d
for (unsigned int s=gridDim.x*blockDim.x/4; s>32; s>>=1) {
if (tid < s) {
s_data[tid] += s_data[tid + s];
}
__syncthreads();
}
// Unwrap last loop
if (tid < 32) {
s_data[tid] += s_data[tid+32];
s_data[tid] += s_data[tid+16];
s_data[tid] += s_data[tid+8];
s_data[tid] += s_data[tid+4];
s_data[tid] += s_data[tid+2];
s_data[tid] += s_data[tid+1];
}
__syncthreads();
}
__device__ void manipulate_constant_data(unsigned int * const data,
const unsigned int tid)
{
// Load data into shared memory while doing an operation at the same time
unsigned int i = gridDim.x*(blockDim.x)/2;
if(tid < i){
data[tid] = const_data_gpu[tid] + const_data_gpu[tid+i];
}
__syncthreads();
// Add up data through reduction
// Divides the probelm in half, but half the threads are also idle.
// I know this isn't the fastest way through, but it's a start
// Then d
for (unsigned int s=gridDim.x*blockDim.x/4; s>32; s>>=1) {
if (tid < s) {
data[tid] += const_data_gpu[tid + s];
}
__syncthreads();
}
// Unwrap last loop
if (tid < 32) {
data[tid] += const_data_gpu[tid+32];
data[tid] += const_data_gpu[tid+16];
data[tid] += const_data_gpu[tid+8];
data[tid] += const_data_gpu[tid+4];
data[tid] += const_data_gpu[tid+2];
data[tid] += const_data_gpu[tid+1];
}
__syncthreads();
}
__device__ void copy_data(const unsigned int * const src,
unsigned int * const dst,
const unsigned int num_elements,
const unsigned int tid)
{
if(tid < num_elements) {
dst[tid] = src[tid];
}
__syncthreads();
}
__global__ void gpu_kernel(unsigned int * data, const unsigned int num_elements)
{
const unsigned int thread_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
__shared__ unsigned int shared[NUM_ELEMENTS];
manipulate_shared_data(data, shared, thread_idx);
copy_data(shared, data, num_elements, thread_idx);
}
__global__ void gpu_kernel_constants(unsigned int * data, const unsigned int num_elements)
{
const unsigned int thread_idx = (blockIdx.x * blockDim.x) + threadIdx.x;
manipulate_constant_data(data, thread_idx);
}
__host__ void generate_rand_data(unsigned int * host_data_ptr)
{
for(unsigned int i=0; i < NUM_ELEMENTS; i++)
{
host_data_ptr[i] = (unsigned int) rand();
}
}
__host__ void generate_sequetial_data(unsigned int * data) {
for(unsigned int i=0; i < NUM_ELEMENTS; i++)
{
data[i] = i;
}
}
__host__ void start_measure(cudaEvent_t * start, cudaEvent_t *stop){
cudaEventCreate(start,0);
cudaEventCreate(stop,0);
cudaEventRecord(*start, 0);
}
__host__ void stop_measure(cudaEvent_t* start, cudaEvent_t * stop, float &time) {
cudaEventRecord(*stop, 0);
cudaEventSynchronize(*stop);
cudaEventElapsedTime(&time, *start, *stop);
}
// Create display function to ensure output is the same for both kernels
__host__ void display_data(const unsigned int * const in_data,
const unsigned int * const out_data,
const unsigned int num_iter) {
for(int i = 0; i < num_iter; i ++){
printf("i=%i, input_data = %u, output_data = %u\n", i, in_data[i], out_data[i]);
}
}
int main ( int argc, char *argv[] )
{
const unsigned int num_elements = NUM_ELEMENTS;
const unsigned int num_threads = MAX_THREADS_PER_BLOCK;
const unsigned int num_blocks = num_elements/num_threads;
const unsigned int num_bytes = num_elements * sizeof(unsigned int);
const unsigned int kernel_loop = KERNEL_LOOP;
// Prep Host data
unsigned int * host_pinned;
unsigned int * host_pinned_final;
cudaMallocHost((void**)&host_pinned, num_bytes);
cudaMallocHost((void**)&host_pinned_final, num_bytes);
generate_rand_data(const_data_host);
generate_sequetial_data(host_pinned);
// Prep Device data
unsigned int * data_gpu;
cudaMalloc(&data_gpu, num_bytes);
// Define measurement
float time;
cudaEvent_t kernel_start, kernel_stop;
cudaEvent_t kernel_start1, kernel_stop1;
// Copy new data to be manipulated
cudaMemcpyToSymbol(const_data_gpu, const_data_host, num_bytes, 0, cudaMemcpyHostToDevice);
cudaThreadSynchronize();
start_measure(&kernel_start1, &kernel_stop1);
cudaMemcpy(data_gpu, host_pinned, num_bytes, cudaMemcpyHostToDevice);
// run gpu kernel
gpu_kernel_constants <<<num_blocks, num_threads>>>(data_gpu, num_elements);
/* Copy back the gpu results to the CPU */
cudaMemcpy(host_pinned_final, data_gpu, num_bytes, cudaMemcpyDeviceToHost);
stop_measure(&kernel_start1, &kernel_stop1, time);
std::cout << "Constant memory kernel took " << time << std::endl;
display_data(const_data_host, host_pinned_final, 5);
/* Allocate arrays on the GPU */
cudaThreadSynchronize();
start_measure(&kernel_start, &kernel_stop);
cudaMemcpy(data_gpu, host_pinned, num_bytes, cudaMemcpyHostToDevice);
// run gpu kernel
gpu_kernel <<<num_blocks, num_threads>>>(data_gpu, num_elements);
/* Copy back the gpu results to the CPU */
cudaMemcpy(host_pinned_final, data_gpu, num_bytes, cudaMemcpyDeviceToHost);
stop_measure(&kernel_start, &kernel_stop, time);
std::cout << "Shared memory kernel took " << time << std::endl;
display_data(host_pinned, host_pinned_final, 5);
/* Free the arrays on the GPU as now we're done with them */
cudaFree(data_gpu);
cudaFreeHost(host_pinned_final);
cudaFreeHost(host_pinned);
}
|
6,302 | #include <cstdlib>
#include <iostream>
#include <math.h>
#include <ctime>
#include <chrono>
#include <vector>
using namespace std;
#define N 4000
#define count N*N
#define threadsPerBlock 1000
#define numberBlocks N*N/threadsPerBlock
// __device__ int partition(double* input, int start, int end)
// {
// double pivot = input[end];
//
// while(start < end){
// while(input[start] < pivot)
// start++;
// while (input[end] > pivot)
// end--;
// if (input[start] == inpucout<<"time: "<<endTime<<" "<<startTime<<" "<<CLOCKS_PER_SEC<<endl;t[end])
// start++;
// else if(start < end){
// double tmp = input[start];
// input[start] = input[end];
// input[end] = tmp;
// }
// }
// return end;
// }
//
// __device__ double quickSelect(double* input, int p, int r, int k){
// if(p == r){
// return input[p];
// }
// int j = partition(input, p, r);
// int length = j - p + 1;
// if (length == k){
// return input[j];
// }cout<<"time: "<<endTime<<" "<<startTime<<" "<<CLOCKS_PER_SEC<<endl;
// else if( k < length ){
// return quickSelect(input, p, j - 1, k);
// }
// else{
// return quickSelect(input, j + 1, r, k - length);
// }
// }
__device__ void sort(double* input){
for(int i=0;i<5;i++){
for(int j=i;j<5;j++){
if(input[j]<input[i]){
double new_temp = input[i];
input[i] = input[j];
input[j] = new_temp;
}
}
}
}
__global__ void median (double *a, double *b) {
int number = blockIdx.x*blockDim.x + threadIdx.x;
// if((number <N) || (number>=N*N-N)||(number/N==0)||(number/N==N-1)){
// b[number]=a[number];
// }
// if((number > N-1) && (threadIdx.x > 0) && (threadIdx.x < N-1) && (number < N*N-N)){
if((number > N-1) && (number%N > 0) && (number%N < N-1) && (number < N*N-N)){
double tempCompare[5];
tempCompare[0] = a[number];
tempCompare[1] = a[number-1];
tempCompare[2] = a[number+1];
tempCompare[3] = a[number-N];
tempCompare[4] = a[number+N];
// b[number] = quickSelect(tempCompare,0,4,2);
// a[number] = tempCompare[2];
sort(tempCompare);
b[number]=tempCompare[2];
}
else if(number < N*N){
b[number]=a[number];
}
__syncthreads();
}
__global__ void move (double *b, double *a) {
int number = blockIdx.x*blockDim.x + threadIdx.x;
a[number] = b[number];
}
__global__ void reduction (double *in, double *out) {
__shared__ double temp[threadsPerBlock];
int id = threadIdx.x;
temp[id] = in[blockIdx.x*blockDim.x + id];
__syncthreads();
if(id<500 && id>11){
temp[id] += temp[id+500]; __syncthreads();
}
__syncthreads();
if(id<256){
temp[id] += temp[id+256]; __syncthreads();
}
if(id<128){
temp[id] += temp[id+128]; __syncthreads();
}
if(id<64){
temp[id] += temp[id+64]; __syncthreads();
}
if(id<32){
temp[id] += temp[id+32]; __syncthreads();
}
if(id<16){
temp[id] += temp[id+16]; __syncthreads();
}
if(id<8){
temp[id] += temp[id+8]; __syncthreads();
}
if(id<4){
temp[id] += temp[id+4]; __syncthreads();
}
if(id<2){
temp[id] += temp[id+2]; __syncthreads();
}
if(id<1){
temp[id] += temp[id+1]; __syncthreads();
}
if(id<1){out[blockIdx.x] = temp[id];}
}
__global__ void sumGen (double *in, double *out) {
for(int i=0;i<(N/threadsPerBlock)*(N/threadsPerBlock);i++){
out[0]+=in[i];
}
}
__global__ void assign (double *a, double *spe) {
spe[0] = a[count/2+N/2];
spe[1] = a[17*N+31];
}
int main(){
double A[count], B[count];
double sum[1], speNum[2];
double *d_a, *d_b, *d_partSum, *d_ppartSum, *d_sum, *d_speNum;
int size = N*N*sizeof(double);
int twosize = 2*sizeof(double);
sum[0]=0;
for(int i=0;i<N;i++){
for(int j=0;j<N;j++){
A[i*N+j] = sin(i*i+j)*sin(i*i+j)+cos(i-j);
// A[i*N+j] = j;
B[i*N+j] = 0;
}
}
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_partSum, size/threadsPerBlock);
cudaMalloc((void **)&d_ppartSum, size/threadsPerBlock/threadsPerBlock);
cudaMalloc((void **)&d_sum, sizeof(double));
cudaMalloc((void **)&d_speNum,twosize);
cudaMemcpy(d_a, A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_sum, sum, sizeof(double), cudaMemcpyHostToDevice);
// clock_t startaaa = clock();
// cudaEvent_t startTime=0, endTime=0;
// cudaEventCreate(&startTime);
// cudaEventCreate(&endTime);
auto start = std::chrono::system_clock::now();
// cudaEventRecord(startTime, 0);
for(int i=0;i<10;i++){
median<<<numberBlocks,threadsPerBlock>>>(d_a,d_b);
cudaDeviceSynchronize();
move<<<numberBlocks,threadsPerBlock>>>(d_b,d_a);
cudaDeviceSynchronize();
}
reduction<<<count/threadsPerBlock, threadsPerBlock>>>(d_a,d_partSum);
reduction<<<(count/threadsPerBlock/threadsPerBlock),threadsPerBlock>>>(d_partSum,d_ppartSum);
sumGen<<<1,1>>>(d_ppartSum,d_sum);
assign<<<1,1>>>(d_a, d_speNum);
cudaDeviceSynchronize();
// clock_t endbbb = clock();
// cudaEventRecord(endTime, 0);
// cudaEventSynchronize(endTime) ;
// float time;
// cudaEventElapsedTime(&time,startTime,endTime);
auto end = std::chrono::system_clock::now();
std::chrono::duration<double> elapsed_seconds = end-start;
cudaMemcpy(sum, d_sum, sizeof(double), cudaMemcpyDeviceToHost);
cudaMemcpy(speNum, d_speNum, twosize, cudaMemcpyDeviceToHost);
cudaMemcpy(B, d_a, size, cudaMemcpyDeviceToHost);
cudaFree(d_a);cudaFree(d_b);cudaFree(d_partSum);cudaFree(d_ppartSum);cudaFree(d_sum);cudaFree(d_speNum);
cout.precision(8);
// cout<<"time: "<<endbbb<<" "<<startaaa<<" "<<CLOCKS_PER_SEC<<endl;
// cout<<"time: "<<(endTime-startTime)/CLOCKS_PER_SEC<<endl;
// cout<<"time: "<<time<<endl;
// cout<<"time: "<<elapsed_seconds<<endl;
printf("time :%f\n",elapsed_seconds);
cout<<"Sum: "<<sum[0]<<endl;
cout<<"A[n/2][n/2]: "<<speNum[0]<<" "<<A[count/2+N/2]<<" "<<B[count/2+N/2]<<endl;
cout<<"A[17][31]: "<<speNum[1]<<" "<<A[17*N+31]<<" "<<B[17*N+31]<<endl;
cout<<"A[999][999]: "<<A[999*N+999]<<" "<<B[999*N+999]<<endl;
cout<<"A[999][500]: "<<A[999*N+500]<<" "<<B[999*N+500]<<endl;
cout<<"A[500][999]: "<<A[500*N+999]<<" "<<B[500*N+999]<<endl;
cout<<"A[500][0]: "<<A[500*N]<<" "<<B[500*N]<<endl;
cout<<"A[501][0]: "<<A[501*N]<<" "<<B[501*N]<<endl;
return 0;
}
|
6,303 | #include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <iostream>
#include <chrono>
int main() {
int N = 2518;
thrust::host_vector<double> host(N);
double tmp;
for(int i = 0; i < N; i++){
std::cin >> tmp;
host[i] = tmp;
}
/* na linha abaixo os dados são copiados
para GPU */
auto start_time = std::chrono::high_resolution_clock::now();
thrust::device_vector<double> dev(host);
auto end_time = std::chrono::high_resolution_clock::now();
auto runtime = std::chrono::duration_cast<std::chrono::milliseconds>(end_time - start_time);
std::cerr << "Alocação e cópia para GPU: " << runtime.count() << "ms\n";
double mediaAnual = thrust::reduce(dev.end() -365, dev.end(), 0.0, thrust::plus<double>()) /N;
std::cout << "Media Anual: " << mediaAnual << "\n";
}
|
6,304 |
#include <stdio.h>
#include <cuda.h>
// Kernel that executes on the CUDA device
__global__ void temp_calc(float *a, float *b, int N, int edge) // a: Source array, b: Target array, N: Total size, edge: Length of edge
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Calculates the row and column number
int row = idx / (edge+1);
int col = idx - row * (edge+1);
if (idx<N) {
if(row>0 && row<edge && col>0 && col<edge) // Not on the edges
b[row*(edge+1)+col]=(a[(row-1)*(edge+1)+col]+a[(row+1)*(edge+1)+col]+a[row*(edge+1)+col-1]+a[row*(edge+1)+col+1])/4.0;
}
}
// main routine that executes on the host
int main(void)
{
//clock_t start = clock();
float *a_h, *a_d, *b_d; // Pointer to host & device arrays
int edge = 1000; // Can be changed
const int N = (edge+1) * (edge+1); // Number of elements in arrays
size_t size = N * sizeof(float);
a_h = (float *)malloc(size); // Allocate array on host
cudaMalloc((void **) &a_d, size); // Allocate array a on device
cudaMalloc((void **) &b_d, size); // Allocate array b on device
// Initialize host array
for (int i=0; i<=edge; i++) {
for (int j=0; j<=edge; j++){
if(i==0){
if(j>=10 && j<=30){
a_h[i*(edge+1)+j]=150.0;
}
else{
a_h[i*(edge+1)+j]=80.0;
}
}
else{
if(i==edge || j==0 || j==edge){
a_h[i*(edge+1)+j]=80.0;
}
else{
a_h[i*(edge+1)+j]=0.0;
}
}
}
}
// Initialize block size and block number
int block_size = 256;
int n_blocks = N/block_size + (N%block_size == 0 ? 0:1);
// Iteration
int iter = 500; // Can be changed
for (int i=0;i<iter;i++){
cudaMemcpy(a_d, a_h, size, cudaMemcpyHostToDevice); // Copy the host array to the CUDA
cudaMemcpy(b_d, a_h, size, cudaMemcpyHostToDevice);
temp_calc <<< n_blocks, block_size >>> (a_d, b_d, N, edge); // Calculate the values on CUDA
cudaMemcpy(a_h, b_d, sizeof(float)*N, cudaMemcpyDeviceToHost); // Retrieve result from device and store it in host array
}
/*
// Print results
for (int i=0; i<=edge; i++) {
for (int j=0; j<=edge; j++)
printf("%f ", a_h[i*(edge+1)+j]);
printf("\n");
}
clock_t end = (clock() - start)/1000;
printf("time: %ldms\n", end);
*/
// Cleanup
free(a_h); cudaFree(a_d);
} |
6,305 | // CUDA_Device_Properties.cpp : Definiert den Einstiegspunkt fr die Konsolenanwendung.
/*
* Description:
* Acquire info about CUDA devices on system.
*
* Author: P Stegmann
* Date: 2014-10-22
*/
//
#include <stdio.h>
#include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
#include "device_launch_parameters.h"
int main()
{
// Get number of devices
int nDevices = 0;
cudaError_t errorcode = cudaSuccess;
errorcode = cudaGetDeviceCount(&nDevices);
std::cout << "Anzahl der CUDA GPUs: " << nDevices << std::endl;
std::cout << cudaGetErrorString(errorcode) << std::endl;
// Loop over all devices
for(int ii = 0; ii < nDevices; ii++)
{
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, ii);
printf("Device number: %d\n", ii);
printf(" Device name: %s\n", prop.name);
printf(" Minor Compute capability: %d\n", prop.minor);
printf(" Major compute capability: %d\n", prop.major);
printf(" Memory Clock Rate (KHz): %d\n", prop.memoryBusWidth);
printf(" Peak Memory Bandwidth (GB/s) : %f\n",
2.0*prop.memoryClockRate*(prop.memoryBusWidth/8)/1.0e6);
printf(" Maximum threads per block: %d\n", prop.maxThreadsPerBlock);
printf(" Size of a warp: %d\n", prop.warpSize);
printf(" Available shared memory: %d\n", prop.sharedMemPerBlock);
printf(" Maximum global memory: %d\n", prop.totalGlobalMem);
}
return 0;
}
|
6,306 | #include <iostream>
#include <fstream>
#include <cstdlib>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
int main(void)
{
using namespace std;
int DIM = 1 << 24;
thrust::host_vector<double> x_h(DIM);
cout << "CUDA-C (Thrust) version\n";
cout << "Filling vector with random doubles (on host).\n";
for(int i=0; i<DIM; i++)
{
x_h[i] = (double) (rand()) / (double) (RAND_MAX);
}
cout << "Done.\n";
cout << "Allocate device vector and copy array to device.\n";
thrust::device_vector<double> x_d = x_h;
cout << "Sorting random array with " << DIM << " double elements using std::sort..."<<"\n";
thrust::sort(x_d.begin(), x_d.end());
cout << "Done.\n";
cout << "Copy array to host.\n";
thrust::copy(x_d.begin(), x_d.end(), x_h.begin());
cout << "Done.\n";
}
|
6,307 | #include <time.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
__global__ void GPuEuler(float *y, float t_0, float y_0 ,int N, float delta) {
int myID = threadIdx.x + blockDim.x * blockIdx.x;
if (myID <N) {
y[myID] = y_0;
for(int i=0;i<myID;i++){
float j_del = delta * i+ t_0;
y[myID] =y[myID]+ delta * (9*j_del*j_del-4*j_del+5);
}
}
}
int main(int argc, char const *argv[])
{
printf("seccion 1.b\n");
int hilos1b = 256,n1b,bloque1b;
float delta_t1b,tiempoGPU1b;
float *dev_e1b,*hst_y;
cudaEvent_t start1b, end1b;
for(int i=1;i<5;i++) {
delta_t1b=powf(10,-i);
n1b=10/delta_t1b +1;
hst_y = (float*) malloc(n1b*sizeof(float));
bloque1b = ceil((float) n1b /hilos1b);
cudaEventCreate(&start1b);
cudaEventCreate(&end1b);
cudaEventRecord(start1b,0);
cudaMalloc( (void**) &dev_e1b, n1b*sizeof(float));
GPuEuler<<<bloque1b,hilos1b>>>(dev_e1b,0,4,n1b,delta_t1b);
cudaEventRecord(end1b,0);
cudaEventSynchronize(end1b);
cudaEventElapsedTime(&tiempoGPU1b,start1b,end1b);
cudaMemcpy(hst_y,dev_e1b,n1b*sizeof(float),cudaMemcpyDeviceToHost);
cudaFree(dev_e1b);
free(hst_y);
cudaEventDestroy(start1b);
cudaEventDestroy(end1b);
printf("%f\n",tiempoGPU1b);
}
return 0;
} |
6,308 | #include "hashtable.cuh"
template <typename T>
__global__ void init_hash_kernel(hashbucket<T>* d_hashtable, const int hash_table_rows)
{
int row = threadIdx.x + blockDim.x * blockIdx.x;
if (row < hash_table_rows) {
d_hashtable[row].key_row = EMPTYMARKER;
d_hashtable[row].max = 0;
d_hashtable[row].min = 0;
d_hashtable[row].sum = 0;
d_hashtable[row].count = 0;
}
}
template <typename T>
__host__ void init_hash_table(hashbucket<T>* d_hashtable, const int hash_table_rows)
{
//launch kernel
dim3 dimBlock(BLOCK_SIZE, 1, 1);
dim3 dimGrid((hash_table_rows + BLOCK_SIZE - 1) / BLOCK_SIZE, 1, 1);
init_hash_kernel<T><<<dimGrid, dimBlock>>>(d_hashtable, hash_table_rows);
cudaDeviceSynchronize();
return;
}
|
6,309 | #include <stdlib.h>
#include <math.h>
__global__ void VecEval(float *C)
{
int i = threadIdx.x;
int j;
for (j=0; j<=100; j++)
C[i] = 1./sqrt(1.+i) + j*sin(2.*sin(0.1*i));
}
int main()
{ const int N=1000000;
float C[N];
// Kernel invocation with N threads
VecEval<<<1, N>>>(C);
}
|
6,310 | #include <cuda.h>
#include <time.h>
#include <stdio.h>
__global__ void vecAddKernel(float* A, float* B, float* C, int n){
int i = blockDim.x*blockIdx.x + threadIdx.x;
if(i<n){
C[i] = A[i] + B[i];
}
}
void vecAdd(float* A, float* B, float* C, int n)
{
int size = n * sizeof(float);
static float *d_A, *d_B, *d_C;
cudaMalloc((void **) &d_A, size); // allocate d_A
cudaMemcpy(d_A, A, size, cudaMemcpyHostToDevice); // copy A to GPU
cudaMalloc((void **) &d_B, size); // allocate d_B
cudaMemcpy(d_B, B, size, cudaMemcpyHostToDevice); // copy B to GPU
cudaMalloc((void **) &d_C, size); // allocate d_C
vecAddKernel<<<ceil(n/256.0), 256>>>(d_A, d_B, d_C, n); // launch kernel
cudaMemcpy(C, d_C, size, cudaMemcpyDeviceToHost); // copy d_C to host
// print result
printf("\nA: ");
for (int i = 0; i < 5; i++) {
printf("%.2f ", A[i]);
}
printf("\nB: ");
for (int i = 0; i < 5; i++) {
printf("%.2f ", B[i]);
}
printf("\n-------------------------------------");
printf("\nC: ");
for (int i = 0; i < 5; i++) {
printf("%.2f ", C[i]);
}
printf("\n-------------------------------------\n");
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
int main() {
int N = 5;
static float h_A[5];
static float h_B[5];
static float h_C[5];
for (int i = 0; i < N; i++) {
h_A[i] = i%2;
h_B[i] = i%3;
}
vecAdd(h_A, h_B, h_C, N);
} |
6,311 | #include <iostream>
#include <algorithm>
#include <list>
#include <vector>
#include <iterator>
#include <functional>
#include <time.h>
#include <chrono>
#include <cstdlib>
using namespace std;
struct triple
{
long long int set; //Set denotes which Elements are in the Subset
double w; //Weight of the Triple
double p; //Profit of the Triple
//Struct Constructor
triple() : set(0),
w(0.0),
p(0.0)
{}
//Comparison Operator Overloadings
bool operator< (const triple &t) const
{ return (w < t.w); }
bool operator> (const triple &t) const
{ return (w > t.w); }
};
void merge_lists(vector<triple> &A, vector<triple> &B,
vector< pair<double, double> > &V)
{
vector<triple> T_p, Tcopy;
triple t;
long long int v1s = V.size() >> 1, v2s = V.size() - v1s;
//Initialisation for A
t.set = 0, t.w = t.p = 0;
A.push_back(t);
//Sort A in Non-Increasing Order
for (long long int i = 0; i < v1s; ++i)
{
T_p.clear();
Tcopy.clear();
//Add Elements to Subset (Triple) ti
//Add ti to T_p
for (long long int j = 0; j < (long long int)A.size(); ++j)
{
t.set = A[j].set + (1 << i);
t.w = A[j].w + V[i].first;
t.p = A[j].p + V[i].second;
T_p.push_back(t);
}
//Merge A, T_p
merge(A.begin(), A.end(), T_p.begin(), T_p.end(), back_inserter(Tcopy));
A = Tcopy;
}
//Initialisation for B
t.set = 0, t.w = t.p = 0;
B.push_back(t);
//Sort B in Non-Increasing Order
for (long long int i = 0; i < v2s; ++i)
{
T_p.clear();
Tcopy.clear();
//Add Elements to Subset (Triple) ti
//Add ti to T_p
for (long long int j = 0; j < (long long int)B.size(); ++j)
{
t.set = B[j].set + (1 << i);
t.w = B[j].w + V[i + v1s].first;
t.p = B[j].p + V[i + v1s].second;
T_p.push_back(t);
}
//Merge B, T_p
merge(B.begin(), B.end(), T_p.begin(), T_p.end(), back_inserter(Tcopy), greater<>());
B = Tcopy;
}
}
void maxScan(vector<triple> &B, vector< pair<double, long long int> > &maxB)
{
long long int Bsize = B.size();
maxB[Bsize - 1].first = B[Bsize - 1].p;
maxB[Bsize - 1].second = Bsize - 1;
for (long long int i = Bsize - 2; i >= 0; i--)
{
if (B[i].p>maxB[i + 1].first)
{
maxB[i].first = B[i].p;
maxB[i].second = i;
}
else
{
maxB[i].first = maxB[i + 1].first;
maxB[i].second = maxB[i + 1].second;
}
}
}
long long int generate_sets(vector<triple> &A, vector<triple> &B, const double &c,
vector< pair<double, long long int> > &maxB, long long int N)
{
double bestValue = 0;
pair<long long int, long long int> bestSet;
long long int i = 0, j = 0;
while(i < N && j < N)
{
if (A[i].w + B[j].w > c)
{
++j;
if (j == N) break;
else continue;
}
if (A[i].p + maxB[j].first > bestValue)
{
bestValue = A[i].p + maxB[j].first;
bestSet = make_pair(A[i].set, maxB[j].second);
}
++i;
}
return bestValue;
}
long long int dp_knapSack(long long int W, double wt[], double val[], long long int n)
{
long long int i, w;
vector< vector<double> > K(n + 1, vector<double>(W + 1));
// Build table K[][] in bottom up manner
for (i = 0; i <= n; i++)
{
for (w = 0; w <= W; w++)
{
if (i == 0 || w == 0)
K[i][w] = 0;
else if (wt[i - 1] <= w)
K[i][w] = max(val[i - 1] + K[i - 1][w - wt[i - 1]], K[i - 1][w]);
else
K[i][w] = K[i - 1][w];
}
}
return K[n][W];
}
int main()
{
vector<triple> A, B;
//Input Data
double c = 0;
vector< pair<double, double> > V;
vector<double> wt_arr, p_arr;
srand(time(0));
//Input Data
for (long long int i = 0; i < 40; ++i)
{
long long int wt = (rand() % (int)1e7) + 1;
long long int p = (rand() % (int)1e7) + 1;
c += wt;
V.push_back(make_pair((double)wt, (double)p));
//wt_arr.push_back(wt);
//p_arr.push_back(p);
}
c /= 2;
//long long int dpSoln = dp_knapSack(c, &wt_arr[0], &p_arr[0], V.size());
//Computation & Timing
auto start = chrono::steady_clock::now();
merge_lists(A, B, V);
vector< pair<double, long long int> > maxB((long long int)B.size());
maxScan(B, maxB);
generate_sets(A, B, c, maxB, (1 << (long long int)(V.size() >> 1)));
auto stop = chrono::steady_clock::now();
cout << "Computational Time : ";
cout << (double)(chrono::duration_cast<chrono::nanoseconds>(stop - start).count()) / 1000000.0;
cout << " ms" << endl;
cin.get();
return 0;
} |
6,312 | #define EIGEN_USE_GPU
#include <cuda.h>
#include <stdio.h>
__global__ void AddOneKernel(const int* in, const int N, int* out) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N;
i += blockDim.x * gridDim.x) {
out[i] = in[i] + 1;
}
}
void AddOneKernelLauncher(const int* in, const int N, int* out) {
AddOneKernel<<<32, 256>>>(in, N, out);
cudaError_t cudaerr = cudaDeviceSynchronize();
if (cudaerr != cudaSuccess)
printf("kernel launch failed with error \"%s\".\n", cudaGetErrorString(cudaerr));
} |
6,313 | //
// Created by Peter Rigole on 2019-04-24.
//
#ifndef AXONBITS_TEST_H
#define AXONBITS_TEST_H
#include <cuda_runtime.h>
#include <string>
#include <assert.h>
#include <stdexcept>
/**
* Test base class.
* Mind that classes with virtual functions can't have a header file.
*/
class Test {
public:
Test() {}
Test(const char* nameInit) : name(nameInit) {}
~Test() {}
__host__
virtual void test() {}
__host__
void checkCudaErrors() {
cudaDeviceSynchronize();
cudaError_t cudaError;
cudaError = cudaGetLastError();
if(cudaError != cudaSuccess) {
printf("%s device failure, cudaGetLastError() returned %d: %s\n", getName(), cudaError, cudaGetErrorString(cudaError));
throw std::runtime_error ("CUDA Error");
} else {
printf("%s device test successful\n", getName());
}
}
__host__
const char* getName() { return name; }
private:
const char* name;
};
#endif //AXONBITS_TEST_H
|
6,314 | #include <thrust/sequence.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
int main(void)
{
int N = 20;
// --- Filter parameters
double alpha = 2.7;
double beta = -0.3;
// --- Defining and initializing the input vector on the device
thrust::device_vector<double> d_input(N,alpha * 1.);
d_input[0] = d_input[0]/alpha;
// --- Defining the output vector on the device
thrust::device_vector<double> d_output(d_input);
// --- Defining the {1/beta^n} sequence
thrust::device_vector<double> d_1_over_beta(N,1./beta);
thrust::device_vector<double> d_1_over_beta_to_the_n(N,1./beta);
thrust::device_vector<double> d_n(N);
thrust::sequence(d_n.begin(), d_n.end());
thrust::inclusive_scan(d_1_over_beta.begin(), d_1_over_beta.end(), d_1_over_beta_to_the_n.begin(), thrust::multiplies<double>());
thrust::transform(d_1_over_beta_to_the_n.begin(), d_1_over_beta_to_the_n.end(), d_input.begin(), d_input.begin(), thrust::multiplies<double>());
thrust::inclusive_scan(d_input.begin(), d_input.end(), d_output.begin(), thrust::plus<double>());
thrust::transform(d_output.begin(), d_output.end(), d_1_over_beta_to_the_n.begin(), d_output.begin(), thrust::divides<double>());
for (int i=0; i<N; i++) {
double val = d_output[i];
printf("Device vector element number %i equal to %f\n",i,val);
}
// --- Defining and initializing the input vector on the host
thrust::host_vector<double> h_input(N,1.);
// --- Defining the output vector on the host
thrust::host_vector<double> h_output(h_input);
h_output[0] = h_input[0];
for(int i=1; i<N; i++)
{
h_output[i] = h_input[i] * alpha + beta * h_output[i-1];
}
for (int i=0; i<N; i++) {
double val = h_output[i];
printf("Host vector element number %i equal to %f\n",i,val);
}
for (int i=0; i<N; i++) {
double val = h_output[i] - d_output[i];
printf("Difference between host and device vector element number %i equal to %f\n",i,val);
}
getchar();
}
|
6,315 | #include <stdio.h>
#include <assert.h>
#include <cuda.h>
// Notice: this function could only be used to detect Cuda function whose return value type is not void.
// we could use cudaGetLastError() to detect such cuda function (ex, self_define kernel function.)
cudaError_t checkCuda(cudaError_t result){
if(result != cudaSuccess){
fprintf(stderr, "CUDA Runtime Error: %s\n", cudaGetErrorString(result));
assert(result == cudaSuccess);
}
return result;
}
cudaError_t checkLastCuda(){
cudaError_t result = cudaGetLastError(); // return the error from above
if(result != cudaSuccess){
fprintf(stderr, "CUDA Runtime Error: %s\n", cudaGetErrorString(result));
assert(result == cudaSuccess);
}
return result;
} |
6,316 | #include <cuComplex.h>
__global__ void get_Gj(double *Fj, double *ph, cuDoubleComplex *Gj)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
double s, c;
sincos(ph[idx], &s, &c);
Gj[idx].x = Fj[idx] * c;
Gj[idx].y = Fj[idx] * s;
}
__global__ void mult_complex_1to1(cuDoubleComplex *A, cuDoubleComplex *B, cuDoubleComplex *C)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * blockDim.x * gridDim.x;
C[idx] = cuCmul(A[idx], B[idx]);
}
__global__ void mult_complex_1ton(cuDoubleComplex *A, cuDoubleComplex *B, cuDoubleComplex *C)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int idx = tid + blockIdx.y * blockDim.x * gridDim.x;
C[idx] = cuCmul(A[tid], B[idx]);
}
__global__ void get_E_Gwkj(double *Fk, cuDoubleComplex *Gkj, double *E, cuDoubleComplex *Gwkj)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x + blockIdx.y * blockDim.x * gridDim.x;
double absGkj = cuCabs(Gkj[idx]);
E[idx] = (Fk[idx] - absGkj) * (Fk[idx] - absGkj);
cuDoubleComplex expGkj = cuCdiv(Gkj[idx], make_cuDoubleComplex(absGkj, 0));
Gwkj[idx] = cuCsub(cuCmul(make_cuDoubleComplex(Fk[idx], 0), expGkj), Gkj[idx]);
}
__global__ void sum_E(int num, double *E)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
double Esum = E[idx];
for(int s = num-1; s > 0; s--)
Esum += E[idx + s * blockDim.x * gridDim.x];
E[idx] = Esum;
}
__global__ void sum_Etot(double *E, double *Ered)
{
extern __shared__ double share[];
int tid = threadIdx.x;
int idx = tid + blockIdx.x * blockDim.x * 2;
share[tid] = E[idx] + E[idx+blockDim.x];
__syncthreads();
for(unsigned int s = blockDim.x/2; s > 0; s >>= 1)
{
if(tid < s)
{
share[tid] += share[tid+s];
}
__syncthreads();
}
if(tid == 0)
Ered[blockIdx.x] = share[0];
}
__global__ void get_dE(int num, cuDoubleComplex *Gj, cuDoubleComplex *Gwjk, double *dE)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
double reGwjk = Gwjk[idx].x;
double imGwjk = -Gwjk[idx].y;
for(int s = num-1; s > 0; s--)
{
reGwjk += Gwjk[idx + s * blockDim.x * gridDim.x].x;
imGwjk -= Gwjk[idx + s * blockDim.x * gridDim.x].y;
}
dE[idx] = (Gj[idx].x * imGwjk + Gj[idx].y * reGwjk) * 2;
}
__global__ void mult_T(int N, double z, double wl, double *r2, cuDoubleComplex *U)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
double s, c;
cuDoubleComplex u = U[idx];
double ph = z * sqrt(1 / wl / wl - r2[idx] / N / N) * 2;
sincospi(ph, &s, &c);
U[idx].x = u.x * c - u.y * s;
U[idx].y = u.x * s + u.y * c;
}
__global__ void mult_ph12(int N, double z, double wl, double *r2, cuDoubleComplex *U)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
double s, c;
cuDoubleComplex u = U[idx];
double ph = 2 * z / wl + r2[idx] / wl / z;
sincospi(ph, &s, &c);
U[idx].x = (u.x * s + u.y * c) / N;
U[idx].y = (-u.x * c + u.y * s) / N;
}
__global__ void mult_ph1(int N, double z, double wl, cuDoubleComplex *U)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
double s, c;
cuDoubleComplex u = U[idx];
double ph = 2 * z / wl;
sincospi(ph, &s, &c);
U[idx].x = -(u.x * s + u.y * c) * N;
U[idx].y = (u.x * c - u.y * s) * N;
}
__global__ void mult_ph2(int N, double z, double wl, double *r2, cuDoubleComplex *U)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
double s, c;
cuDoubleComplex u = U[idx];
double ph = r2[idx] / wl / z;
sincospi(ph, &s, &c);
U[idx].x = u.x * c - u.y * s;
U[idx].y = u.x * s + u.y * c;
}
|
6,317 | /**
* File: fw-cuda.cu
*
* Floyd Warshall algorithm CUDA implementation
*
* @author Mateusz Bojanowski
* @source https://github.com/MTB90/CUDA_Blocked_Floyd-Warshall
*/
#include<stdio.h>
#include<stdlib.h>
#include<unistd.h>
// CUDA Headers
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
// Helper definition
#define VAR(v, i) __typeof(i) v=(i)
#define FOR(i, j, k) for (int i = (j); i <= (k); ++i)
#define FORD(i, j, k)for (int i=(j); i >= (k); --i)
#define FORE(i, c) for(VAR(i, (c).begin()); i != (c).end(); ++i)
#define REP(i, n) for(int i = 0;i <(n); ++i)
// CONSTS
#define INF 1061109567 // 3F 3F 3F 3F
#define CHARINF 63 // 3F
#define CHARBIT 8
#define NONE -1
#define CMCPYHTD cudaMemcpyHostToDevice
#define CMCPYDTH cudaMemcpyDeviceToHost
// CONSTS for compute capability 2.0
#define BLOCK_WIDTH 16
#define WARP 32
bool gPrint = false; // print graph d or not
bool gDebug = false; // print more deatails to debug
/** Cuda handle error, if err is not success print error and line in code
*
* @param status CUDA Error types
*/
#define HANDLE_ERROR(err) \
{ \
if (err != cudaSuccess) \
{ \
fprintf(stderr, "%s failed at line %d \nError message: %s \n", \
__FILE__, __LINE__ ,cudaGetErrorString(err)); \
exit(EXIT_FAILURE); \
} \
}
/**Kernel for wake gpu
*
* @param reps dummy variable only to perform some action
*/
__global__ void wake_gpu_kernel(int reps)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= reps) return;
}
/**Kernel for parallel Floyd Warshall algorithm on gpu
*
* @param u number vertex of which is performed relaxation paths [v1, v2]
* @param n number of vertices in the graph G:=(V,E), n := |V(G)|
* @param d matrix of shortest paths d(G)
* @param p matrix of predecessors p(G)
*/
__global__ void fw_kernel(const unsigned int u, const unsigned int n, int * const d, int * const p)
{
int v1 = blockDim.y * blockIdx.y + threadIdx.y;
int v2 = blockDim.x * blockIdx.x + threadIdx.x;
if (v1 < n && v2 < n)
{
int newPath = d[v1 * n + u] + d[u * n + v2];
int oldPath = d[v1 * n + v2];
if (oldPath > newPath)
{
d[v1 * n + v2] = newPath;
p[v1 * n + v2] = p[u * n + v2];
}
}
}
/** Parallel Floyd Warshall algorithm using gpu
*
* @param n number of vertices in the graph G:=(V,E), n := |V(G)|
* @param G is a the graph G:=(V,E)
* @param d matrix of shortest paths d(G)
* @param p matrix of predecessors p(G)
*/
extern "C" void fw_gpu(const unsigned int n, const int * const G, int * const d, int * const p)
{
int *dev_d = 0;
int *dev_p = 0;
cudaError_t cudaStatus;
cudaStream_t cpyStream;
// Choose which GPU to run on, change this on a multi-GPU system.
cudaStatus = cudaSetDevice(0);
HANDLE_ERROR(cudaStatus);
// Initialize the grid and block dimensions here
dim3 dimGrid((n - 1) / BLOCK_WIDTH + 1, (n - 1) / BLOCK_WIDTH + 1, 1);
dim3 dimBlock(BLOCK_WIDTH, BLOCK_WIDTH, 1);
if (gDebug)
{
printf("|V| %d\n", n);
printf("Dim Grid:\nx - %d\ny - %d\nz - %d\n", dimGrid.x, dimGrid.y, dimGrid.z);
printf("Dim Block::\nx - %d\ny - %d\nz - %d\n", dimBlock.x, dimBlock.y, dimBlock.z);
}
// Create new stream to copy data
cudaStatus = cudaStreamCreate(&cpyStream);
HANDLE_ERROR(cudaStatus);
// Allocate GPU buffers for matrix of shortest paths d(G) and predecessors p(G)
cudaStatus = cudaMalloc((void**)&dev_d, n * n * sizeof(int));
HANDLE_ERROR(cudaStatus);
cudaStatus = cudaMalloc((void**)&dev_p, n * n * sizeof(int));
HANDLE_ERROR(cudaStatus);
// Wake up gpu
wake_gpu_kernel<<<1, dimBlock>>>(32);
// Copy input from host memory to GPU buffers.
cudaStatus = cudaMemcpyAsync(dev_d, G, n * n * sizeof(int), CMCPYHTD, cpyStream);
cudaStatus = cudaMemcpyAsync(dev_p, p, n * n * sizeof(int), CMCPYHTD, cpyStream);
// cudaDeviceSynchronize waits for the kernel to finish, and returns
cudaStatus = cudaDeviceSynchronize();
HANDLE_ERROR(cudaStatus);
cudaFuncSetCacheConfig(fw_kernel, cudaFuncCachePreferL1 );
FOR(u, 0, n - 1)
{
fw_kernel<<<dimGrid, dimBlock>>>(u, n, dev_d, dev_p);
}
// Check for any errors launching the kernel
cudaStatus = cudaGetLastError();
HANDLE_ERROR(cudaStatus);
// cudaDeviceSynchronize waits for the kernel to finish, and returns
// any errors encountered during the launch.
cudaStatus = cudaDeviceSynchronize();
HANDLE_ERROR(cudaStatus);
cudaStatus = cudaMemcpy(d, dev_d, n * n * sizeof(int), CMCPYDTH);
HANDLE_ERROR(cudaStatus);
cudaStatus = cudaMemcpy(p, dev_p, n * n * sizeof(int), CMCPYDTH);
HANDLE_ERROR(cudaStatus);
cudaStatus = cudaFree(dev_d);
HANDLE_ERROR(cudaStatus);
cudaStatus = cudaFree(dev_p);
HANDLE_ERROR(cudaStatus);
return;
}
/**
* Print graph G as a matrix
*
* @param n number of vertices in the graph G:=(V,E), n := |V(G)|
* @param G is a the graph G:=(V,E)
*/
void print_graph(const unsigned int n, const int * const G)
{
FOR(v1, 0, n - 1)
{
FOR(v2, 0, n - 1)
{
if (G[v1 * n + v2] < INF)
printf("%d ", G[v1 * n + v2]);
else
printf("INF ");
}
printf("\n");
}
printf("\n");
}
/**
* Reconstruct Path
*
* @param i, j id vertex
* @param G is a the graph G:=(V,E)
* @param p matrix of predecessors p(G)
*/
int reconstruct_path(unsigned int n, unsigned int i, unsigned int j, const int * const p, const int * const G)
{
if (i == j )
return 0;
else if ( p[i * n + j] == NONE)
return INF;
else
{
int path = reconstruct_path(n, i, p[i * n + j], p, G);
if (path == INF)
return INF;
else
return path + G[ p [i * n + j] * n + j];
}
}
/**
* Check paths
*
* @param n number of vertices in the graph G:=(V,E), n := |V(G)|
* @param G is a the graph G:=(V,E)
* @param d matrix of shortest paths d(G)
* @param p matrix of predecessors p(G)
*/
bool check_paths(const unsigned int n, const int * const G, const int * const d, const int * const p)
{
FOR (i, 0, n - 1)
{
FOR (j, 0, n - 1)
{
int path = reconstruct_path(n, i, j, p, G);
if (gDebug)
printf("%d %d %d == %d \n", i, j, path, d[i * n + j]);
if (path != d[i * n + j])
return false;
}
}
return true;
}
|
6,318 | //xfail:BOOGIE_ERROR
//--blockDim=32 --gridDim=64 --no-inline
//error: possible write-write race on
#include <stdio.h>
#include "cuda.h"
#include <assert.h>
#include <cuda_runtime_api.h>
#define M 2//32
#define N 4//64
__global__ void foo(int* p) {
__shared__ unsigned char x[N];
for (unsigned int i=0; i<(N/4); i++) {
((unsigned int*)x)[i] = 1;//0;
}
/*
for (int i = 0; i < N/4; i++) {
p[i] = x[i];
}
*/
}
int main(){
int *a;
int *dev_a;
int size = N*sizeof(int);
cudaMalloc((void**)&dev_a, size);
a = (int*)malloc(size);
for (int i = 0; i < N; i++)
a[i] = 2;
cudaMemcpy(dev_a,a,size, cudaMemcpyHostToDevice);
printf("old a: ");
for (int i = 0; i < N; i++)
printf("%d ", a[i]);
foo<<<M,N>>>(dev_a);
//ESBMC_verify_kernel(foo,M,N,dev_a);
cudaMemcpy(a,dev_a,size,cudaMemcpyDeviceToHost);
printf("\nnew a: ");
for (int i = 0; i < N; i++)
printf("%d ", a[i]);
free(a);
cudaFree(dev_a);
return 0;
}
|
6,319 | #include <stdio.h>
#define BLOCK_SIZE 128
#define BLOCK_SIZE_F 128.0
__global__
void sumRedKernel(float *A, int n){
__shared__ float partialSum[BLOCK_SIZE*2];
int i = (threadIdx.x + blockDim.x * blockIdx.x)*2;
unsigned int t = threadIdx.x * 2;
partialSum[t] = A[i];
partialSum[t+1] = A[i+1];
t = threadIdx.x;
for(unsigned int stride = blockDim.x; stride > 0; stride /= 2){
__syncthreads();
if(t < stride && (t+stride) < n) partialSum[t] += partialSum[t+stride];
}
__syncthreads();
if(threadIdx.x == 0){
A[blockIdx.x] = partialSum[0];
}
}
void sumRed(float* A, int n){
int size = n*sizeof(float);
float *d_A;
cudaMalloc((void **) &d_A, size);
cudaMemcpy(d_A,A,size,cudaMemcpyHostToDevice);
sumRedKernel<<<ceil(n/(BLOCK_SIZE_F*2)), BLOCK_SIZE>>>(d_A,n);
cudaMemcpy(A,d_A,size,cudaMemcpyDeviceToHost);
cudaFree(d_A);
}
int main(){
int n,i;
float *h_A;
scanf("%d", &n);
h_A = (float*) malloc(n*sizeof(float));
for(i = 0; i < n; i++){
//scanf("%f", &h_A[i]);
h_A[i] = 1;
}
while(n > 1){
sumRed(h_A,n);
n = ceil(n/(BLOCK_SIZE_F*2));
}
printf("%f", h_A[0]);
printf("\n");
return 0;
}
|
6,320 | //#include<iostream>
#include<stdio.h>
#include<stdlib.h>
#include <cuda.h>
//#include <math.h>
#define BLOCK_SIZE 512
int checkResults(float*res, float* cudaRes,int length)
{
int nDiffs=0;
const float smallVal = 0.01f; // Keeping this extra high as we have repetitive addition and sequence matters
for(int i=0; i<length; i++)
if(fabs(cudaRes[i]-res[i])>smallVal)
nDiffs++;
//if(1)
//printf("%d :: %f %f\n", i, cudaRes[i], res[i]);
return nDiffs;
}
void initializeArray(FILE* fp,float* arr, int nElements)
{
for( int i=0; i<nElements; i++){
int r=fscanf(fp,"%f",&arr[i]);
if(r == EOF){
rewind(fp);
}
arr[i]-=5; // This is to make the data zero mean. Otherwise we reach large numbers and lose precision
}
}
void inclusiveScan_SEQ(float *in, float *out,int length) {
float sum=0.f;
for (int i =0; i < length; i++) {
sum+=in[i];
out[i]=sum;
}
}
__global__ void fixup(float *input, float *aux, int len) {
unsigned int t = threadIdx.x, start = 2 * blockIdx.x * BLOCK_SIZE;
if (blockIdx.x) {
if (start + t < len)
input[start + t] += aux[blockIdx.x - 1];
if (start + BLOCK_SIZE + t < len)
input[start + BLOCK_SIZE + t] += aux[blockIdx.x - 1];
}
}
__global__ void test(float * input, float * output, float *aux, int len) {
// Load a segment of the input vector into shared memory
__shared__ float scan_array[BLOCK_SIZE << 1];
unsigned int t = threadIdx.x, start = 2 * blockIdx.x * BLOCK_SIZE;
if (start + t < len)
scan_array[t] = input[start + t];
else
scan_array[t] = 0;
if (start + BLOCK_SIZE + t < len)
scan_array[BLOCK_SIZE + t] = input[start + BLOCK_SIZE + t];
else
scan_array[BLOCK_SIZE + t] = 0;
__syncthreads();
// Reduction
int stride;
for (stride = 1; stride <= BLOCK_SIZE; stride <<= 1) {
int index = (t + 1) * stride * 2 - 1;
if (index < 2 * BLOCK_SIZE)
scan_array[index] += scan_array[index - stride];
__syncthreads();
}
// Post reduction
for (stride = BLOCK_SIZE >> 1; stride; stride >>= 1) {
int index = (t + 1) * stride * 2 - 1;
if (index + stride < 2 * BLOCK_SIZE)
scan_array[index + stride] += scan_array[index];
__syncthreads();
}
if (start + t < len)
output[start + t] = scan_array[t];
if (start + BLOCK_SIZE + t < len)
output[start + BLOCK_SIZE + t] = scan_array[BLOCK_SIZE + t];
if (aux && t == 0)
aux[blockIdx.x] = scan_array[2 * BLOCK_SIZE - 1];
}
int main(int argc, char* argv[]) {
if(argc!=2){
printf("Usage %s N\n",argv[0]);
return 1;
}
int N=atoi(argv[1]);
FILE *fp = fopen("problem1.inp","r");
int size = N * sizeof(float);
//allocate resources
float *in = (float *)malloc(size);
float *out = (float *)malloc(size);
float *cuda_out= (float *)malloc(size);
float *d_in, *d_out, * tmp;
float * deviceAuxScannedArray;
cudaMalloc(&d_in, size);
cudaMalloc(&d_out, size);
cudaMalloc(&tmp, (BLOCK_SIZE << 1) * sizeof(float));
cudaMalloc(&deviceAuxScannedArray, (BLOCK_SIZE << 1) * sizeof(float));
float time = 0.f;
initializeArray(fp,in, N);
// Your code here
cudaEvent_t start, end;
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaEventRecord(start, 0);
cudaMemcpy(d_in, in, size, cudaMemcpyHostToDevice);
int block_num = N / BLOCK_SIZE;
if(block_num == 0) block_num++;
test<<<block_num, BLOCK_SIZE>>>(d_in, d_out, tmp, N);
cudaDeviceSynchronize();
test<<<dim3(1,1,1), BLOCK_SIZE>>>(tmp, deviceAuxScannedArray, NULL, BLOCK_SIZE << 1);
cudaDeviceSynchronize();
fixup<<<N/BLOCK_SIZE, BLOCK_SIZE>>>(d_out, deviceAuxScannedArray, N);
cudaDeviceSynchronize();
cudaMemcpy(cuda_out, d_out, size, cudaMemcpyDeviceToHost);
cudaEventRecord(end, 0);
cudaEventSynchronize(end);
cudaEventElapsedTime(&time, start, end);
inclusiveScan_SEQ(in, out,N);
int nDiffs = checkResults(out, cuda_out,N);
//if(nDiffs)printf("Test Failed\n"); // This should never print
printf("%d\n%f\n%f\n",N,cuda_out[N-1],time);
//free resources
free(in); free(out); free(cuda_out);
return 0;
} |
6,321 | #include <cuda.h>
#include <vector>
#include <iostream>
#include <stdio.h>
/*
To compile, use: nvcc -Xcompiler /wd4819 test1.cu
*/
void vecAddonHost(double *h_A,double *h_B,double *h_C,int n) {
for (int i=0; i<n; i++) {
h_C[i] = h_A[i] + h_B[i];
}
}
// CUDA kernel
// each thread for each element
__global__
void vecAddKernel(double *A, double *B, double *C, int n) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i<n) C[i] = A[i] + B[i];
}
void vecAddonDevice(double *h_A,double *h_B,double *h_C,int n) {
int size = n * sizeof(double);
double *d_A, *d_B, *d_C;
cudaError_t error;
error=cudaMalloc((void **) &d_A, size);
if (error != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(error),__FILE__,__LINE__);
exit(EXIT_FAILURE);
}
error=cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
if (error != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(error),__FILE__,__LINE__);
exit(EXIT_FAILURE);
}
error=cudaMalloc((void **) &d_B, size);
if (error != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(error),__FILE__,__LINE__);
exit(EXIT_FAILURE);
}
error=cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
if (error != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(error),__FILE__,__LINE__);
exit(EXIT_FAILURE);
}
error=cudaMalloc((void **) &d_C, size);
if (error != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(error),__FILE__,__LINE__);
exit(EXIT_FAILURE);
}
vecAddKernel<<<ceil(n/256.0), 256>>>(d_A, d_B, d_C, n);
error=cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
if (error != cudaSuccess) {
printf("%s in %s at line %d\n", cudaGetErrorString(error),__FILE__,__LINE__);
exit(EXIT_FAILURE);
}
// free memory
cudaFree(d_A); cudaFree(d_B); cudaFree(d_C);
}
int main(int argc, char **argv) {
int N = 1000;
double *h_A = new double[N];
double *h_B = new double[N];
double *h_C = new double[N];
// initialize on host
for (int i=0; i<N; i++) {
h_A[i] = i;
h_B[i] = i*i;
}
for (int i=0; i<N; i++) h_C[i] = 0.0;
// perform C=A+B on host
vecAddonHost( h_A, h_B, h_C, N);
// output
std::cout << "host answer (first 5 only):" << std::endl;
for (int i=0; i<5; i++) {
std::cout << h_C[i] << " ";
}
std::cout << std::endl;
// clean up C
std::cout << "cleaning up" << std::endl;
for (int i=0; i<N; i++) h_C[i] = 0.0;
// run cuda add
std::cout << "run cuda add" << std::endl;
vecAddonDevice( h_A, h_B, h_C, N);
// output
std::cout << "device answer (first 5 only):" << std::endl;
for (int i=0; i<5; i++) {
std::cout << h_C[i] << " ";
}
std::cout << std::endl;
delete[] h_A;
delete[] h_B;
delete[] h_C;
return 0;
} |
6,322 | // Author: Ayush Kumar
// Roll No: 170195
// Compile: nvcc -g -G -arch=sm_61 -std=c++11 assignment5-p3.cu -o assignment5-p3
#include <cmath>
#include <iostream>
#include <sys/time.h>
#define SIZE 1024
#define BLOCK_SIZE 16
#define THRESHOLD (0.000001)
using std::cerr;
using std::cout;
using std::endl;
double rtclock() { // Seconds
struct timezone Tzp;
struct timeval Tp;
int stat;
stat = gettimeofday(&Tp, &Tzp);
if (stat != 0) {
cout << "Error return from gettimeofday: " << stat << "\n";
}
return (Tp.tv_sec + Tp.tv_usec * 1.0e-6);
}
__host__ void ATAonCPU(double* M, double* P) {
for (int k = 0; k < SIZE; k++) {
for (int i = 0; i < SIZE; i++) {
for (int j = 0; j < SIZE; j++)
P[i*SIZE + j] += M[k*SIZE + i] * M[k*SIZE + j];
}
}
}
__global__ void ATAkernel1(double* A, double* B) {
// TODO: Fill in
int i = blockIdx.y * blockDim.y + threadIdx.y;
int j = blockIdx.x * blockDim.x + threadIdx.x;
for (int k = 0; k < SIZE; k++) {
// atomicAdd(&B[i*SIZE + j], A[k*SIZE + i] * A[k*SIZE + j]);
B[i*SIZE + j] += A[k*SIZE + i] * A[k*SIZE + j];
}
}
__global__ void ATAkernel2(double* A, double* B) {
// TODO: Fill in
// Block row and column
int block_i = blockIdx.y;
int block_j = blockIdx.x;
// Each thread block computes one sub-matrix B_sub of B
double* B_sub = &B[block_i * blockDim.y * SIZE + block_j * blockDim.x];
// Each thread computes one element of B_sub
// by accumulating results into B_value
double B_value = 0;
// Thread row and column within B_sub
int thread_i = threadIdx.y;
int thread_j = threadIdx.x;
// Loop over all the sub-matrices of A^T and A that are
// required to compute B_sub
// Multiply each pair of sub-matrices together
// and accumulate the results
for (int m = 0; m < SIZE/blockDim.x; m++) {
// Get sub-matrix AT_sub of AT
double* AT_sub = &A[m * blockDim.y * SIZE + block_i * blockDim.x];
// Get sub-matrix A_sub of A
double* A_sub = &A[m * blockDim.y * SIZE + block_j * blockDim.x];
// Shared memory used to store AT_sub and A_sub respectively
__shared__ double ATs[BLOCK_SIZE][BLOCK_SIZE];
__shared__ double As[BLOCK_SIZE][BLOCK_SIZE];
// Load AT_sub and A_sub from device memory to shared memory
// Each thread loads one element of each sub-matrix
ATs[thread_i][thread_j] = AT_sub[thread_i * SIZE + thread_j];
As[thread_i][thread_j] = A_sub[thread_i * SIZE + thread_j];
// Synchronize to make sure the sub-matrices are loaded
// before starting the computation
__syncthreads();
// Multiply AT_sub and A_sub together
for (int k = 0; k < BLOCK_SIZE; k += 16) {
B_value += ATs[k][thread_i] * As[k][thread_j];
B_value += ATs[k+1][thread_i] * As[k+1][thread_j];
B_value += ATs[k+2][thread_i] * As[k+2][thread_j];
B_value += ATs[k+3][thread_i] * As[k+3][thread_j];
B_value += ATs[k+4][thread_i] * As[k+4][thread_j];
B_value += ATs[k+5][thread_i] * As[k+5][thread_j];
B_value += ATs[k+6][thread_i] * As[k+6][thread_j];
B_value += ATs[k+7][thread_i] * As[k+7][thread_j];
B_value += ATs[k+8][thread_i] * As[k+8][thread_j];
B_value += ATs[k+9][thread_i] * As[k+9][thread_j];
B_value += ATs[k+10][thread_i] * As[k+10][thread_j];
B_value += ATs[k+11][thread_i] * As[k+11][thread_j];
B_value += ATs[k+12][thread_i] * As[k+12][thread_j];
B_value += ATs[k+13][thread_i] * As[k+13][thread_j];
B_value += ATs[k+14][thread_i] * As[k+14][thread_j];
B_value += ATs[k+15][thread_i] * As[k+15][thread_j];
}
// Synchronize to make sure that the preceding
// computation is done before loading two new
// sub-matrices of AT and A in the next iteration
__syncthreads();
}
// Write B_sub to device memory
// Each thread writes one element
B_sub[thread_i * SIZE + thread_j] = B_value;
}
__host__ void check_result(double* Test, double* Ref) {
double maxdiff = 0, rel_diff = 0;
int numdiffs = 0;
for (int i = 0; i < SIZE; i++) {
for (int j = 0; j < SIZE; j++) {
rel_diff = (Test[i*SIZE + j] - Ref[i*SIZE + j]);
if (fabs(rel_diff) > THRESHOLD) {
numdiffs++;
if (rel_diff > maxdiff)
maxdiff = rel_diff;
}
}
}
if (numdiffs > 0)
cout << numdiffs << " Diffs found over THRESHOLD " << THRESHOLD << " Max Diff = " << maxdiff
<< "\n";
else
cout << "No differences found between base and test versions\n";
}
int main() {
cout << "Matrix Size = " << SIZE << "\n";
double* h_in = new double[SIZE*SIZE];
double* h_cpu_out = new double[SIZE*SIZE];
double* h_dev_out = new double[SIZE*SIZE];
for (int i = 0; i < SIZE; i++) {
for (int j = 0; j < SIZE; j++) {
h_in[i*SIZE + j] = i * j * 0.25;
h_cpu_out[i*SIZE + j] = 0;
h_dev_out[i*SIZE + j] = 0;
}
}
double clkbegin = rtclock();
ATAonCPU(h_in, h_cpu_out);
double clkend = rtclock();
double cpu_time = clkend - clkbegin;
cout << "A^T.A on CPU: " << ((2.0 * SIZE * SIZE * SIZE) / cpu_time)
<< " GFLOPS; Time = " << cpu_time * 1000 << " msec" << endl;
cudaError_t status;
cudaEvent_t start, end;
double* d_in;
double* d_out;
float kernel_time;
// TODO: Fill in
cudaMalloc(&d_in, sizeof(double)*SIZE*SIZE);
cudaMalloc(&d_out, sizeof(double)*SIZE*SIZE);
dim3 threads_in_block1(BLOCK_SIZE, BLOCK_SIZE);
dim3 blocks_in_grid1(SIZE/threads_in_block1.x, SIZE/threads_in_block1.y);
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaEventRecord(start, 0);
/************** CUDA **************/
cudaMemcpy(d_in, h_in, sizeof(double)*SIZE*SIZE, cudaMemcpyHostToDevice);
cudaMemset(d_out, 0, sizeof(double)*SIZE*SIZE);
ATAkernel1<<<blocks_in_grid1, threads_in_block1>>>(d_in, d_out);
cudaMemcpy(h_dev_out, d_out, sizeof(double)*SIZE*SIZE, cudaMemcpyDeviceToHost);
/************** CUDA **************/
cudaEventRecord(end, 0);
cudaEventSynchronize(end);
cudaEventElapsedTime(&kernel_time, start, end);
cudaEventDestroy(start);
cudaEventDestroy(end);
check_result(h_cpu_out, h_dev_out);
cout << "A^T.A on GPU Kernel 1: " << ((2.0 * SIZE * SIZE * SIZE) / (kernel_time * 1.0e-03))
<< " GFLOPS; Time = " << kernel_time << " msec" << endl;
status = cudaGetLastError();
if (status != cudaSuccess) {
cerr << "CUDA Error: " << cudaGetErrorString(status) << endl;
}
dim3 threads_in_block2(BLOCK_SIZE, BLOCK_SIZE);
dim3 blocks_in_grid2(SIZE/threads_in_block2.x, SIZE/threads_in_block2.y);
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaEventRecord(start, 0);
/************** CUDA **************/
cudaMemcpy(d_in, h_in, sizeof(double)*SIZE*SIZE, cudaMemcpyHostToDevice);
cudaMemset(d_out, 0, sizeof(double)*SIZE*SIZE);
ATAkernel2<<<blocks_in_grid2, threads_in_block2>>>(d_in, d_out);
cudaMemcpy(h_dev_out, d_out, sizeof(double)*SIZE*SIZE, cudaMemcpyDeviceToHost);
/************** CUDA **************/
cudaEventRecord(end, 0);
cudaEventSynchronize(end);
cudaEventElapsedTime(&kernel_time, start, end);
cudaEventDestroy(start);
cudaEventDestroy(end);
check_result(h_cpu_out, h_dev_out);
cout << "A^T.A on GPU Kernel 2: " << ((2.0 * SIZE * SIZE * SIZE) / (kernel_time * 1.0e-03))
<< " GFLOPS; Time = " << kernel_time << " msec" << endl;
status = cudaGetLastError();
if (status != cudaSuccess) {
cerr << "CUDA Error: " << cudaGetErrorString(status) << endl;
}
// Free device memory
cudaFree(d_in);
cudaFree(d_out);
// Free host memory
delete[] h_in;
delete[] h_cpu_out;
delete[] h_dev_out;
return EXIT_SUCCESS;
}
|
6,323 | #include "includes.h"
__global__ void sneldiv(unsigned short *inA, float *inB, int *sub, int Nprj, int snno)
{
int idz = threadIdx.x + blockDim.x*blockIdx.x;
if (blockIdx.y<Nprj && idz<snno) {
// inB > only active bins of the subset
// inA > all sinogram bins
float a = (float)inA[snno*sub[blockIdx.y] + idz];
a /= inB[snno*blockIdx.y + idz];//sub[blockIdx.y]
inB[snno*blockIdx.y + idz] = a; //sub[blockIdx.y]
}
} |
6,324 | #include <stdio.h>
#include <stdlib.h>
#include <string.h> /* memcpy */
/* C99 syntax test (currently only a subset is parsed) */
/* @todo Also typedef struct */
typedef struct Car
{
const char *name;
int year;
float max_speed;
} Car; /* @todo Allow unnecessary ; */
int main()
{
int test_integer;
test_integer = -134;
int another_test_integer = 1;
/* @todo Make work */
/*char test_str[] = "foo"; */
/* @todo This parses, but needs conversion to C89 (because decl is separated from init) */
Car car;
/* @todo This parses, but need to make conversion from compound literals and designated initializers to C89 */
/*car = (Car) {
.year = 1,
.name = "bbb",
.max_speed = -2.0
};*/
/* @todo Rest of C99 */
/* @todo Make work */
/*(void)car; */
/*(void)test_integer; */
/*(void)another_test_integer; */
int x = car.year + test_integer + another_test_integer;
return x;
}
|
6,325 | #include <cuda_runtime.h>
#include <stdio.h>
__global__ void checkIndex(void){
printf("threadIdx:(%d, %d, %d) \n blockIdx:(%d, %d, %d) \n blockDim:(%d, %d, %d) \n gridDim:(%d, %d, %d) \n",
threadIdx.x, threadIdx.y, threadIdx.z,
blockIdx.x, blockIdx.y, blockIdx.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 you leave
cudaDeviceReset();
return 0;
}
|
6,326 | #include "includes.h"
__global__ void DataPointMap(int size, const double *inputX, const double *inputY, double *output, const double *inFreeArray, int length) {
const long ix = threadIdx.x + blockIdx.x * (long)blockDim.x;
if (ix < size) {
// copy int array
const double *inArrayBody = &inputX[ix* length];
double *outArrayBody = &output[ix* length];
for (long i = 0; i < length; i++) {
outArrayBody[i] = inArrayBody[i] + inFreeArray[i];
}
}
} |
6,327 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#include <string.h>
#include <curand.h>
#include <curand_kernel.h>
//to get data yahoo finance
//time period: Apr 01 2016 -> Apr 01 2019
//freq: Weekly
#define NUM_ELEMENTS 100 //why when I change this everything breaks & this should not change because constant memory
#define NUM_PORTFOLIOS atoi(argv[argc])
#define MAX_NUM_OF_STOCKS 85
#define DEBUG 0
float* readFile(char* filename){
float* ret = (float*) malloc(NUM_ELEMENTS*sizeof(float));
FILE* ptr = fopen(filename,"r");
if (ptr==NULL)
{
printf("Error reading file");
return 0;
}
char line[255];
char* token;
int lineCount = 0;
fgets(line, 255, ptr); //grab the first line and do nothing
while (fgets(line, 255, ptr) != 0 && lineCount < NUM_ELEMENTS){ //for each line
int dataCount = 0;
token = strtok(line, ",");
while (token != 0) { //for each word in line
if (dataCount == 5) {
ret[lineCount] = atof(token);
}
token = strtok(0, ",");
dataCount++;
}
lineCount++;
}
fclose(ptr);
return ret;
}
void writeFile(char* filename, float* returns, float* risk, int len){
FILE* ptr = fopen(filename, "w");
for (int a = 0; a < len; a++){
fprintf(ptr, "%f %f\n", risk[a], returns[a]);
}
fclose(ptr);
}
float getAverage(float* nums, int len){
float sum = 0;
for (int a = 0; a < len; a++){
sum += nums[a];
}
return sum/len;
}
float* getPercentReturns(float* nums, int len){
float* ret = (float*) malloc(sizeof(float)*(len-1));
for (int a = 0; a < len-1; a++){
ret[a] = (nums[a+1]-nums[a])/nums[a];
}
return ret;
}
//a few possible errors in here
//still need to plot
//why am I mallocing
void gold(int argc, char* argv[]){
argc--;
if (argc < 3) {
printf("%s\n", "Expected more arguments");
exit(0);
}
float** closingPrices = (float**) malloc(sizeof(float*)*(argc-1));
float** returns = (float**) malloc(sizeof(float*)*(argc-1));
float* averages = (float*) malloc(sizeof(float)*(argc-1));
for (int a = 1; a < argc; a++){
closingPrices[a-1] = readFile(argv[a]);
returns[a-1] = getPercentReturns(closingPrices[a-1], NUM_ELEMENTS);
averages[a-1] = getAverage(returns[a-1], NUM_ELEMENTS-1);
}
if (DEBUG){
for (int a = 0; a < (argc-1); a++){
for (int b = 0; b < (NUM_ELEMENTS-1); b++){
printf("Returns %d %d: %f \n", a, b, returns[a][b]);
}
}
for (int a = 0; a < argc-1; a++){
printf("avg %d: %f\n", a, averages[a]);
}
}
//calculate the covariances for each of the stocks
//doing extra things [0][4] will be the same as [4][0]
float** covariance = (float**) malloc(sizeof(float*)*(argc-1));
for (int a = 0; a < argc-1; a++){
covariance[a] = (float*) malloc(sizeof(float)*(argc-1));
for (int b = 0; b < argc-1; b++){
float sum = 0;
for (int c = 0; c < NUM_ELEMENTS-1; c++){
sum += (returns[a][c] - averages[a]) * (returns[b][c] - averages[b]);
}
sum /= NUM_ELEMENTS-2;
covariance[a][b] = sum;
}
}
//retiming -malloc, transfers -constant *
//transpose data array vs constant *
//cutting down on local variables in portfolios
//remove std all together *
//move all the constants to pt2
//dont double calculate for the covariance *
//where do you put the write file *
//time to choose the weights for the given portfolios
//PSUDEO:
//for doing random weights
//if x stocks
//then choose x numbers
//then find the sum of the randoms
//then divide each random number by sum
clock_t start = clock(), diff;
srand(time(NULL)); // Initialization, should only be called once.
float* risk = (float*) malloc(sizeof(float)* NUM_PORTFOLIOS);
float* reward = (float*) malloc(sizeof(float)* NUM_PORTFOLIOS);
for (int a = 0; a < NUM_PORTFOLIOS; a++){//find the risk & reward for each portfolio
float randomWeights[argc-1]; //may actually want to save this for later
int totalWeight = 0;
for (int b = 0; b < argc-1; b++){//choose random weights
int r = rand() % 100; //RAND MIGHT BE DOING THE SAME VAL EVERYTIME
totalWeight += r;
randomWeights[b] = (float) r;
}
for (int b = 0; b < argc-1; b++){//now random weight has the correct weights
randomWeights[b] /= totalWeight;
}
//first find the reward
float totalReward = 0;
for (int b = 0; b < argc-1; b++){
totalReward += averages[b]*randomWeights[b];
}
reward[a] = totalReward;
//find the risk of the portfolio
float totalRisk = 0;
float work[argc-1];
for (int b = 0; b < argc-1; b++){
work[b] = 0;
for (int c = 0; c < argc-1; c++){
work[b] += randomWeights[c]*covariance[c][b];
}
}
for (int b = 0; b < argc-1; b++){
totalRisk += work[b] * randomWeights[b];
}
risk[a] = sqrt(totalRisk);
if (a==0 && DEBUG){
for (int r = 0; r < argc-1;r++) printf("randomWeights: %f\n", randomWeights[r]);
printf("Risk: %f\n", risk[a]);
for (int r = 0; r < argc-1; r++){
for (int rr = 0; rr < argc-1; rr++){
printf("Cov of %d %d : %f\n", r, rr, covariance[r][rr]);
}
}
}
}
diff = clock() - start;
float msec = (float) diff * 1000 / (float) CLOCKS_PER_SEC;
printf("CPU time portfolio %f seconds\n", msec/1000);
//plot the data
if (DEBUG) writeFile("riskreturngold.txt", reward, risk, NUM_PORTFOLIOS);
}
__constant__ float c_returns[MAX_NUM_OF_STOCKS * 99];
__constant__ float c_averages[MAX_NUM_OF_STOCKS];
__constant__ float c_covariance[MAX_NUM_OF_STOCKS*MAX_NUM_OF_STOCKS];
__global__ void GPercentReturns(float* closingPrices, float* returns, int numOfStocks)
{
__shared__ float closing[NUM_ELEMENTS];
int stockId = blockIdx.x;
int returnId = threadIdx.x;
int grab = returnId + (stockId * NUM_ELEMENTS); //also write 2
//everyone load into shared
closing[returnId] = closingPrices[grab];
__syncthreads();
if (returnId != NUM_ELEMENTS-1){//last thread should do this
int to = returnId + (stockId*(NUM_ELEMENTS-1));
returns[to] = (closing[returnId+1]-closing[returnId])/closing[returnId];
}
}
__global__ void GReduceAverageR(float* average, int numOfStocks, int mid){
__shared__ float reduce[99];
int returnId = threadIdx.x;
int stockId = blockIdx.x;
int dim = blockDim.x;
reduce[returnId] = c_returns[returnId + (stockId*dim)];
__syncthreads();
if (returnId>=mid){
reduce[returnId-mid]+=reduce[returnId];
}
__syncthreads();
for (int s = mid/2; s > 0; s/=2){
if (returnId < s) {
reduce[returnId] += reduce[returnId+s];
}
__syncthreads();
}
//REMOVED IF
if (returnId == 0) average[stockId] = reduce[0]/99.0;
}
//try flipping the memory
__global__ void GCovariance(float* covariance, int numberOfStocks){
int b = threadIdx.x;
int a = blockIdx.x;
if (a > b) return;
float sum = 0;
for (int c = 0; c < NUM_ELEMENTS-1; c++)
sum += (c_returns[a*(NUM_ELEMENTS-1)+c] - c_averages[a]) * (c_returns[b*(NUM_ELEMENTS-1)+c] - c_averages[b]);
sum /= NUM_ELEMENTS-2;
covariance[a*numberOfStocks+b] = sum;
covariance[b*numberOfStocks+a] = sum;
}
__global__ void GPortfolio(float* risk, float* reward, int numberOfStocks, int mid){
//obscene amount of global calls here
//only one call to risk[] and reward[] at the end
//also there might be a GPU version of sqrt()
extern __shared__ float sharedMemory[];
float* randomWeights = (float*) &sharedMemory[0];
float* scratch = (float*) &sharedMemory[numberOfStocks];
//__shared__ float randomWeights[16];
//__shared__ float scratch[16];
int tid = threadIdx.x;
int bid = blockIdx.x;
curandState state;
curand_init(tid+bid*blockDim.x,10,0,&state);
float r = curand_uniform(&state);
//RAN WEIGHT
//FAST- WORKS
randomWeights[tid] = r;
__syncthreads();
//quick reduce
if (tid >= mid){
randomWeights[tid-mid] += randomWeights[tid];
}
__syncthreads();
for (int s = mid/2; s > 0; s /= 2){
if (tid < s)
randomWeights[tid] += randomWeights[tid+s];
__syncthreads();
}
float totalWeight = randomWeights[0];
__syncthreads();
randomWeights[tid] = (float) r/ totalWeight;
//RETURN
//FAST
scratch[tid] = c_averages[tid]*randomWeights[tid];
__syncthreads();
if (tid >= mid){
scratch[tid-mid] += scratch[tid];
if (tid >= numberOfStocks) printf("%d\n", tid);
if (tid-mid < 0) printf("%d", tid-mid);
}
__syncthreads();
for (int s = mid/2; s > 0; s /= 2){
if (tid < s) {
scratch[tid] += scratch[tid+s];
}
__syncthreads();
}
if (tid == 0) reward[bid] = scratch[0];
__syncthreads();
//RISK
//FAST
float work = 0;
for (int c = 0; c < numberOfStocks; c++){
work += randomWeights[c]*c_covariance[c*numberOfStocks+tid];
}
scratch[tid] = work*randomWeights[tid];
__syncthreads();
if (tid >= mid){
scratch[tid-mid] += scratch[tid];
}
__syncthreads();
for (int s = mid/2; s > 0; s /= 2){
if (tid < s)
scratch[tid] += scratch[tid+s];
__syncthreads();
}
if (tid == 0) risk[bid] = sqrt(scratch[0]);
}
void gpu (int argc, char* argv[]) {
argc--;
float* closingPrices = (float*) malloc(sizeof(float)*(argc-1)*NUM_ELEMENTS);
float* returns = (float*) malloc(sizeof(float)*(argc-1)*(NUM_ELEMENTS-1));
float* averages = (float*) malloc(sizeof(float)*(argc-1));
float* std = (float*) malloc(sizeof(float)*(argc-1));
float* covariance = (float*) malloc(sizeof(float)*(argc-1)*(argc-1));
for (int a = 1; a < argc; a++){
float* add = readFile(argv[a]);
for (int b = 0; b < NUM_ELEMENTS; b++){
closingPrices[(a-1)*NUM_ELEMENTS+b] = add[b];
}
}
float* d_closingPrices;
cudaMalloc(&d_closingPrices, sizeof(float) * (argc-1)*NUM_ELEMENTS);
float* d_all;
cudaMalloc(&d_all, sizeof(float) * (argc-1)*(NUM_ELEMENTS-1));
cudaMemcpy(d_closingPrices, closingPrices, sizeof(float)*(argc-1)*NUM_ELEMENTS, cudaMemcpyHostToDevice);
GPercentReturns<<<argc-1,NUM_ELEMENTS>>>(d_closingPrices, d_all, argc-1);
cudaMemcpy(returns, d_all, sizeof(float)*(argc-1)*(NUM_ELEMENTS-1), cudaMemcpyDeviceToHost);
cudaMemcpyToSymbol(c_returns, returns, sizeof(float) * (argc-1)*(NUM_ELEMENTS-1));
if (DEBUG){
for (int a = 0; a < (argc-1); a++){
for (int b = 0; b < (NUM_ELEMENTS-1); b++){
printf("Returns %d %d: %f \n", a, b, returns[a*(NUM_ELEMENTS-1)+b]);
}
}
}
int mid = 1;
while (mid * 2 <= NUM_ELEMENTS-1) {
mid *= 2;
}
GReduceAverageR<<<argc-1, NUM_ELEMENTS-1>>>(d_all, argc-1, mid);
cudaMemcpy(averages, d_all, sizeof(float)*(argc-1), cudaMemcpyDeviceToHost);
cudaMemcpyToSymbol(c_averages, averages, sizeof(float) * (argc-1));
if (DEBUG){
for (int a = 0; a < argc-1; a++){
printf("avg %d: %f\n", a, averages[a]);
}
}
GCovariance<<<argc-1,argc-1>>>(d_all, argc-1);
cudaMemcpy(covariance, d_all, sizeof(float)*(argc-1)*(argc-1), cudaMemcpyDeviceToHost);
cudaMemcpyToSymbol(c_covariance, covariance, sizeof(float) * (argc-1)*(argc-1));
if (DEBUG){
for (int a = 0; a < argc-1; a++){
for (int b = 0; b <argc-1; b++){
printf("Cov %d %d: %f\n", a, b, covariance[a*(argc-1)+b]);
}
}
}
//timing just for portfolio
float time;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
//END
float* risk = (float*) malloc(sizeof(float)*NUM_PORTFOLIOS);
float* reward = (float*) malloc(sizeof(float)*NUM_PORTFOLIOS);
float* d_risk;
float* d_reward;
cudaMalloc(&d_risk, sizeof(float)*NUM_PORTFOLIOS);
cudaMalloc(&d_reward, sizeof(float)*NUM_PORTFOLIOS);
mid = 1;
while (mid * 2 <= argc-1){
mid *= 2;
}
GPortfolio<<<NUM_PORTFOLIOS, argc-1, (sizeof(float)*(argc-1))*2>>>(d_risk, d_reward, argc-1, mid);
cudaMemcpy(risk, d_risk, sizeof(float)*NUM_PORTFOLIOS, cudaMemcpyDeviceToHost);
cudaMemcpy(reward, d_reward, sizeof(float)*NUM_PORTFOLIOS, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
//START
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaError_t code = cudaEventElapsedTime(&time, start, stop);
if (code != cudaSuccess) {
fprintf(stderr,"GPUassert: %s\n", cudaGetErrorName(code));
}
//END
printf("Time for portfolio: %f s\n", time/1000);
if (DEBUG) writeFile("riskreturn.txt", reward, risk, NUM_PORTFOLIOS);
}
//to plot
//in terminal do
//gnuplot
//plot 'riskreturn.txt' with points pt 3
int main( int argc, char* argv[])
{
printf("Num stocks: %i \n", argc-2);
printf("Num port: %i \n", atoi(argv[argc-1]));
clock_t start = clock(), diff;
gold(argc, argv);
diff = clock() - start;
float msec = (float) diff * 1000 / (float) CLOCKS_PER_SEC;
printf("Total CPU time taken %f seconds\n", msec/1000);
clock_t start2 = clock(), diff2;
gpu(argc, argv);
diff2 = clock() - start2;
float msec2 = (float) diff2 * 1000 / (float) CLOCKS_PER_SEC;
printf("Total GPU time taken %f seconds \n", msec2/1000);
return 0;
} |
6,328 | #include "includes.h"
__global__ void Naive_Hist(int* d_result, int* d_hist, int n_vertices) {
//each block compares the same row to all others row2
int row = blockIdx.x;
int row2 = threadIdx.x;
bool equal;
//shared count for whole block/same vertice
__shared__ int count;
//one thread sets count to zero and syncsthreads.
if(row2 == 0)
count = 0;
__syncthreads();
//checks equality to other vertices
if(row < n_vertices && row2 < n_vertices)
for(int i = row2; i < n_vertices; i += blockDim.x) {
//checks equality of vertices lcm
equal = false;
for(int j = 0; j < n_vertices; j++) {
if(d_result[row*n_vertices +j] == d_result[i*n_vertices + j])
equal = true;
else {
equal = false;
break;
}
}
//adds to count if vertices are equal
if(equal)
atomicAdd(&count, 1);
}
//syncsthreads so count is done and increments hist[count]
__syncthreads();
if(row < n_vertices && row2 == 0 && count > 0)
atomicAdd(&d_hist[count], 1);
} |
6,329 | #include "includes.h"
__global__ void inc (int n, float* a) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
a[i] += 1;
}
}; |
6,330 | #include <stdio.h>
#include <stdlib.h>
#include <curand_kernel.h>
#define TRIALS_PER_THREAD 1000
#define BLOCKS 256
#define THREADS 256
#define PI 3.14159265358979 // known value of pi
long pi_mc(unsigned long long trials) {
double x, y;
long points_in_circle=0;
for(long i = 0; i < trials; i++) {
x = rand() / (double) RAND_MAX;
y = rand() / (double) RAND_MAX;
points_in_circle += (x*x + y*y <= 1.0f);
}
return points_in_circle;
}
int main (int argc, char *argv[]) {
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
unsigned long long points_in_circle = 0;
unsigned long long total_points = 0;
cudaEventRecord(start);
printf(" time (ms) | total points | points in 1/4 circle | estimated pi | error \n");
printf("------------------------------------------------------------------------------------------------------------\n");
for (int j=1; j < 10000; j++)
{
points_in_circle += pi_mc((unsigned long long)BLOCKS * (unsigned long long)THREADS * (unsigned long long)TRIALS_PER_THREAD);
total_points += (unsigned long long)BLOCKS * (unsigned long long)THREADS * (unsigned long long)TRIALS_PER_THREAD;
long double pi = 4 * (long double) points_in_circle / (long double)total_points;
long double error = pi - (long double) PI;
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("%14.0f\t%16lld\t%16lld\t%20.14lf\t%20.14lf\n", milliseconds, total_points, points_in_circle, pi, error);
}
return 0;
}
|
6,331 | //#ifndef _MATRIXMUL_KERNEL_H_
//#define _MATRIXMUL_KERNEL_H_
#define BLOCK_SIZE_X 16
#define BLOCK_SIZE_Y 16
#define GRID_SIZE_X 8
#define GRID_SIZE_Y 8
extern "C"
/* Signature:
float* d_array_in, int count, uint single_in
->
float* d_array_out, uint* d_array_len, int* memstruct, uint* single_out
*/
__global__ void
addfloat(
float* d_array_in,
size_t count,
size_t width, size_t height, size_t depth,
float d_single_in,
float* d_array_out,
size_t* d_array_out_len,
float val)
{
int idx = threadIdx.x + BLOCK_SIZE_X * blockIdx.x;
int idy = threadIdx.y + BLOCK_SIZE_Y * blockIdx.y;
int thd_width = BLOCK_SIZE_X * GRID_SIZE_X;
int linpos = thd_width * idy + idx;
*(d_array_out+linpos) = d_array_in[linpos] + val;
__syncthreads();
}
|
6,332 | //pass
//--blockDim=64 --gridDim=64 --no-inline
#include "cuda.h"
__device__ void bar(int x) {
}
__global__ void foo() {
bar(5);
}
|
6,333 | // Add a scalar to the vector
//
// IMPORTANT: Prevent symbol mangling by setting: extern "C"
extern "C"
__global__ void vadd(int *const v, int const a, size_t const len) {
const unsigned int gid = blockDim.x * blockIdx.x + threadIdx.x;
const unsigned int gsize = gridDim.x * blockDim.x;
for (size_t i = gid; i < len; i += gsize) {
v[i] += a;
}
}
|
6,334 | #include <stdio.h>
#include <cuda.h>
#define N 1<<7
#define THREADS_PER_BLOCK 1024
__global__ void dot(float *a, float *b, float *c) {
__shared__ float temp[THREADS_PER_BLOCK];
int index = threadIdx.x + blockIdx.x * blockDim.x;
if (index >= N) return;
temp[threadIdx.x] = a[index] * b[index];
__syncthreads();
if (0 == threadIdx.x) {
float sum = 0.0;
int max = THREADS_PER_BLOCK;
if (N < max) max = N;
for (int i = 0; i < max; i++) {
sum += temp[i];
}
//c[0] = sum;
atomicAdd(c, sum);
}
}
void random_floats(float *a, float size)
{
int i;
for (i=0; i<size; i++)
a[i] = i;
return;
}
int main(void) {
int num_SMs;
int devId = 0; // Unique GPU
cudaDeviceGetAttribute(&num_SMs, cudaDevAttrMultiProcessorCount, devId); // Y devId?
printf("Number of SMs: %d\n", num_SMs);
int i;
float result;
float *a, *b, *c; // host copies of a, b, c
float *dev_a, *dev_b, *dev_c; // device copies of a, b, c
int size = N * sizeof(float); // we need space for N floats
// allocate device copies of a, b, c
cudaMalloc( (void**)&dev_a, size );
cudaMalloc( (void**)&dev_b, size );
cudaMalloc( (void**)&dev_c, sizeof(float) );
a = (float*)malloc( size );
b = (float*)malloc( size );
c = (float*)malloc( sizeof(float) );
random_floats( a, N );
random_floats( b, N );
/*
printf("a = ");
for (i=0; i<N; i++) printf("%d, ", a[i]);
printf("\n");
printf("b = ");
for (i=0; i<N; i++) printf("%d, ", b[i]);
printf("\n");
*/
result = 0;
for (i=0; i<N; i++) result += a[i] * b[i];
*c = 0;
// copy inputs to device
cudaMemcpy( dev_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy( dev_b, b, size, cudaMemcpyHostToDevice);
int blocks = (int)(N/THREADS_PER_BLOCK) + 1; // ceil(...)
//if(blocks<1) blocks=1;
// launch dot() kernel
dot <<< blocks, THREADS_PER_BLOCK >>> (dev_a, dev_b, dev_c);
// copy device result back to host copy of c
cudaMemcpy(c, dev_c, sizeof(float) , cudaMemcpyDeviceToHost);
printf("*c = %.2f\n", *c);
printf("result = %.2f\n", result);
free(a); free(b); free(c);
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
return 0;
}
|
6,335 | #include <iostream>
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <random>
#include <vector>
#include <chrono>
#define TILE_DIM 32
#define BLOCK_ROWS 8
__global__ void transposeNaive(int *odata, const int *idata, const int n, const int m) {
int x = blockIdx.x * TILE_DIM + threadIdx.x;
int y = blockIdx.y * TILE_DIM + threadIdx.y;
for (int j = 0; j < TILE_DIM; j+= BLOCK_ROWS) {
if (x < m && (y+j) < n) {
odata[x*n + (y+j)] = idata[(y+j)*m + x];
}
}
}
__global__ void transposeSharedMem(int *odata, const int *idata, const int n, const int m) {
__shared__ int tile[TILE_DIM * (TILE_DIM+1)];
int x = blockIdx.x * TILE_DIM + threadIdx.x;
int y = blockIdx.y * TILE_DIM + threadIdx.y;
for (int j = 0; j < TILE_DIM; j += BLOCK_ROWS) {
tile[(threadIdx.y+j)*(TILE_DIM+1) + threadIdx.x] = idata[(y+j)*m + x];
}
__syncthreads();
x = blockIdx.y * TILE_DIM + threadIdx.x;
y = blockIdx.x * TILE_DIM + threadIdx.y;
for (int j = 0; j < TILE_DIM; j+= BLOCK_ROWS) {
if (x < n && (y+j) < m) {
odata[(y+j)*n + x] = tile[threadIdx.x*(TILE_DIM+1) + threadIdx.y+j];
}
}
}
std::vector<std::vector<int>> random_matrix(const int num_rows, const int num_cols, const int min_val=0.0, const int max_val=1000.0) {
std::vector<std::vector<int>> my_arr;
static std::random_device rd;
static std::mt19937 mte(rd());
std::uniform_int_distribution<int> dist(min_val, max_val);
for (int i = 0; i < num_rows; i++) {
std::vector<int> my_arr_col;
for (int j = 0; j < num_cols; j++) {
my_arr_col.push_back(dist(mte));
}
my_arr.push_back(my_arr_col);
}
return my_arr;
}
bool check_correctness(int *odata, const int *idata, const int n, const int m) {
for (int i = 0; i < n*m; i++) {
int y = i/m;
int x = i % m;
if ((n*x + y) >= n*m || odata[n*x + y] != idata[i]) {
return false;
}
}
return true;
}
int main(void) {
int n = 2000;
int m = 5000;
dim3 dimGrid((m + TILE_DIM - 1)/TILE_DIM, (n + TILE_DIM - 1)/TILE_DIM, 1);
dim3 dimBlock(TILE_DIM, BLOCK_ROWS, 1);
int *idata, *odata;
cudaMallocManaged(&idata, n*m*sizeof(int));
cudaMallocManaged(&odata, n*m*sizeof(int));
std::vector<std::vector<int>> my_arr = random_matrix(n, m, 0.0, 100.0);
for (int i = 0; i < n; i++) {
for (int j = 0; j < m; j++) {
idata[m*i + j] = my_arr[i][j];
}
}
auto t1 = std::chrono::high_resolution_clock::now();
transposeSharedMem<<<dimGrid, dimBlock>>>(odata, idata, n, m);
cudaDeviceSynchronize();
auto t2 = std::chrono::high_resolution_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::milliseconds>( t2 - t1 ).count();
std::cout << duration << std::endl;
std::cout << check_correctness(odata, idata, n, m) << std::endl;
cudaFree(idata);
cudaFree(odata);
return 0;
}
|
6,336 | #include <stdio.h>
#define N 2048
#define N2 N*N
#define BLOCK_SIZE 32
__global__ void matrix_mult( const int *dev_a, const int *dev_b, int *dev_c)
{
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + threadIdx.x;
int tmp_sum = 0;
for(int k = 0; k < N; ++k){
tmp_sum += dev_a[row * N + k] * dev_b[k * N + col];
}
dev_c[row * N + col] = tmp_sum;
}
void random_ints(int *p, int n) {
int i;
for(i=0; i<n; i++) {
p[i]=rand();
}
}
int main() {
int *host_a, *host_b, *host_c, *host_d; // host copies of host_a, host_b, host_c
int *dev_a, *dev_b, *dev_c; // device copies of dev_a, dev_b, dev_c
int size = N2 * sizeof( int ); // we need space for N
int i, j, k;
// allocate device copies of host_a, host_b, host_c
cudaMalloc( (void**)&dev_a, size );
cudaMalloc( (void**)&dev_b, size );
cudaMalloc( (void**)&dev_c, size );
host_a = (int*)malloc( size );
host_b = (int*)malloc( size );
host_c = (int*)malloc( size );
host_d = (int*)malloc( size );
random_ints( host_a, N2 );
random_ints( host_b, N2 );
dim3 Block_Dim (BLOCK_SIZE, BLOCK_SIZE, 1);
dim3 Grid_Dim (N/BLOCK_SIZE, N/BLOCK_SIZE, 1);
// copy inputs to device
cudaMemcpy( dev_a, host_a, size, cudaMemcpyHostToDevice );
cudaMemcpy( dev_b, host_b, size, cudaMemcpyHostToDevice );
// launch an rev() kernel with N threads
matrix_mult<<< Grid_Dim, Block_Dim >>>(dev_a, dev_b, dev_c);
// copy device result back to host copy of host_c
cudaMemcpy( host_c, dev_c, size, cudaMemcpyDeviceToHost );
cudaDeviceSynchronize();
int sum;
int errors = 0;
for(i=0; i<N; i++) {
for(j=0; j<N; j++) {
sum = 0;
for(k=0; k<N; k++) {
sum += host_a[i*N+k]*host_b[k*N+j];
}
host_d[i*N+j] = sum;
if(host_c[i*N+j] != host_d[i*N+j]) {
printf(" %i \n", host_c[i*N+j]);
printf(" %i \n", host_d[i*N+j]);
errors += 1;
break;
}
}
}
if(errors==0) printf("%i errors: correct! \n", errors);
else printf("%i errors: not correct! \n", errors);
free( host_a ); free( host_b ); free( host_c ); free( host_d );
cudaFree( dev_a );
cudaFree( dev_b );
cudaFree( dev_c );
return 0;
} |
6,337 | #include "includes.h"
__global__ void ForwardSoftmax(float *Z, int nColsZ, float *sumExp, float *A)
{
int row = threadIdx.x;
int col = blockIdx.x;
atomicAdd(&sumExp[col], exp(Z[row * nColsZ + col]));
__syncthreads();
A[row * nColsZ + col] = exp(Z[row * nColsZ + col]) / sumExp[col];
} |
6,338 | #include <iostream>
#include <math.h>
#include <cuda_profiler_api.h>
// function to add the elements of two arrays
void add_cpu(int n, float *x, float *y)
{
for (int i = 0; i < n; i++)
y[i] = x[i] + y[i];
}
void on_cpu(){
std::cout << "Running on cpu." << std::endl;
int N = 1<<20; // 1M elements
float *x = new float[N];
float *y = new float[N];
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f;
}
// Run kernel on 1M elements on the CPU
add_cpu(N, x, y);
// Check for errors (all values should be 3.0f)
float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(y[i]-3.0f));
std::cout << "Max error: " << maxError << std::endl;
// Free memory
delete [] x;
delete [] y;
}
__global__
void add_gpu(int n, float *x, float *y)
{
for (int i = 0; i < n; i++)
y[i] = x[i] + y[i];
}
void on_gpu()
{
cudaProfilerStart();
std::cout << "Running on gpu." << std::endl;
int N = 1<<20;
float *x, *y;
// Allocate Unified Memory accessible from CPU or GPU
cudaMallocManaged(&x, N*sizeof(float));
cudaMallocManaged(&y, N*sizeof(float));
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f;
}
// Run kernel on 1M elements on the GPU
add_gpu<<<1, 1>>>(N, x, y);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
// Check for errors (all values should be 3.0f)
float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(y[i]-3.0f));
std::cout << "Max error: " << maxError << std::endl;
// Free memory
cudaFree(x);
cudaFree(y);
cudaDeviceReset();
cudaProfilerStop();
}
int main(void)
{
on_cpu();
on_gpu();
return 0;
}
|
6,339 | #include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
__global__ void initialize (int N, float *a, float *b, float *c){
int i = (blockIdx.x * blockDim.x) + threadIdx.x;
c[i] = 0;
a[i] = 1 + i;
b[i] = 1 - i;
}
__global__ void addVectors (int N, float *a, float *b, float *c){
int i = (blockIdx.x * blockDim.x) + threadIdx.x;
c[i] = a[i] + b[i];
}
int main (int argc, char **argv){
if (argc != 2) exit (1);
int N = atoi(argv[1]);
float *a, *b, *c;
cudaMallocManaged((void**)&a, N*sizeof(float));
cudaMallocManaged((void**)&b, N*sizeof(float));
cudaMallocManaged((void**)&c, N*sizeof(float));
dim3 block(1024);
dim3 grid(((N -1) / 1024) + 1);
initialize<<<grid,block>>>(N,a,b,c);
addVectors<<<grid,block>>>(N,a,b,c);
cudaDeviceSynchronize();
for (int i = 0; i < 5; i++) {
printf("%f\n", c[i]);
}
cudaFree(a);
cudaFree(b);
cudaFree(c);
}
|
6,340 | /**
* @file : activationf.cu
* @brief : activation functions content/source file in CUDA C++14,
* @author : Ernest Yeung <ernestyalumni@gmail.com>
* @date : 20171020
* @ref :
*
* If you find this code useful, feel free to donate directly and easily at this direct PayPal link:
*
* https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=ernestsaveschristmas%2bpaypal%40gmail%2ecom&lc=US&item_name=ernestyalumni¤cy_code=USD&bn=PP%2dDonationsBF%3abtn_donateCC_LG%2egif%3aNonHosted
*
* which won't go through a 3rd. party such as indiegogo, kickstarter, patreon.
* Otherwise, I receive emails and messages on how all my (free) material on
* physics, math, and engineering have helped students with their studies,
* and I know what it's like to not have money as a student, but love physics
* (or math, sciences, etc.), so I am committed to keeping all my material
* open-source and free, whether or not
* sufficiently crowdfunded, under the open-source MIT license:
* feel free to copy, edit, paste, make your own versions, share, use as you wish.
* Just don't be an asshole and not give credit where credit is due.
* Peace out, never give up! -EY
*
* */
/*
* COMPILATION TIP
* nvcc -std=c++14 -lcublas -dc Axon.cu -o Axon.o
*
* */
#include "activationf.h"
// 0
__global__ void identity_kernel(const int SIZE, float*z) {
}
__global__ void D_identity_kernel(const int SIZE, const float* z, float* d_a) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
d_a[tid]= 1.0f;
}
}
// 1
__global__ void sigmoid_kernel(const int SIZE, float*z) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = 1.f/(1.f + expf(-a_val));
z[tid]=a_val;
}
}
__global__ void D_sigmoid_kernel(const int SIZE, const float* z, float* d_a) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = 1.f/(1.f + expf(-a_val));
a_val = a_val * ( 1.0f - a_val );
d_a[tid]=a_val;
}
}
// 2
__global__ void tanh_kernel(const int SIZE, float*z)
{
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = tanhf(a_val);
z[tid] = a_val;
}
}
__global__ void D_tanh_kernel(const int SIZE, const float* z, float*d_a) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = tanhf(a_val);
a_val = 1.0f - (a_val)*a_val;
d_a[tid] = a_val;
}
}
__global__ void arctan_kernel(const int SIZE, float*z) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = atanf(a_val);
z[tid] = a_val;
}
}
__global__ void D_arctan_kernel(const int SIZE, const float* z, float*d_a) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = 1.0f / ( 1.0f + a_val*a_val);
d_a[tid] = a_val;
}
}
__global__ void ReLU_kernel(const int SIZE, float*z) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
if (a_val < 0.f) {
z[tid] = 0.f;
}
}
}
__global__ void D_ReLU_kernel(const int SIZE, const float*z, float*d_a) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
if (a_val < 0.f) {
d_a[tid] = 0.f;
} else {
d_a[tid] = 1.0f;
}
}
}
/**
* @fn Gaussian_kernel
* @param c
* @param sigma_dev
* @note exp(-(z-c)^2 / (2.f * sigma_dev*sigma_dev) )
* */
__global__ void Gaussian_kernel(const int SIZE, float* z) {
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = expf( -1.0f * ( a_val - 0.f)*(a_val-0.f) / 2.0f / (1.f*1.f) ) ;
z[tid] = a_val;
}
}
/**
* @fn D_Gaussian_kernel
* @brief derivative of Gaussian_kernel
* @param c
* @param sigma_dev
* @note -(z-c) / ( sigma_dev*sigma_dev) * exp(-(z-c)^2 / (2.f * sigma_dev*sigma_dev) )
* */
__global__ void D_Gaussian_kernel(const int SIZE, const float* z, float*d_a)
{
int kx = threadIdx.x + blockDim.x * blockIdx.x;
if (kx >= SIZE) { return; }
for (int tid=kx; tid < SIZE; tid += gridDim.x*blockDim.x)
{
float a_val = z[tid];
a_val = -1.0f * (a_val - 0.f)/(1.f*1.f) *
expf( -1.0f * ( a_val - 0.f)*(a_val-0.f) / 2.0f / (1.f*1.f) ) ;
d_a[tid] = a_val;
}
}
|
6,341 | #include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
/* Initialize the two arrays referenced by the first two parameters in preparation for
* jacobi iteration. The width and height of the arrays are given by the integer parameters.
* Border elements are set to 5.0 for both arrays, and the interior elements of a1 are
* set to 1.0. Interior elements of a2 are not initialized.
*/
void initializeArrays(int *a1, int width, int height){
int i, j;
for(i=0; i<height; i++){
for(j=0; j<width; j++){
if(i==0 || j ==0 || i==height-1 || j==width-1){
a1[i*width + j] = 1;
}
else {
a1[i*width + j] = 0;
}
}
}
}
/* Print the 2D array of floats referenced by the first parameter. The second and third
* parameters specify its dimensions, while the last argument indicates whether printing
* is actually descired at all. No output is produced if shouldPrint == 0.
*/
void printArray(int *arr, int rows, int cols, int shouldPrint){
if (!shouldPrint)
return;
int i,j;
for(i=0; i<rows; i++){
for(j=0; j<cols; j++){
printf("%d ", arr[i*cols + j]);
}
printf("\n");
}
printf("\n");
}
int strToInt(char * str){
int length = strlen(str);
int integer = 0;
for (int i=0; i<length; i++){
integer = integer + (str[i]-'0')*pow((double)10, (double)(length-1-i));
}
return integer;
}
|
6,342 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <assert.h>
#include <sys/time.h>
#define THREADS 512
#ifdef __cplusplus
extern "C"
{
#endif
__global__ void bitonic_sort(float *arr, int i, int j)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int p = index ^ j;
int q = index & i;
float temp;
if (p > index) {
if (((q==0) && (arr[index]>arr[p])) || ((q!=0) && (arr[index]<arr[p]))) {
temp = arr[index];
arr[index] = arr[p];
arr[p] = temp;
}
}
}
int cuda_sort(int number_of_elements, float *a)
{
float *arr;
cudaMalloc((void**) &arr, number_of_elements * sizeof(float));
cudaMemcpy(arr, a, number_of_elements * sizeof(float), cudaMemcpyHostToDevice);
dim3 dimGrid(number_of_elements/THREADS);
dim3 dimBlock(THREADS);
int i = 2;
while (i <= number_of_elements) {
int j = i/2;
while (j > 0){
bitonic_sort<<<dimGrid, dimBlock>>>(arr, i, j);
j/=2;
}
i*=2;
}
cudaMemcpy(a, arr, number_of_elements * sizeof(float), cudaMemcpyDeviceToHost);
cudaFree(arr);
return 0;
}
#ifdef __cplusplus
}
#endif
|
6,343 | #include<cuda.h>
#include<stdio.h>
#include<cuda_runtime.h>
void matricMul(int *A, int *B, int *C, int size) {
for (int col = 0; col < size; col++) {
for (int row = 0; row < size; row++) {
int outidx = col * size + row;
for (int idx = 0; idx < size; idx++) {
C[outidx] += A[col*size + idx] * B[idx*size + row];
}
}
}
}
void matrixMulCheck(int *C_cpu, int *C_gpu, int size) {
bool ResultFlag = true;
// Print the result
for (int i = 0; i < size; i++) {
if (C_cpu[i] != C_gpu[i]) {
ResultFlag = false;
printf("Error: C_cpu[%d] = %d; C_gpu[%d] = %d;\n", i, C_cpu[i], i, C_gpu[i]);
break;
}
}
if (ResultFlag == true) printf("Matrix Multiplication OK!\n");
else printf("Matrix Multiplication Error!\n");
}
__global__ void matrixMulGmem(int *A, int *B, int *C, int size) {
int tid, tx, ty;
tx = threadIdx.x + blockDim.x * blockIdx.x;
ty = threadIdx.y + blockDim.y * blockIdx.y;
tid = size * ty + tx;
int Aval = 0;
int Bval = 0;
int Cval = 0;
for (int i = 0; i < size; i++) {
Aval = A[ty * size + i];
Bval = B[i * size + tx];
Cval += Aval * Bval;
}
C[tid] = Cval;
}
int main() {
int nx = 1600;
int ny = 1600;
int dimx = 32;
int dimy = 16;
dim3 block(dimx, dimy); // Block dimension 32x16
dim3 grid((nx+block.x-1)/block.x, (ny+block.y-1)/block.y);
int MatrixSize = nx * ny;
int BufferSize = MatrixSize * sizeof(int);
// Create events and streams
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
int *h_A, *h_B, *h_C;
int *C_cpu;
// Host memory allocation
h_A = (int*)malloc(BufferSize);
h_B = (int*)malloc(BufferSize);
h_C = (int*)malloc(BufferSize);
C_cpu = (int*)malloc(BufferSize);
// Data input
for (int i = 0; i < MatrixSize; i++) {
h_A[i] = i % 100;
h_B[i] = i % 100;
h_C[i] = 0;
C_cpu[i] = 0;
}
int *d_A, *d_B, *d_C;
// Device memory allocation
cudaMalloc((void**)&d_A, BufferSize);
cudaMalloc((void**)&d_B, BufferSize);
cudaMalloc((void**)&d_C, BufferSize);
cudaEventRecord(start);
// Copy data from Host to Device
cudaMemcpy(d_A, h_A, BufferSize, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, BufferSize, cudaMemcpyHostToDevice);
// Matrix Multiplication using only the global memory
matrixMulGmem<<<grid, block>>>(d_A, d_B, d_C, nx);
// Copy result from Device to Host
cudaMemcpy(h_C, d_C, BufferSize, cudaMemcpyDeviceToHost);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float time;
cudaEventElapsedTime(&time, start, stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
// Check result
matricMul(h_A, h_B, C_cpu, nx);
matrixMulCheck(C_cpu, h_C, nx);
// Free memory
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
free(h_A);
free(h_B);
free(h_C);
free(C_cpu);
return 0;
} |
6,344 | #include "includes.h"
const int Nthreads = 1024, maxFR = 10000, NrankMax = 3, nt0max=81, NchanMax = 17;
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////
__global__ void computeProjections(const double *Params, const float *dataraw, const int *iC, const int *st, const int *id, const float *W, float *feat){
float x;
int tidx, nt0min, tidy, my_chan, this_chan, tid, bid, nt0, NchanNear, j, t, NT, NrankPC;
volatile __shared__ float sW[nt0max*NrankMax], sD[nt0max*NchanMax];
NT = (int) Params[0];
NchanNear = (int) Params[2];
nt0 = (int) Params[3];
NrankPC = (int) Params[6];
nt0min = (int) Params[4];
tidx = threadIdx.x;
tidy = threadIdx.y;
bid = blockIdx.x;
// move wPCA to shared memory
while (tidx<nt0){
sW[tidx + tidy*nt0] = W[tidx + tidy*nt0];
tidx+=blockDim.x;
}
tidx = threadIdx.x;
tid = tidx + tidy*blockDim.x;
// move raw data to shared memory
while (tid<nt0){
my_chan = id[bid];
for (j=0;j<NchanNear;j++){
this_chan = iC[j + NchanNear*my_chan];
sD[tid + nt0*j] = dataraw[tid + st[bid]+nt0min-1 + NT * this_chan];
}
tid+=blockDim.x*blockDim.y;
}
__syncthreads();
x = 0.0f;
for (t=0;t<nt0;t++)
x += sD[t + nt0*tidx] * sW[t + nt0*tidy];
feat[tidy + tidx*NrankPC + NrankPC*NchanNear*bid] = x;
} |
6,345 | #include "credit.cuh"
__device__ double RationalApproximation(double t)
{
double c[] = {2.515517, 0.802853, 0.010328};
double d[] = {1.432788, 0.189269, 0.001308};
return t - ((c[2]*t + c[1])*t + c[0]) /
(((d[2]*t + d[1])*t + d[0])*t + 1.0);
}
__device__ double qNorm(double p)
{
if ( (p < 0.5) & (p > 0))
{
return -RationalApproximation( sqrt(-2.0*log(p)) );
}
else if ( (p >= 0.5) & (p < 1))
{
return RationalApproximation( sqrt(-2.0*log(1-p)) );
}
}
Credit::Credit() {}
Credit::~Credit() {}
Credit::Credit(double PD, double EAD, double LGD, double FG, double FL) : PD(PD), EAD(EAD), LGD(LGD), FG(FG), FL(FL)
{
this->rho = pow(FG, 2) + pow(FL, 2);
this->FI = sqrt( 1 - this->rho );
}
__device__ double Credit::loss(double rG, double rL, curandState * state)
{
double Y = sqrt(this->rho) * ( this->FG * rG + FL * rL ) + sqrt( 1 - this->rho ) * curand_normal(state);
return (qNorm(this->PD) >= Y) * EAD * LGD;
}
__device__ Portfolio::Portfolio(Credit *cartera, int n): carte(cartera), n(n) {}
__device__ Portfolio::~Portfolio() {}
__device__ double Portfolio::loss(double fg, double fl, curandState *state)
{
double loss = 0;
for (int i = 0; i < this->n; i++)
{
loss += this->carte[i].loss(fg, fl, state);
}
return loss;
}
|
6,346 | #include "includes.h"
__device__ void trace_subm(int j, int k, int *daG, int *dbG, double *AB, double *A){
int l;
for(l=0; l<(*dbG); l++){
*(A+j*(*daG)+k) += *(AB+j*(*dbG)+l+k*(*dbG)+l);
}
}
__global__ void ptrBp(int *daG, int *dbG, double *ABg, double *Ag) {
int k = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
trace_subm(j, k, daG, dbG, ABg, Ag);
} |
6,347 | extern "C" {
/*
* Kernel to separate RGB channels
*/
__global__
void separateChannels(const uchar4* const inputImageRGBA,
int numRows,
int numCols,
unsigned char* const redChannel,
unsigned char* const greenChannel,
unsigned char* const blueChannel)
{
int absolute_image_position_x = blockDim.x * blockIdx.x + threadIdx.x;
int absolute_image_position_y = blockDim.y * blockIdx.y + threadIdx.y;
if ( absolute_image_position_x >= numCols ||
absolute_image_position_y >= numRows )
{
return;
}
int thread_1D_pos = absolute_image_position_y * numCols + absolute_image_position_x;
redChannel[thread_1D_pos] = inputImageRGBA[thread_1D_pos].x;
greenChannel[thread_1D_pos] = inputImageRGBA[thread_1D_pos].y;
blueChannel[thread_1D_pos] = inputImageRGBA[thread_1D_pos].z;
}
/*
* Kernel for combining RGB channels
*/
__global__
void recombineChannels(const unsigned char* const redChannel,
const unsigned char* const greenChannel,
const unsigned char* const blueChannel,
uchar4* const outputImageRGBA,
int numRows,
int numCols)
{
const int2 thread_2D_pos = make_int2( blockIdx.x * blockDim.x + threadIdx.x,
blockIdx.y * blockDim.y + threadIdx.y);
const int thread_1D_pos = thread_2D_pos.y * numCols + thread_2D_pos.x;
if (thread_2D_pos.x >= numCols || thread_2D_pos.y >= numRows)
return;
unsigned char red = redChannel[thread_1D_pos];
unsigned char green = greenChannel[thread_1D_pos];
unsigned char blue = blueChannel[thread_1D_pos];
//Alpha should be 255 for no transparency
uchar4 outputPixel = make_uchar4(red, green, blue, 255);
outputImageRGBA[thread_1D_pos] = outputPixel;
}
} |
6,348 | #include <cuda.h>
#include <cuComplex.h>
#include <math_constants.h>
#define __SET_MAP \
const int \
x = blockIdx.x * blockDim.x + threadIdx.x \
, y = blockIdx.y * blockDim.y + threadIdx.y \
;
template <int max_support>
__device__ __inline__
void ucs_common(
cuDoubleComplex mesh[max_support][max_support]
, double t2
){
__SET_MAP
const int max_half_support = max_support / 2;
double
t2_div_sc = t2 / double(max_half_support)
, xs = double(x - max_half_support) * t2_div_sc
, ys = double(y - max_half_support) * t2_div_sc
;
mesh[x][y] = make_cuDoubleComplex(xs * xs + ys * ys, 0.0);
}
extern "C" __global__ void r2(cuDoubleComplex mesh[256][256], double t2) {
ucs_common<256>(mesh, t2);
}
template <int max_support>
__device__ __inline__
void calc(
cuDoubleComplex dst[max_support][max_support]
, const cuDoubleComplex src[max_support][max_support]
, double w
){
__SET_MAP
double ph = w * (1.0 - sqrt(1.0 - src[x][y].x));
double s, c;
sincos(2.0 * CUDART_PI * ph, &s, &c);
dst[x][y] = make_cuDoubleComplex(c, -s); // to get rid of conj later
}
extern "C" __global__ void wkernff(
cuDoubleComplex dst[256][256]
, const cuDoubleComplex src[256][256]
, double w
){
calc<256>(dst, src, w);
}
template <
int max_support
, int oversample
>
__device__ __inline__
void copy_ucs_2_over(
cuDoubleComplex dst[max_support * oversample][max_support * oversample]
, const cuDoubleComplex src[max_support][max_support]
){
__SET_MAP
const int max_half_support = max_support / 2;
const int dst_center = max_support * oversample / 2 - max_half_support;
dst[dst_center + x][dst_center + y] = src[x][y];
}
extern "C" __global__ void copy_2_over(
cuDoubleComplex dst[2048][2048]
, const cuDoubleComplex src[256][256]
){
copy_ucs_2_over<256,8>(dst, src);
}
// We use 3rd grid dimension to cover oversample range
template <
int max_support
, int oversample
>
__device__ __inline__
void transpose_over(
cuDoubleComplex dst[oversample][oversample][max_support][max_support]
, const cuDoubleComplex src[max_support * oversample][max_support * oversample]
) {
__SET_MAP
const int
overx = blockIdx.z / oversample
, overy = blockIdx.z % oversample
, sx = x * oversample + overx
, sy = y * oversample + overy
;
dst[overx][overy][x][y] = src[sx][sy];
}
extern "C" __global__ void transpose_over0(
cuDoubleComplex dst[8][8][256][256]
, const cuDoubleComplex src[2048][2048]
){
transpose_over<256,8>(dst, src);
}
template <
int max_support
, int oversample
>
__device__ __inline__
void cut_out(
int supp
, cuDoubleComplex * dst
, const cuDoubleComplex src[max_support][max_support]
) {
__SET_MAP
if (x >= supp || y >= supp) return;
dst[x * supp + y] = src[max_support/2-supp/2+x][max_support/2-supp/2+y];
}
extern "C" __global__ void wextract1(
cuDoubleComplex * dst
, const cuDoubleComplex src[256][256]
, int supp
){
cut_out<256,8>(supp, dst, src);
}
|
6,349 | #include<iostream>
#include<cuda.h>
#include<cuda_runtime.h>
#define SIZE 9
using namespace std;
__global__ void vectoradd(int *p,int *q,int *result){
int tid = threadIdx.x + blockDim.x * blockIdx.x;
if(tid<SIZE){
// for(int i=0;i<SIZE;i++){
result[tid] = p[tid] + q[tid];
// }
}
}
int main(int argc, char const *argv[]){
int v1[SIZE],v2[SIZE],z[SIZE];
for(int i=0;i<SIZE;i++){
v1[i] = rand()%100+1;
v2[i] = rand()%50+1;
z[i] = 0;
}
printf("First Vector:\n");
for(int i=0;i<SIZE;i++){
printf("%d ",v1[1]);
}
printf("\nSecond Vector:\n");
for(int i=0;i<SIZE;i++){
printf("%d ",v2[1]);
}
int byte_size = SIZE * sizeof(int);
int *a,*b,*c;
cudaMalloc(&a,byte_size);
cudaMalloc(&b,byte_size);
cudaMalloc(&c,byte_size);
cudaMemcpy(a,v1,byte_size,cudaMemcpyHostToDevice);
cudaMemcpy(b,v2,byte_size,cudaMemcpyHostToDevice);
cudaMemcpy(c,z,byte_size,cudaMemcpyHostToDevice);
vectoradd<<<2,SIZE>>>(a,b,c);
cudaMemcpy(&z,c,byte_size,cudaMemcpyDeviceToHost);
printf("\nResult:\n");
for(int i=0;i<SIZE;i++){
printf("%d ",z[1]);
}
return 0;
} |
6,350 | #include <iostream>
#include <cuda.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>
#include "parallel.cuh"
#define ELEMENTS 5
#define errCheck(code) { errorCheck((code), __FILE__, __LINE__); }
void addWithCuda(const int* a, const int* b, int* c, int elements);
using std::cout;
using std::flush;
using std::endl;
inline void errorCheck(cudaError_t code, const char* file, int line) {
if (cudaSuccess != code) {
std::cout << "[" << file << ", line " << line << "]" << std::flush;
std::cout << " CUDA error <" << cudaGetErrorString(code) << "> received." << std::endl << std::flush;
exit(EXIT_FAILURE);
}
}
__global__ void addKernel(const int* a, const int* b, int* c, int elements) {
int x = blockIdx.x * blockDim.x + threadIdx.x;
printf("x = %d, BlockIdx = %d, BlockDimx = %d, threadIdx = %d\n", x, blockIdx.x, blockDim.x, threadIdx.x);
if (x < elements) {
c[x] = a[x] + b[x];
}
}
void solvePar(int rows, int cols, int iterations, double td, double h, double** matrix) {
cout << "Do cuda related stuff here!" << endl << flush;
// Example.
// int elements = 5;
const int a[ELEMENTS] = { 1, 2, 3, 4, 5 };
const int b[ELEMENTS] = { 5, 4, 3, 2, 1 };
int c[ELEMENTS] = { 0 };
addWithCuda(a, b, c, ELEMENTS);
}
// before in lab 3 (too complex in the end)
// get initial totalMatrix
// extract partialMatrix
// in the main calculation method
// for each iteration
// ||calculate the heatmap of partialMatrix||
// end for loop
// copy/paste the values back to totalMatrix
// AFTER in lab 3 (this final method)
// in the main calculation method
// for each iteration
// get initial totalMatrix
// extract partialMatrix
// ||calculate the heatmap of partialMatrix||
// sync
// copy/paste the values back to totalMatrix
// end for loop
// for lab 4
// for each iteration
// get totalMatrix
// extract partialMatrix
// || parallel calculate partialMatrix ||
// copy paste partial to total
// end for
void addWithCuda(const int* a, const int* b, int* c, int elements) {
int* dev_a = nullptr;
int* dev_b = nullptr;
int* dev_c = nullptr;
dim3 dimGrid(1, 1, 1);
dim3 dimBlock(5, 1, 1);
errCheck(cudaSetDevice(0));
errCheck(cudaMalloc((void**)&dev_c, elements * sizeof(int)));
errCheck(cudaMalloc((void**)&dev_a, elements * sizeof(int)));
errCheck(cudaMalloc((void**)&dev_b, elements * sizeof(int)));
errCheck(cudaMemcpy(dev_a, a, elements * sizeof(int), cudaMemcpyHostToDevice));
errCheck(cudaMemcpy(dev_b, b, elements * sizeof(int), cudaMemcpyHostToDevice));
// for should be here
// extract partial matrix from total matrix
// in our addKernel, arguments should be
// totalInputMatrix, partialMatrix, totalOutputMatrix
addKernel << <dimGrid, dimBlock >> > (dev_a, dev_b, dev_c, elements);
errCheck(cudaGetLastError());
errCheck(cudaDeviceSynchronize());
errCheck(cudaMemcpy(c, dev_c, elements * sizeof(int), cudaMemcpyDeviceToHost));
errCheck(cudaFree(dev_a));
errCheck(cudaFree(dev_b));
errCheck(cudaFree(dev_c));
errCheck(cudaDeviceReset());
cout << "c = { " << c[0] << flush;
for (int i = 1; i < elements; i++) {
cout << ", " << c[i] << flush;
}
cout << " }" << endl << flush;
}
|
6,351 | #include <stdio.h>
#include <stdlib.h>
#define RADIUS 3
#define BLK_SIZE 256
#define NUM_ELEMENTS (BLK_SIZE * 32) // 256 * 32 = 8192
__global__ void stencil_1d(int *d_in, int *d_out){
__shared__ int temp[ BLK_SIZE + 2*RADIUS ]; // Stored in shared memory
int gindex = (blockIdx.x * blockDim.x) + threadIdx.x + RADIUS;
int lindex = threadIdx.x + RADIUS;
// Load input elements from global memory to shared memory
temp[lindex] = d_in[gindex];
if (threadIdx.x < RADIUS){
temp[lindex - RADIUS] = d_in[gindex - RADIUS];
temp[lindex + BLK_SIZE] = d_in[gindex + BLK_SIZE];
}
// Synchronize (ensure all the data is available)
__syncthreads();
int result = 0;
for (int offset = -RADIUS; offset <= RADIUS; offset++)
result += temp[lindex + offset];
// Store the result to global memory
d_out[gindex - RADIUS] = result;
}
int main(void){
int h_in[ NUM_ELEMENTS + (2*RADIUS) ];
int h_out[ NUM_ELEMENTS ];
int *d_in, *d_out;
// Initialize host input values
for (int i=0; i<(NUM_ELEMENTS + 2*RADIUS); i++)
h_in[i] = 1;
// Allocate device global memory
cudaMalloc( &d_in, (NUM_ELEMENTS + 2*RADIUS) * sizeof(int) );
cudaMalloc( &d_out, NUM_ELEMENTS * sizeof(int) );
// Copy HOST -> DEVICE
cudaMemcpy( d_in, h_in, (NUM_ELEMENTS + 2*RADIUS) * sizeof(int), cudaMemcpyHostToDevice);
// Launch kernel
stencil_1d<<< NUM_ELEMENTS/BLK_SIZE, BLK_SIZE>>>(d_in, d_out);
// NUM_ELEMENTS / BLK_SIZE = 8192 / 256 = 32
// BLK_SIZE = 256
// Copy result DEVICE -> HOST
cudaMemcpy( h_out, d_out, NUM_ELEMENTS * sizeof(int), cudaMemcpyDeviceToHost);
// Verify results
int cnt = 0;
for (int i=0; i<NUM_ELEMENTS; i++){
if (h_out[i] != 7){
printf("h_out[%d] == %d != 7\n", i, h_out[i]);
cnt++;
break;
}
}
if (cnt!=0){
printf("Wrong result\n");
}else{
printf("Success\n");
}
// Clean up
cudaFree(d_in);
cudaFree(d_out);
return 0;
}
|
6,352 | // probado en cuda 10.1 agosto 2020
// nvcc suma.cu -o v && ./v
#include <bits/stdc++.h>
using namespace std;
#define THREADS_PER_BLOCK 1024 //depende de la arquitectura
//#define g 10/2
void random_ints(int *a, int tam){
for (int i =0; i < tam; ++i){
a[i] = 1;//+rand()%10;
}
}
void imprimir(int *&v, int tam){
for(int i=0;i<tam;i++){
cout<<v[i]<<" ";
}
cout<<endl;
}
//=================cuda=================
__global__ void add(int *a, int *b, int *r) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
r[index] = a[index] + b[index];
}
__global__ void sumar(int *a, int *b, int *r, int tam) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
if( index < tam){
r[index] = a[index] + b[index];
}
}
void cuda_suma(int *a, int *b, int *r, int tam ){
int *d_a, *d_b, *d_r; //device copies of a,b,c
int size = tam*sizeof(int);
//dar memoria en GPU
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_r, size);
//copiar HOST -> DEVICE
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);//memoria device
cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);//memoria device
//run kernel //almenos debe contener un bloque
sumar<<<(tam+THREADS_PER_BLOCK-1)/THREADS_PER_BLOCK,THREADS_PER_BLOCK>>>(d_a, d_b, d_r, tam);//run
//copiar DEVICE -> HOST
cudaMemcpy(r, d_r, size, cudaMemcpyDeviceToHost);//retornar a host
//liberar memoria
cudaFree(d_a); cudaFree(d_b); cudaFree(d_r);
}
//======================================
int main(void){
int tam = 300*300; //250 000
int *a, *b, *r;
a = (int*)malloc(tam*sizeof(int));
b = (int*)malloc(tam*sizeof(int));
r = (int*)malloc(tam*sizeof(int));
random_ints(a,tam);
random_ints(b,tam);
//suma(a,b,r,tam);
cuda_suma(a,b,r,tam);
//imprimir(a,tam);
//imprimir(b,tam);
imprimir(r,tam);
free(a); free(b); free(r);
return 0;
}
|
6,353 | #include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#define ASCII_CHARS 128
int mod(int a, int b) {
return (a%b+b)%b;
}
void printrow(float *msg_v_list) {
int i;
for (i = 0; i < 16; i++) {
printf("PRINTROW: %f\n", msg_v_list[i]);
}
}
__global__ void mtxEncrypt(float *secretKey, float *msg, float *result, int matrix_dims) {
int tx = threadIdx.x;
int ty = threadIdx.y;
float Pvalue = 0;
int i;
for (i = 0; i < 16; i++) {
// printf("PRINTROW: %f\n", msg[i]);
}
for (int k = 0; k < matrix_dims; ++k) {
float secretElement = secretKey[ty * matrix_dims + k];
float msgElement = msg[k * matrix_dims + tx];
// printf("\nMultiplying %f by %f: \n", secretElement, msgElement);
Pvalue += secretElement * msgElement;
}
//printf("PValue: %f %d %d\n", Pvalue, (int)Pvalue, ((int)Pvalue%ASCII_CHARS+ASCII_CHARS)%ASCII_CHARS);
int mod_result = ((int)Pvalue%ASCII_CHARS+ASCII_CHARS)%ASCII_CHARS; // actual modulus function
result[ty * matrix_dims + tx] = mod_result;
// printf("result: %f\n", result[ty * matrix_dims + tx]);
}
void secretKey(float **SKey, float **invSKey, int matrix_dims) {
int k,z;
float xSKey[4][4] = {{8, 6, 9, 5},
{6, 9, 5, 10},
{5, 8, 4, 9},
{10, 6, 11, 4}};
float xinvSKey[4][4] = {{-3, 20, -21, 1},
{2, -41, 44, 1},
{2, -6, 6, -1},
{-1, 28, -30, -1}};
int i, j;
for (i = 0; i < 4; i++) {
for (j = 0; j < 4; j++) {
SKey[0][i*matrix_dims + j] = xSKey[i][j];
invSKey[0][i*matrix_dims + j] = xinvSKey[i][j];
printf("%f : %f \n", i*matrix_dims + j, SKey[0][i*matrix_dims + j]);
}
}
}
void pad_msg(char *msg, int matrix_dims) {
printf("Padding..\n");
int i;
unsigned int msg_len = strlen(msg);
int matrix_size = matrix_dims*matrix_dims;
int extra_chars = matrix_size - msg_len%matrix_size;
char *space = (char *)malloc(extra_chars);
for (i = 0; i < extra_chars; i++) {
space[i] = ' ';
}
strcat(msg, space);
}
void encode_msg(char *msg, float *msg_vector, int matrix_dims) {
int matrix_size = matrix_dims * matrix_dims;
int i = 0;
while (msg[i] != '\0') {
msg_vector[i] = (int)(msg[i]);
i++;
}
}
int main(int argc, char *argv[]) {
char *msg;
if (argc > 1) {
if (fopen(argv[1], "r")) {
printf("reading file\n\n");
FILE *msg_file;
msg_file = fopen(argv[1], "r");
fseek(msg_file, 0, SEEK_END);
// ftell() gives current position in the stream
long msg_file_size = ftell(msg_file);
// rewind to beginning of file now that we have size
fseek(msg_file, 0, SEEK_SET);
// allocate memory for msg var, read file stream into memory
char *msg_file_text = (char *)malloc(msg_file_size + 1);
fread(msg_file_text, msg_file_size, 1, msg_file);
fclose(msg_file);
// printf("%s", msg_file_text);
msg = msg_file_text;
} else {
msg = (char *)malloc(strlen(argv[1]) + 15);
strcpy(msg, argv[1]);
}
} else {
const char* jack_msg = "All work and no play makes Jack a dull boy.";
// const char *jack_msg = "kkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk";
msg = (char *) malloc(strlen(jack_msg));
strcpy(msg, jack_msg);
}
char *ascii_dict = (char *)malloc(128);
int matrix_dims = 4;
int matrix_size = matrix_dims*matrix_dims;
// malloc() for 4 (int*) pointers to 4 rows, also malloc'd (int)
int z;
float **SKey = (float **) malloc(matrix_dims * sizeof(float *));
for (z = 0; z < matrix_dims; z++) {
SKey[z] = (float *)malloc(matrix_dims * sizeof(float));
}
float **invSKey = (float **)malloc(matrix_dims * sizeof(float *));
for (z = 0; z < matrix_dims; z++) {
invSKey[z] = (float *)malloc(matrix_dims * sizeof(float));
}
secretKey(SKey, invSKey, matrix_dims);
int i, j;
printf("%s", msg);
for (i = 0; i < 4; i++) {
for (j = 0; j < 4; j++) {
printf("Skey: %f\n", SKey[0][i*4 + j]);
}
}
// pad message if not divisible by matrix_dims^2
int msg_size;
if (strlen(msg) % matrix_size != 0) {
pad_msg(msg, matrix_dims);
}
msg_size = strlen(msg);
int msg_parts = msg_size / matrix_size;
printf("%s", msg);
float *msg_vector = (float *)malloc(strlen(msg)*sizeof(float));
// message encoded, results stored in msg_vector
encode_msg(msg, msg_vector, matrix_dims);
printf("ENCODED!\n");
float msg_vector_list[msg_parts][matrix_size];
for (i = 0; i < msg_parts; i++) {
printf("\n\nCount: %d %d\n", i, msg_size%matrix_size);
for (j = 0; j < matrix_size; j++) {
msg_vector_list[i][j] = msg_vector[i*matrix_size + j];
printf("%f\n", msg_vector_list[i][j], "\n");
}
}
int nBytes = matrix_size*sizeof(float);
printf("1\n");
float **results = (float **)malloc(msg_size * sizeof(float *));
printf("2\n");
for (z = 0; z < msg_parts; z++) {
results[z] = (float *)malloc(matrix_size * sizeof(float));
memset(results[z], 0, nBytes);
}
printf("3\n");
cudaDeviceProp deviceProp;
cudaGetDeviceProperties(&deviceProp, 0);
cudaSetDevice(0);
float *secretGpu, *msgGpu, *resultGpu;
cudaMalloc((void **)&secretGpu, nBytes);
printf("msg_parts: %d \n", msg_parts);
printf("\nmsg_vector_list[1]: %d\n", *msg_vector_list[1]);
cudaMalloc((void **)&resultGpu, nBytes);
cudaMalloc((void **)&msgGpu, nBytes);
cudaMemcpy(secretGpu, *SKey, nBytes, cudaMemcpyHostToDevice);
dim3 block(matrix_dims, matrix_dims);
dim3 grid((matrix_dims+block.x-1)/block.x, (matrix_dims+block.y-1)/block.y);
for (i = 0; i < msg_parts; i++) {
cudaMemcpy(msgGpu, msg_vector_list[i], nBytes, cudaMemcpyHostToDevice);
mtxEncrypt<<<grid, block>>>(secretGpu, msgGpu, resultGpu, matrix_dims);
cudaMemcpy(results[i], resultGpu, nBytes, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
}
for (i = 0; i < msg_parts; i++) {
for (j = 0; j < matrix_size; j++) {
printf("%c", (int)results[i][j], char((int)results[i][j]));
}
}
float **unEncrypted = (float **)malloc(msg_parts * sizeof(float *));
for (z = 0; z < msg_parts; z++) {
unEncrypted[z] = (float *)malloc(matrix_size * sizeof(float));
memset(unEncrypted[z], 0, nBytes);
}
cudaMemcpy(secretGpu, *invSKey, nBytes, cudaMemcpyHostToDevice);
for (i = 0; i < msg_parts; i++) {
cudaMemcpy(msgGpu, results[i], nBytes, cudaMemcpyHostToDevice);
mtxEncrypt<<<grid, block>>>(secretGpu, msgGpu, resultGpu, matrix_dims);
cudaMemcpy(unEncrypted[i], resultGpu, nBytes, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
}
for (i = 0; i < msg_parts; i++) {
for (j = 0; j < matrix_size; j++) {
printf("%c", (int)unEncrypted[i][j], char((int)unEncrypted[i][j]));
}
}
free(results);
cudaFree(secretGpu);
cudaFree(msgGpu);
cudaFree(resultGpu);
cudaDeviceReset();
}
|
6,354 | //optimization homework #4 cs 677 Theodore Jagodits
#include <time.h>
#include <stdio.h>
#include <stdlib.h>
#include "string.h"
#include <iostream>
#define DEFAULT_ROW 128
#define DEFAULT_COL 128
#define TILE_SIZE 16
#define MAX_CONST 16000
#define GPU_SPEED_LO_LIMIT 1
//add constant memory
__constant__ float c_inp[MAX_CONST];
//tiling complete
__global__ void unknown_algo_inp2_chunk(float *result, float *inp2, int row, int col, int num_tiles, int chunk){
//add shared memory
__shared__ float temp_shared_2[TILE_SIZE * TILE_SIZE];
//get row col idx
int tx = blockIdx.x * blockDim.x + threadIdx.x;
int ty = blockIdx.y * blockDim.y + threadIdx.y;
float temp = 0.0f;
unsigned int curr = 0, pos_x;
for(unsigned int count = 0; count < num_tiles; count++){
//find position
pos_x = count * TILE_SIZE + threadIdx.x;
//check bounds and load tile
if(pos_x < col && ty < row){
temp_shared_2[threadIdx.y * TILE_SIZE + threadIdx.x] = inp2[ty*row + pos_x];
}
__syncthreads();
for(unsigned int tile = 0; tile < TILE_SIZE; tile++){
if(curr <= tx){
temp += temp_shared_2[threadIdx.y * TILE_SIZE + tile];
}
curr ++;
}
}
if(ty < row && tx < col){
result[ty*row + tx] = temp;
}
}
//tiling complete
__global__ void unknown_algo_inp1_chunk(float *result, int row, int col, int num_tiles, int stride, int chunk){
int tx = blockDim.x * blockIdx.x + threadIdx.x;
int ty = blockDim.y * blockIdx.y + threadIdx.y;
float temp, local;
temp = 0.0f;
if(ty < row && tx < col){
local = c_inp[ty*row +tx];
result[ty*row + tx] = temp;
}
for(int i = 0; i < col; i++){
temp += local * c_inp[stride + i];
}
if(ty < row && tx < col){
result[ty*row + tx] = temp;
}
}
//tiling complete
__global__ void unknown_algo_inp2_no_chunk(float *result, int row, int col, int num_tiles){
//add shared memory
__shared__ float temp_shared_2[TILE_SIZE * TILE_SIZE];
//get row col idx
int tx = blockIdx.x * blockDim.x + threadIdx.x;
int ty = blockIdx.y * blockDim.y + threadIdx.y;
float temp = 0.0f;
unsigned int curr = 0, pos_x;
for(unsigned int count = 0; count < num_tiles; count++){
//find position
pos_x = count * TILE_SIZE + threadIdx.x;
//check bounds and load tile
if(pos_x < col && ty < row){
temp_shared_2[threadIdx.y * TILE_SIZE + threadIdx.x] = c_inp[ty*row + pos_x];
}
__syncthreads();
for(unsigned int tile = 0; tile < TILE_SIZE; tile++){
if(curr <= tx){
temp += temp_shared_2[threadIdx.y * TILE_SIZE + tile];
}
curr ++;
}
}
if(ty < row && tx < col){
result[ty*row + tx] = temp;
}
}
//tiling complete no point of tiling here
__global__ void unknown_algo_inp1_no_chunk(float *result, int row, int col, int num_tiles, int stride){
int tx = blockDim.x * blockIdx.x + threadIdx.x;
int ty = blockDim.y * blockIdx.y + threadIdx.y;
float temp, local;
temp = 0.0f;
if(ty < row && tx < col){
local = c_inp[ty*row +tx];
result[ty*row + tx] = temp;
}
for(int i = 0; i < col; i++){
temp += local * c_inp[stride + i];
}
if(ty < row && tx < col){
result[ty*row + tx] = temp;
}
}
int main( int argc, char **argv ){
int row = DEFAULT_ROW;
int col = DEFAULT_COL;
if(argc == 3){
row = atoi(argv[1]);
col = atoi(argv[2]);
}
int num_bytes = row* col * sizeof(float);
int stride = row * col;
float *h_temp = (float *) calloc(col, sizeof(float)); //store temp results
float *d_temp = (float *) calloc(col, sizeof(float)); //store temp results
float *d_result = (float *) malloc(num_bytes);
float *h_input = (float *) malloc(num_bytes);
float *h_result = (float *) malloc(num_bytes);
//cudaMalloc(&d_input2, num_bytes);
cudaMalloc(&d_result, num_bytes);
cudaMalloc(&d_temp, col * sizeof(float));
//put in data
for(int o = 0; o < row; o++){
for(int p = 0; p < col; p++){
h_input[row * o + p] = 1.0f;
}
}
// add input 1 to input
for(int p = 0; p < col; p++){
h_input[stride + p] = 1.0f;
}
//declare block and grid size for kernel
int block_size = TILE_SIZE;
//make grids x y
int grid_x = (int)ceil((float)col/block_size);
int grid_y = (int)ceil((float)row/block_size);
dim3 dim_grid (grid_x, grid_y);
dim3 dim_block (block_size, block_size);
//run different configs depending on size
if(col * row + col> MAX_CONST){ //greater than limit of const mem
/*
float *temp_chunk = (float*) calloc(MAX_CONST,sizeof(float));
for(int chunk = 0; chunk < (int)ceil((float)(row*col)/MAX_CONST); chunk++){
for(int i = 0; i < MAX_CONST; i++){
if(i<stride)
temp_chunk[i] = h_input[chunk*MAX_CONST + i];
else
break;
}
cudaMemcpyToSymbol(c_inp, temp_chunk, MAX_CONST);
unknown_algo_inp2_chunk<<< dim_grid, dim_block >>> (d_result, d_temp, row, col, num_tiles, chunk);
}
for(int chunk = 0; chunk < (int)ceil((float)(row*col)/MAX_CONST); chunk++){
unknown_algo_inp1<<< dim_grid, dim_block >>> (d_result, row, col, 0);
}
free(temp_chunk);
*/
} else if(col * row > GPU_SPEED_LO_LIMIT){//check
cudaMemcpyToSymbol(c_inp, h_input, num_bytes);
unknown_algo_inp2_no_chunk<<< dim_grid, dim_block >>> (d_result, row, col, grid_x);
unknown_algo_inp1_no_chunk<<< dim_grid, dim_block >>> (d_result, row, col, grid_x, stride);
} else{ // run one kernel
//combined_algo<<< dim_grid, dim_block >>> (d_result, row, col, 0);
}
// Copy result back to host
cudaMemcpy(h_result, d_result, num_bytes, cudaMemcpyDeviceToHost);
//print error check
for(int i = 0; i < row; i++){
printf("%d. ", i);
for(int j = 0; j < col; j++){
printf("%d ", (int)h_result[i*row + j]);
}
printf("\n");
}
free(h_input);
free(h_result);
cudaFree(d_result);
cudaFree(d_temp);
return 0;
} |
6,355 | #include "includes.h"
__global__ void reduce_v2(float* in,float* out, int n){
int tx = threadIdx.x;
int bx = blockIdx.x;
int BX = blockDim.x; //same as THEAD_MAX
int i = bx*BX+tx;
__shared__ float S[THEAD_MAX];
S[tx] = i < n ? in[i] : 0;
__syncthreads();
for(int s=BX/2; s>0 ;s>>=1){
if(tx < s)
S[tx] += S[tx+s];
__syncthreads();
}
if(tx==0)
out[bx] = S[0];
} |
6,356 | #include "includes.h"
__global__ void mult3_kernel(double *g_out, double *a, double *b, double *ct, int n) {
const int j2 = blockIdx.x * blockDim.x + threadIdx.x;
double wkr, wki, xr, xi, yr, yi, ajr, aji, akr, aki, bjr, bji, bkr, bki;
double new_ajr, new_aji, new_akr, new_aki;
const int m = n >> 1;
const int nc = n >> 2;
const int j = j2 << 1;
if (j2) {
int nminusj = n - j;
wkr = 0.5 - ct[nc - j2];
wki = ct[j2];
ajr = a[j];
aji = a[1 + j];
akr = a[nminusj];
aki = a[1 + nminusj];
xr = ajr - akr;
xi = aji + aki;
yr = wkr * xr - wki * xi;
yi = wkr * xi + wki * xr;
ajr -= yr;
aji -= yi;
akr += yr;
aki -= yi;
bjr = b[j];
bji = b[1 + j];
bkr = b[nminusj];
bki = b[1 + nminusj];
xr = bjr - bkr;
xi = bji + bki;
yr = wkr * xr - wki * xi;
yi = wkr * xi + wki * xr;
bjr -= yr;
bji -= yi;
bkr += yr;
bki -= yi;
new_aji = ajr * bji + bjr * aji;
new_ajr = ajr * bjr - aji * bji;
new_aki = akr * bki + bkr * aki;
new_akr = akr * bkr - aki * bki;
xr = new_ajr - new_akr;
xi = new_aji + new_aki;
yr = wkr * xr + wki * xi;
yi = wkr * xi - wki * xr;
g_out[j] = new_ajr - yr;
g_out[1 + j] = yi - new_aji;
g_out[nminusj] = new_akr + yr;
g_out[1 + nminusj] = yi - new_aki;
} else {
xr = a[0];
xi = a[1];
yr = b[0];
yi = b[1];
g_out[0] = xr * yr + xi * yi;
g_out[1] = -xr * yi - xi * yr;
xr = a[0 + m];
xi = a[1 + m];
yr = b[0 + m];
yi = b[1 + m];
g_out[1 + m] = -xr * yi - xi * yr;
g_out[0 + m] = xr * yr - xi * yi;
}
} |
6,357 | #include <thrust/sort.h>
#include <thrust/device_ptr.h>
//---------------------------------------------------------------------------
// NVCC is not yet able to compile C++11 code.
// Hence the need to keep Thrust and VexCL code in separate files.
//---------------------------------------------------------------------------
template <typename T>
void thrust_sort(T *begin, T *end) {
thrust::sort(
thrust::device_pointer_cast(begin),
thrust::device_pointer_cast(end)
);
}
//---------------------------------------------------------------------------
// Due to the code separation we also need to explicitly instantiate the
// necessary templates.
//---------------------------------------------------------------------------
#define VEXCL_INSTANTIATE_THRUST_SORT(T) \
template void thrust_sort<T>(T * begin, T * end)
VEXCL_INSTANTIATE_THRUST_SORT(int);
#undef VEXCL_INSTANTIATE_THRUST_SORT
|
6,358 | #include "includes.h"
__global__ void initializeElementsTo(int initialValue, int *a, int N)
{
int i = threadIdx.x + blockIdx.x * blockDim.x;
a[i] = initialValue;
} |
6,359 | /*
*
* GPU Computing: Benchmarking CUDA with the Scalar-Vector Product
* Computes only one element in a single thread and uses N threads with 1
* Single Thread-Per-Block.
*
*/
#include <stdio.h>
#include <time.h>
#include <cuda.h>
#define SCALAR 3
float printRunTime(const char *string, struct timespec *ti1, struct timespec *ti2)
{
float runtime;
runtime = (ti2->tv_sec - ti1->tv_sec ) + 1e-9*(ti2->tv_nsec - ti1->tv_nsec);
fprintf(stderr,"Run time %s : %f microsecs.\n", string, runtime * 1e6);
return runtime;
}
void show(float *a, long n)
{
int i,j;
int max = 10;
j = (n > max) ? n-max : 0;
printf("Results (max last %d) : \n",max);
for(i=j; i<n; i++)
printf("%3d : %6.2f\n",i,a[i]);
}
// This runs on the CPU :
float multiplyOnHost(float *a, long n, float s)
{
long i;
struct timespec ti1,ti2;
fprintf(stderr,"Now computing on CPU : \n");
clock_gettime(CLOCK_REALTIME,&ti1); // read starttime into t1
for(i=0;i<n;i++)
a[i] *= s;
clock_gettime(CLOCK_REALTIME,&ti2); // read endtime into t2
return printRunTime("CPU",&ti1,&ti2);
}
// This is the kernel that runs on the GPU :
__global__ void multiplyKernel(float *b, float s)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
b[i] *= s;
}
// This part also runs on the CPU (and not on the GPU !) :
float multiplyOnDevice(float *b_h, long n, float s)
{
float *a_d; // Pointer to device array
struct timespec ti1,ti2,ti3,ti4;
int nBlocks;
// Do the actual calculation on N threads inside the GPU device:
nBlocks = n;
if(nBlocks > 65535)
{
fprintf(stderr,"Maximum number of blocks exceeded : %d > 65535 !\n", nBlocks);
exit(1);
}
fprintf(stderr,"Now computing on GPU (%d blocks, %d threads per block): \n", nBlocks, 1);
cudaMalloc((void **) &a_d, n * sizeof(float)); // Allocate array on device
clock_gettime(CLOCK_REALTIME,&ti1); // read starttime into t1
cudaMemcpy(a_d, b_h, n * sizeof(float), cudaMemcpyHostToDevice);
clock_gettime(CLOCK_REALTIME,&ti2); // read starttime into t2
multiplyKernel <<< nBlocks, 1 >>> (a_d, s);
cudaDeviceSynchronize(); // We make sure all threads finished before stopping clock
clock_gettime(CLOCK_REALTIME,&ti3); // read endtime into t3
// Retrieve result from device and store it in host array
cudaMemcpy(b_h, a_d, sizeof(float)*n, cudaMemcpyDeviceToHost);
clock_gettime(CLOCK_REALTIME,&ti4); // read endtime into t4
cudaFree(a_d);
printRunTime("GPU (no data transfer)",&ti2,&ti3);
return printRunTime("GPU (including data transfer)",&ti1,&ti4);
}
// main routine that executes on the host
int main(int argc, char **argv)
{
float *a_h,*b_h; // Pointers to host arrays
long n = 4096L; // Number of elements in arrays (problemsize)
long i;
float ts,tp;
float s = SCALAR;
if(argc >=2 )
sscanf(argv[1],"%ld",&n);
fprintf(stderr,"Starting with n=%ld ....\n",n);
// Allocate arrays on host
a_h = (float *)malloc(n * sizeof(float));
b_h = (float *)malloc(n * sizeof(float));
// Initialize host arrays
for (i=0; i<n; i++)
b_h[i] = a_h[i] = (float)i;
// Run sequential algorithm on CPU
ts = multiplyOnHost(a_h, n, s);
// Run parallel algorithm on GPU
tp = multiplyOnDevice(b_h, n, s);
// check results
#if (DEBUG > 0)
show(a_h,n);
printf("Comparing results ....\n");
int diffresults=0;
for (i=0; i<n; i++)
{
float f = fabs(a_h[i] - b_h[i]);
if(f > 1e-6)
{
printf("CPU: %f , GPU: %f , diff: %f\n",a_h[i],b_h[i],f);
diffresults=1;
}
}
if(diffresults == 0)
printf("Both results are equal up to 1e-6\n");
#endif
printf("Ratio execution time GPU/CPU: %f\n",tp/ts);
free(a_h);
free(b_h);
exit(0);
}
|
6,360 | #include "includes.h"
__device__ float f(float x)
{
return 4.f / (1.f + x * x);
}
__global__ void searchGPU(float *data, const float x, int *result)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (data[idx] == x)
{
result[0] = static_cast<int>(data[idx]);
result[1] = idx;
}
} |
6,361 | #include <cstdio>
#include <cuda_runtime.h>
#include "ray.cuh"
__global__
void cudaRayKernel(float *vel, float *thick, float *rho, float *c_rho,
int n_layers, int l_rho, float start, float end, float threshold) {
/* get current thread's id */
unsigned int index = blockIdx.x * blockDim.x + threadIdx.x;
/* create float to hold error */
float err;
/* while this thread is dealing with a valid index */
while (index < l_rho) {
/* initialize error */
err = start;
/* sum over all layers with this rho value */
for(int k = 0; k < n_layers * 2; k++){
err += (rho[index] * vel[k] * thick[k]) / sqrt(1 - (rho[index] * rho[index] * vel[k] * vel[k]));
}
/* calculate and save error */
err = err - end;
/* check if error is within threshold */
if(abs(err) < threshold){
* c_rho = rho[index];
}
/* advance thread id */
index += blockDim.x * gridDim.x;
}
}
void cudaCallRayKernel(const unsigned int blocks,
const unsigned int threadsPerBlock,
float *vel,
float *thick,
float *rho,
float *c_rho,
int n_layers,
int l_rho,
float start,
float end,
float threshold){
// Call the kernel above this function.
cudaRayKernel<<<blocks, threadsPerBlock>>>(vel, thick, rho, c_rho,
n_layers, l_rho, start, end, threshold);
}
|
6,362 | #include <stdio.h>
#include <math.h>
#define N (2048*2048)
#define THREAD_PER_BLOCK 512
__global__ void mul( int *a, int *b, int *c) {
int i = blockIdx.x/4;
int j = (blockIdx.x%4) * blockDim.x + threadIdx.x;
c[i*2048+j] = 0;
for(int k=0; k<N; ++k){
c[i*2048+j] += a[i*2048+k]*a[k*2048+j];
}
}
void random_ints(int *p, int n) {
int i;
for(i=0; i<n; i++) {
p[i]=rand();
}
}
int main( void ) {
int *a, *b, *c, *d; // host copies of a, b, c
int *dev_a, *dev_b, *dev_c; // device copies of a, b, c
int size = N * sizeof( int ); // we need space for N
int i, j, k;
// allocate device copies of a, b, c
cudaMalloc( (void**)&dev_a, size );
cudaMalloc( (void**)&dev_b, size );
cudaMalloc( (void**)&dev_c, size );
a = (int*)malloc( size );
b = (int*)malloc( size );
c = (int*)malloc( size );
d = (int*)malloc( size );
random_ints( a, N );
random_ints( b, N );
// copy inputs to device
cudaMemcpy( dev_a, a, size, cudaMemcpyHostToDevice );
cudaMemcpy( dev_b, b, size, cudaMemcpyHostToDevice );
// launch an rev() kernel with N threads
mul<<< N/THREAD_PER_BLOCK, THREAD_PER_BLOCK >>>( dev_a, dev_b, dev_c);
// copy device result back to host copy of c
cudaMemcpy( c, dev_c, size, cudaMemcpyDeviceToHost );
for(i=0; i<N; i++) {
d[i] = 0;
}
for(i=0; i<2048; i++) {
for(j=0; j<2048; j++) {
for(k=0; k<2048; k++) {
d[i*2048+j] += a[i*2048+k]*b[k*2048+j];
}
if(c[i*2048+j]!=d[i*2048+j]) {
printf("error: expected %d, got %d!\n",d[i*2048+j], c[i*2048+j]);
break;
}
}
}
if(i==N) {printf("correct! \n");}
free( a ); free( b ); free( c ); free( d );
cudaFree( dev_a );
cudaFree( dev_b );
cudaFree( dev_c );
return 0;
} |
6,363 | #include "includes.h"
__global__ void gpu_array_dot_product_r8__(size_t tsize, const double *arr1, const double *arr2, volatile double *dprod)
{
extern __shared__ double dprs_r8[]; //volume = blockDim.x
size_t l;
int i,j;
double dpr;
dpr=0.0; for(l=blockIdx.x*blockDim.x+threadIdx.x;l<tsize;l+=gridDim.x*blockDim.x){dpr+=arr1[l]*arr2[l];}
dprs_r8[threadIdx.x]=dpr;
__syncthreads();
i=1; while(i < blockDim.x){j=threadIdx.x*(i*2); if(j+i < blockDim.x) dprs_r8[j]+=dprs_r8[j+i]; i*=2;}
__syncthreads();
if(threadIdx.x == 0){
i=1; while(i == 1){i=atomicMax(&dot_product_wr_lock,1);} //waiting for a lock to unlock, then lock
*dprod+=dprs_r8[0];
__threadfence();
i=atomicExch(&dot_product_wr_lock,0); //unlock
}
__syncthreads();
return;
} |
6,364 | float h_A[]= {
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0.8981878048439349, 0.7284526468360941, 0.9045342481983578, 0.8912820563474912, 0.8868903813507512, 0.716384416728994, 0.5541972786094421, 0.7180850636327027, 0.9969116342119186, 0.6969653141603387, 0.9591358290956418, 0.9071342803134956, 0.6739288242500394, 0.5746286749172023, 0.5733749128948586, 0.6946747089802279, 0.9355226678251845, 0.5042364394811916, 0.9802579157576002, 0.686515410090131, 0.8707226378959546, 0.8776113970527495, 0.8498978197467467, 0.9340240999220863, 0.6075689289970767, 0.5586036629490954, 0.5084313604824819, 0.9465630166280761, 0.7355268754144731, 0.865705536279632, 0.8420352201008432, 0.9666942400653543, 0.6374042045276761, 0.8752630035897329, 0.9494632187653813, 0.5587096482925681, 0.6913615017339274, 0.8492296154368619, 0.7698530980532853, 0.5417072491626533, 0.5595827470804314, 0.6396928525227497, 0.5517907016335322, 0.6628072216916872, 0.863068578906792, 0.8392135645776647, 0.9516197130323213, 0.6516409443442889, 0.884506342027248, 0.9286024174785221, 0.9494903497302265, 0.9088994110221776, 0.7941758365811513, 0.5041288103351683, 0.581472202943031, 0.8389319021900805, 0.7467619592472843, 0.9608396998198812, 0.6260072756260459, 0.5656202873597103, 0.5536568686401813, 0.8593269697453869, 0.8087681895368505, 0.7266620057665549, 0.9416794254746113, 0.5553787349889704, 0.5502975969417139, 0.7000388926087249, 0.9327937405959654, 0.9708766675973982, 0.6658848161611707, 0.8418283157862885, 0.8231479095679375, 0.7181676149198362, 0.503068333106038, 0.5055394220105669, 0.7244310132663117, 0.9887388563551569, 0.7157187359921858, 0.535974874286931, 0.5934154832467804, 0.5066661830865147, 0.9307740750816724, 0.5187012677593679, 0.9788166759915911, 0.8325165414294495, 0.5617702973786088, 0.8667429720348836, 0.7696433177643606, 0.7899648516589132, 0.5781599977652676, 0.6619773641337268, 0.9735639405561753, 0.8798883893335955, 0.6953456274162342, 0.8367925962613612, 0.7630389970831122, 0.7313301666836242, 0.5713209353088201, 0.6138643736782707, 0.7848549433392049, 0.8025100621761108, 0.7565502662428885, 0.6238388607840951, 0.8220963449123955, 0.7775074951181126, 0.8536725010001984, 0.5389729286822329, 0.6483614885739187, 0.7025740407503932, 0.6565064393000917, 0.9690383850074876, 0.6321321884399536, 0.7187713488651681, 0.7585075067413782, 0.9383396881612067, 0.6573150901779409, 0.5281389529728997, 0.6571033117767886, 0.969153432963686, 0.8583408803660696, 0.92605489162383, 0.9702759202911789, 50000.0};
int h_B[]= {
3, 5, 7, 9, 11, 13, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 373, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 395, 397, 399, 401, 403, 405, 407, 409, 411, 413, 415, 417, 419, 421, 423, 425, 427, 429, 431, 433, 435, 437, 439, 441, 443, 445, 447, 449, 451, 453, 455, 457, 459, 461, 463, 465, 467, 469, 471, 473, 476, 478, 480, 482, 485, 487, 489, 491, 493, 495, 497, 499, 501, 503, 505, 507, 509, 511, 513, 515, 517, 519, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 559, 561, 563, 565, 567, 569, 571, 573, 575, 577, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 642, 644, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 668, 670, 672, 674, 677, 679, 681, 683, 687, 689, 691, 693, 695, 697, 700, 702, 704, 706, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 768, 770, 773, 775, 778, 780, 782, 784, 786, 788, 790, 792, 795, 797, 800, 802, 805, 807, 809, 811, 813, 815, 817, 819, 822, 824, 827, 829, 831, 833, 836, 838, 840, 842, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 867, 869, 872, 874, 877, 879, 881, 883, 885, 887, 890, 892, 894, 896, 898, 900, 903, 905, 908, 910, 913, 915, 918, 920, 923, 925, 928, 930, 933, 935, 938, 940, 942, 944, 946, 948, 951, 953, 955, 957, 959, 961, 964, 966, 968, 970, 972, 974, 977, 979, 982, 984, 987, 989, 992, 994, 996, 998, 1001, 1003, 1005, 1007, 1010, 1012, 1016, 1018, 1020, 1022, 1025, 1027, 1030, 1032, 1035, 1037, 1040, 1042, 1045, 1047, 1050, 1052, 1055, 1057, 1060, 1062, 1065, 1067, 1070, 1072, 1075, 1077, 1080, 1082, 1085, 1087, 1090, 1092, 1095, 1097, 1100, 1102, 1104, 1106, 1108, 1110, 1113, 1115, 1118, 1120, 1123, 1125, 1128, 1130, 1133, 1135, 1138, 1140, 1143, 1145, 1148, 1150, 1153, 1155, 1158, 1160, 1163, 1165, 1168, 1170, 1172, 1174, 1176, 1178, 1181, 1183, 1186, 1188, 1191, 1193, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1210, 1212, 1214, 1216, 1218, 1220, 1222, 1224, 1226, 1228, 1230, 1232, 1234, 1236, 1238, 1240, 1242, 1244, 1246, 1248, 1250, 1252, 1254, 1256, 1258, 1260, 1262, 1264, 1266, 1268, 1270, 1272, 1274, 1276, 1278, 1280, 1282, 1284, 1286, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1326, 1328, 1330, 1332, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1355, 1357, 1359, 1361, 1363, 1365, 1367, 1369, 1371, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1389, 1391, 1393, 1395, 1397, 1399, 1401, 1403, 1405, 1407, 1409, 1411, 1413, 1415, 1418, 1420, 1423, 1425, 1428, 1430, 1433, 1435, 1438, 1440, 1443, 1445, 1448, 1450, 1453, 1455, 1457, 1459, 1461, 1463, 1466, 1468, 1471, 1473, 1475, 1477, 1479, 1481, 1483, 1485, 1488, 1490, 1492, 1494, 1497, 1499, 1501, 1503, 1506, 1508, 1512, 1514, 1516, 1518, 1520, 1522, 1525, 1527, 1530, 1532, 1537, 1539, 1541, 1543, 1545, 1547, 1550, 1552, 1555, 1557, 1560, 1562, 1565, 1567, 1569, 1571, 1573, 1575, 1578, 1580, 1583, 1585, 1588, 1590, 1593, 1595, 1598, 1600, 1603, 1605, 1608, 1610, 1613, 1615, 1618, 1620, 1623, 1625, 1628, 1630, 1633, 1635, 1637, 1639, 1641, 1643, 1645, 1647, 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, 1738, 1740, 1742, 1744, 1747, 1749, 1752, 1754, 1757, 1759, 1762, 1764, 1767, 1769, 1772, 1774, 1777, 1779, 1781, 1783, 1785, 1787, 1790, 1792, 1795, 1797, 1800, 1802, 1805, 1807, 1810, 1812, 1815, 1817, 1820, 1822, 1825, 1827, 1830, 1832, 1835, 1837, 1840, 1842, 1845, 1847, 1850, 1852, 1855, 1857, 1860, 1862, 1865, 1867, 1869, 1871, 1873, 1875, 1878, 1880, 1883, 1885, 1888, 1890, 1893, 1895, 1898, 1900, 1903, 1905, 1910, 1912, 1914, 1916, 1918, 1920, 1922, 1924, 1926, 1928, 1930, 1932, 1934, 1936, 1938, 1940, 1943, 1945, 1948, 1950, 1952, 1954, 1956, 1958, 1960, 1962, 1964, 1966, 1968, 1970, 1972, 1974, 1976, 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026, 2028, 2030, 2032, 2034, 2036, 2038, 2040, 2042, 2044, 2046, 2048, 2050, 2052, 2054, 2056, 2058, 2060, 2062, 2064, 2066, 2068, 2070, 2072, 2074, 2076, 2078, 2080, 2082, 2084, 2086, 2088, 2090, 2092, 2094, 2096, 2098, 2100, 2102, 2104, 2106, 2109, 2111, 2113, 2115, 2117, 2119, 2121, 2123, 2125, 2127, 2129, 2131, 2133, 2135, 2137, 2139, 2141, 2143, 2145, 2147, 2149, 2151, 2153, 2155, 2157, 2159, 2161, 2163, 2165, 2167, 2169, 2171, 2174, 2176, 2178, 2180, 2182, 2184, 2186, 2188, 2190, 2192, 2194, 2196, 2199, 2201, 2203, 2205, 2208, 2210, 2212, 2214, 2217, 2219, 2221, 2223, 2226, 2228, 2230, 2232, 2234, 2236, 2238, 2240, 2242, 2244, 2246, 2248, 2250, 2252, 2254, 2256, 2258, 2260, 2262, 2264, 2266, 2268, 2270, 2272, 2274, 2276, 2278, 2280, 2282, 2284, 2287, 2289, 2292, 2294, 2296, 2298, 2300, 2302, 2304, 2306, 2308, 2310, 2312, 2314, 2316, 2318, 2320, 2322, 2324, 2326, 2328, 2330, 2332, 2334, 2336, 2338, 2340, 2342, 2344, 2346, 2348, 2350, 2352, 2354, 2356, 2358, 2360, 2362, 2364, 2366, 2368, 2370, 2372, 2374, 2376, 2378, 2380, 2382, 2384, 2386, 2388, 2390, 2392, 2394, 2396, 2398, 2400, 2402, 2404, 2406, 2408, 2410, 2412, 2414, 2416, 2418, 2420, 2422, 2424, 2426, 2428, 2430, 2432, 2434, 2436, 2438, 2440, 2442, 2444, 2446, 2448, 2450, 2452, 2454, 2456, 2458, 2460, 2462, 2464, 2466, 2468, 2470, 2473, 2475, 2477, 2479, 2481, 2483, 2485, 2487, 2489, 2491, 2493, 2495, 2497, 2499, 2501, 2503, 2505, 2507, 2509, 2511, 2513, 2515, 2517, 2519, 2521, 2523, 2525, 2527, 2529, 2531, 2533, 2535, 2537, 2539, 2541, 2543, 2545, 2547, 2549, 2551, 2553, 2555, 2557, 2559, 2562, 2564, 2566, 2568, 2571, 2573, 2575, 2577, 2579, 2581, 2583, 2585, 2587, 2589, 2592, 2594, 2596, 2598, 2601, 2603, 2605, 2607, 2610, 2612, 2615, 2617, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2643, 2645, 2647, 2649, 2651, 2653, 2655, 2658, 2660, 2662, 2664, 2667, 2669, 2672, 2674, 2677, 2679, 2682, 2684, 2687, 2689, 2691, 2693, 2695, 2697, 2700, 2702, 2705, 2707, 2710, 2712, 2715, 2717, 2720, 2722, 2725, 2727, 2729, 2731, 2733, 2735, 2738, 2740, 2743, 2745, 2748, 2750, 2753, 2755, 2758, 2760, 2763, 2765, 2768, 2770, 2773, 2775, 2778, 2780, 2783, 2785, 2788, 2790, 2793, 2795, 2798, 2800, 2803, 2805, 2808, 2810, 2813, 2815, 2817, 2819, 2821, 2823, 2826, 2828, 2831, 2833, 2836, 2838, 2841, 2843, 2846, 2848, 2851, 2853, 2856, 2858, 2861, 2863, 2865, 2867, 2869, 2871, 2874, 2876, 2879, 2881, 2884, 2886, 2889, 2891, 2894, 2896, 2899, 2901, 2904, 2906, 2909, 2911, 2914, 2916, 2919, 2921, 2924, 2926, 2929, 2931, 2934, 2936, 2939, 2941, 2944, 2946, 2949, 2951, 2954, 2956, 2959, 2961, 2964, 2966, 2969, 2971, 2974, 2976, 2979, 2981, 2984, 2986, 2989, 2991, 2994, 2996, 2999, 3001, 3004, 3006, 3009, 3011, 3013, 3015, 3017, 3019, 3022, 3024, 3027, 3029, 3032, 3034, 3037, 3039, 3041, 3043, 3046, 3048, 3051, 3053, 3059, 3061, 3063, 3065, 3067, 3069, 3072, 3074, 3077, 3079, 3082, 3084, 3087, 3089, 3092, 3094, 3097, 3099, 3101, 3103, 3105, 3107, 3109, 3111, 3113, 3115, 3117, 3119, 3122, 3124, 3127, 3129, 3132, 3134, 3137, 3139, 3142, 3144, 3147, 3149, 3155, 3157, 3159, 3161, 3163, 3165, 3168, 3170, 3172, 3174, 3176, 3178, 3181, 3183, 3186, 3188, 3194, 3196, 3199, 3201, 3204, 3206, 3208, 3210, 3213, 3215, 3217, 3219, 3224, 3226, 3228, 3230, 3232, 3234, 3236, 3238, 3241, 3243, 3245, 3247, 3249, 3251, 3254, 3256, 3259, 3261, 3264, 3266, 3269, 3271, 3273, 3275, 3277, 3279, 3281, 3283, 3285, 3287, 3290, 3292, 3295, 3297, 3300, 3302, 3305, 3307, 3310, 3312, 3315, 3317, 3320, 3322, 3325, 3327, 3330, 3332, 3334, 3336, 3338, 3340, 3342, 3344, 3346, 3348, 3351, 3353, 3356, 3358, 3360, 3362, 3364, 3366, 3368, 3370, 3372, 3374, 3376, 3378, 3380, 3382, 3385, 3387, 3389, 3391, 3393, 3395, 3398, 3400, 3403, 3405, 3407, 3409, 3411, 3413, 3416, 3418, 3421, 3423, 3426, 3428, 3431, 3433, 3436, 3438, 3441, 3443, 3446, 3448, 3451, 3453, 3456, 3458, 3460, 3462, 3464, 3466, 3468, 3470, 3472, 3474, 3476, 3478, 3480, 3482, 3484, 3486, 3488, 3490, 3492, 3494, 3497, 3499, 3501, 3503, 3505, 3507, 3510, 3512, 3515, 3517, 3519, 3521, 3523, 3525, 3528, 3530, 3533, 3535, 3538, 3540, 3543, 3545, 3547, 3549, 3551, 3553, 3556, 3558, 3561, 3563, 3566, 3568, 3571, 3573, 3576, 3578, 3582, 3584, 3586, 3588, 3593, 3595, 3598, 3600, 3603, 3605, 3608, 3610, 3613, 3615, 3617, 3619, 3622, 3624, 3627, 3629, 3641, 3643, 3646, 3648, 3651, 3653, 3656, 3658, 3661, 3663, 3665, 3667, 3669, 3671, 3674, 3676, 3679, 3681, 3684, 3686, 3689, 3691, 3694, 3696, 3699, 3701, 3703, 3705, 3708, 3710, 3713, 3715, 3721, 3723, 3725, 3727, 3729, 3731, 3734, 3736, 3739, 3741, 3744, 3746, 3749, 3751, 3753, 3755, 3757, 3759, 3762, 3764, 3767, 3769, 3772, 3774, 3777, 3779, 3781, 3783, 3785, 3787, 3790, 3792, 3795, 3797, 3800, 3802, 3805, 3807, 3810, 3812, 3815, 3817, 3820, 3822, 3825, 3827, 3829, 3831, 3834, 3836, 3839, 3841, 3847, 3849, 3852, 3854, 3857, 3859, 3862, 3864, 3867, 3869, 3872, 3874, 3877, 3879, 3882, 3884, 3887, 3889, 3891, 3893, 3895, 3897, 3900, 3902, 3905, 3907, 3910, 3912, 3915, 3917, 3920, 3922, 3925, 3927, 3930, 3932, 3935, 3937, 3939, 3941, 3943, 3945, 3948, 3950, 3953, 3955, 3958, 3960, 3963, 3965, 3967, 3969, 3971, 3973, 3975, 3977, 3980, 3982, 3984, 3986, 3990, 3992, 3995, 3997, 4000, 4002, 4005, 4007, 4010, 4012, 4014, 4016, 4018, 4020, 4022, 4024, 4026, 4028, 4030, 4032, 4034, 4036, 4039, 4041, 4044, 4046, 4049, 4051, 4054, 4056, 4059, 4061, 4064, 4066, 4069, 4071, 4074, 4076, 4079, 4081, 4084, 4086, 4089, 4091, 4093, 4095, 4097, 4099, 4102, 4104, 4107, 4109, 4112, 4114, 4117, 4119, 4122, 4124, 4127, 4129, 4132, 4134, 4137, 4139, 4142, 4144, 4147, 4149, 4152, 4154, 4157, 4159, 4161, 4163, 4165, 4167, 4170, 4172, 4175, 4177, 4180, 4182, 4185, 4187, 4189, 4191, 4193, 4195, 4197, 4199, 4202, 4204, 4207, 4209, 4215, 4217, 4219, 4221, 4223, 4225, 4228, 4230, 4233, 4235, 4238, 4240, 4243, 4245, 4248, 4250, 4253, 4255, 4258, 4260, 4263, 4265, 4267, 4269, 4271, 4273, 4275, 4277, 4279, 4281, 4283, 4285, 4287, 4289, 4291, 4293, 4295, 4297, 4299, 4301, 4303, 4305, 4307, 4309, 4311, 4313, 4315, 4317, 4319, 4321, 4323, 4325, 4327, 4329, 4331, 4333, 4335, 4337, 4339, 4341, 4343, 4345, 4347, 4349, 4351, 4353, 4355, 4357, 4359, 4361, 4363, 4365, 4367, 4369, 4371, 4373, 4375, 4377, 4379, 4381, 4383, 4385, 4387, 4389, 4391, 4393, 4395, 4397, 4399, 4401, 4403, 4405, 4407, 4409, 4411, 4413, 4415, 4417, 4419, 4421, 4424, 4426, 4428, 4430, 4433, 4435, 4437, 4439, 4442, 4444, 4446, 4448, 4451, 4453, 4456, 4458, 4460, 4462, 4464, 4466, 4468, 4470, 4472, 4474, 4476, 4478, 4480, 4482, 4484, 4486, 4488, 4490, 4492, 4494, 4496, 4498, 4500, 4502, 4504, 4506, 4508, 4510, 4512, 4514, 4516, 4518, 4521, 4523, 4525, 4527, 4530, 4532, 4534, 4536, 4538, 4540, 4542, 4544, 4546, 4548, 4550, 4552, 4554, 4556, 4558, 4560, 4562, 4564, 4566, 4568, 4570, 4572, 4575, 4577, 4579, 4581, 4584, 4586, 4589, 4591, 4593, 4595, 4597, 4599, 4601, 4603, 4606, 4608, 4610, 4612, 4617, 4619, 4621, 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37175, 37177, 37179, 37182, 37183, 37185, 37187, 37188, 37189, 37192, 37193, 37196, 36902, 37202, 37204, 37205, 37206, 36907, 37210, 37216, 37213, 37221, 37218, 37222, 37002, 37092, 37133, 37143, 37150, 37157, 37092, 37002, 37133, 37143, 37150, 37157, 37091, 37092, 37133, 37143, 37150, 37157, 37011, 36738, 37107, 37016, 37109, 36832, 37115, 37089, 36808, 37125, 37096, 37100, 37107, 37105, 37109, 36832, 37115, 37089, 36808, 37125, 37190, 36900, 37198, 37190, 36900, 37059, 37057, 36560, 37062, 37068, 37076, 37074, 36798, 37133, 37133, 37089, 36808, 37092, 37091, 37143, 37150, 37157, 37096, 37100, 37107, 37105, 37109, 36832, 37115, 37122, 36840, 37125, 37131, 37129, 37133, 37143, 37150, 37157, 37176, 37190, 36900, 37198, 37225, 37229, 37232, 37233, 37235, 37237, 36614, 37239, 37228, 37241, 37228, 37241, 37243, 37248, 37256, 37228, 37241, 37228, 37241, 37228, 37241, 37267, 37246, 37250, 37252, 37254, 37258, 36973, 37246, 37250, 37252, 37254, 37258, 36976, 37263, 37265, 37269, 36229, 37263, 37265, 37269, 36223, 37277, 37263, 37265, 37269, 36982, 37263, 37265, 37269, 37280, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37319, 37333, 37345, 37349, 37351, 37358, 37363, 37365, 37367, 37369, 37215, 37312, 37313, 37314, 37315, 37220, 37401, 37402, 37403, 37361, 37362, 37404, 37405, 37366, 37406, 37368, 37316, 37167, 37165, 37407, 37408, 37409, 37361, 37362, 37410, 37411, 37366, 37412, 37368, 37317, 37167, 37165, 37413, 37414, 37415, 37361, 37362, 37416, 37417, 37366, 37418, 37368, 37318, 37167, 37165, 37419, 37098, 37420, 36818, 37015, 37013, 37421, 37422, 37423, 37357, 37356, 37424, 37425, 37426, 37427, 37428, 36743, 37429, 37098, 36818, 37430, 37103, 37431, 37432, 37433, 37357, 37323, 37434, 37435, 37436, 37437, 37438, 36747, 37324, 37325, 37326, 37327, 37328, 37329, 37331, 36757, 37335, 37336, 37337, 37339, 36768, 37342, 37380, 37382, 37383, 37385, 37439, 37386, 37023, 37388, 37389, 37440, 37441, 37207, 37392, 37203, 37394, 37211, 37325, 37326, 37327, 37328, 37329, 37331, 36757, 37335, 37336, 37337, 37339, 36768, 37342, 37380, 37382, 37383, 37385, 37442, 37386, 37055, 37388, 37389, 37443, 37444, 37445, 37207, 37392, 37203, 37394, 37211, 37446, 37447, 37344, 37343, 37448, 37449, 37450, 37348, 37347, 37451, 37452, 37453, 37366, 37368, 37454, 37455, 37456, 37457, 37458, 37459, 37460, 37461, 37098, 36818, 37462, 37103, 37463, 37464, 37465, 37357, 37356, 37466, 37467, 37468, 37469, 37470, 37128, 37471, 37472, 37473, 37361, 37362, 37474, 37475, 37366, 37476, 37368, 37370, 37167, 37165, 37373, 37374, 37375, 37377, 37477, 37379, 37380, 37382, 37383, 37385, 37478, 37386, 37194, 37388, 37389, 37479, 37480, 37207, 37392, 37203, 37394, 37211, 37482, 37231, 36936, 36938, 37487, 37489, 37226, 37490, 37491, 37226, 37492, 37496, 37226, 37497, 37498, 37226, 37499, 37500, 37226, 37501, 37493, 37503, 37494, 37504, 37505, 37506, 37495, 37507, 37493, 37509, 37494, 37510, 37511, 37512, 37495, 37513, 37515, 37516, 37502, 37517, 37518, 37519, 37520, 37502, 37521, 37522, 37524, 37525, 37502, 37526, 37528, 37529, 37502, 37530, 37271, 37273, 37279, 37282, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37397, 37579, 37580, 37581, 37582, 37399, 37584, 37587, 37588, 37574, 37146, 37591, 37153, 37593, 37005, 37594, 37595, 37596, 37597, 37600, 37601, 37574, 37146, 37604, 37153, 37606, 37160, 37607, 37608, 37609, 37610, 37613, 37614, 37574, 37146, 37617, 37153, 37619, 37005, 37620, 37621, 37622, 37010, 37624, 37626, 37627, 37628, 37629, 37632, 37633, 37634, 37017, 37639, 37095, 37641, 37642, 37644, 37645, 37648, 37649, 37650, 37020, 37655, 37656, 37657, 37658, 37659, 37660, 37661, 37662, 37663, 37334, 37664, 37665, 37666, 37667, 37668, 37669, 37670, 37671, 37672, 37673, 37675, 37676, 37677, 37678, 37681, 37682, 37683, 37684, 37685, 37686, 37687, 37688, 37689, 37690, 37691, 37692, 37334, 37693, 37694, 37695, 37696, 37697, 37698, 37699, 37700, 37701, 37702, 37704, 37705, 37706, 37707, 37709, 37711, 37712, 37713, 37714, 37715, 37718, 37719, 37346, 37721, 37723, 37724, 37350, 37574, 37728, 37729, 37732, 37095, 37738, 37739, 37741, 37742, 37745, 37746, 37747, 37359, 37752, 37753, 37756, 37757, 37574, 37146, 37760, 37153, 37762, 37160, 37763, 37764, 37765, 37766, 37767, 37768, 37769, 37771, 37772, 37773, 37774, 37775, 37777, 37778, 37779, 37780, 37783, 37784, 37785, 37786, 37787, 37788, 37794, 37238, 37236, 37400, 37797, 37238, 37236, 37400, 37637, 37653, 37800, 37238, 37236, 37488, 37803, 37238, 37236, 37488, 37731, 37731, 37750, 37806, 37238, 37236, 37488, 37808, 37810, 37814, 37816, 37818, 37822, 37826, 37274, 37831, 37276, 37836, 37840, 37813, 37842, 37821, 37843, 37825, 37830, 37835, 37844, 37839, 37845, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37897, 37898, 37590, 37900, 37592, 37902, 37904, 37909, 37910, 37603, 37912, 37605, 37914, 37916, 37921, 37922, 37616, 37924, 37618, 37926, 37928, 37930, 37625, 37933, 37936, 37939, 37941, 37943, 37946, 37949, 37959, 37973, 37974, 37986, 38000, 38002, 38007, 38009, 38011, 38013, 38014, 38018, 38020, 38023, 38026, 38031, 38032, 37759, 38034, 37761, 38036, 38038, 38052, 38053, 37957, 37955, 37953, 37963, 37961, 37965, 37969, 37967, 37970, 37951, 37978, 37984, 37982, 37980, 37996, 37994, 37997, 37999, 38006, 37793, 37217, 38060, 38061, 38062, 37957, 37955, 37953, 37963, 37892, 37890, 37965, 37969, 37967, 37970, 37951, 37978, 37796, 37224, 38064, 38065, 38066, 37223, 37586, 37896, 37599, 37908, 37612, 37920, 37957, 37955, 37953, 37963, 37961, 37965, 37969, 37967, 37970, 37951, 37978, 37984, 37982, 37980, 37996, 37994, 37997, 37999, 38006, 37224, 38067, 37940, 37945, 38068, 37950, 37755, 38030, 37957, 37955, 37953, 37963, 37961, 37965, 37969, 37967, 37970, 37951, 37978, 37984, 37982, 37980, 37999, 38006, 37799, 36927, 38070, 38071, 38072, 37957, 37955, 37953, 37963, 37961, 37965, 37969, 37967, 37970, 37972, 37978, 37984, 37982, 37980, 37990, 37988, 37992, 37996, 37994, 37997, 37999, 38006, 37802, 36928, 38074, 38075, 38076, 38022, 38077, 38027, 37726, 38030, 37727, 38022, 38078, 37755, 38030, 38022, 38079, 38027, 37755, 38030, 38043, 38041, 38044, 38048, 38046, 38049, 38051, 38057, 37805, 37484, 38081, 38082, 38083, 37809, 37811, 38096, 37815, 37817, 37819, 38098, 37823, 38100, 37827, 37275, 38101, 37832, 37523, 38102, 37837, 38104, 37841, 35679, 35680, 35683, 35684, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37589, 37602, 37615, 38175, 38176, 38178, 38179, 37758, 38196, 38197, 38198, 38199, 38200, 38174, 38201, 38202, 38203, 38204, 38205, 38206, 38207, 38208, 38209, 38210, 38211, 38177, 38212, 38213, 38214, 38215, 38216, 38218, 38219, 37795, 38222, 38223, 38224, 38174, 38225, 38226, 38227, 38228, 38229, 38230, 38231, 38232, 38233, 38235, 38236, 37798, 38239, 38240, 38241, 38149, 38147, 38150, 38242, 38243, 38156, 38154, 38157, 38244, 38245, 38163, 38161, 38164, 38246, 38247, 38248, 38174, 38249, 38250, 38251, 38252, 38253, 38254, 38255, 38256, 38257, 38258, 38259, 38177, 38260, 38261, 38262, 38263, 38264, 38265, 37623, 37935, 38166, 38168, 38169, 38267, 37640, 38268, 38171, 38172, 38173, 38270, 38271, 38272, 38194, 38192, 38195, 38273, 38274, 38275, 38174, 38276, 38277, 38278, 38279, 38280, 38281, 38282, 38283, 38284, 38285, 38286, 38177, 38287, 38288, 38290, 38291, 37801, 38294, 38295, 38296, 38174, 38297, 38298, 38299, 38300, 38301, 38302, 38303, 38304, 38305, 38306, 38307, 38177, 38308, 38309, 38310, 38311, 38312, 38313, 38314, 38315, 38317, 38318, 37804, 37737, 38321, 38186, 38180, 38181, 38182, 38183, 38323, 38324, 38325, 38194, 38192, 38190, 38195, 38326, 38194, 38192, 38190, 37737, 38327, 38186, 38187, 38188, 38329, 38330, 38194, 38192, 38190, 38195, 37737, 38331, 38186, 38187, 38188, 38333, 38334, 38335, 38194, 38192, 38195, 38336, 38337, 38338, 38339, 38340, 38341, 38342, 38343, 38345, 38346, 37807, 38349, 38350, 38352, 38353, 38354, 38356, 38358, 38359, 38361, 38362, 38364, 38366, 38367, 38368, 38369, 38370, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38410, 38413, 38414, 38417, 38404, 38422, 38425, 38426, 38406, 38434, 38437, 38438, 38442, 38404, 38453, 38454, 38145, 38455, 38458, 38459, 38152, 38460, 38463, 38464, 38159, 38465, 38466, 38469, 38470, 38473, 38404, 38478, 38481, 38482, 38406, 38488, 38489, 38490, 38491, 38492, 38494, 38496, 38497, 38498, 38502, 38503, 38190, 38504, 38505, 38508, 38509, 38512, 38404, 38517, 38520, 38406, 38526, 38529, 38530, 38533, 38404, 38538, 38541, 38542, 38545, 38406, 38553, 38555, 38556, 38557, 38558, 38559, 38563, 38564, 38565, 38566, 38568, 38569, 38570, 38571, 38573, 38574, 38575, 38578, 38579, 38580, 38581, 38582, 38584, 38585, 38586, 38590, 38591, 38190, 38592, 38593, 38596, 38409, 38431, 38433, 38447, 38449, 38450, 38603, 38493, 38499, 38452, 38457, 38462, 38587, 38589, 38487, 38603, 38493, 38499, 38501, 38587, 38589, 38523, 38525, 38550, 38552, 38560, 38562, 38577, 38587, 38577, 38587, 38589, 38601, 38603, 38606, 38605, 38609, 38608, 38610, 35681, 38612, 35682, 38614, 38615, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38656, 38660, 38661, 38664, 38665, 38667, 38669, 38672, 38670, 38676, 38674, 38680, 38678, 38682, 38686, 38687, 38690, 38692, 38495, 38702, 38700, 38704, 38708, 38709, 38711, 38712, 38716, 38717, 38721, 38554, 38728, 38732, 38572, 38739, 38583, 38749, 38747, 38753, 38419, 38416, 38428, 38544, 38754, 38755, 38444, 38756, 38757, 38475, 38472, 38484, 38544, 38758, 38759, 38760, 38695, 38761, 38699, 38762, 38763, 38764, 38765, 38746, 38766, 38475, 38472, 38484, 38544, 38767, 38768, 38769, 38695, 38770, 38699, 38771, 38772, 38746, 38773, 38514, 38511, 38547, 38544, 38774, 38775, 38535, 38532, 38547, 38544, 38776, 38777, 38778, 38727, 38725, 38779, 38780, 38781, 38738, 38782, 38783, 38746, 38784, 38598, 38595, 38785, 38786, 38787, 38788, 38789, 38790, 38791, 38792, 38793, 38794, 38795, 38796, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38421, 38430, 38446, 38856, 38858, 38860, 38477, 38486, 38868, 38516, 38522, 38537, 38549, 38878, 38879, 38881, 38884, 38600, 38886, 38887, 38657, 38888, 38889, 38662, 38892, 38441, 38666, 38895, 38896, 38683, 38897, 38898, 38688, 38865, 38902, 38866, 38904, 38882, 38909, 38911, 38912, 38683, 38913, 38914, 38688, 38865, 38918, 38866, 38920, 38882, 38923, 38925, 38926, 38705, 38927, 38928, 38710, 38931, 38932, 38713, 38933, 38934, 38718, 38877, 38938, 38939, 38880, 38943, 38882, 38946, 38948, 38949, 38891, 38894, 38900, 38951, 38916, 38930, 38936, 38951, 38952, 38954, 38618, 38959, 38957, 38619, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38996, 38994, 38976, 38999, 38997, 38977, 39001, 39002, 38978, 39005, 39003, 38982, 39008, 39006, 38983, 39009, 38901, 39011, 38903, 38673, 38677, 38681, 39013, 38908, 38750, 39017, 39015, 38982, 39020, 39018, 38983, 39021, 38917, 39023, 38919, 38703, 39025, 38922, 38750, 39029, 39027, 38985, 39032, 39030, 38986, 39035, 39033, 38987, 39038, 39036, 38988, 39039, 38937, 38731, 38742, 39042, 38942, 38742, 39044, 38945, 38750, 39046, 38993, 39048, 39049, 39050, 39051, 39052, 39053, 39054, 39055, 39058, 39059, 38617, 39060, 38616, 39061, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39105, 39106, 39108, 39109, 39000, 39112, 39114, 39115, 39117, 39118, 39123, 39124, 39125, 39128, 39130, 39131, 39133, 39134, 39139, 39142, 39144, 39145, 39147, 39148, 39150, 39151, 39153, 39154, 39156, 39157, 39158, 39161, 39164, 39166, 39122, 39120, 39127, 39138, 39136, 39141, 39122, 39120, 39127, 39138, 39136, 39141, 39160, 39163, 39177, 39179, 39175, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39236, 39266, 39267, 39244, 39243, 39242, 39268, 39245, 39235, 39233, 39259, 39269, 39270, 39250, 39271, 39251, 39241, 39239, 39260, 39261, 39265, 39272, 39273, 39244, 39243, 39242, 39274, 39245, 39249, 39247, 39275, 39276, 39250, 39277, 39251, 39255, 39253, 39259, 39257, 39260, 39261, 39262, 39278, 39263, 39279, 39264, 39265, 39280, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39297, 39299, 39300, 39301, 39303, 39304, 39305, 39306, 39237, 39307, 39309, 39311, 39312, 39313, 39314, 39315, 39316, 39317, 39319, 39320, 39321, 39323, 39324, 39325, 39326, 39328, 39330, 39331, 39332, 39333, 39334, 39335, 39336, 39337, 39339, 39341, 39342, 39282, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39361, 39365, 39368, 39372, 39378, 39382, 39387, 39389, 39364, 39371, 39370, 39395, 39394, 39393, 39375, 39170, 39381, 39386, 39385, 39395, 39394, 39393, 39392, 39174, 39397, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39424, 39367, 39428, 39432, 39167, 39433, 39434, 39169, 39435, 39436, 39437, 39438, 39439, 39440, 39171, 39441, 39442, 39173, 39172, 39443, 39444, 39445, 39446, 39447, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39488, 39168, 39492, 39493, 39495, 39496, 39498, 39490, 39502, 39503, 39505, 39506, 39507, 39509, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39552, 39553, 39557, 39559, 39562, 39564, 39556, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39491, 39617, 39501, 39500, 39622, 39511, 39620, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39683, 39681, 39685, 39686, 39560, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39745, 39744, 39748, 39746, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39809, 39811, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39180, 39448, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39936, 39937, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 40000, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
int h_C[]= {
4, 6, 8, 10, 12, 14, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 286, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 396, 398, 400, 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, 468, 470, 472, 474, 477, 479, 481, 483, 486, 488, 490, 492, 494, 496, 498, 500, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 558, 560, 562, 564, 566, 568, 570, 572, 574, 576, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 643, 645, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 669, 671, 673, 675, 678, 680, 682, 684, 688, 690, 692, 694, 696, 698, 701, 703, 705, 707, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 769, 771, 774, 776, 779, 781, 783, 785, 787, 789, 791, 793, 796, 798, 801, 803, 806, 808, 810, 812, 814, 816, 818, 820, 823, 825, 828, 830, 832, 834, 837, 839, 841, 843, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 868, 870, 873, 875, 878, 880, 882, 884, 886, 888, 891, 893, 895, 897, 899, 901, 904, 906, 909, 911, 914, 916, 919, 921, 924, 926, 929, 931, 934, 936, 939, 941, 943, 945, 947, 949, 952, 954, 956, 958, 960, 962, 965, 967, 969, 971, 973, 975, 978, 980, 983, 985, 988, 990, 993, 995, 997, 999, 1002, 1004, 1006, 1008, 1011, 1013, 1017, 1019, 1021, 1023, 1026, 1028, 1031, 1033, 1036, 1038, 1041, 1043, 1046, 1048, 1051, 1053, 1056, 1058, 1061, 1063, 1066, 1068, 1071, 1073, 1076, 1078, 1081, 1083, 1086, 1088, 1091, 1093, 1096, 1098, 1101, 1103, 1105, 1107, 1109, 1111, 1114, 1116, 1119, 1121, 1124, 1126, 1129, 1131, 1134, 1136, 1139, 1141, 1144, 1146, 1149, 1151, 1154, 1156, 1159, 1161, 1164, 1166, 1169, 1171, 1173, 1175, 1177, 1179, 1182, 1184, 1187, 1189, 1192, 1194, 1197, 1199, 1201, 1203, 1205, 1207, 1209, 1211, 1213, 1215, 1217, 1219, 1221, 1223, 1225, 1227, 1229, 1231, 1233, 1235, 1237, 1239, 1241, 1243, 1245, 1247, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 1277, 1279, 1281, 1283, 1285, 1287, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1327, 1329, 1331, 1333, 1336, 1338, 1340, 1342, 1344, 1346, 1348, 1350, 1352, 1354, 1356, 1358, 1360, 1362, 1364, 1366, 1368, 1370, 1372, 1374, 1376, 1378, 1380, 1382, 1384, 1386, 1390, 1392, 1394, 1396, 1398, 1400, 1402, 1404, 1406, 1408, 1410, 1412, 1414, 1416, 1419, 1421, 1424, 1426, 1429, 1431, 1434, 1436, 1439, 1441, 1444, 1446, 1449, 1451, 1454, 1456, 1458, 1460, 1462, 1464, 1467, 1469, 1472, 1474, 1476, 1478, 1480, 1482, 1484, 1486, 1489, 1491, 1493, 1495, 1498, 1500, 1502, 1504, 1507, 1509, 1513, 1515, 1517, 1519, 1521, 1523, 1526, 1528, 1531, 1533, 1538, 1540, 1542, 1544, 1546, 1548, 1551, 1553, 1556, 1558, 1561, 1563, 1566, 1568, 1570, 1572, 1574, 1576, 1579, 1581, 1584, 1586, 1589, 1591, 1594, 1596, 1599, 1601, 1604, 1606, 1609, 1611, 1614, 1616, 1619, 1621, 1624, 1626, 1629, 1631, 1634, 1636, 1638, 1640, 1642, 1644, 1646, 1648, 1650, 1652, 1654, 1656, 1658, 1660, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1694, 1696, 1698, 1700, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1718, 1720, 1722, 1724, 1726, 1728, 1730, 1732, 1734, 1736, 1739, 1741, 1743, 1745, 1748, 1750, 1753, 1755, 1758, 1760, 1763, 1765, 1768, 1770, 1773, 1775, 1778, 1780, 1782, 1784, 1786, 1788, 1791, 1793, 1796, 1798, 1801, 1803, 1806, 1808, 1811, 1813, 1816, 1818, 1821, 1823, 1826, 1828, 1831, 1833, 1836, 1838, 1841, 1843, 1846, 1848, 1851, 1853, 1856, 1858, 1861, 1863, 1866, 1868, 1870, 1872, 1874, 1876, 1879, 1881, 1884, 1886, 1889, 1891, 1894, 1896, 1899, 1901, 1904, 1906, 1911, 1913, 1915, 1917, 1919, 1921, 1923, 1925, 1927, 1929, 1931, 1933, 1935, 1937, 1939, 1941, 1944, 1946, 1949, 1951, 1953, 1955, 1957, 1959, 1961, 1963, 1965, 1967, 1969, 1971, 1973, 1975, 1977, 1979, 1981, 1983, 1985, 1987, 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023, 2025, 2027, 2029, 2031, 2033, 2035, 2037, 2039, 2041, 2043, 2045, 2047, 2049, 2051, 2053, 2055, 2057, 2059, 2061, 2063, 2065, 2067, 2069, 2071, 2073, 2075, 2077, 2079, 2081, 2083, 2085, 2087, 2089, 2091, 2093, 2095, 2097, 2099, 2101, 2103, 2105, 2107, 2110, 2112, 2114, 2116, 2118, 2120, 2122, 2124, 2126, 2128, 2130, 2132, 2134, 2136, 2138, 2140, 2142, 2144, 2146, 2148, 2150, 2152, 2154, 2156, 2158, 2160, 2162, 2164, 2166, 2168, 2170, 2172, 2175, 2177, 2179, 2181, 2183, 2185, 2187, 2189, 2191, 2193, 2195, 2197, 2200, 2202, 2204, 2206, 2209, 2211, 2213, 2215, 2218, 2220, 2222, 2224, 2227, 2229, 2231, 2233, 2235, 2237, 2239, 2241, 2243, 2245, 2247, 2249, 2251, 2253, 2255, 2257, 2259, 2261, 2263, 2265, 2267, 2269, 2271, 2273, 2275, 2277, 2279, 2281, 2283, 2285, 2288, 2290, 2293, 2295, 2297, 2299, 2301, 2303, 2305, 2307, 2309, 2311, 2313, 2315, 2317, 2319, 2321, 2323, 2325, 2327, 2329, 2331, 2333, 2335, 2337, 2339, 2341, 2343, 2345, 2347, 2349, 2351, 2353, 2355, 2357, 2359, 2361, 2363, 2365, 2367, 2369, 2371, 2373, 2375, 2377, 2379, 2381, 2383, 2385, 2387, 2389, 2391, 2393, 2395, 2397, 2399, 2401, 2403, 2405, 2407, 2409, 2411, 2413, 2415, 2417, 2419, 2421, 2423, 2425, 2427, 2429, 2431, 2433, 2435, 2437, 2439, 2441, 2443, 2445, 2447, 2449, 2451, 2453, 2455, 2457, 2459, 2461, 2463, 2465, 2467, 2469, 2471, 2474, 2476, 2478, 2480, 2482, 2484, 2486, 2488, 2490, 2492, 2494, 2496, 2498, 2500, 2502, 2504, 2506, 2508, 2510, 2512, 2514, 2516, 2518, 2520, 2522, 2524, 2526, 2528, 2530, 2532, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554, 2556, 2558, 2560, 2563, 2565, 2567, 2569, 2572, 2574, 2576, 2578, 2580, 2582, 2584, 2586, 2588, 2590, 2593, 2595, 2597, 2599, 2602, 2604, 2606, 2608, 2611, 2613, 2616, 2618, 2622, 2624, 2626, 2628, 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2644, 2646, 2648, 2650, 2652, 2654, 2656, 2659, 2661, 2663, 2665, 2668, 2670, 2673, 2675, 2678, 2680, 2683, 2685, 2688, 2690, 2692, 2694, 2696, 2698, 2701, 2703, 2706, 2708, 2711, 2713, 2716, 2718, 2721, 2723, 2726, 2728, 2730, 2732, 2734, 2736, 2739, 2741, 2744, 2746, 2749, 2751, 2754, 2756, 2759, 2761, 2764, 2766, 2769, 2771, 2774, 2776, 2779, 2781, 2784, 2786, 2789, 2791, 2794, 2796, 2799, 2801, 2804, 2806, 2809, 2811, 2814, 2816, 2818, 2820, 2822, 2824, 2827, 2829, 2832, 2834, 2837, 2839, 2842, 2844, 2847, 2849, 2852, 2854, 2857, 2859, 2862, 2864, 2866, 2868, 2870, 2872, 2875, 2877, 2880, 2882, 2885, 2887, 2890, 2892, 2895, 2897, 2900, 2902, 2905, 2907, 2910, 2912, 2915, 2917, 2920, 2922, 2925, 2927, 2930, 2932, 2935, 2937, 2940, 2942, 2945, 2947, 2950, 2952, 2955, 2957, 2960, 2962, 2965, 2967, 2970, 2972, 2975, 2977, 2980, 2982, 2985, 2987, 2990, 2992, 2995, 2997, 3000, 3002, 3005, 3007, 3010, 3012, 3014, 3016, 3018, 3020, 3023, 3025, 3028, 3030, 3033, 3035, 3038, 3040, 3042, 3044, 3047, 3049, 3052, 3054, 3060, 3062, 3064, 3066, 3068, 3070, 3073, 3075, 3078, 3080, 3083, 3085, 3088, 3090, 3093, 3095, 3098, 3100, 3102, 3104, 3106, 3108, 3110, 3112, 3114, 3116, 3118, 3120, 3123, 3125, 3128, 3130, 3133, 3135, 3138, 3140, 3143, 3145, 3148, 3150, 3156, 3158, 3160, 3162, 3164, 3166, 3169, 3171, 3173, 3175, 3177, 3179, 3182, 3184, 3187, 3189, 3195, 3197, 3200, 3202, 3205, 3207, 3209, 3211, 3214, 3216, 3218, 3220, 3225, 3227, 3229, 3231, 3233, 3235, 3237, 3239, 3242, 3244, 3246, 3248, 3250, 3252, 3255, 3257, 3260, 3262, 3265, 3267, 3270, 3272, 3274, 3276, 3278, 3280, 3282, 3284, 3286, 3288, 3291, 3293, 3296, 3298, 3301, 3303, 3306, 3308, 3311, 3313, 3316, 3318, 3321, 3323, 3326, 3328, 3331, 3333, 3335, 3337, 3339, 3341, 3343, 3345, 3347, 3349, 3352, 3354, 3357, 3359, 3361, 3363, 3365, 3367, 3369, 3371, 3373, 3375, 3377, 3379, 3381, 3383, 3386, 3388, 3390, 3392, 3394, 3396, 3399, 3401, 3404, 3406, 3408, 3410, 3412, 3414, 3417, 3419, 3422, 3424, 3427, 3429, 3432, 3434, 3437, 3439, 3442, 3444, 3447, 3449, 3452, 3454, 3457, 3459, 3461, 3463, 3465, 3467, 3469, 3471, 3473, 3475, 3477, 3479, 3481, 3483, 3485, 3487, 3489, 3491, 3493, 3495, 3498, 3500, 3502, 3504, 3506, 3508, 3511, 3513, 3516, 3518, 3520, 3522, 3524, 3526, 3529, 3531, 3534, 3536, 3539, 3541, 3544, 3546, 3548, 3550, 3552, 3554, 3557, 3559, 3562, 3564, 3567, 3569, 3572, 3574, 3577, 3579, 3583, 3585, 3587, 3589, 3594, 3596, 3599, 3601, 3604, 3606, 3609, 3611, 3614, 3616, 3618, 3620, 3623, 3625, 3628, 3630, 3642, 3644, 3647, 3649, 3652, 3654, 3657, 3659, 3662, 3664, 3666, 3668, 3670, 3672, 3675, 3677, 3680, 3682, 3685, 3687, 3690, 3692, 3695, 3697, 3700, 3702, 3704, 3706, 3709, 3711, 3714, 3716, 3722, 3724, 3726, 3728, 3730, 3732, 3735, 3737, 3740, 3742, 3745, 3747, 3750, 3752, 3754, 3756, 3758, 3760, 3763, 3765, 3768, 3770, 3773, 3775, 3778, 3780, 3782, 3784, 3786, 3788, 3791, 3793, 3796, 3798, 3801, 3803, 3806, 3808, 3811, 3813, 3816, 3818, 3821, 3823, 3826, 3828, 3830, 3832, 3835, 3837, 3840, 3842, 3848, 3850, 3853, 3855, 3858, 3860, 3863, 3865, 3868, 3870, 3873, 3875, 3878, 3880, 3883, 3885, 3888, 3890, 3892, 3894, 3896, 3898, 3901, 3903, 3906, 3908, 3911, 3913, 3916, 3918, 3921, 3923, 3926, 3928, 3931, 3933, 3936, 3938, 3940, 3942, 3944, 3946, 3949, 3951, 3954, 3956, 3959, 3961, 3964, 3966, 3968, 3970, 3972, 3974, 3976, 3978, 3981, 3983, 3985, 3987, 3991, 3993, 3996, 3998, 4001, 4003, 4006, 4008, 4011, 4013, 4015, 4017, 4019, 4021, 4023, 4025, 4027, 4029, 4031, 4033, 4035, 4037, 4040, 4042, 4045, 4047, 4050, 4052, 4055, 4057, 4060, 4062, 4065, 4067, 4070, 4072, 4075, 4077, 4080, 4082, 4085, 4087, 4090, 4092, 4094, 4096, 4098, 4100, 4103, 4105, 4108, 4110, 4113, 4115, 4118, 4120, 4123, 4125, 4128, 4130, 4133, 4135, 4138, 4140, 4143, 4145, 4148, 4150, 4153, 4155, 4158, 4160, 4162, 4164, 4166, 4168, 4171, 4173, 4176, 4178, 4181, 4183, 4186, 4188, 4190, 4192, 4194, 4196, 4198, 4200, 4203, 4205, 4208, 4210, 4216, 4218, 4220, 4222, 4224, 4226, 4229, 4231, 4234, 4236, 4239, 4241, 4244, 4246, 4249, 4251, 4254, 4256, 4259, 4261, 4264, 4266, 4268, 4270, 4272, 4274, 4276, 4278, 4280, 4282, 4284, 4286, 4288, 4290, 4292, 4294, 4296, 4298, 4300, 4302, 4304, 4306, 4308, 4310, 4312, 4314, 4316, 4318, 4320, 4322, 4324, 4326, 4328, 4330, 4332, 4334, 4336, 4338, 4340, 4342, 4344, 4346, 4348, 4350, 4352, 4354, 4356, 4358, 4360, 4362, 4364, 4366, 4368, 4370, 4372, 4374, 4376, 4378, 4380, 4382, 4384, 4386, 4388, 4390, 4392, 4394, 4396, 4398, 4400, 4402, 4404, 4406, 4408, 4410, 4412, 4414, 4416, 4418, 4420, 4422, 4425, 4427, 4429, 4431, 4434, 4436, 4438, 4440, 4443, 4445, 4447, 4449, 4452, 4454, 4457, 4459, 4461, 4463, 4465, 4467, 4469, 4471, 4473, 4475, 4477, 4479, 4481, 4483, 4485, 4487, 4489, 4491, 4493, 4495, 4497, 4499, 4501, 4503, 4505, 4507, 4509, 4511, 4513, 4515, 4517, 4519, 4522, 4524, 4526, 4528, 4531, 4533, 4535, 4537, 4539, 4541, 4543, 4545, 4547, 4549, 4551, 4553, 4555, 4557, 4559, 4561, 4563, 4565, 4567, 4569, 4571, 4573, 4576, 4578, 4580, 4582, 4585, 4587, 4590, 4592, 4594, 4596, 4598, 4600, 4602, 4604, 4607, 4609, 4611, 4613, 4618, 4620, 4622, 4624, 4626, 4628, 4631, 4633, 4636, 4638, 4641, 4643, 4646, 4648, 4651, 4653, 4656, 4658, 4661, 4663, 4666, 4668, 4671, 4673, 4676, 4678, 4681, 4683, 4686, 4688, 4690, 4692, 4695, 4697, 4700, 4702, 4708, 4710, 4712, 4714, 4716, 4718, 4721, 4723, 4726, 4728, 4731, 4733, 4736, 4738, 4741, 4743, 4746, 4748, 4751, 4753, 4755, 4757, 4759, 4761, 4764, 4766, 4769, 4771, 4774, 4776, 4779, 4781, 4783, 4785, 4787, 4789, 4791, 4793, 4795, 4797, 4799, 4801, 4803, 4805, 4807, 4809, 4811, 4813, 4815, 4817, 4819, 4821, 4823, 4825, 4827, 4829, 4831, 4833, 4835, 4837, 4839, 4841, 4843, 4845, 4848, 4850, 4852, 4854, 4856, 4858, 4861, 4863, 4866, 4868, 4871, 4873, 4876, 4878, 4880, 4882, 4885, 4887, 4889, 4891, 4895, 4897, 4900, 4902, 4905, 4907, 4910, 4912, 4914, 4916, 4919, 4921, 4923, 4925, 4929, 4931, 4934, 4936, 4939, 4941, 4944, 4946, 4949, 4951, 4954, 4956, 4959, 4961, 4963, 4965, 4968, 4970, 4973, 4975, 4981, 4983, 4985, 4987, 4990, 4992, 4994, 4996, 4999, 5001, 5005, 5007, 5009, 5011, 5014, 5016, 5019, 5021, 5024, 5026, 5029, 5031, 5033, 5035, 5038, 5040, 5043, 5045, 5048, 5050, 5052, 5054, 5056, 5058, 5061, 5063, 5066, 5068, 5071, 5073, 5076, 5078, 5081, 5083, 5086, 5088, 5091, 5093, 5096, 5098, 5100, 5102, 5104, 5106, 5109, 5111, 5114, 5116, 5119, 5121, 5124, 5126, 5128, 5130, 5132, 5134, 5137, 5139, 5142, 5144, 5147, 5149, 5152, 5154, 5156, 5158, 5160, 5162, 5164, 5166, 5168, 5170, 5172, 5174, 5176, 5178, 5180, 5182, 5184, 5186, 5189, 5191, 5194, 5196, 5199, 5201, 5203, 5205, 5207, 5209, 5212, 5214, 5216, 5218, 5220, 5222, 5225, 5227, 5229, 5231, 5233, 5235, 5238, 5240, 5243, 5245, 5248, 5250, 5253, 5255, 5258, 5260, 5263, 5265, 5268, 5270, 5273, 5275, 5277, 5279, 5281, 5283, 5286, 5288, 5290, 5292, 5294, 5296, 5298, 5300, 5302, 5304, 5306, 5308, 5310, 5312, 5314, 5316, 5318, 5320, 5322, 5324, 5326, 5328, 5331, 5333, 5335, 5337, 5340, 5342, 5345, 5347, 5353, 5355, 5357, 5359, 5361, 5363, 5366, 5368, 5371, 5373, 5376, 5378, 5381, 5383, 5385, 5387, 5389, 5391, 5393, 5395, 5397, 5399, 5402, 5404, 5406, 5408, 5410, 5412, 5414, 5416, 5419, 5421, 5424, 5426, 5432, 5434, 5437, 5439, 5442, 5444, 5447, 5449, 5451, 5453, 5455, 5457, 5459, 5461, 5463, 5465, 5468, 5470, 5473, 5475, 5478, 5480, 5483, 5485, 5488, 5490, 5493, 5495, 5498, 5500, 5502, 5504, 5506, 5508, 5511, 5513, 5515, 5517, 5519, 5521, 5524, 5526, 5529, 5531, 5534, 5536, 5539, 5541, 5543, 5545, 5548, 5550, 5553, 5555, 5558, 5560, 5562, 5564, 5566, 5568, 5570, 5572, 5574, 5576, 5578, 5580, 5582, 5584, 5586, 5588, 5590, 5592, 5594, 5596, 5598, 5600, 5602, 5604, 5606, 5608, 5610, 5612, 5614, 5616, 5618, 5620, 5622, 5624, 5626, 5628, 5630, 5632, 5634, 5636, 5638, 5640, 5642, 5644, 5646, 5648, 5650, 5652, 5654, 5656, 5658, 5660, 5662, 5664, 5667, 5669, 5672, 5674, 5676, 5678, 5680, 5682, 5684, 5686, 5688, 5690, 5692, 5694, 5696, 5698, 5700, 5702, 5704, 5706, 5708, 5710, 5712, 5714, 5716, 5718, 5720, 5722, 5724, 5726, 5728, 5730, 5732, 5734, 5736, 5738, 5740, 5742, 5744, 5746, 5748, 5750, 5752, 5754, 5756, 5758, 5761, 5763, 5765, 5767, 5770, 5772, 5774, 5776, 5779, 5781, 5783, 5785, 5787, 5789, 5791, 5793, 5795, 5797, 5799, 5801, 5803, 5805, 5807, 5809, 5811, 5813, 5816, 5818, 5820, 5822, 5824, 5826, 5829, 5831, 5833, 5835, 5838, 5840, 5842, 5844, 5847, 5849, 5851, 5853, 5855, 5857, 5859, 5861, 5863, 5865, 5867, 5869, 5871, 5873, 5875, 5877, 5879, 5881, 5883, 5885, 5887, 5889, 5891, 5893, 5896, 5898, 5900, 5902, 5905, 5907, 5909, 5911, 5913, 5915, 5918, 5920, 5922, 5924, 5926, 5928, 5930, 5932, 5934, 5936, 5938, 5940, 5942, 5944, 5946, 5948, 5950, 5952, 5954, 5956, 5958, 5960, 5962, 5964, 5966, 5968, 5970, 5972, 5974, 5976, 5978, 5980, 5982, 5984, 5986, 5988, 5990, 5992, 5994, 5996, 5999, 6001, 6003, 6005, 6007, 6009, 6011, 6013, 6015, 6017, 6020, 6022, 6028, 6030, 6033, 6035, 6038, 6040, 6042, 6044, 6047, 6049, 6052, 6054, 6060, 6062, 6064, 6066, 6068, 6070, 6073, 6075, 6078, 6080, 6083, 6085, 6088, 6090, 6092, 6094, 6096, 6098, 6101, 6103, 6106, 6108, 6111, 6113, 6116, 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16374, 16378, 38237, 38238, 16383, 16460, 16461, 38148, 38146, 37903, 16466, 16467, 38155, 38153, 37915, 16472, 16473, 38162, 38160, 37927, 16478, 16479, 16480, 37958, 16482, 16483, 16484, 16485, 16486, 16487, 16488, 16489, 16491, 16492, 16493, 37985, 16495, 16496, 16497, 16498, 16499, 16501, 38165, 38167, 37931, 37631, 37938, 16508, 38170, 16510, 37942, 37647, 37948, 16515, 16516, 16517, 38193, 38191, 38037, 16522, 16523, 16524, 37958, 16526, 16527, 16528, 16529, 16530, 16531, 16532, 16533, 16535, 16536, 16537, 37985, 16539, 16540, 16544, 38292, 38293, 16549, 16550, 16551, 37958, 16553, 16554, 16555, 16556, 16557, 16558, 16559, 16560, 16562, 16563, 16564, 37985, 16566, 16567, 16568, 16569, 16570, 16571, 16572, 16573, 16577, 38319, 38320, 38185, 16583, 38019, 37717, 37720, 38010, 37725, 16590, 16591, 16592, 38016, 38015, 38184, 38037, 16597, 38016, 38015, 38184, 38185, 16602, 38019, 37744, 38025, 16607, 16608, 37736, 37735, 37734, 38037, 38185, 16614, 38019, 37744, 38025, 16619, 16620, 16621, 38193, 38191, 38037, 16626, 16627, 16628, 16629, 16630, 16631, 16632, 16633, 16637, 38347, 38348, 16907, 16908, 16910, 16914, 16915, 16917, 16922, 16923, 16927, 16928, 16932, 16967, 16985, 16988, 16994, 17008, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38411, 16335, 38415, 38418, 38403, 38423, 16348, 38427, 38405, 38435, 16365, 38439, 38443, 38403, 16462, 16463, 38400, 16465, 16468, 16469, 38401, 16471, 16474, 16475, 38402, 16477, 38467, 16481, 38471, 38474, 38403, 38479, 16494, 38483, 38405, 16502, 16503, 16504, 16505, 16506, 16509, 16511, 16512, 16513, 16518, 16519, 38407, 16521, 38506, 16525, 38510, 38513, 38403, 38518, 16538, 38405, 38527, 16552, 38531, 38534, 38403, 38539, 16565, 38543, 38546, 38405, 16582, 16584, 16585, 16586, 16587, 16588, 16593, 16594, 16595, 16596, 16598, 16599, 16600, 16601, 16603, 16604, 16605, 16609, 16610, 16611, 16612, 16613, 16615, 16616, 16617, 16622, 16623, 38407, 16625, 38594, 38597, 38408, 38217, 38432, 38234, 38448, 38344, 38602, 38266, 38269, 38451, 38456, 38461, 38332, 38588, 38344, 38602, 38266, 38269, 38500, 38332, 38588, 38289, 38524, 38316, 38551, 38322, 38561, 38567, 38328, 38576, 38332, 38588, 38344, 38602, 38351, 38604, 38355, 38607, 38357, 38611, 38360, 38613, 38363, 38365, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38412, 16344, 38424, 16354, 38436, 38440, 16375, 16464, 38671, 16470, 38675, 16476, 38679, 38468, 16490, 38480, 16500, 38693, 38697, 16520, 38701, 38507, 16534, 38519, 16541, 38528, 16561, 38540, 16574, 38723, 38729, 38733, 38736, 38740, 38744, 16624, 38748, 16634, 38659, 38658, 38663, 38719, 16695, 16696, 38668, 16701, 16702, 38685, 38684, 38689, 38719, 16711, 16712, 16787, 38694, 16790, 38698, 16792, 16794, 16796, 16799, 38745, 16801, 38685, 38684, 38689, 38719, 16811, 16812, 16814, 38694, 16817, 38698, 16819, 16822, 38745, 16824, 38707, 38706, 38720, 38719, 16834, 16835, 38715, 38714, 38720, 38719, 16844, 16845, 16847, 38726, 38724, 16850, 16852, 16855, 38737, 16857, 16860, 38745, 16862, 38752, 38751, 16867, 16868, 16983, 16984, 16986, 16987, 16989, 16990, 16991, 16992, 16993, 17007, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38849, 38851, 38854, 38855, 38857, 38859, 38862, 38864, 38867, 38870, 38872, 38874, 38876, 38730, 38734, 38741, 38883, 38885, 16687, 16688, 38848, 16691, 16692, 38850, 16697, 38853, 38852, 16703, 16704, 38861, 16707, 16708, 38863, 38691, 16788, 38696, 16791, 38743, 16800, 16803, 16804, 38861, 16807, 16808, 38863, 38691, 16815, 38696, 16818, 38743, 16823, 16826, 16827, 38869, 16830, 16831, 38871, 16836, 16837, 38873, 16840, 16841, 38875, 38722, 16848, 16849, 38735, 16856, 38743, 16861, 16864, 16865, 38890, 38893, 38899, 38950, 38915, 38929, 38935, 38950, 38953, 38955, 38960, 38958, 38956, 38961, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 16689, 38995, 38420, 16693, 38998, 38429, 16698, 16699, 38445, 16705, 39004, 38476, 16709, 39007, 38485, 16786, 39010, 16789, 39012, 38979, 38980, 38981, 16798, 39014, 38992, 16805, 39016, 38476, 16809, 39019, 38485, 16813, 39022, 16816, 39024, 38984, 16821, 39026, 38992, 16828, 39028, 38515, 16832, 39031, 38521, 16838, 39034, 38536, 16842, 39037, 38548, 16846, 39040, 38989, 38990, 16854, 39043, 38991, 16859, 39045, 38992, 39047, 38599, 16891, 16894, 16902, 16906, 16945, 16953, 16956, 16965, 17012, 17013, 39057, 17015, 39056, 17020, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39104, 16690, 39107, 16694, 39110, 16700, 39113, 16706, 39116, 16710, 16793, 16795, 16797, 16802, 39129, 16806, 39132, 16810, 16820, 16825, 39143, 16829, 39146, 16833, 39149, 16839, 39152, 16843, 39041, 16851, 16853, 16858, 16863, 16866, 39121, 39119, 39126, 39137, 39135, 39140, 39121, 39119, 39126, 39137, 39135, 39140, 39159, 39162, 17014, 17016, 39176, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39111, 16882, 16883, 38907, 38906, 38905, 16887, 38910, 39234, 39232, 39258, 16895, 16896, 38921, 16898, 38924, 39240, 39238, 39155, 38940, 39165, 16936, 16937, 38907, 38906, 38905, 16941, 38910, 39248, 39246, 16946, 16947, 38921, 16949, 38924, 39254, 39252, 39258, 39256, 39155, 38940, 38941, 16960, 38944, 16962, 38947, 39165, 39178, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39298, 16884, 16885, 16886, 16888, 16889, 16890, 16892, 39296, 39308, 16897, 16899, 16900, 16901, 16903, 16904, 16905, 39318, 16938, 16939, 16940, 16942, 16943, 16944, 39327, 16948, 16950, 16951, 16952, 16954, 16955, 16957, 16958, 16959, 16961, 16963, 16964, 39343, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39362, 39366, 16893, 39373, 39379, 39383, 39388, 39390, 39302, 39310, 39369, 39340, 39338, 39338, 39374, 39376, 39322, 39329, 39384, 39340, 39338, 39338, 39391, 39396, 39281, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39363, 39426, 39380, 16971, 39425, 16975, 16976, 39427, 16978, 16979, 16980, 16981, 16982, 16995, 39429, 16998, 16999, 39431, 39430, 17002, 17003, 17004, 17005, 17006, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39360, 39489, 16974, 39494, 16977, 39497, 39499, 39377, 16997, 39504, 17000, 17001, 39508, 39510, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 16972, 16973, 39558, 16996, 39563, 39565, 39555, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39616, 39554, 39619, 39618, 17010, 39621, 39561, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 17009, 39680, 17017, 17018, 39682, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 17011, 39684, 17019, 39747, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39808, 39810, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 39873, 39872, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 17021, 17022, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 40001, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
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, 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1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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 64
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 17024
#define SIZE_OF_AC 23104
__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[627*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];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 266*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 + 267*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 + 268*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 + 269*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 + 270*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 + 271*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 + 272*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 + 273*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 + 274*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 + 275*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 + 276*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 + 277*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 + 278*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 + 279*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 + 280*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 + 281*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 + 282*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 + 283*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 + 284*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 + 285*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 + 286*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 + 287*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 + 288*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 + 289*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 + 290*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 + 291*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 + 292*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 + 293*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 + 294*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 + 295*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 + 296*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 + 297*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 + 298*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 + 299*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 + 300*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 + 301*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 + 302*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 + 303*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 + 304*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 + 305*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 + 306*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 + 307*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 + 308*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 + 309*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 + 310*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 + 311*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 + 312*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 + 313*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 + 314*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 + 315*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 + 316*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 + 317*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 + 318*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 + 319*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 + 320*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 + 321*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 + 322*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 + 323*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 + 324*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 + 325*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 + 326*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 + 327*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 + 328*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 + 329*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 + 330*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 + 331*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 + 332*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 + 333*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 + 334*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 + 335*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 + 336*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 + 337*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 + 338*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 + 339*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 + 340*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 + 341*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 + 342*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 + 343*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 + 344*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 + 345*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 + 346*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 + 347*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 + 348*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 + 349*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]];
__syncthreads();
R[i + 350*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 + 351*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 + 352*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 + 353*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 + 354*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 + 355*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 + 356*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 + 357*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 + 358*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 + 359*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 + 360*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 + 361*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 + 362*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 + 363*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 + 364*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 + 365*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 + 366*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 + 367*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 + 368*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 + 369*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 + 370*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 + 371*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 + 372*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 + 373*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 + 374*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 + 375*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 + 376*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 + 377*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 + 378*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 + 379*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 + 380*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 + 381*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 + 382*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 + 383*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 + 384*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 + 385*t] = Op[i + 119*t] ? R[B[i + 119*t]] * R[C[i + 119*t]] : R[B[i + 119*t]] + R[C[i + 119*t]];
R[i + 386*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 + 387*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 + 388*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 + 389*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 + 390*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 + 391*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 + 392*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 + 393*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 + 394*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 + 395*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 + 396*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]];
__syncthreads();
R[i + 397*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 + 398*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 + 399*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 + 400*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 + 401*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 + 402*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 + 403*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 + 404*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 + 405*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 + 406*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 + 407*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 + 408*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 + 409*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 + 410*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 + 411*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 + 412*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 + 413*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 + 414*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 + 415*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 + 416*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 + 417*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 + 418*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 + 419*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 + 420*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 + 421*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 + 422*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 + 423*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 + 424*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 + 425*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 + 426*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 + 427*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 + 428*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 + 429*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 + 430*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 + 431*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 + 432*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 + 433*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 + 434*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 + 435*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 + 436*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 + 437*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 + 438*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 + 439*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 + 440*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 + 441*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 + 442*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 + 443*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 + 444*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]];
__syncthreads();
R[i + 445*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 + 446*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 + 447*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 + 448*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 + 449*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 + 450*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 + 451*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 + 452*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 + 453*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 + 454*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 + 455*t] = Op[i + 189*t] ? R[B[i + 189*t]] * R[C[i + 189*t]] : R[B[i + 189*t]] + R[C[i + 189*t]];
R[i + 456*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 + 457*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 + 458*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 + 459*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 + 460*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 + 461*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 + 462*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 + 463*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 + 464*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 + 465*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 + 466*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 + 467*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 + 468*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 + 469*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 + 470*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 + 471*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 + 472*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 + 473*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 + 474*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 + 475*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 + 476*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 + 477*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 + 478*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 + 479*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 + 480*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 + 481*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 + 482*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 + 483*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 + 484*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 + 485*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 + 486*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 + 487*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 + 488*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]];
__syncthreads();
R[i + 489*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 + 490*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 + 491*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 + 492*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 + 493*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 + 494*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 + 495*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 + 496*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 + 497*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 + 498*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 + 499*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 + 500*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 + 501*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 + 502*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 + 503*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 + 504*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 + 505*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 + 506*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 + 507*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 + 508*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 + 509*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]];
__syncthreads();
R[i + 510*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 + 511*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 + 512*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 + 513*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 + 514*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 + 515*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 + 516*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 + 517*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 + 518*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 + 519*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 + 520*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 + 521*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 + 522*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 + 523*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 + 524*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 + 525*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 + 526*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 + 527*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 + 528*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 + 529*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 + 530*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]];
__syncthreads();
R[i + 531*t] = Op[i + 265*t] ? R[B[i + 265*t]] * R[C[i + 265*t]] : R[B[i + 265*t]] + R[C[i + 265*t]];
R[i + 532*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 + 533*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 + 534*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 + 535*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 + 536*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 + 537*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 + 538*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 + 539*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 + 540*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 + 541*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 + 542*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 + 543*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 + 544*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 + 545*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 + 546*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 + 547*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 + 548*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]];
__syncthreads();
R[i + 549*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 + 550*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 + 551*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 + 552*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 + 553*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 + 554*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 + 555*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 + 556*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 + 557*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]];
__syncthreads();
R[i + 558*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 + 559*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 + 560*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 + 561*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 + 562*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 + 563*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 + 564*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 + 565*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 + 566*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]];
__syncthreads();
R[i + 567*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 + 568*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 + 569*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 + 570*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 + 571*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 + 572*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]];
__syncthreads();
R[i + 573*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 + 574*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 + 575*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 + 576*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 + 577*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]];
__syncthreads();
R[i + 578*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 + 579*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 + 580*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 + 581*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 + 582*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]];
__syncthreads();
R[i + 583*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 + 584*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 + 585*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 + 586*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]];
__syncthreads();
R[i + 587*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 + 588*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 + 589*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 + 590*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 + 591*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]];
__syncthreads();
R[i + 592*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 + 593*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 + 594*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 + 595*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]];
__syncthreads();
R[i + 596*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 + 597*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 + 598*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 + 599*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]];
__syncthreads();
R[i + 600*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 + 601*t] = Op[i + 335*t] ? R[B[i + 335*t]] * R[C[i + 335*t]] : R[B[i + 335*t]] + R[C[i + 335*t]];
R[i + 602*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 + 603*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]];
__syncthreads();
R[i + 604*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 + 605*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 + 606*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]];
__syncthreads();
R[i + 607*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 + 608*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]];
__syncthreads();
R[i + 609*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 + 610*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]];
__syncthreads();
R[i + 611*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 + 612*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]];
__syncthreads();
R[i + 613*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]];
__syncthreads();
R[i + 614*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]];
__syncthreads();
R[i + 615*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]];
__syncthreads();
R[i + 616*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]];
__syncthreads();
R[i + 617*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]];
__syncthreads();
R[i + 618*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]];
__syncthreads();
R[i + 619*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]];
__syncthreads();
R[i + 620*t] = Op[i + 354*t] ? R[B[i + 354*t]] * R[C[i + 354*t]] : R[B[i + 354*t]] + R[C[i + 354*t]];
__syncthreads();
R[i + 621*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]];
__syncthreads();
R[i + 622*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]];
__syncthreads();
R[i + 623*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]];
__syncthreads();
R[i + 624*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]];
__syncthreads();
R[i + 625*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]];
__syncthreads();
R[i + 626*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]];
if (i==0) { final += R[626*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
6,365 | #include <float.h>
/*
* Author:
* Oren Freifeld, freifeld@csail.mit.edu
*/
__global__ void honeycomb( int* seg,
double* centers,
int K,
int nPts, int xdim, int ydim
){
int idx = threadIdx.x + blockIdx.x*blockDim.x;
if (idx>=nPts)
return;
int x = idx % xdim;
int y = idx / xdim;
double dx,dy;
double D2 = DBL_MAX;
//double D2 = (xdim*xdim+ydim*ydim )*10000000; // some large number
double d2;
for (int j=0; j < K; j++){
dx = (x - centers[j*2+0]);
dy = (y - centers[j*2+1]);
d2 = dx*dx + dy*dy;
if ( d2 <= D2){
D2 = d2;
seg[idx]=j;
}
}
return;
}
|
6,366 | /* computes the force array */
#include <math.h>
#include <stdio.h>
__global__ void force_aux(long, double *, float *, float *, double *,double *);
__global__ void force_aux2(long, double, double, double *, double *, float *, float *);
void force(long n, long nblock, long nthread, double *mx, double *my, double *magx, double *magy, double *r_gpu,
double *f_gpu, float *sinr_gpu, float *cosr_gpu, double *magx_gpu, double *magy_gpu)
{
long i;
force_aux<<<nblock,nthread>>>(n,r_gpu,sinr_gpu,cosr_gpu,magx_gpu,magy_gpu);
cudaMemcpy(magx,magx_gpu,nblock*sizeof(double),cudaMemcpyDeviceToHost);
cudaMemcpy(magy,magy_gpu,nblock*sizeof(double),cudaMemcpyDeviceToHost);
*mx=0.0;
*my=0.0;
for (i=0;i<nblock;i++)
{
*mx+=magx[i];
*my+=magy[i];
};
*mx=*mx/((double) n);
*my=*my/((double) n);
force_aux2<<<nblock,nthread>>>(n,*mx,*my,r_gpu,f_gpu,sinr_gpu,cosr_gpu);
return;
}
|
6,367 | #include <iostream>
#include <math.h>
// CUDA kernel to add elements of two arrays
__global__
void add(int n, float *x, float *y)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride)
y[i] = x[i] + y[i];
}
int main(void)
{
int N = 1<<20;
float *x, *y;
int device = -1;
// Allocate Unified Memory -- accessible from CPU or GPU
cudaMallocManaged(&x, N*sizeof(float));
cudaMallocManaged(&y, N*sizeof(float));
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f;
}
cudaGetDevice(&device);
// GPU prefetches unified memory memory
cudaMemPrefetchAsync(x, N*sizeof(float), device, NULL);
cudaMemPrefetchAsync(y, N*sizeof(float), device, NULL);
// Launch kernel on 1M elements on the GPU
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
add<<<numBlocks, blockSize>>>(N, x, y);
// Host prefecthes Memory
cudaMemPrefetchAsync(y, N*sizeof(float), cudaCpuDeviceId, NULL);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
// Check for errors (all values should be 3.0f)
float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(y[i]-3.0f));
std::cout << "Max error: " << maxError << std::endl;
// Free memory
cudaFree(x);
cudaFree(y);
return 0;
}
|
6,368 | #include <iostream>
#include <random>
#include "cuda_runtime_api.h"
double *InitializeArray(const int length, const int seed) {
double *A = (double*)malloc(length * sizeof(double));
std::default_random_engine e;
std::uniform_real_distribution<double> rand(0, 10);
e.seed(seed);
for (int i = 0; i < length; ++i) {
A[i] = rand(e);
}
return A;
}
void PrintArray(double *A, const int length, std::string str) {
std::cout <<"Array " << str << ":";
for (int i = 0; i < length; ++i) {
std::cout << " " << A[i];
}
std::cout << std::endl;
}
__host__ __device__ double MaxElement(double a, double b, double c) {
if ((a >= b) && (a >= c)) {
return a;
}
if (b >= c) {
return b;
}
return c;
}
double *MaxElements(double *A, double *B, double *C, const int length) {
double *D = (double*)malloc(length * sizeof(double));
for (int i = 0; i < length; ++i) {
D[i] = MaxElement(A[i], B[i], C[i]);
}
return D;
}
__global__ void MaxElementsKernel(double *A, double *B, double *C, double *D) {
int i = threadIdx.x;
D[i] = MaxElement(A[i], B[i], C[i]);
}
int main() {
const int length = 10;
const size_t size = length * sizeof(double);
double *h_A, *h_B, *h_C, *h_D;
h_A = (double*)malloc(size);
h_B = (double*)malloc(size);
h_C = (double*)malloc(size);
h_D = (double*)malloc(size);
h_A = InitializeArray(length, 0);
h_B = InitializeArray(length, 5);
h_C = InitializeArray(length, 10);
PrintArray(h_A, length, "A");
PrintArray(h_B, length, "B");
PrintArray(h_C, length, "C");
std::cout << "CPU Result:\n";
PrintArray(MaxElements(h_A, h_B, h_C, length), length, "D");
std::cout << "GPU Result:\n";
const int dev = 0;
cudaSetDevice(dev);
double *d_A, *d_B, *d_C, *d_D;
cudaMalloc((void **)&d_A, size);
cudaMalloc((void **)&d_B, size);
cudaMalloc((void **)&d_C, size);
cudaMalloc((void **)&d_D, size);
cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_C, h_C, size, cudaMemcpyHostToDevice);
MaxElementsKernel<<<1, length>>>(d_A, d_B, d_C, d_D);
cudaMemcpy(h_D, d_D, size, cudaMemcpyDeviceToHost);
PrintArray(h_D, length, "D");
free(h_A);
free(h_B);
free(h_C);
free(h_D);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
cudaFree(d_D);
return 0;
}
|
6,369 | //
// include files
//
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
//
// CPU addition
//
void VecAdd(float* A, float* B, float* C, int N)
{
for (int i = 0; i < N; i++)
C[i] = A[i] + B[i];
}
//
// main code
//
int main(int argc, char **argv)
{
cudaSetDevice(1);
// Input the vector length
int N = atoi(argv[1]);
// Number of bytes to allocate for N float
size_t bytes = N*sizeof(float);
// Generate randomly vectors A and B
float *A = (float *)malloc(bytes);
float *B = (float *)malloc(bytes);
float *C = (float *)malloc(bytes);
for (int i = 0; i < N; i++)
{
A[i] = rand()%100;
B[i] = rand()%100;
}
VecAdd(A, B, C, N);
int s = 0;
for (int j = 0; j < N; j++) s += C[j];
printf("\nCPU Vector Length: %d Sum: %d\n", N, s);
// Free CPU memory
free(A);
free(B);
free(C);
// CUDA exit -- needed to flush printf write buffer
cudaDeviceReset();
return 1;
} |
6,370 | #include <stdio.h>
__global__ void hello_from_gpu()
{
const int bid = blockIdx.x;
const int tid = threadIdx.x;
printf("Hello World from the GPU in block %d thread %d!\n", bid, tid);
}
int main(void)
{
hello_from_gpu<<<2, 4>>>();
cudaDeviceSynchronize();
return 0;
} |
6,371 | int main0(int argc, const char **argv) {
}
int main1(int argc, const char *argv[]) {
}
void fp0(const int *x) {
}
void fp1(const int *x = (const int*)0) {
}
void fa0(int x[]) {
}
|
6,372 | #include<stdio.h>
#include<stdlib.h>
#include<string.h>
#include<float.h> //DBL_MAX
#include <cuda_runtime_api.h>
#define restrict __restrict__
#define PADDINGCLASS -2
#define EXP 2
#define OUTPUT_FILE "ocuda"
#define INPUT_FILE "data"
void printStats(size_t bytes, cudaEvent_t before, cudaEvent_t after, const char *msg);
void check_error(cudaError_t err, const char *msg);
void readInput(FILE* file, double* coords, double* coordsnew, int* classes, int spacedim, int numels, int newels);
void writeOutput(double* coords, int* classes, int spacedim, int numels);
__device__ int findMode(double4* elements, int classes_num, int k);
__global__ void findClass(double* coords, double* coordsnew, int* input_classes, double4* d_output, int spacedim, int classes_num, int numels, int offset, int newPointIndex, int newels, double* d_coordsDistances);
__device__ double distance(double* coords, double* coords2, int spacedim);
__global__ void findMin(double4* input, double* coords, double* coordsnew, int* classes, int classes_num, int spacedim, int numels, int offset, double4* result, int k, int newPointIndex, int eleInBlock, int newels, double* coordsDistances);
__device__ void swapdouble(double* x, double* y);
__device__ void swapInt(int* x, int* y);
__global__ void calcDistances(double* coords, double* coordsnew, int spacedim, int numels, int newels, double* coordsDistances);
//Declaration of shared-memory. It's going to contains partial minimum of distances
extern __shared__ double4 mPartial[];
int main(int argc, char *argv[])
{
int newels; //number of points we want classify
int k; //number of nearest points we use to classify
int numels; //total element already classified
int spacedim;
char filePath[255]; //path + filname of input file
int classes_num; //number of classes
double* h_coords; //coords of existing points with a class
double* h_coordsnew; //coords of points we want to classify
int* h_classes; //array contains the class for each points
//*** Device-variables-declaration ***
double* d_coords;
double* d_coordsnew;
double* d_coordsDistances;
double* d_newcoordsDistances;
double4* d_result;
int* d_classes;
double4* d_output;
//*** end-device-declaration
//***cudaEvent-declaration***
cudaEvent_t before_allocation, before_input, before_upload, before_knn, before_download;
cudaEvent_t after_allocation, after_input, after_upload, after_knn, after_download;
//***end-cudaEvent-declaration***
if (argc > 2)
{
strcpy(filePath, argv[1]);
k = atoi(argv[2]);
}
else
{
printf("how-to-use: knn <inputfile> <k> \n");
exit(1);
}
//***cuda-init-event***
check_error(cudaEventCreate(&before_allocation), "create before_allocation cudaEvent");
check_error(cudaEventCreate(&before_input), "create before_input cudaEvent");
check_error(cudaEventCreate(&before_upload), "create before_upload cudaEvent");
check_error(cudaEventCreate(&before_knn), "create before_knn cudaEvent");
check_error(cudaEventCreate(&before_download), "create before_download cudaEvent");
check_error(cudaEventCreate(&after_allocation), "create after_allocation cudaEvent");
check_error(cudaEventCreate(&after_input), "create after_input cudaEvent");
check_error(cudaEventCreate(&after_upload), "create after_upload cudaEvent");
check_error(cudaEventCreate(&after_knn), "create after_knn cudaEvent");
check_error(cudaEventCreate(&after_download), "create after_download cudaEvent");
//***end-cuda-init-event***
FILE *fp;
if((fp = fopen(filePath, "r")) == NULL)
{
printf("No such file\n");
exit(1);
}
fseek(fp, 0L, SEEK_END);
float fileSize = ftell(fp);
rewind(fp);
int count = fscanf(fp, "%d,%d,%d,%d\n", &numels, &newels, &classes_num, &spacedim);
int totalElements = numels + newels;
//*** allocation ***
cudaEventRecord(before_allocation);
h_coords = (double*) malloc(sizeof(double)*totalElements*spacedim);
h_coordsnew = (double*) malloc(sizeof(double)*newels*spacedim);
h_classes = (int*) malloc(sizeof(int)*totalElements);
const int blockSize = 512;
int numBlocks = (totalElements + blockSize - 1)/blockSize;
//*** device-allocation ***
check_error(cudaMalloc(&d_coords, totalElements*spacedim*sizeof(double)), "alloc d_coords_x");
check_error(cudaMalloc(&d_output, ((totalElements + blockSize - 1)/blockSize)*4*sizeof(double)), "alloc d_output");
check_error(cudaMalloc(&d_classes, totalElements*sizeof(int)), "alloc d_classes");
check_error(cudaMalloc(&d_result, 4*k*sizeof(double)), "alloc d_result");
check_error(cudaMalloc(&d_coordsDistances, (newels*totalElements)*sizeof(double)), "alloc d_coordsDistances");
check_error(cudaMalloc(&d_newcoordsDistances, (newels*newels)*sizeof(double)), "alloc d_newcoordsDistances");
check_error(cudaMalloc(&d_coordsnew, newels*spacedim*sizeof(double)), "alloc d_coordsnew");
//*** end-device-allocation ***
cudaEventRecord(after_allocation);
///***input-from-file***
cudaEventRecord(before_input);
readInput(fp, h_coords, h_coordsnew, h_classes, spacedim, numels, newels);
cudaEventRecord(after_input);
fclose(fp);
///***end-input-from-file***
//***copy-arrays-on-device***
cudaEventRecord(before_upload);
check_error(cudaMemcpy(d_coords, h_coords, totalElements*spacedim*sizeof(double), cudaMemcpyHostToDevice), "copy d_coords");
check_error(cudaMemcpy(d_classes, h_classes, totalElements*sizeof(int), cudaMemcpyHostToDevice), "copy d_classes");
check_error(cudaMemcpy(d_coordsnew, h_coordsnew, newels*spacedim*sizeof(double), cudaMemcpyHostToDevice), "copy d_coordsnew");
cudaEventRecord(after_upload);
//***end-copy-arrays-on-device***
cudaEventRecord(before_knn);
calcDistances<<<numBlocks, blockSize>>>(d_coords, d_coordsnew, spacedim, numels, newels, d_coordsDistances);
int i, j;
for (i = 0; i < newels; i++)
{
numBlocks = (numels + blockSize - 1)/blockSize;
j = 0;
for (j = 0; j < k; j++)
{
findClass<<<numBlocks, blockSize, blockSize*4*sizeof(double)>>>(
d_coords, d_coordsnew, d_classes,
d_output,
spacedim, classes_num,
numels, j, i, newels, d_coordsDistances);
findMin<<<1, blockSize, blockSize*4*sizeof(double)>>>(d_output, d_coords, d_coordsnew, d_classes, classes_num, spacedim, numels, j, d_result, k, i, numBlocks, newels, d_coordsDistances);
}
numels++;
}
cudaEventRecord(after_knn);
cudaEventRecord(before_download);
check_error(cudaMemcpy(h_coords, d_coords, spacedim*totalElements*sizeof(double), cudaMemcpyDeviceToHost), "download coords");
check_error(cudaMemcpy(h_classes, d_classes, totalElements*sizeof(int), cudaMemcpyDeviceToHost), "download classes");
cudaEventRecord(after_download);
check_error(cudaEventSynchronize(after_download), "sync cudaEvents");
printStats((totalElements+newels)*(1+spacedim)*sizeof(double) + totalElements*sizeof(int), before_allocation, after_allocation, "[time] allocation");
printStats(fileSize, before_input, after_input, "[time] read input file");
printStats(fileSize, before_upload, after_upload, "[time] upload host->device");
printStats((spacedim*totalElements*sizeof(double) + totalElements*sizeof(int))*newels, before_knn, after_knn, "[time] knn algorithm");
printStats((spacedim*totalElements*sizeof(double) + totalElements*sizeof(int))*newels, before_download, after_download, "[time] download device->host");
writeOutput(h_coords, h_classes, spacedim, numels);
return 0;
}
void check_error(cudaError_t err, const char *msg)
{
if (err != cudaSuccess)
{
fprintf(stderr, "%s : error %d (%s)\n", msg, err, cudaGetErrorString(err));
exit(err);
}
}
float runtime;
void printStats(size_t bytes, cudaEvent_t before, cudaEvent_t after, const char *msg)
{
check_error(cudaEventElapsedTime(&runtime, before, after), msg);
printf("%s %gms, %g GB/s\n", msg, runtime, bytes/runtime/(1024*1024));
}
//Parallel reduction to find the k-minimum distances
__global__ void findClass(
double* coords, double* coordsnew,
int* input_classes, double4* d_output,
int spacedim, int classes_num, int numels, int offset, int newPointIndex, int newels, double* d_coordsDistances)
{
int gid = offset + threadIdx.x + blockIdx.x*blockDim.x;
int lid = threadIdx.x;
mPartial[lid] = make_double4(-1, PADDINGCLASS, -1, -1);
if (gid >= numels) return;
double min = d_coordsDistances[gid*newels + newPointIndex];
double d;
int c = input_classes[gid];
int minID = gid;
while (gid < numels)
{
d = d_coordsDistances[gid*newels + newPointIndex];
if(d < min)
{
min = d;
minID = gid;
c = input_classes[gid];
}
gid += gridDim.x*blockDim.x;
}
mPartial[lid] = make_double4(min, (double)c, minID, -1);
//Part 2: reduction in shared memory
int stride = (blockDim.x)/2;
while (stride > 0)
{
__syncthreads();
if (lid < stride && mPartial[lid+stride].y != PADDINGCLASS && mPartial[lid].y != PADDINGCLASS && mPartial[lid+stride].x < mPartial[lid].x)
mPartial[lid] = mPartial[lid+stride];
stride /= 2;
}
/* Part 3: save the block's result in global memory */
if (lid == 0)
d_output[blockIdx.x] = mPartial[0];
}
__global__ void findMin(double4* input, double* coords, double* coordsnew, int* classes, int classes_num, int spacedim, int numels, int offset, double4* result, int k, int newPointIndex, int eleInBlock, int newels, double* coordsDistances)
{
int gid = threadIdx.x + blockIdx.x*blockDim.x;
int lid = threadIdx.x;
mPartial[lid] = make_double4(-1, PADDINGCLASS, -1, -1);
if (gid >= eleInBlock || gid >= blockDim.x) return;
double distmin = input[gid].x;
double classmin = input[gid].y;
double gidMin = input[gid].z;
while (gid < eleInBlock)
{
if(input[gid].x < distmin)
{
distmin = input[gid].x;
classmin = input[gid].y;
gidMin = input[gid].z;
}
gid += gridDim.x*blockDim.x;
}
mPartial[lid] = make_double4(distmin, classmin, gidMin, -1);
//Part 2: reduction in shared memory
int stride = (blockDim.x)/2;
while (stride > 0)
{
__syncthreads();
if (lid < stride && mPartial[lid+stride].y != PADDINGCLASS && mPartial[lid].y != PADDINGCLASS && mPartial[lid+stride].x < mPartial[lid].x)
mPartial[lid] = mPartial[lid + stride];
stride /= 2;
}
/* Part 3: save the block's result in global memory */
if (lid == 0)
{
input[0] = mPartial[0];
int minID = mPartial[0].z;
int i = 0;
for (i = 0; i < spacedim; i++)
swapdouble(&(coords[spacedim*minID+i]), &(coords[offset*spacedim+i]));
for (i = 0; i < newels; i++)
swapdouble(&(coordsDistances[newels*minID + i]), &(coordsDistances[newels*offset+i]));
swapInt(&(classes[minID]), &(classes[offset]));
result[offset] = input[0];
if (offset == k-1)
{
int j;
for (j = 0; j < spacedim; j++)
coords[spacedim*numels+j] = coordsnew[spacedim*newPointIndex + j];
classes[numels] = findMode(result, classes_num, k);
}
}
}
__global__ void calcDistances(double* coords, double* coordsnew, int spacedim, int numels, int newels, double* coordsDistances)
{
int point = threadIdx.x + blockIdx.x*blockDim.x;
int totalElements = numels + newels;
if (point >= totalElements) return;
int i = 0;
if (point < numels)
{
//per ogni punto - mi calcolo le distanze con i punti newles
for (i = 0; i < newels; i++)
coordsDistances[point*newels+i] = distance((point*spacedim+coords), (i*spacedim+coordsnew), spacedim);
}
else
{
//punto da determinare, mi calcolo la distanza con il resto dei punti newels
int index = point - numels;
for (i = 0; i < newels; i++)
{
//distance per me stesso
if (i == index)
coordsDistances[point*newels+i] = DBL_MAX;
else
coordsDistances[point*newels+i] = distance((index*spacedim+coordsnew), (i*spacedim+coordsnew), spacedim);
}
}
}
// read input from file
void readInput(FILE* file, double* coords, double* coordsnew, int* classes, int spacedim, int numels, int newels)
{
int i, j;
int count;
for(i=0; i<numels; i++)
{
for (j = 0; j < spacedim; j++)
count = fscanf(file, "%lf,", &(coords[i*spacedim +j]));
count = fscanf(file, "%d\n", &(classes[i]));
}
for(i = 0; i < newels; i++)
{
for (j = 0; j < spacedim; j++)
count = fscanf(file, "%lf,", &(coordsnew[i*spacedim+j]));
count = fscanf(file, "-1\n");
}
count++;
}
//Write Output on file
void writeOutput(double* coords, int* classes, int spacedim, int numels)
{
FILE *fp;
fp = fopen(OUTPUT_FILE, "w");
int i, j;
for( i = 0; i < numels; i++)
{
for (j = 0; j < spacedim; j++)
fprintf(fp, "%lf,", coords[i*spacedim+j]);
fprintf(fp, "%d\n", classes[i]);
}
fclose(fp);
}
//multidimensional euclidian distance
__device__ double distance(double* coords, double* coords2, int spacedim)
{
double sum = 0;
int i;
for (i = 0; i < spacedim; i++)
{
double diff = coords[i] - coords2[i];
sum += diff*diff;
}
return sum;
}
__device__ void swapdouble(double* x, double* y)
{
double tmp = *x;
*x = *y;
*y = tmp;
}
__device__ void swapInt(int* x, int* y)
{
int tmp = *x;
*x = *y;
*y = tmp;
}
__device__ int findMode(double4* elements, int classes_num, int k)
{
int* classCount = (int*) (malloc(sizeof(int)*classes_num));
int i;
for (i = 0; i < classes_num; i++)
classCount[i] = 0;
for (i = 0; i < k; i++)
classCount[(int)(elements[i].y)]++;
int max = 0;
int maxValue = classCount[0];
for (i = 1; i < classes_num; i++)
{
int value = classCount[i];
if (value > maxValue)
{
max = i;
maxValue = value;
}
else if (value != 0 && maxValue == value)
{
int j = 0;
for (j = 0; j < k; j++)
{
if (elements[j].y == i)
{
max = i;
break;
}
else if (elements[j].y == max)
break;
}
}
}
free(classCount);
return max;
}
|
6,373 |
#include <stdio.h>
#include <stdlib.h>
#include <stdint.h>
#include <string.h>
#include <math.h>
#define SIZE 32
/* Autores:
*
* Antonio J. Cabrera
* Paul Gazel-Anthoine
*/
// Structs (H)
typedef struct bmpFileHeader {
/* 2 bytes de identificación */
uint32_t size; /* Tamaño del archivo */
uint16_t resv1; /* Reservado */
uint16_t resv2; /* Reservado */
uint32_t offset; /* Offset hasta hasta los datos de imagen */
} bmpFileHeader;
typedef struct bmpInfoHeader {
uint32_t headersize; /* Tamaño de la cabecera */
uint32_t width; /* Ancho */
uint32_t height; /* Alto */
uint16_t planes; /* Planos de color (Siempre 1) */
uint16_t bpp; /* bits por pixel */
uint32_t compress; /* compresion */
uint32_t imgsize; /* tamaño de los datos de imagen */
uint32_t bpmx; /* Resolucion X en bits por metro */
uint32_t bpmy; /* Resolucion Y en bits por metro */
uint32_t colors; /* colors used en la paleta */
uint32_t imxtcolors; /* Colores importantes. 0 si son todos */
} bmpInfoHeader;
// Rutinas BMP (C)
unsigned char *LoadBMP(char *filename, bmpInfoHeader *bInfoHeader) {
FILE *f;
bmpFileHeader header; /* cabecera */
unsigned char *imgdata; /* datos de imagen */
uint16_t type; /* 2 bytes identificativos */
f=fopen (filename, "r");
if (!f) { /* Si no podemos leer, no hay imagen */
printf("NO se puede abrir el fichero %s\n", filename);
return NULL;
}
/* Leemos los dos primeros bytes y comprobamos el formato */
fread(&type, sizeof(uint16_t), 1, f);
if (type !=0x4D42) {
fclose(f);
printf("%s NO es una imagen BMP\n", filename);
return NULL;
}
/* Leemos la cabecera del fichero */
fread(&header, sizeof(bmpFileHeader), 1, f);
printf("File size: %u\n", header.size);
printf("Reservado: %u\n", header.resv1);
printf("Reservado: %u\n", header.resv2);
printf("Offset: %u\n", header.offset);
/* Leemos la cabecera de información del BMP */
fread(bInfoHeader, sizeof(bmpInfoHeader), 1, f);
/* Reservamos memoria para la imagen, lo que indique imgsize */
if (bInfoHeader->imgsize == 0) bInfoHeader->imgsize = ((bInfoHeader->width*3 +3) / 4) * 4 * bInfoHeader->height;
imgdata = (unsigned char*) malloc(bInfoHeader->imgsize);
if (imgdata == NULL) {
printf("Fallo en el malloc, del fichero %s\n", filename);
exit(0);
}
/* Nos situamos en donde empiezan los datos de imagen, lo indica el offset de la cabecera de fichero */
fseek(f, header.offset, SEEK_SET);
/* Leemos los datos de la imagen, tantos bytes como imgsize */
fread(imgdata, bInfoHeader->imgsize,1, f);
/* Cerramos el fichero */
fclose(f);
/* Devolvemos la imagen */
return imgdata;
}
bmpInfoHeader *createInfoHeader(uint32_t width, uint32_t height, uint32_t ppp) {
bmpInfoHeader *InfoHeader;
bool IH;
IH = malloc(sizeof(bmpInfoHeader));
if (!IH) return NULL;
InfoHeader->headersize = sizeof(bmpInfoHeader);
InfoHeader->width = width;
InfoHeader->height = height;
InfoHeader->planes = 1;
InfoHeader->bpp = 24;
InfoHeader->compress = 0;
/* 3 bytes por pixel, width*height pixels, el tamaño de las filas ha de ser multiplo de 4 */
InfoHeader->imgsize = ((width*3 + 3) / 4) * 4 * height;
InfoHeader->bpmx = (unsigned) ((double)ppp*100/2.54);
InfoHeader->bpmy= InfoHeader->bpmx; /* Misma resolucion vertical y horiontal */
InfoHeader->colors = 0;
InfoHeader->imxtcolors = 0;
return InfoHeader;
}
void SaveBMP(char *filename, bmpInfoHeader *InfoHeader, unsigned char *imgdata) {
bmpFileHeader header;
FILE *f;
uint16_t type;
f=fopen(filename, "w+");
header.size = InfoHeader->imgsize + sizeof(bmpFileHeader) + sizeof(bmpInfoHeader) +2;//2
header.resv1 = 0;
header.resv2 = 0;
/* El offset será el tamaño de las dos cabeceras + 2 (información de fichero)*/
header.offset=sizeof(bmpFileHeader)+sizeof(bmpInfoHeader) +2;//2
/* Escribimos la identificación del archivo */
type=0x4D42;
fwrite(&type, sizeof(type),1,f);
/* Escribimos la cabecera de fichero */
fwrite(&header, sizeof(bmpFileHeader),1,f);
/* Escribimos la información básica de la imagen */
fwrite(InfoHeader, sizeof(bmpInfoHeader),1,f);
/* Escribimos la imagen */
fwrite(imgdata, InfoHeader->imgsize, 1, f);
fclose(f);
}
void DisplayInfo(char *FileName, bmpInfoHeader *InfoHeader)
{
printf("\n");
printf("Informacion de %s\n", FileName);
printf("Tamaño de la cabecera: %u bytes\n", InfoHeader->headersize);
printf("Anchura: %d pixels\n", InfoHeader->width);
printf("Altura: %d pixels\n", InfoHeader->height);
printf("Planos (1): %d\n", InfoHeader->planes);
printf("Bits por pixel: %d\n", InfoHeader->bpp);
printf("Compresion: %d\n", InfoHeader->compress);
printf("Tamaño de la imagen: %u bytes\n", InfoHeader->imgsize);
printf("Resolucion horizontal: %u px/m\n", InfoHeader->bpmx);
printf("Resolucion vertical: %u px/m\n", InfoHeader->bpmy);
if (InfoHeader->bpmx == 0)
InfoHeader->bpmx = (unsigned) ((double)24*100/2.54);
if (InfoHeader->bpmy == 0)
InfoHeader->bpmy = (unsigned) ((double)24*100/2.54);
printf("Colores en paleta: %d\n", InfoHeader->colors);
printf("Colores importantes: %d\n", InfoHeader->imxtcolors);
}
/*
------------------------------------------------
Nuestro Código
------------------------------------------------
*/
__global__ void KernelByN (int N, int M, unsigned char *A) {
int row = blockIdx.y * blockDim.y + threadIdx.y;
int col = blockIdx.x * blockDim.x + 3*threadIdx.x;
if(row < M && col < N)
A[row*N+col] = A[row*N+col+1] = A[row*N+col+2] = (A[row*N+col] + A[row*N+col+1] + A[row*N+col+2])/3;
}
int main(int argc, char** argv)
{
unsigned int N, M;
unsigned int numBytes;
unsigned int nBlocks, nThreads;
float TiempoTotal, TiempoKernel;
cudaEvent_t E0, E1, E2, E3;
unsigned char *d_A;
if (argc != 3) { printf("Usage: ./exe img.bmp prefix\n"); exit(0); }
printf("INICIO\n");
bmpInfoHeader header;
unsigned char *image;
image = LoadBMP(argv[1], &header);
unsigned int N3 = header.width * 3;
N = (N3+3) & 0xFFFFFFFC; // Fila multiplo de 4 (BMP)
M = header.height;
// numero de Threads en cada dimension
nThreads = SIZE;
// numero de Blocks en cada dimension
nBlocks = (N+nThreads-1)/nThreads;
numBytes = N * M * sizeof(unsigned char);
dim3 dimGrid(nBlocks, nBlocks, 1);
dim3 dimBlock(nThreads, nThreads, 1);
cudaEventCreate(&E0);
cudaEventCreate(&E1);
cudaEventCreate(&E2);
cudaEventCreate(&E3);
cudaEventRecord(E0, 0);
cudaEventSynchronize(E0);
// Obtener Memoria en el device
cudaMalloc((unsigned char**)&d_A, numBytes);
// Copiar datos desde el host en el device
cudaMemcpy(d_A, image, numBytes, cudaMemcpyHostToDevice);
cudaEventRecord(E1, 0);
cudaEventSynchronize(E1);
// Ejecutar el kernel
KernelByN<<<dimGrid, dimBlock>>>(N, M, d_A);
cudaEventRecord(E2, 0);
cudaEventSynchronize(E2);
// Obtener el resultado desde el host
cudaMemcpy(image, d_A, numBytes, cudaMemcpyDeviceToHost);
// Liberar Memoria del device
cudaFree(d_A);
cudaEventRecord(E3, 0);
cudaEventSynchronize(E3);
cudaEventElapsedTime(&TiempoTotal, E0, E3);
cudaEventElapsedTime(&TiempoKernel, E1, E2);
printf("\nKERNEL ByN\n");
printf("Dimensiones: %dx%d\n", N, M);
printf("nThreads: %dx%d (%d)\n", nThreads, nThreads, nThreads*nThreads);
printf("nBlocks: %dx%d (%d)\n", nBlocks, nBlocks, nBlocks*nBlocks);
printf("Tiempo Global: %4.6f milseg\n", TiempoTotal);
printf("Tiempo Kernel: %4.6f milseg\n", TiempoKernel);
cudaEventDestroy(E0); cudaEventDestroy(E1); cudaEventDestroy(E2); cudaEventDestroy(E3);
char nom[32];
strcpy(nom, argv[2]);
strcat(nom, "_");
strcat(nom,argv[1]);
SaveBMP(nom, &header, image);
}
|
6,374 | /*
1. Recibimos como parametros, el canal RGB con el que se va a trabajar
2. lugar en donde se va almacenar el resultado
3. ancho de la imagen
4. alto de la imagen
5. matriz de gauss
6. dimension de la matriz de gauss 3 o 5
*/
__global__ void aplicarFiltroGauss(const unsigned char *inputEspacioColor,
unsigned char *outputEspacioColor,
const unsigned int ancho,
const unsigned int alto,
const float *gausskernel,
const unsigned int kernel) {
// Obtenemos las columnas resultantes de la multiplicacion del numero
// de hilos*tamano del bloque *dimension del bloque todas en el espacio de X
const unsigned int columnas = threadIdx.x + blockIdx.x * blockDim.x;
// Obtenemos las filas resultantes de la multiplicacion del numero
// de hilos*tamano del bloque *dimension del bloque todas en el espacio de Y
const unsigned int filas = threadIdx.y + blockIdx.y * blockDim.y;
//Comprobacion para ver si no se ha superado las dimensiones de la imagen
if(filas < alto && columnas < ancho) {
// Obtenemos el valor central de la matriz 5//2 = 2.5 = 2 #
const int mitadSizeKernel = (int)kernel / 2;
// Variable que van a almacenar la suma de producto de los canales rgb por cada valor de la matriz de gauss
float pixel = 0.0;
// Bucles para recorrer la matriz de gauss desde (-2,2]#
for(int i = -mitadSizeKernel; i <= mitadSizeKernel; i++) {
for(int j = -mitadSizeKernel; j <= mitadSizeKernel; j++) {
// Obtenemos la posicion "x" y "y" de un pixel en especifico para manipularlo. #
// Con el min aseguramos no pasarnos del ancho o alto de la imagen #
// Con el max aseguramos obtener solo valores positivos y no se problemas con la dimension de la imagen
const unsigned int y = max(0, min(alto - 1, filas + i));
const unsigned int x = max(0, min(ancho - 1, columnas + j));
//Recordamos que la matriz en este caso es una lista no una matriz seguida por lenguaje C
//entonces para solo se necesita la posicion una posicion para el kernel que buscamos.
const float valorGauss = gausskernel[(j + mitadSizeKernel) + (i + mitadSizeKernel) * kernel];
//ahora multiplicamos el valor de la matriz de gauss por el pixel en la posicion x,y
pixel += valorGauss * inputEspacioColor[x + y * ancho];
}
}
//printf("%.0f",pixel);
// Obtenemos la posicion del pixel haciendo uso de columnas, filas y ancho, luego asignamos el valor del pixel modificado.
outputEspacioColor[columnas + filas * ancho] = static_cast<unsigned char>(pixel);
}
} |
6,375 | //cuda.cu
//simple CUDA functions
//by Wasit 20-8-2011
#include <stdio.h>
#include <cuda_runtime.h>
//memory allocation on device side
extern "C" void CUDA_Constructor(int** g_A,int** g_B,int size){
cudaMalloc(g_A,sizeof(int)*size);
cudaMalloc(g_B,sizeof(int)*size);
}
//copying data from host to device
extern "C" void CUDA_SetData(int* g_dist, int* h_src,int size){
cudaMemcpy(g_dist,h_src,sizeof(int)*size,cudaMemcpyHostToDevice);
}
//CUDA Kernel block and thread ID are indicated by blockIdx and threadIdx, respectively
__global__ void Kernel_Add(int* g_A,int* g_B){
unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
g_A[x]=g_A[x]+g_B[x];
}
//Addition function
//number of thread and block is set before call Kernel
extern "C" void CUDA_Add(int* g_A,int* g_B,int size){
int threadnum=16;
int blocknum=size/threadnum;
Kernel_Add<<<threadnum,blocknum>>>(g_A,g_B);
}
//read data back to host
extern "C" void CUDA_GetData(int* h_dist, int* g_src,int size){
cudaMemcpy(h_dist,g_src,sizeof(int)*size,cudaMemcpyDeviceToHost);
}
|
6,376 | __global__ void edge(int* src, int* des, int* w, int* m, int* dist){
const int e0 = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
const int offset = blockDim.x * blockDim.y * blockDim.z;
int e = -1;
__shared__ int quickBreak[1];
while(1){
e = e0;
if(e == 0){
quickBreak[0] = 0;
}
__syncthreads();
while(e < (*m)){
if(dist[des[e]] > dist[src[e]] + w[e]){
atomicMin(&dist[des[e]], dist[src[e]] + w[e]);
if(dist[des[e]] = dist[src[e]] + w[e]){
quickBreak[0] = 1;
}
}
e += offset;
}
__syncthreads();
if(quickBreak[0] == 0){
break;
}
}
} |
6,377 | #include <stdio.h>
#include <math.h>
#define NBLOCKS 1024
#define NTHREADS 256
//Model-1 & 4
__global__ void dvc_ScaLBL_D3Q19_AAodd_GreyscaleColor(int *neighborList, int *Map, double *dist, double *Aq, double *Bq, double *Den,
double *Phi, double *GreySolidGrad, double *Poros,double *Perm, double *Velocity,
double rhoA, double rhoB, double tauA, double tauB,double tauA_eff,double tauB_eff,double alpha, double beta,
double Gx, double Gy, double Gz, int strideY, int strideZ, int start, int finish, int Np){
int n,nn,ijk,nread;
int nr1,nr2,nr3,nr4,nr5,nr6;
int nr7,nr8,nr9,nr10;
int nr11,nr12,nr13,nr14;
//int nr15,nr16,nr17,nr18;
double fq;
// conserved momemnts
double rho,jx,jy,jz;
double vx,vy,vz,v_mag;
double ux,uy,uz,u_mag;
// non-conserved moments
double m1,m2,m4,m6,m8,m9,m10,m11,m12,m13,m14,m15,m16,m17,m18;
double m3,m5,m7;
double nA,nB; // number density
double a1,b1,a2,b2,nAB,delta;
double C,nx,ny,nz; //color gradient magnitude and direction
double phi,tau,rho0,rlx_setA,rlx_setB;
double GeoFun=0.0;//geometric function from Guo's PRE 66, 036304 (2002)
double porosity;
double perm;//voxel permeability
double c0, c1; //Guo's model parameters
double tau_eff;
double mu_eff;//kinematic viscosity
double nx_gs,ny_gs,nz_gs;//grey-solid color gradient
double nx_phase,ny_phase,nz_phase,C_phase;
double Fx,Fy,Fz;
const double mrt_V1=0.05263157894736842;
const double mrt_V2=0.012531328320802;
const double mrt_V3=0.04761904761904762;
const double mrt_V4=0.004594820384294068;
const double mrt_V5=0.01587301587301587;
const double mrt_V6=0.0555555555555555555555555;
const double mrt_V7=0.02777777777777778;
const double mrt_V8=0.08333333333333333;
const double mrt_V9=0.003341687552213868;
const double mrt_V10=0.003968253968253968;
const double mrt_V11=0.01388888888888889;
const double mrt_V12=0.04166666666666666;
int S = Np/NBLOCKS/NTHREADS + 1;
for (int s=0; s<S; s++){
//........Get 1-D index for this thread....................
n = S*blockIdx.x*blockDim.x + s*blockDim.x + threadIdx.x + start;
if (n<finish) {
// read the component number densities
nA = Den[n];
nB = Den[Np + n];
porosity = Poros[n];
perm = Perm[n];
nx_gs = GreySolidGrad[n+0*Np];
ny_gs = GreySolidGrad[n+1*Np];
nz_gs = GreySolidGrad[n+2*Np];
// compute phase indicator field
phi=(nA-nB)/(nA+nB);
// local density
rho0=rhoA + 0.5*(1.0-phi)*(rhoB-rhoA);
// local relaxation time
tau=tauA + 0.5*(1.0-phi)*(tauB-tauA);
tau_eff=tauA_eff + 0.5*(1.0-phi)*(tauB_eff-tauA_eff);
rlx_setA = 1.f/tau;
rlx_setB = 8.f*(2.f-rlx_setA)/(8.f-rlx_setA);
mu_eff = (tau_eff-0.5)/3.0;//kinematic viscosity
// Get the 1D index based on regular data layout
ijk = Map[n];
// COMPUTE THE COLOR GRADIENT
//........................................................................
//.................Read Phase Indicator Values............................
//........................................................................
nn = ijk-1; // neighbor index (get convention)
m1 = Phi[nn]; // get neighbor for phi - 1
//........................................................................
nn = ijk+1; // neighbor index (get convention)
m2 = Phi[nn]; // get neighbor for phi - 2
//........................................................................
nn = ijk-strideY; // neighbor index (get convention)
m3 = Phi[nn]; // get neighbor for phi - 3
//........................................................................
nn = ijk+strideY; // neighbor index (get convention)
m4 = Phi[nn]; // get neighbor for phi - 4
//........................................................................
nn = ijk-strideZ; // neighbor index (get convention)
m5 = Phi[nn]; // get neighbor for phi - 5
//........................................................................
nn = ijk+strideZ; // neighbor index (get convention)
m6 = Phi[nn]; // get neighbor for phi - 6
//........................................................................
nn = ijk-strideY-1; // neighbor index (get convention)
m7 = Phi[nn]; // get neighbor for phi - 7
//........................................................................
nn = ijk+strideY+1; // neighbor index (get convention)
m8 = Phi[nn]; // get neighbor for phi - 8
//........................................................................
nn = ijk+strideY-1; // neighbor index (get convention)
m9 = Phi[nn]; // get neighbor for phi - 9
//........................................................................
nn = ijk-strideY+1; // neighbor index (get convention)
m10 = Phi[nn]; // get neighbor for phi - 10
//........................................................................
nn = ijk-strideZ-1; // neighbor index (get convention)
m11 = Phi[nn]; // get neighbor for phi - 11
//........................................................................
nn = ijk+strideZ+1; // neighbor index (get convention)
m12 = Phi[nn]; // get neighbor for phi - 12
//........................................................................
nn = ijk+strideZ-1; // neighbor index (get convention)
m13 = Phi[nn]; // get neighbor for phi - 13
//........................................................................
nn = ijk-strideZ+1; // neighbor index (get convention)
m14 = Phi[nn]; // get neighbor for phi - 14
//........................................................................
nn = ijk-strideZ-strideY; // neighbor index (get convention)
m15 = Phi[nn]; // get neighbor for phi - 15
//........................................................................
nn = ijk+strideZ+strideY; // neighbor index (get convention)
m16 = Phi[nn]; // get neighbor for phi - 16
//........................................................................
nn = ijk+strideZ-strideY; // neighbor index (get convention)
m17 = Phi[nn]; // get neighbor for phi - 17
//........................................................................
nn = ijk-strideZ+strideY; // neighbor index (get convention)
m18 = Phi[nn]; // get neighbor for phi - 18
//............Compute the Color Gradient...................................
nx_phase = -(m1-m2+0.5*(m7-m8+m9-m10+m11-m12+m13-m14));
ny_phase = -(m3-m4+0.5*(m7-m8-m9+m10+m15-m16+m17-m18));
nz_phase = -(m5-m6+0.5*(m11-m12-m13+m14+m15-m16-m17+m18));
C_phase = sqrt(nx_phase*nx_phase+ny_phase*ny_phase+nz_phase*nz_phase);
//correct the normal color gradient by considering the effect of grey solid
nx = nx_phase + (1.0-porosity)*nx_gs;
ny = ny_phase + (1.0-porosity)*ny_gs;
nz = nz_phase + (1.0-porosity)*nz_gs;
if (C_phase==0.0){
nx = nx_phase;
ny = ny_phase;
nz = nz_phase;
}
//...........Normalize the Color Gradient.................................
C = sqrt(nx*nx+ny*ny+nz*nz);
double ColorMag = C;
if (C==0.0) ColorMag=1.0;
nx = nx/ColorMag;
ny = ny/ColorMag;
nz = nz/ColorMag;
// q=0
fq = dist[n];
rho = fq;
m1 = -30.0*fq;
m2 = 12.0*fq;
// q=1
//nread = neighborList[n]; // neighbor 2
//fq = dist[nread]; // reading the f1 data into register fq
nr1 = neighborList[n];
fq = dist[nr1]; // reading the f1 data into register fq
rho += fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jx = fq;
m4 = -4.0*fq;
m9 = 2.0*fq;
m10 = -4.0*fq;
// f2 = dist[10*Np+n];
//nread = neighborList[n+Np]; // neighbor 1 ( < 10Np => even part of dist)
//fq = dist[nread]; // reading the f2 data into register fq
nr2 = neighborList[n+Np]; // neighbor 1 ( < 10Np => even part of dist)
fq = dist[nr2]; // reading the f2 data into register fq
rho += fq;
m1 -= 11.0*(fq);
m2 -= 4.0*(fq);
jx -= fq;
m4 += 4.0*(fq);
m9 += 2.0*(fq);
m10 -= 4.0*(fq);
// q=3
//nread = neighborList[n+2*Np]; // neighbor 4
//fq = dist[nread];
nr3 = neighborList[n+2*Np]; // neighbor 4
fq = dist[nr3];
rho += fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jy = fq;
m6 = -4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 = fq;
m12 = -2.0*fq;
// q = 4
//nread = neighborList[n+3*Np]; // neighbor 3
//fq = dist[nread];
nr4 = neighborList[n+3*Np]; // neighbor 3
fq = dist[nr4];
rho+= fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jy -= fq;
m6 += 4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 += fq;
m12 -= 2.0*fq;
// q=5
//nread = neighborList[n+4*Np];
//fq = dist[nread];
nr5 = neighborList[n+4*Np];
fq = dist[nr5];
rho += fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jz = fq;
m8 = -4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 -= fq;
m12 += 2.0*fq;
// q = 6
//nread = neighborList[n+5*Np];
//fq = dist[nread];
nr6 = neighborList[n+5*Np];
fq = dist[nr6];
rho+= fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jz -= fq;
m8 += 4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 -= fq;
m12 += 2.0*fq;
// q=7
//nread = neighborList[n+6*Np];
//fq = dist[nread];
nr7 = neighborList[n+6*Np];
fq = dist[nr7];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jy += fq;
m6 += fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 = fq;
m16 = fq;
m17 = -fq;
// q = 8
//nread = neighborList[n+7*Np];
//fq = dist[nread];
nr8 = neighborList[n+7*Np];
fq = dist[nr8];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jy -= fq;
m6 -= fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 += fq;
m16 -= fq;
m17 += fq;
// q=9
//nread = neighborList[n+8*Np];
//fq = dist[nread];
nr9 = neighborList[n+8*Np];
fq = dist[nr9];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jy -= fq;
m6 -= fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 -= fq;
m16 += fq;
m17 += fq;
// q = 10
//nread = neighborList[n+9*Np];
//fq = dist[nread];
nr10 = neighborList[n+9*Np];
fq = dist[nr10];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jy += fq;
m6 += fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 -= fq;
m16 -= fq;
m17 -= fq;
// q=11
//nread = neighborList[n+10*Np];
//fq = dist[nread];
nr11 = neighborList[n+10*Np];
fq = dist[nr11];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jz += fq;
m8 += fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 = fq;
m16 -= fq;
m18 = fq;
// q=12
//nread = neighborList[n+11*Np];
//fq = dist[nread];
nr12 = neighborList[n+11*Np];
fq = dist[nr12];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jz -= fq;
m8 -= fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 += fq;
m16 += fq;
m18 -= fq;
// q=13
//nread = neighborList[n+12*Np];
//fq = dist[nread];
nr13 = neighborList[n+12*Np];
fq = dist[nr13];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jz -= fq;
m8 -= fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 -= fq;
m16 -= fq;
m18 -= fq;
// q=14
//nread = neighborList[n+13*Np];
//fq = dist[nread];
nr14 = neighborList[n+13*Np];
fq = dist[nr14];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jz += fq;
m8 += fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 -= fq;
m16 += fq;
m18 += fq;
// q=15
nread = neighborList[n+14*Np];
fq = dist[nread];
//fq = dist[17*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy += fq;
m6 += fq;
jz += fq;
m8 += fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 = fq;
m17 += fq;
m18 -= fq;
// q=16
nread = neighborList[n+15*Np];
fq = dist[nread];
//fq = dist[8*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy -= fq;
m6 -= fq;
jz -= fq;
m8 -= fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 += fq;
m17 -= fq;
m18 += fq;
// q=17
//fq = dist[18*Np+n];
nread = neighborList[n+16*Np];
fq = dist[nread];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy += fq;
m6 += fq;
jz -= fq;
m8 -= fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 -= fq;
m17 += fq;
m18 += fq;
// q=18
nread = neighborList[n+17*Np];
fq = dist[nread];
//fq = dist[9*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy -= fq;
m6 -= fq;
jz += fq;
m8 += fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 -= fq;
m17 -= fq;
m18 -= fq;
// Compute greyscale related parameters
c0 = 0.5*(1.0+porosity*0.5*mu_eff/perm);
if (porosity==1.0) c0 = 0.5;//i.e. apparent pore nodes
//GeoFun = 1.75/sqrt(150.0*porosity*porosity*porosity);
c1 = porosity*0.5*GeoFun/sqrt(perm);
if (porosity==1.0) c1 = 0.0;//i.e. apparent pore nodes
vx = jx/rho0+0.5*(porosity*Gx);
vy = jy/rho0+0.5*(porosity*Gy);
vz = jz/rho0+0.5*(porosity*Gz);
v_mag=sqrt(vx*vx+vy*vy+vz*vz);
ux = vx/(c0+sqrt(c0*c0+c1*v_mag));
uy = vy/(c0+sqrt(c0*c0+c1*v_mag));
uz = vz/(c0+sqrt(c0*c0+c1*v_mag));
u_mag=sqrt(ux*ux+uy*uy+uz*uz);
//Update the total force to include linear (Darcy) and nonlinear (Forchheimer) drags due to the porous medium
Fx = rho0*(-porosity*mu_eff/perm*ux - porosity*GeoFun/sqrt(perm)*u_mag*ux + porosity*Gx);
Fy = rho0*(-porosity*mu_eff/perm*uy - porosity*GeoFun/sqrt(perm)*u_mag*uy + porosity*Gy);
Fz = rho0*(-porosity*mu_eff/perm*uz - porosity*GeoFun/sqrt(perm)*u_mag*uz + porosity*Gz);
if (porosity==1.0){
Fx=rho0*(Gx);
Fy=rho0*(Gy);
Fz=rho0*(Gz);
}
// write the velocity
Velocity[n] = ux;
Velocity[Np+n] = uy;
Velocity[2*Np+n] = uz;
//........................................................................
//..............carry out relaxation process..............................
//..........Toelke, Fruediger et. al. 2006................................
//---------------- NO higher-order force -------------------------------//
if (C == 0.0) nx = ny = nz = 0.0;
m1 = m1 + rlx_setA*((19*(ux*ux+uy*uy+uz*uz)*rho0/porosity - 11*rho) -19*alpha*C - m1);
m2 = m2 + rlx_setA*((3*rho - 5.5*(ux*ux+uy*uy+uz*uz)*rho0/porosity)- m2);
jx = jx + Fx;
m4 = m4 + rlx_setB*((-0.6666666666666666*ux*rho0)- m4)
+ (1-0.5*rlx_setB)*(-0.6666666666666666*Fx);
jy = jy + Fy;
m6 = m6 + rlx_setB*((-0.6666666666666666*uy*rho0)- m6)
+ (1-0.5*rlx_setB)*(-0.6666666666666666*Fy);
jz = jz + Fz;
m8 = m8 + rlx_setB*((-0.6666666666666666*uz*rho0)- m8)
+ (1-0.5*rlx_setB)*(-0.6666666666666666*Fz);
m9 = m9 + rlx_setA*(((2*ux*ux-uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(2*nx*nx-ny*ny-nz*nz) - m9);
m10 = m10 + rlx_setA*( - m10);
//m10 = m10 + rlx_setA*(-0.5*rho0*((2*ux*ux-uy*uy-uz*uz)/porosity)- m10);
m11 = m11 + rlx_setA*(((uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(ny*ny-nz*nz)- m11);
m12 = m12 + rlx_setA*( - m12);
//m12 = m12 + rlx_setA*(-0.5*(rho0*(uy*uy-uz*uz)/porosity)- m12);
m13 = m13 + rlx_setA*( (ux*uy*rho0/porosity) + 0.5*alpha*C*nx*ny - m13);
m14 = m14 + rlx_setA*( (uy*uz*rho0/porosity) + 0.5*alpha*C*ny*nz - m14);
m15 = m15 + rlx_setA*( (ux*uz*rho0/porosity) + 0.5*alpha*C*nx*nz - m15);
m16 = m16 + rlx_setB*( - m16);
m17 = m17 + rlx_setB*( - m17);
m18 = m18 + rlx_setB*( - m18);
//----------------------------------------------------------------------//
//----------------With higher-order force ------------------------------//
//if (C == 0.0) nx = ny = nz = 0.0;
//m1 = m1 + rlx_setA*((19*(ux*ux+uy*uy+uz*uz)*rho0/porosity - 11*rho) -19*alpha*C - m1)
// + (1-0.5*rlx_setA)*38*(Fx*ux+Fy*uy+Fz*uz)/porosity;
//m2 = m2 + rlx_setA*((3*rho - 5.5*(ux*ux+uy*uy+uz*uz)*rho0/porosity)- m2)
// + (1-0.5*rlx_setA)*11*(-Fx*ux-Fy*uy-Fz*uz)/porosity;
//jx = jx + Fx;
//m4 = m4 + rlx_setB*((-0.6666666666666666*ux*rho0)- m4)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fx);
//jy = jy + Fy;
//m6 = m6 + rlx_setB*((-0.6666666666666666*uy*rho0)- m6)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fy);
//jz = jz + Fz;
//m8 = m8 + rlx_setB*((-0.6666666666666666*uz*rho0)- m8)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fz);
//m9 = m9 + rlx_setA*(((2*ux*ux-uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(2*nx*nx-ny*ny-nz*nz) - m9)
// + (1-0.5*rlx_setA)*(4*Fx*ux-2*Fy*uy-2*Fz*uz)/porosity;
////m10 = m10 + rlx_setA*( - m10);
//m10 = m10 + rlx_setA*(-0.5*rho0*((2*ux*ux-uy*uy-uz*uz)/porosity)- m10)
// + (1-0.5*rlx_setA)*(-2*Fx*ux+Fy*uy+Fz*uz)/porosity;
//m11 = m11 + rlx_setA*(((uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(ny*ny-nz*nz)- m11)
// + (1-0.5*rlx_setA)*(2*Fy*uy-2*Fz*uz)/porosity;
////m12 = m12 + rlx_setA*( - m12);
//m12 = m12 + rlx_setA*(-0.5*(rho0*(uy*uy-uz*uz)/porosity)- m12)
// + (1-0.5*rlx_setA)*(-Fy*uy+Fz*uz)/porosity;
//m13 = m13 + rlx_setA*( (ux*uy*rho0/porosity) + 0.5*alpha*C*nx*ny - m13);
// + (1-0.5*rlx_setA)*(Fy*ux+Fx*uy)/porosity;
//m14 = m14 + rlx_setA*( (uy*uz*rho0/porosity) + 0.5*alpha*C*ny*nz - m14);
// + (1-0.5*rlx_setA)*(Fz*uy+Fy*uz)/porosity;
//m15 = m15 + rlx_setA*( (ux*uz*rho0/porosity) + 0.5*alpha*C*nx*nz - m15);
// + (1-0.5*rlx_setA)*(Fz*ux+Fx*uz)/porosity;
//m16 = m16 + rlx_setB*( - m16);
//m17 = m17 + rlx_setB*( - m17);
//m18 = m18 + rlx_setB*( - m18);
//----------------------------------------------------------------------//
//.................inverse transformation......................................................
// q=0
fq = mrt_V1*rho-mrt_V2*m1+mrt_V3*m2;
dist[n] = fq;
// q = 1
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jx-m4)+mrt_V6*(m9-m10);
//nread = neighborList[n+Np];
dist[nr2] = fq;
// q=2
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m4-jx)+mrt_V6*(m9-m10);
//nread = neighborList[n];
dist[nr1] = fq;
// q = 3
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jy-m6)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
//nread = neighborList[n+3*Np];
dist[nr4] = fq;
// q = 4
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m6-jy)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
//nread = neighborList[n+2*Np];
dist[nr3] = fq;
// q = 5
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jz-m8)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
//nread = neighborList[n+5*Np];
dist[nr6] = fq;
// q = 6
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m8-jz)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
//nread = neighborList[n+4*Np];
dist[nr5] = fq;
// q = 7
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx+jy)+0.025*(m4+m6)+
mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12+0.25*m13+0.125*(m16-m17);
//nread = neighborList[n+7*Np];
dist[nr8] = fq;
// q = 8
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jy)-0.025*(m4+m6) +mrt_V7*m9+mrt_V11*m10+mrt_V8*m11
+mrt_V12*m12+0.25*m13+0.125*(m17-m16);
//nread = neighborList[n+6*Np];
dist[nr7] = fq;
// q = 9
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx-jy)+0.025*(m4-m6)+
mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13+0.125*(m16+m17);
//nread = neighborList[n+9*Np];
dist[nr10] = fq;
// q = 10
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jy-jx)+0.025*(m6-m4)+
mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13-0.125*(m16+m17);
//nread = neighborList[n+8*Np];
dist[nr9] = fq;
// q = 11
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jx+jz)+0.025*(m4+m8)
+mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
-mrt_V12*m12+0.25*m15+0.125*(m18-m16);
//nread = neighborList[n+11*Np];
dist[nr12] = fq;
// q = 12
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jz)-0.025*(m4+m8)+
mrt_V7*m9+mrt_V11*m10-mrt_V8*m11-mrt_V12*m12+0.25*m15+0.125*(m16-m18);
//nread = neighborList[n+10*Np];
dist[nr11]= fq;
// q = 13
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jx-jz)+0.025*(m4-m8)
+mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
-mrt_V12*m12-0.25*m15-0.125*(m16+m18);
//nread = neighborList[n+13*Np];
dist[nr14] = fq;
// q= 14
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jz-jx)+0.025*(m8-m4)
+mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
-mrt_V12*m12-0.25*m15+0.125*(m16+m18);
//nread = neighborList[n+12*Np];
dist[nr13] = fq;
// q = 15
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jy+jz)+0.025*(m6+m8)
-mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m17-m18);
nread = neighborList[n+15*Np];
dist[nread] = fq;
// q = 16
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2-0.1*(jy+jz)-0.025*(m6+m8)
-mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m18-m17);
nread = neighborList[n+14*Np];
dist[nread] = fq;
// q = 17
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jy-jz)+0.025*(m6-m8)
-mrt_V6*m9-mrt_V7*m10-0.25*m14+0.125*(m17+m18);
nread = neighborList[n+17*Np];
dist[nread] = fq;
// q = 18
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jz-jy)+0.025*(m8-m6)
-mrt_V6*m9-mrt_V7*m10-0.25*m14-0.125*(m17+m18);
nread = neighborList[n+16*Np];
dist[nread] = fq;
//........................................................................
// Instantiate mass transport distributions
// Stationary value - distribution 0
nAB = 1.0/(nA+nB);
Aq[n] = 0.3333333333333333*nA;
Bq[n] = 0.3333333333333333*nB;
//...............................................
// q = 0,2,4
// Cq = {1,0,0}, {0,1,0}, {0,0,1}
delta = beta*nA*nB*nAB*0.1111111111111111*nx;
if (!(nA*nB*nAB>0)) delta=0;
a1 = nA*(0.1111111111111111*(1+4.5*ux))+delta;
b1 = nB*(0.1111111111111111*(1+4.5*ux))-delta;
a2 = nA*(0.1111111111111111*(1-4.5*ux))-delta;
b2 = nB*(0.1111111111111111*(1-4.5*ux))+delta;
// q = 1
//nread = neighborList[n+Np];
Aq[nr2] = a1;
Bq[nr2] = b1;
// q=2
//nread = neighborList[n];
Aq[nr1] = a2;
Bq[nr1] = b2;
//...............................................
// Cq = {0,1,0}
delta = beta*nA*nB*nAB*0.1111111111111111*ny;
if (!(nA*nB*nAB>0)) delta=0;
a1 = nA*(0.1111111111111111*(1+4.5*uy))+delta;
b1 = nB*(0.1111111111111111*(1+4.5*uy))-delta;
a2 = nA*(0.1111111111111111*(1-4.5*uy))-delta;
b2 = nB*(0.1111111111111111*(1-4.5*uy))+delta;
// q = 3
//nread = neighborList[n+3*Np];
Aq[nr4] = a1;
Bq[nr4] = b1;
// q = 4
//nread = neighborList[n+2*Np];
Aq[nr3] = a2;
Bq[nr3] = b2;
//...............................................
// q = 4
// Cq = {0,0,1}
delta = beta*nA*nB*nAB*0.1111111111111111*nz;
if (!(nA*nB*nAB>0)) delta=0;
a1 = nA*(0.1111111111111111*(1+4.5*uz))+delta;
b1 = nB*(0.1111111111111111*(1+4.5*uz))-delta;
a2 = nA*(0.1111111111111111*(1-4.5*uz))-delta;
b2 = nB*(0.1111111111111111*(1-4.5*uz))+delta;
// q = 5
//nread = neighborList[n+5*Np];
Aq[nr6] = a1;
Bq[nr6] = b1;
// q = 6
//nread = neighborList[n+4*Np];
Aq[nr5] = a2;
Bq[nr5] = b2;
//...............................................
}
}
}
//Model-1 & 4
__global__ void dvc_ScaLBL_D3Q19_AAeven_GreyscaleColor(int *Map, double *dist, double *Aq, double *Bq, double *Den,
double *Phi, double *GreySolidGrad, double *Poros,double *Perm, double *Velocity,
double rhoA, double rhoB, double tauA, double tauB,double tauA_eff,double tauB_eff, double alpha, double beta,
double Gx, double Gy, double Gz, int strideY, int strideZ, int start, int finish, int Np){
int ijk,nn,n;
double fq;
// conserved momemnts
double rho,jx,jy,jz;
double vx,vy,vz,v_mag;
double ux,uy,uz,u_mag;
// non-conserved moments
double m1,m2,m4,m6,m8,m9,m10,m11,m12,m13,m14,m15,m16,m17,m18;
double m3,m5,m7;
double nA,nB; // number density
double a1,b1,a2,b2,nAB,delta;
double C,nx,ny,nz; //color gradient magnitude and direction
double phi,tau,rho0,rlx_setA,rlx_setB;
double GeoFun=0.0;//geometric function from Guo's PRE 66, 036304 (2002)
double porosity;
double perm;//voxel permeability
double c0, c1; //Guo's model parameters
double tau_eff;
double mu_eff;//kinematic viscosity
double nx_gs,ny_gs,nz_gs;//grey-solid color gradient
double nx_phase,ny_phase,nz_phase,C_phase;
double Fx,Fy,Fz;
const double mrt_V1=0.05263157894736842;
const double mrt_V2=0.012531328320802;
const double mrt_V3=0.04761904761904762;
const double mrt_V4=0.004594820384294068;
const double mrt_V5=0.01587301587301587;
const double mrt_V6=0.0555555555555555555555555;
const double mrt_V7=0.02777777777777778;
const double mrt_V8=0.08333333333333333;
const double mrt_V9=0.003341687552213868;
const double mrt_V10=0.003968253968253968;
const double mrt_V11=0.01388888888888889;
const double mrt_V12=0.04166666666666666;
int S = Np/NBLOCKS/NTHREADS + 1;
for (int s=0; s<S; s++){
//........Get 1-D index for this thread....................
n = S*blockIdx.x*blockDim.x + s*blockDim.x + threadIdx.x + start;
if (n<finish) {
// read the component number densities
nA = Den[n];
nB = Den[Np + n];
porosity = Poros[n];
perm = Perm[n];
nx_gs = GreySolidGrad[n+0*Np];
ny_gs = GreySolidGrad[n+1*Np];
nz_gs = GreySolidGrad[n+2*Np];
// compute phase indicator field
phi=(nA-nB)/(nA+nB);
// local density
rho0=rhoA + 0.5*(1.0-phi)*(rhoB-rhoA);
// local relaxation time
tau=tauA + 0.5*(1.0-phi)*(tauB-tauA);
tau_eff=tauA_eff + 0.5*(1.0-phi)*(tauB_eff-tauA_eff);
rlx_setA = 1.f/tau;
rlx_setB = 8.f*(2.f-rlx_setA)/(8.f-rlx_setA);
mu_eff = (tau_eff-0.5)/3.0;//kinematic viscosity
// Get the 1D index based on regular data layout
ijk = Map[n];
// COMPUTE THE COLOR GRADIENT
//........................................................................
//.................Read Phase Indicator Values............................
//........................................................................
nn = ijk-1; // neighbor index (get convention)
m1 = Phi[nn]; // get neighbor for phi - 1
//........................................................................
nn = ijk+1; // neighbor index (get convention)
m2 = Phi[nn]; // get neighbor for phi - 2
//........................................................................
nn = ijk-strideY; // neighbor index (get convention)
m3 = Phi[nn]; // get neighbor for phi - 3
//........................................................................
nn = ijk+strideY; // neighbor index (get convention)
m4 = Phi[nn]; // get neighbor for phi - 4
//........................................................................
nn = ijk-strideZ; // neighbor index (get convention)
m5 = Phi[nn]; // get neighbor for phi - 5
//........................................................................
nn = ijk+strideZ; // neighbor index (get convention)
m6 = Phi[nn]; // get neighbor for phi - 6
//........................................................................
nn = ijk-strideY-1; // neighbor index (get convention)
m7 = Phi[nn]; // get neighbor for phi - 7
//........................................................................
nn = ijk+strideY+1; // neighbor index (get convention)
m8 = Phi[nn]; // get neighbor for phi - 8
//........................................................................
nn = ijk+strideY-1; // neighbor index (get convention)
m9 = Phi[nn]; // get neighbor for phi - 9
//........................................................................
nn = ijk-strideY+1; // neighbor index (get convention)
m10 = Phi[nn]; // get neighbor for phi - 10
//........................................................................
nn = ijk-strideZ-1; // neighbor index (get convention)
m11 = Phi[nn]; // get neighbor for phi - 11
//........................................................................
nn = ijk+strideZ+1; // neighbor index (get convention)
m12 = Phi[nn]; // get neighbor for phi - 12
//........................................................................
nn = ijk+strideZ-1; // neighbor index (get convention)
m13 = Phi[nn]; // get neighbor for phi - 13
//........................................................................
nn = ijk-strideZ+1; // neighbor index (get convention)
m14 = Phi[nn]; // get neighbor for phi - 14
//........................................................................
nn = ijk-strideZ-strideY; // neighbor index (get convention)
m15 = Phi[nn]; // get neighbor for phi - 15
//........................................................................
nn = ijk+strideZ+strideY; // neighbor index (get convention)
m16 = Phi[nn]; // get neighbor for phi - 16
//........................................................................
nn = ijk+strideZ-strideY; // neighbor index (get convention)
m17 = Phi[nn]; // get neighbor for phi - 17
//........................................................................
nn = ijk-strideZ+strideY; // neighbor index (get convention)
m18 = Phi[nn]; // get neighbor for phi - 18
//............Compute the Color Gradient...................................
nx_phase = -(m1-m2+0.5*(m7-m8+m9-m10+m11-m12+m13-m14));
ny_phase = -(m3-m4+0.5*(m7-m8-m9+m10+m15-m16+m17-m18));
nz_phase = -(m5-m6+0.5*(m11-m12-m13+m14+m15-m16-m17+m18));
C_phase = sqrt(nx_phase*nx_phase+ny_phase*ny_phase+nz_phase*nz_phase);
//correct the normal color gradient by considering the effect of grey solid
nx = nx_phase + (1.0-porosity)*nx_gs;
ny = ny_phase + (1.0-porosity)*ny_gs;
nz = nz_phase + (1.0-porosity)*nz_gs;
if (C_phase==0.0){
nx = nx_phase;
ny = ny_phase;
nz = nz_phase;
}
//...........Normalize the Color Gradient.................................
C = sqrt(nx*nx+ny*ny+nz*nz);
double ColorMag = C;
if (C==0.0) ColorMag=1.0;
nx = nx/ColorMag;
ny = ny/ColorMag;
nz = nz/ColorMag;
// q=0
fq = dist[n];
rho = fq;
m1 = -30.0*fq;
m2 = 12.0*fq;
// q=1
fq = dist[2*Np+n];
rho += fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jx = fq;
m4 = -4.0*fq;
m9 = 2.0*fq;
m10 = -4.0*fq;
// f2 = dist[10*Np+n];
fq = dist[1*Np+n];
rho += fq;
m1 -= 11.0*(fq);
m2 -= 4.0*(fq);
jx -= fq;
m4 += 4.0*(fq);
m9 += 2.0*(fq);
m10 -= 4.0*(fq);
// q=3
fq = dist[4*Np+n];
rho += fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jy = fq;
m6 = -4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 = fq;
m12 = -2.0*fq;
// q = 4
fq = dist[3*Np+n];
rho+= fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jy -= fq;
m6 += 4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 += fq;
m12 -= 2.0*fq;
// q=5
fq = dist[6*Np+n];
rho += fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jz = fq;
m8 = -4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 -= fq;
m12 += 2.0*fq;
// q = 6
fq = dist[5*Np+n];
rho+= fq;
m1 -= 11.0*fq;
m2 -= 4.0*fq;
jz -= fq;
m8 += 4.0*fq;
m9 -= fq;
m10 += 2.0*fq;
m11 -= fq;
m12 += 2.0*fq;
// q=7
fq = dist[8*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jy += fq;
m6 += fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 = fq;
m16 = fq;
m17 = -fq;
// q = 8
fq = dist[7*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jy -= fq;
m6 -= fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 += fq;
m16 -= fq;
m17 += fq;
// q=9
fq = dist[10*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jy -= fq;
m6 -= fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 -= fq;
m16 += fq;
m17 += fq;
// q = 10
fq = dist[9*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jy += fq;
m6 += fq;
m9 += fq;
m10 += fq;
m11 += fq;
m12 += fq;
m13 -= fq;
m16 -= fq;
m17 -= fq;
// q=11
fq = dist[12*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jz += fq;
m8 += fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 = fq;
m16 -= fq;
m18 = fq;
// q=12
fq = dist[11*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jz -= fq;
m8 -= fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 += fq;
m16 += fq;
m18 -= fq;
// q=13
fq = dist[14*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx += fq;
m4 += fq;
jz -= fq;
m8 -= fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 -= fq;
m16 -= fq;
m18 -= fq;
// q=14
fq = dist[13*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jx -= fq;
m4 -= fq;
jz += fq;
m8 += fq;
m9 += fq;
m10 += fq;
m11 -= fq;
m12 -= fq;
m15 -= fq;
m16 += fq;
m18 += fq;
// q=15
fq = dist[16*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy += fq;
m6 += fq;
jz += fq;
m8 += fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 = fq;
m17 += fq;
m18 -= fq;
// q=16
fq = dist[15*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy -= fq;
m6 -= fq;
jz -= fq;
m8 -= fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 += fq;
m17 -= fq;
m18 += fq;
// q=17
fq = dist[18*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy += fq;
m6 += fq;
jz -= fq;
m8 -= fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 -= fq;
m17 += fq;
m18 += fq;
// q=18
fq = dist[17*Np+n];
rho += fq;
m1 += 8.0*fq;
m2 += fq;
jy -= fq;
m6 -= fq;
jz += fq;
m8 += fq;
m9 -= 2.0*fq;
m10 -= 2.0*fq;
m14 -= fq;
m17 -= fq;
m18 -= fq;
// Compute greyscale related parameters
c0 = 0.5*(1.0+porosity*0.5*mu_eff/perm);
if (porosity==1.0) c0 = 0.5;//i.e. apparent pore nodes
//GeoFun = 1.75/sqrt(150.0*porosity*porosity*porosity);
c1 = porosity*0.5*GeoFun/sqrt(perm);
if (porosity==1.0) c1 = 0.0;//i.e. apparent pore nodes
vx = jx/rho0+0.5*(porosity*Gx);
vy = jy/rho0+0.5*(porosity*Gy);
vz = jz/rho0+0.5*(porosity*Gz);
v_mag=sqrt(vx*vx+vy*vy+vz*vz);
ux = vx/(c0+sqrt(c0*c0+c1*v_mag));
uy = vy/(c0+sqrt(c0*c0+c1*v_mag));
uz = vz/(c0+sqrt(c0*c0+c1*v_mag));
u_mag=sqrt(ux*ux+uy*uy+uz*uz);
//Update the total force to include linear (Darcy) and nonlinear (Forchheimer) drags due to the porous medium
Fx = rho0*(-porosity*mu_eff/perm*ux - porosity*GeoFun/sqrt(perm)*u_mag*ux + porosity*Gx);
Fy = rho0*(-porosity*mu_eff/perm*uy - porosity*GeoFun/sqrt(perm)*u_mag*uy + porosity*Gy);
Fz = rho0*(-porosity*mu_eff/perm*uz - porosity*GeoFun/sqrt(perm)*u_mag*uz + porosity*Gz);
if (porosity==1.0){
Fx=rho0*(Gx);
Fy=rho0*(Gy);
Fz=rho0*(Gz);
}
// write the velocity
Velocity[n] = ux;
Velocity[Np+n] = uy;
Velocity[2*Np+n] = uz;
//........................................................................
//..............carry out relaxation process..............................
//..........Toelke, Fruediger et. al. 2006................................
//---------------- NO higher-order force -------------------------------//
if (C == 0.0) nx = ny = nz = 0.0;
m1 = m1 + rlx_setA*((19*(ux*ux+uy*uy+uz*uz)*rho0/porosity - 11*rho) -19*alpha*C - m1);
m2 = m2 + rlx_setA*((3*rho - 5.5*(ux*ux+uy*uy+uz*uz)*rho0/porosity)- m2);
jx = jx + Fx;
m4 = m4 + rlx_setB*((-0.6666666666666666*ux*rho0)- m4)
+ (1-0.5*rlx_setB)*(-0.6666666666666666*Fx);
jy = jy + Fy;
m6 = m6 + rlx_setB*((-0.6666666666666666*uy*rho0)- m6)
+ (1-0.5*rlx_setB)*(-0.6666666666666666*Fy);
jz = jz + Fz;
m8 = m8 + rlx_setB*((-0.6666666666666666*uz*rho0)- m8)
+ (1-0.5*rlx_setB)*(-0.6666666666666666*Fz);
m9 = m9 + rlx_setA*(((2*ux*ux-uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(2*nx*nx-ny*ny-nz*nz) - m9);
m10 = m10 + rlx_setA*( - m10);
//m10 = m10 + rlx_setA*(-0.5*rho0*((2*ux*ux-uy*uy-uz*uz)/porosity)- m10);
m11 = m11 + rlx_setA*(((uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(ny*ny-nz*nz)- m11);
m12 = m12 + rlx_setA*( - m12);
//m12 = m12 + rlx_setA*(-0.5*(rho0*(uy*uy-uz*uz)/porosity)- m12);
m13 = m13 + rlx_setA*( (ux*uy*rho0/porosity) + 0.5*alpha*C*nx*ny - m13);
m14 = m14 + rlx_setA*( (uy*uz*rho0/porosity) + 0.5*alpha*C*ny*nz - m14);
m15 = m15 + rlx_setA*( (ux*uz*rho0/porosity) + 0.5*alpha*C*nx*nz - m15);
m16 = m16 + rlx_setB*( - m16);
m17 = m17 + rlx_setB*( - m17);
m18 = m18 + rlx_setB*( - m18);
//----------------------------------------------------------------------//
//----------------With higher-order force ------------------------------//
//if (C == 0.0) nx = ny = nz = 0.0;
//m1 = m1 + rlx_setA*((19*(ux*ux+uy*uy+uz*uz)*rho0/porosity - 11*rho) -19*alpha*C - m1)
// + (1-0.5*rlx_setA)*38*(Fx*ux+Fy*uy+Fz*uz)/porosity;
//m2 = m2 + rlx_setA*((3*rho - 5.5*(ux*ux+uy*uy+uz*uz)*rho0/porosity)- m2)
// + (1-0.5*rlx_setA)*11*(-Fx*ux-Fy*uy-Fz*uz)/porosity;
//jx = jx + Fx;
//m4 = m4 + rlx_setB*((-0.6666666666666666*ux*rho0)- m4)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fx);
//jy = jy + Fy;
//m6 = m6 + rlx_setB*((-0.6666666666666666*uy*rho0)- m6)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fy);
//jz = jz + Fz;
//m8 = m8 + rlx_setB*((-0.6666666666666666*uz*rho0)- m8)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fz);
//m9 = m9 + rlx_setA*(((2*ux*ux-uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(2*nx*nx-ny*ny-nz*nz) - m9)
// + (1-0.5*rlx_setA)*(4*Fx*ux-2*Fy*uy-2*Fz*uz)/porosity;
////m10 = m10 + rlx_setA*( - m10);
//m10 = m10 + rlx_setA*(-0.5*rho0*((2*ux*ux-uy*uy-uz*uz)/porosity)- m10)
// + (1-0.5*rlx_setA)*(-2*Fx*ux+Fy*uy+Fz*uz)/porosity;
//m11 = m11 + rlx_setA*(((uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(ny*ny-nz*nz)- m11)
// + (1-0.5*rlx_setA)*(2*Fy*uy-2*Fz*uz)/porosity;
////m12 = m12 + rlx_setA*( - m12);
//m12 = m12 + rlx_setA*(-0.5*(rho0*(uy*uy-uz*uz)/porosity)- m12)
// + (1-0.5*rlx_setA)*(-Fy*uy+Fz*uz)/porosity;
//m13 = m13 + rlx_setA*( (ux*uy*rho0/porosity) + 0.5*alpha*C*nx*ny - m13);
// + (1-0.5*rlx_setA)*(Fy*ux+Fx*uy)/porosity;
//m14 = m14 + rlx_setA*( (uy*uz*rho0/porosity) + 0.5*alpha*C*ny*nz - m14);
// + (1-0.5*rlx_setA)*(Fz*uy+Fy*uz)/porosity;
//m15 = m15 + rlx_setA*( (ux*uz*rho0/porosity) + 0.5*alpha*C*nx*nz - m15);
// + (1-0.5*rlx_setA)*(Fz*ux+Fx*uz)/porosity;
//m16 = m16 + rlx_setB*( - m16);
//m17 = m17 + rlx_setB*( - m17);
//m18 = m18 + rlx_setB*( - m18);
//----------------------------------------------------------------------//
//.................inverse transformation......................................................
// q=0
fq = mrt_V1*rho-mrt_V2*m1+mrt_V3*m2;
dist[n] = fq;
// q = 1
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jx-m4)+mrt_V6*(m9-m10);
dist[1*Np+n] = fq;
// q=2
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m4-jx)+mrt_V6*(m9-m10);
dist[2*Np+n] = fq;
// q = 3
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jy-m6)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
dist[3*Np+n] = fq;
// q = 4
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m6-jy)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
dist[4*Np+n] = fq;
// q = 5
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jz-m8)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
dist[5*Np+n] = fq;
// q = 6
fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m8-jz)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
dist[6*Np+n] = fq;
// q = 7
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx+jy)+0.025*(m4+m6)+
mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12+0.25*m13+0.125*(m16-m17);
dist[7*Np+n] = fq;
// q = 8
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jy)-0.025*(m4+m6) +mrt_V7*m9+mrt_V11*m10+mrt_V8*m11
+mrt_V12*m12+0.25*m13+0.125*(m17-m16);
dist[8*Np+n] = fq;
// q = 9
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx-jy)+0.025*(m4-m6)+
mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13+0.125*(m16+m17);
dist[9*Np+n] = fq;
// q = 10
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jy-jx)+0.025*(m6-m4)+
mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13-0.125*(m16+m17);
dist[10*Np+n] = fq;
// q = 11
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jx+jz)+0.025*(m4+m8)
+mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
-mrt_V12*m12+0.25*m15+0.125*(m18-m16);
dist[11*Np+n] = fq;
// q = 12
fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jz)-0.025*(m4+m8)+
mrt_V7*m9+mrt_V11*m10-mrt_V8*m11-mrt_V12*m12+0.25*m15+0.125*(m16-m18);
dist[12*Np+n] = fq;
// q = 13
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jx-jz)+0.025*(m4-m8)
+mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
-mrt_V12*m12-0.25*m15-0.125*(m16+m18);
dist[13*Np+n] = fq;
// q= 14
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jz-jx)+0.025*(m8-m4)
+mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
-mrt_V12*m12-0.25*m15+0.125*(m16+m18);
dist[14*Np+n] = fq;
// q = 15
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jy+jz)+0.025*(m6+m8)
-mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m17-m18);
dist[15*Np+n] = fq;
// q = 16
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2-0.1*(jy+jz)-0.025*(m6+m8)
-mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m18-m17);
dist[16*Np+n] = fq;
// q = 17
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jy-jz)+0.025*(m6-m8)
-mrt_V6*m9-mrt_V7*m10-0.25*m14+0.125*(m17+m18);
dist[17*Np+n] = fq;
// q = 18
fq = mrt_V1*rho+mrt_V9*m1
+mrt_V10*m2+0.1*(jz-jy)+0.025*(m8-m6)
-mrt_V6*m9-mrt_V7*m10-0.25*m14-0.125*(m17+m18);
dist[18*Np+n] = fq;
//........................................................................
// Instantiate mass transport distributions
// Stationary value - distribution 0
nAB = 1.0/(nA+nB);
Aq[n] = 0.3333333333333333*nA;
Bq[n] = 0.3333333333333333*nB;
//...............................................
// q = 0,2,4
// Cq = {1,0,0}, {0,1,0}, {0,0,1}
delta = beta*nA*nB*nAB*0.1111111111111111*nx;
if (!(nA*nB*nAB>0)) delta=0;
a1 = nA*(0.1111111111111111*(1+4.5*ux))+delta;
b1 = nB*(0.1111111111111111*(1+4.5*ux))-delta;
a2 = nA*(0.1111111111111111*(1-4.5*ux))-delta;
b2 = nB*(0.1111111111111111*(1-4.5*ux))+delta;
Aq[1*Np+n] = a1;
Bq[1*Np+n] = b1;
Aq[2*Np+n] = a2;
Bq[2*Np+n] = b2;
//...............................................
// q = 2
// Cq = {0,1,0}
delta = beta*nA*nB*nAB*0.1111111111111111*ny;
if (!(nA*nB*nAB>0)) delta=0;
a1 = nA*(0.1111111111111111*(1+4.5*uy))+delta;
b1 = nB*(0.1111111111111111*(1+4.5*uy))-delta;
a2 = nA*(0.1111111111111111*(1-4.5*uy))-delta;
b2 = nB*(0.1111111111111111*(1-4.5*uy))+delta;
Aq[3*Np+n] = a1;
Bq[3*Np+n] = b1;
Aq[4*Np+n] = a2;
Bq[4*Np+n] = b2;
//...............................................
// q = 4
// Cq = {0,0,1}
delta = beta*nA*nB*nAB*0.1111111111111111*nz;
if (!(nA*nB*nAB>0)) delta=0;
a1 = nA*(0.1111111111111111*(1+4.5*uz))+delta;
b1 = nB*(0.1111111111111111*(1+4.5*uz))-delta;
a2 = nA*(0.1111111111111111*(1-4.5*uz))-delta;
b2 = nB*(0.1111111111111111*(1-4.5*uz))+delta;
Aq[5*Np+n] = a1;
Bq[5*Np+n] = b1;
Aq[6*Np+n] = a2;
Bq[6*Np+n] = b2;
//...............................................
}
}
}
////Model-2&3
//__global__ void dvc_ScaLBL_D3Q19_AAodd_GreyscaleColor(int *neighborList, int *Map, double *dist, double *Aq, double *Bq, double *Den,
// double *Phi, double *GreySolidGrad, double *Poros,double *Perm, double *Velocity,
// double rhoA, double rhoB, double tauA, double tauB,double tauA_eff,double tauB_eff,double alpha, double beta,
// double Gx, double Gy, double Gz, int strideY, int strideZ, int start, int finish, int Np){
//
// int n,nn,ijk,nread;
// int nr1,nr2,nr3,nr4,nr5,nr6;
// int nr7,nr8,nr9,nr10;
// int nr11,nr12,nr13,nr14;
// //int nr15,nr16,nr17,nr18;
// double fq;
// // conserved momemnts
// double rho,jx,jy,jz;
// double vx,vy,vz,v_mag;
// double ux,uy,uz,u_mag;
// // non-conserved moments
// double m1,m2,m4,m6,m8,m9,m10,m11,m12,m13,m14,m15,m16,m17,m18;
// double m3,m5,m7;
// double t1,t2,t4,t6,t8,t9,t10,t11,t12,t13,t14,t15,t16,t17,t18;
// double t3,t5,t7;
// double nA,nB; // number density
// double a1,b1,a2,b2,nAB,delta;
// double C,nx,ny,nz; //color gradient magnitude and direction
// double phi,tau,rho0,rlx_setA,rlx_setB;
//
// double GeoFun=0.0;//geometric function from Guo's PRE 66, 036304 (2002)
// double porosity;
// double perm;//voxel permeability
// double c0, c1; //Guo's model parameters
// double tau_eff;
// double mu_eff;//kinematic viscosity
// double nx_phase,ny_phase,nz_phase,C_phase;
// double Fx,Fy,Fz;
//
// const double mrt_V1=0.05263157894736842;
// const double mrt_V2=0.012531328320802;
// const double mrt_V3=0.04761904761904762;
// const double mrt_V4=0.004594820384294068;
// const double mrt_V5=0.01587301587301587;
// const double mrt_V6=0.0555555555555555555555555;
// const double mrt_V7=0.02777777777777778;
// const double mrt_V8=0.08333333333333333;
// const double mrt_V9=0.003341687552213868;
// const double mrt_V10=0.003968253968253968;
// const double mrt_V11=0.01388888888888889;
// const double mrt_V12=0.04166666666666666;
//
// int S = Np/NBLOCKS/NTHREADS + 1;
// for (int s=0; s<S; s++){
// //........Get 1-D index for this thread....................
// n = S*blockIdx.x*blockDim.x + s*blockDim.x + threadIdx.x + start;
// if (n<finish) {
// // read the component number densities
// nA = Den[n];
// nB = Den[Np + n];
// porosity = Poros[n];
// perm = Perm[n];
//
// // compute phase indicator field
// phi=(nA-nB)/(nA+nB);
//
// // local density
// rho0=rhoA + 0.5*(1.0-phi)*(rhoB-rhoA);
// // local relaxation time
// tau=tauA + 0.5*(1.0-phi)*(tauB-tauA);
// tau_eff=tauA_eff + 0.5*(1.0-phi)*(tauB_eff-tauA_eff);
// rlx_setA = 1.f/tau;
// rlx_setB = 8.f*(2.f-rlx_setA)/(8.f-rlx_setA);
// mu_eff = (tau_eff-0.5)/3.0;//kinematic viscosity
//
// // Get the 1D index based on regular data layout
// ijk = Map[n];
// // COMPUTE THE COLOR GRADIENT
// //........................................................................
// //.................Read Phase Indicator Values............................
// //........................................................................
// nn = ijk-1; // neighbor index (get convention)
// m1 = Phi[nn]; // get neighbor for phi - 1
// t1 = m1+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t1)>1.0) t1 =((t1>0.0)-(t1<0.0))*(1.0-fabs(t1))+t1;
// //........................................................................
// nn = ijk+1; // neighbor index (get convention)
// m2 = Phi[nn]; // get neighbor for phi - 2
// t2 = m2+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t2)>1.0) t2 =((t2>0.0)-(t2<0.0))*(1.0-fabs(t2))+t2;
// //........................................................................
// nn = ijk-strideY; // neighbor index (get convention)
// m3 = Phi[nn]; // get neighbor for phi - 3
// t3 = m3+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t3)>1.0) t3 =((t3>0.0)-(t3<0.0))*(1.0-fabs(t3))+t3;
// //........................................................................
// nn = ijk+strideY; // neighbor index (get convention)
// m4 = Phi[nn]; // get neighbor for phi - 4
// t4 = m4+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t4)>1.0) t4 =((t4>0.0)-(t4<0.0))*(1.0-fabs(t4))+t4;
// //........................................................................
// nn = ijk-strideZ; // neighbor index (get convention)
// m5 = Phi[nn]; // get neighbor for phi - 5
// t5 = m5+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t5)>1.0) t5 =((t5>0.0)-(t5<0.0))*(1.0-fabs(t5))+t5;
// //........................................................................
// nn = ijk+strideZ; // neighbor index (get convention)
// m6 = Phi[nn]; // get neighbor for phi - 6
// t6 = m6+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t6)>1.0) t6 =((t6>0.0)-(t6<0.0))*(1.0-fabs(t6))+t6;
// //........................................................................
// nn = ijk-strideY-1; // neighbor index (get convention)
// m7 = Phi[nn]; // get neighbor for phi - 7
// t7 = m7+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t7)>1.0) t7 =((t7>0.0)-(t7<0.0))*(1.0-fabs(t7))+t7;
// //........................................................................
// nn = ijk+strideY+1; // neighbor index (get convention)
// m8 = Phi[nn]; // get neighbor for phi - 8
// t8 = m8+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t8)>1.0) t8 =((t8>0.0)-(t8<0.0))*(1.0-fabs(t8))+t8;
// //........................................................................
// nn = ijk+strideY-1; // neighbor index (get convention)
// m9 = Phi[nn]; // get neighbor for phi - 9
// t9 = m9+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t9)>1.0) t9 =((t9>0.0)-(t9<0.0))*(1.0-fabs(t9))+t9;
// //........................................................................
// nn = ijk-strideY+1; // neighbor index (get convention)
// m10 = Phi[nn]; // get neighbor for phi - 10
// t10 = m10+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t10)>1.0) t10 =((t10>0.0)-(t10<0.0))*(1.0-fabs(t10))+t10;
// //........................................................................
// nn = ijk-strideZ-1; // neighbor index (get convention)
// m11 = Phi[nn]; // get neighbor for phi - 11
// t11 = m11+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t11)>1.0) t11 =((t11>0.0)-(t11<0.0))*(1.0-fabs(t11))+t11;
// //........................................................................
// nn = ijk+strideZ+1; // neighbor index (get convention)
// m12 = Phi[nn]; // get neighbor for phi - 12
// t12 = m12+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t12)>1.0) t12 =((t12>0.0)-(t12<0.0))*(1.0-fabs(t12))+t12;
// //........................................................................
// nn = ijk+strideZ-1; // neighbor index (get convention)
// m13 = Phi[nn]; // get neighbor for phi - 13
// t13 = m13+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t13)>1.0) t13 =((t13>0.0)-(t13<0.0))*(1.0-fabs(t13))+t13;
// //........................................................................
// nn = ijk-strideZ+1; // neighbor index (get convention)
// m14 = Phi[nn]; // get neighbor for phi - 14
// t14 = m14+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t14)>1.0) t14 =((t14>0.0)-(t14<0.0))*(1.0-fabs(t14))+t14;
// //........................................................................
// nn = ijk-strideZ-strideY; // neighbor index (get convention)
// m15 = Phi[nn]; // get neighbor for phi - 15
// t15 = m15+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t15)>1.0) t15 =((t15>0.0)-(t15<0.0))*(1.0-fabs(t15))+t15;
// //........................................................................
// nn = ijk+strideZ+strideY; // neighbor index (get convention)
// m16 = Phi[nn]; // get neighbor for phi - 16
// t16 = m16+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t16)>1.0) t16 =((t16>0.0)-(t16<0.0))*(1.0-fabs(t16))+t16;
// //........................................................................
// nn = ijk+strideZ-strideY; // neighbor index (get convention)
// m17 = Phi[nn]; // get neighbor for phi - 17
// t17 = m17+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t17)>1.0) t17 =((t17>0.0)-(t17<0.0))*(1.0-fabs(t17))+t17;
// //........................................................................
// nn = ijk-strideZ+strideY; // neighbor index (get convention)
// m18 = Phi[nn]; // get neighbor for phi - 18
// t18 = m18+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t18)>1.0) t18 =((t18>0.0)-(t18<0.0))*(1.0-fabs(t18))+t18;
// //............Compute the Color Gradient...................................
// nx_phase = -(m1-m2+0.5*(m7-m8+m9-m10+m11-m12+m13-m14));
// ny_phase = -(m3-m4+0.5*(m7-m8-m9+m10+m15-m16+m17-m18));
// nz_phase = -(m5-m6+0.5*(m11-m12-m13+m14+m15-m16-m17+m18));
// C_phase = sqrt(nx_phase*nx_phase+ny_phase*ny_phase+nz_phase*nz_phase);
// //correct the normal color gradient by considering the effect of grey solid
// nx = -(t1-t2+0.5*(t7-t8+t9-t10+t11-t12+t13-t14));
// ny = -(t3-t4+0.5*(t7-t8-t9+t10+t15-t16+t17-t18));
// nz = -(t5-t6+0.5*(t11-t12-t13+t14+t15-t16-t17+t18));
//
// if (C_phase==0.0){//i.e. if in a bulk phase, there is no need for grey-solid correction
// nx = nx_phase;
// ny = ny_phase;
// nz = nz_phase;
// }
//
// //...........Normalize the Color Gradient.................................
// C = sqrt(nx*nx+ny*ny+nz*nz);
// double ColorMag = C;
// if (C==0.0) ColorMag=1.0;
// nx = nx/ColorMag;
// ny = ny/ColorMag;
// nz = nz/ColorMag;
//
// // q=0
// fq = dist[n];
// rho = fq;
// m1 = -30.0*fq;
// m2 = 12.0*fq;
//
// // q=1
// //nread = neighborList[n]; // neighbor 2
// //fq = dist[nread]; // reading the f1 data into register fq
// nr1 = neighborList[n];
// fq = dist[nr1]; // reading the f1 data into register fq
// rho += fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jx = fq;
// m4 = -4.0*fq;
// m9 = 2.0*fq;
// m10 = -4.0*fq;
//
// // f2 = dist[10*Np+n];
// //nread = neighborList[n+Np]; // neighbor 1 ( < 10Np => even part of dist)
// //fq = dist[nread]; // reading the f2 data into register fq
// nr2 = neighborList[n+Np]; // neighbor 1 ( < 10Np => even part of dist)
// fq = dist[nr2]; // reading the f2 data into register fq
// rho += fq;
// m1 -= 11.0*(fq);
// m2 -= 4.0*(fq);
// jx -= fq;
// m4 += 4.0*(fq);
// m9 += 2.0*(fq);
// m10 -= 4.0*(fq);
//
// // q=3
// //nread = neighborList[n+2*Np]; // neighbor 4
// //fq = dist[nread];
// nr3 = neighborList[n+2*Np]; // neighbor 4
// fq = dist[nr3];
// rho += fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jy = fq;
// m6 = -4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 = fq;
// m12 = -2.0*fq;
//
// // q = 4
// //nread = neighborList[n+3*Np]; // neighbor 3
// //fq = dist[nread];
// nr4 = neighborList[n+3*Np]; // neighbor 3
// fq = dist[nr4];
// rho+= fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jy -= fq;
// m6 += 4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 += fq;
// m12 -= 2.0*fq;
//
// // q=5
// //nread = neighborList[n+4*Np];
// //fq = dist[nread];
// nr5 = neighborList[n+4*Np];
// fq = dist[nr5];
// rho += fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jz = fq;
// m8 = -4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 -= fq;
// m12 += 2.0*fq;
//
//
// // q = 6
// //nread = neighborList[n+5*Np];
// //fq = dist[nread];
// nr6 = neighborList[n+5*Np];
// fq = dist[nr6];
// rho+= fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jz -= fq;
// m8 += 4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 -= fq;
// m12 += 2.0*fq;
//
// // q=7
// //nread = neighborList[n+6*Np];
// //fq = dist[nread];
// nr7 = neighborList[n+6*Np];
// fq = dist[nr7];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jy += fq;
// m6 += fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 = fq;
// m16 = fq;
// m17 = -fq;
//
// // q = 8
// //nread = neighborList[n+7*Np];
// //fq = dist[nread];
// nr8 = neighborList[n+7*Np];
// fq = dist[nr8];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jy -= fq;
// m6 -= fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 += fq;
// m16 -= fq;
// m17 += fq;
//
// // q=9
// //nread = neighborList[n+8*Np];
// //fq = dist[nread];
// nr9 = neighborList[n+8*Np];
// fq = dist[nr9];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jy -= fq;
// m6 -= fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 -= fq;
// m16 += fq;
// m17 += fq;
//
// // q = 10
// //nread = neighborList[n+9*Np];
// //fq = dist[nread];
// nr10 = neighborList[n+9*Np];
// fq = dist[nr10];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jy += fq;
// m6 += fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 -= fq;
// m16 -= fq;
// m17 -= fq;
//
// // q=11
// //nread = neighborList[n+10*Np];
// //fq = dist[nread];
// nr11 = neighborList[n+10*Np];
// fq = dist[nr11];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jz += fq;
// m8 += fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 = fq;
// m16 -= fq;
// m18 = fq;
//
// // q=12
// //nread = neighborList[n+11*Np];
// //fq = dist[nread];
// nr12 = neighborList[n+11*Np];
// fq = dist[nr12];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jz -= fq;
// m8 -= fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 += fq;
// m16 += fq;
// m18 -= fq;
//
// // q=13
// //nread = neighborList[n+12*Np];
// //fq = dist[nread];
// nr13 = neighborList[n+12*Np];
// fq = dist[nr13];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jz -= fq;
// m8 -= fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 -= fq;
// m16 -= fq;
// m18 -= fq;
//
// // q=14
// //nread = neighborList[n+13*Np];
// //fq = dist[nread];
// nr14 = neighborList[n+13*Np];
// fq = dist[nr14];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jz += fq;
// m8 += fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 -= fq;
// m16 += fq;
// m18 += fq;
//
// // q=15
// nread = neighborList[n+14*Np];
// fq = dist[nread];
// //fq = dist[17*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy += fq;
// m6 += fq;
// jz += fq;
// m8 += fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 = fq;
// m17 += fq;
// m18 -= fq;
//
// // q=16
// nread = neighborList[n+15*Np];
// fq = dist[nread];
// //fq = dist[8*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy -= fq;
// m6 -= fq;
// jz -= fq;
// m8 -= fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 += fq;
// m17 -= fq;
// m18 += fq;
//
// // q=17
// //fq = dist[18*Np+n];
// nread = neighborList[n+16*Np];
// fq = dist[nread];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy += fq;
// m6 += fq;
// jz -= fq;
// m8 -= fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 -= fq;
// m17 += fq;
// m18 += fq;
//
// // q=18
// nread = neighborList[n+17*Np];
// fq = dist[nread];
// //fq = dist[9*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy -= fq;
// m6 -= fq;
// jz += fq;
// m8 += fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 -= fq;
// m17 -= fq;
// m18 -= fq;
//
// // Compute greyscale related parameters
// c0 = 0.5*(1.0+porosity*0.5*mu_eff/perm);
// if (porosity==1.0) c0 = 0.5;//i.e. apparent pore nodes
// //GeoFun = 1.75/sqrt(150.0*porosity*porosity*porosity);
// c1 = porosity*0.5*GeoFun/sqrt(perm);
// if (porosity==1.0) c1 = 0.0;//i.e. apparent pore nodes
//
// vx = jx/rho0+0.5*(porosity*Gx);
// vy = jy/rho0+0.5*(porosity*Gy);
// vz = jz/rho0+0.5*(porosity*Gz);
// v_mag=sqrt(vx*vx+vy*vy+vz*vz);
// ux = vx/(c0+sqrt(c0*c0+c1*v_mag));
// uy = vy/(c0+sqrt(c0*c0+c1*v_mag));
// uz = vz/(c0+sqrt(c0*c0+c1*v_mag));
// u_mag=sqrt(ux*ux+uy*uy+uz*uz);
//
// //Update the total force to include linear (Darcy) and nonlinear (Forchheimer) drags due to the porous medium
// Fx = rho0*(-porosity*mu_eff/perm*ux - porosity*GeoFun/sqrt(perm)*u_mag*ux + porosity*Gx);
// Fy = rho0*(-porosity*mu_eff/perm*uy - porosity*GeoFun/sqrt(perm)*u_mag*uy + porosity*Gy);
// Fz = rho0*(-porosity*mu_eff/perm*uz - porosity*GeoFun/sqrt(perm)*u_mag*uz + porosity*Gz);
// if (porosity==1.0){
// Fx=rho0*(Gx);
// Fy=rho0*(Gy);
// Fz=rho0*(Gz);
// }
//
// // write the velocity
// Velocity[n] = ux;
// Velocity[Np+n] = uy;
// Velocity[2*Np+n] = uz;
//
// //........................................................................
// //..............carry out relaxation process..............................
// //..........Toelke, Fruediger et. al. 2006................................
// if (C == 0.0) nx = ny = nz = 0.0;
// m1 = m1 + rlx_setA*((19*(ux*ux+uy*uy+uz*uz)*rho0/porosity - 11*rho) -19*alpha*C - m1);
// m2 = m2 + rlx_setA*((3*rho - 5.5*(ux*ux+uy*uy+uz*uz)*rho0/porosity)- m2);
// jx = jx + Fx;
// m4 = m4 + rlx_setB*((-0.6666666666666666*ux*rho0)- m4)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fx);
// jy = jy + Fy;
// m6 = m6 + rlx_setB*((-0.6666666666666666*uy*rho0)- m6)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fy);
// jz = jz + Fz;
// m8 = m8 + rlx_setB*((-0.6666666666666666*uz*rho0)- m8)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fz);
// m9 = m9 + rlx_setA*(((2*ux*ux-uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(2*nx*nx-ny*ny-nz*nz) - m9);
// m10 = m10 + rlx_setA*( - m10);
// //m10 = m10 + rlx_setA*(-0.5*rho0*((2*ux*ux-uy*uy-uz*uz)/porosity)- m10);
// m11 = m11 + rlx_setA*(((uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(ny*ny-nz*nz)- m11);
// m12 = m12 + rlx_setA*( - m12);
// //m12 = m12 + rlx_setA*(-0.5*(rho0*(uy*uy-uz*uz)/porosity)- m12);
// m13 = m13 + rlx_setA*( (ux*uy*rho0/porosity) + 0.5*alpha*C*nx*ny - m13);
// m14 = m14 + rlx_setA*( (uy*uz*rho0/porosity) + 0.5*alpha*C*ny*nz - m14);
// m15 = m15 + rlx_setA*( (ux*uz*rho0/porosity) + 0.5*alpha*C*nx*nz - m15);
// m16 = m16 + rlx_setB*( - m16);
// m17 = m17 + rlx_setB*( - m17);
// m18 = m18 + rlx_setB*( - m18);
//
// //.................inverse transformation......................................................
// // q=0
// fq = mrt_V1*rho-mrt_V2*m1+mrt_V3*m2;
// dist[n] = fq;
//
// // q = 1
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jx-m4)+mrt_V6*(m9-m10);
// //nread = neighborList[n+Np];
// dist[nr2] = fq;
//
// // q=2
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m4-jx)+mrt_V6*(m9-m10);
// //nread = neighborList[n];
// dist[nr1] = fq;
//
// // q = 3
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jy-m6)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
// //nread = neighborList[n+3*Np];
// dist[nr4] = fq;
//
// // q = 4
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m6-jy)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
// //nread = neighborList[n+2*Np];
// dist[nr3] = fq;
//
// // q = 5
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jz-m8)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
// //nread = neighborList[n+5*Np];
// dist[nr6] = fq;
//
// // q = 6
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m8-jz)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
// //nread = neighborList[n+4*Np];
// dist[nr5] = fq;
//
// // q = 7
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx+jy)+0.025*(m4+m6)+
// mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12+0.25*m13+0.125*(m16-m17);
// //nread = neighborList[n+7*Np];
// dist[nr8] = fq;
//
// // q = 8
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jy)-0.025*(m4+m6) +mrt_V7*m9+mrt_V11*m10+mrt_V8*m11
// +mrt_V12*m12+0.25*m13+0.125*(m17-m16);
// //nread = neighborList[n+6*Np];
// dist[nr7] = fq;
//
// // q = 9
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx-jy)+0.025*(m4-m6)+
// mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13+0.125*(m16+m17);
// //nread = neighborList[n+9*Np];
// dist[nr10] = fq;
//
// // q = 10
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jy-jx)+0.025*(m6-m4)+
// mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13-0.125*(m16+m17);
// //nread = neighborList[n+8*Np];
// dist[nr9] = fq;
//
// // q = 11
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jx+jz)+0.025*(m4+m8)
// +mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
// -mrt_V12*m12+0.25*m15+0.125*(m18-m16);
// //nread = neighborList[n+11*Np];
// dist[nr12] = fq;
//
// // q = 12
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jz)-0.025*(m4+m8)+
// mrt_V7*m9+mrt_V11*m10-mrt_V8*m11-mrt_V12*m12+0.25*m15+0.125*(m16-m18);
// //nread = neighborList[n+10*Np];
// dist[nr11]= fq;
//
// // q = 13
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jx-jz)+0.025*(m4-m8)
// +mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
// -mrt_V12*m12-0.25*m15-0.125*(m16+m18);
// //nread = neighborList[n+13*Np];
// dist[nr14] = fq;
//
// // q= 14
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jz-jx)+0.025*(m8-m4)
// +mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
// -mrt_V12*m12-0.25*m15+0.125*(m16+m18);
// //nread = neighborList[n+12*Np];
// dist[nr13] = fq;
//
//
// // q = 15
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jy+jz)+0.025*(m6+m8)
// -mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m17-m18);
// nread = neighborList[n+15*Np];
// dist[nread] = fq;
//
// // q = 16
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2-0.1*(jy+jz)-0.025*(m6+m8)
// -mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m18-m17);
// nread = neighborList[n+14*Np];
// dist[nread] = fq;
//
//
// // q = 17
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jy-jz)+0.025*(m6-m8)
// -mrt_V6*m9-mrt_V7*m10-0.25*m14+0.125*(m17+m18);
// nread = neighborList[n+17*Np];
// dist[nread] = fq;
//
// // q = 18
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jz-jy)+0.025*(m8-m6)
// -mrt_V6*m9-mrt_V7*m10-0.25*m14-0.125*(m17+m18);
// nread = neighborList[n+16*Np];
// dist[nread] = fq;
// //........................................................................
//
// // Instantiate mass transport distributions
// // Stationary value - distribution 0
// nAB = 1.0/(nA+nB);
// Aq[n] = 0.3333333333333333*nA;
// Bq[n] = 0.3333333333333333*nB;
//
// //...............................................
// // q = 0,2,4
// // Cq = {1,0,0}, {0,1,0}, {0,0,1}
// delta = beta*nA*nB*nAB*0.1111111111111111*nx;
// if (!(nA*nB*nAB>0)) delta=0;
// a1 = nA*(0.1111111111111111*(1+4.5*ux))+delta;
// b1 = nB*(0.1111111111111111*(1+4.5*ux))-delta;
// a2 = nA*(0.1111111111111111*(1-4.5*ux))-delta;
// b2 = nB*(0.1111111111111111*(1-4.5*ux))+delta;
//
// // q = 1
// //nread = neighborList[n+Np];
// Aq[nr2] = a1;
// Bq[nr2] = b1;
// // q=2
// //nread = neighborList[n];
// Aq[nr1] = a2;
// Bq[nr1] = b2;
//
// //...............................................
// // Cq = {0,1,0}
// delta = beta*nA*nB*nAB*0.1111111111111111*ny;
// if (!(nA*nB*nAB>0)) delta=0;
// a1 = nA*(0.1111111111111111*(1+4.5*uy))+delta;
// b1 = nB*(0.1111111111111111*(1+4.5*uy))-delta;
// a2 = nA*(0.1111111111111111*(1-4.5*uy))-delta;
// b2 = nB*(0.1111111111111111*(1-4.5*uy))+delta;
//
// // q = 3
// //nread = neighborList[n+3*Np];
// Aq[nr4] = a1;
// Bq[nr4] = b1;
// // q = 4
// //nread = neighborList[n+2*Np];
// Aq[nr3] = a2;
// Bq[nr3] = b2;
//
// //...............................................
// // q = 4
// // Cq = {0,0,1}
// delta = beta*nA*nB*nAB*0.1111111111111111*nz;
// if (!(nA*nB*nAB>0)) delta=0;
// a1 = nA*(0.1111111111111111*(1+4.5*uz))+delta;
// b1 = nB*(0.1111111111111111*(1+4.5*uz))-delta;
// a2 = nA*(0.1111111111111111*(1-4.5*uz))-delta;
// b2 = nB*(0.1111111111111111*(1-4.5*uz))+delta;
//
// // q = 5
// //nread = neighborList[n+5*Np];
// Aq[nr6] = a1;
// Bq[nr6] = b1;
// // q = 6
// //nread = neighborList[n+4*Np];
// Aq[nr5] = a2;
// Bq[nr5] = b2;
// //...............................................
// }
// }
//}
//
////Model-2&3
//__global__ void dvc_ScaLBL_D3Q19_AAeven_GreyscaleColor(int *Map, double *dist, double *Aq, double *Bq, double *Den,
// double *Phi, double *GreySolidGrad, double *Poros,double *Perm, double *Velocity,
// double rhoA, double rhoB, double tauA, double tauB,double tauA_eff,double tauB_eff, double alpha, double beta,
// double Gx, double Gy, double Gz, int strideY, int strideZ, int start, int finish, int Np){
// int ijk,nn,n;
// double fq;
// // conserved momemnts
// double rho,jx,jy,jz;
// double vx,vy,vz,v_mag;
// double ux,uy,uz,u_mag;
// // non-conserved moments
// double m1,m2,m4,m6,m8,m9,m10,m11,m12,m13,m14,m15,m16,m17,m18;
// double m3,m5,m7;
// double t1,t2,t4,t6,t8,t9,t10,t11,t12,t13,t14,t15,t16,t17,t18;
// double t3,t5,t7;
// double nA,nB; // number density
// double a1,b1,a2,b2,nAB,delta;
// double C,nx,ny,nz; //color gradient magnitude and direction
// double phi,tau,rho0,rlx_setA,rlx_setB;
//
// double GeoFun=0.0;//geometric function from Guo's PRE 66, 036304 (2002)
// double porosity;
// double perm;//voxel permeability
// double c0, c1; //Guo's model parameters
// double tau_eff;
// double mu_eff;//kinematic viscosity
// double nx_phase,ny_phase,nz_phase,C_phase;
// double Fx,Fy,Fz;
//
// const double mrt_V1=0.05263157894736842;
// const double mrt_V2=0.012531328320802;
// const double mrt_V3=0.04761904761904762;
// const double mrt_V4=0.004594820384294068;
// const double mrt_V5=0.01587301587301587;
// const double mrt_V6=0.0555555555555555555555555;
// const double mrt_V7=0.02777777777777778;
// const double mrt_V8=0.08333333333333333;
// const double mrt_V9=0.003341687552213868;
// const double mrt_V10=0.003968253968253968;
// const double mrt_V11=0.01388888888888889;
// const double mrt_V12=0.04166666666666666;
//
// int S = Np/NBLOCKS/NTHREADS + 1;
// for (int s=0; s<S; s++){
// //........Get 1-D index for this thread....................
// n = S*blockIdx.x*blockDim.x + s*blockDim.x + threadIdx.x + start;
// if (n<finish) {
//
// // read the component number densities
// nA = Den[n];
// nB = Den[Np + n];
// porosity = Poros[n];
// perm = Perm[n];
//
// // compute phase indicator field
// phi=(nA-nB)/(nA+nB);
//
// // local density
// rho0=rhoA + 0.5*(1.0-phi)*(rhoB-rhoA);
// // local relaxation time
// tau=tauA + 0.5*(1.0-phi)*(tauB-tauA);
// tau_eff=tauA_eff + 0.5*(1.0-phi)*(tauB_eff-tauA_eff);
// rlx_setA = 1.f/tau;
// rlx_setB = 8.f*(2.f-rlx_setA)/(8.f-rlx_setA);
// mu_eff = (tau_eff-0.5)/3.0;//kinematic viscosity
//
// // Get the 1D index based on regular data layout
// ijk = Map[n];
// // COMPUTE THE COLOR GRADIENT
// //........................................................................
// //.................Read Phase Indicator Values............................
// //........................................................................
// nn = ijk-1; // neighbor index (get convention)
// m1 = Phi[nn]; // get neighbor for phi - 1
// t1 = m1+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t1)>1.0) t1 =((t1>0.0)-(t1<0.0))*(1.0-fabs(t1))+t1;
// //........................................................................
// nn = ijk+1; // neighbor index (get convention)
// m2 = Phi[nn]; // get neighbor for phi - 2
// t2 = m2+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t2)>1.0) t2 =((t2>0.0)-(t2<0.0))*(1.0-fabs(t2))+t2;
// //........................................................................
// nn = ijk-strideY; // neighbor index (get convention)
// m3 = Phi[nn]; // get neighbor for phi - 3
// t3 = m3+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t3)>1.0) t3 =((t3>0.0)-(t3<0.0))*(1.0-fabs(t3))+t3;
// //........................................................................
// nn = ijk+strideY; // neighbor index (get convention)
// m4 = Phi[nn]; // get neighbor for phi - 4
// t4 = m4+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t4)>1.0) t4 =((t4>0.0)-(t4<0.0))*(1.0-fabs(t4))+t4;
// //........................................................................
// nn = ijk-strideZ; // neighbor index (get convention)
// m5 = Phi[nn]; // get neighbor for phi - 5
// t5 = m5+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t5)>1.0) t5 =((t5>0.0)-(t5<0.0))*(1.0-fabs(t5))+t5;
// //........................................................................
// nn = ijk+strideZ; // neighbor index (get convention)
// m6 = Phi[nn]; // get neighbor for phi - 6
// t6 = m6+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t6)>1.0) t6 =((t6>0.0)-(t6<0.0))*(1.0-fabs(t6))+t6;
// //........................................................................
// nn = ijk-strideY-1; // neighbor index (get convention)
// m7 = Phi[nn]; // get neighbor for phi - 7
// t7 = m7+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t7)>1.0) t7 =((t7>0.0)-(t7<0.0))*(1.0-fabs(t7))+t7;
// //........................................................................
// nn = ijk+strideY+1; // neighbor index (get convention)
// m8 = Phi[nn]; // get neighbor for phi - 8
// t8 = m8+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t8)>1.0) t8 =((t8>0.0)-(t8<0.0))*(1.0-fabs(t8))+t8;
// //........................................................................
// nn = ijk+strideY-1; // neighbor index (get convention)
// m9 = Phi[nn]; // get neighbor for phi - 9
// t9 = m9+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t9)>1.0) t9 =((t9>0.0)-(t9<0.0))*(1.0-fabs(t9))+t9;
// //........................................................................
// nn = ijk-strideY+1; // neighbor index (get convention)
// m10 = Phi[nn]; // get neighbor for phi - 10
// t10 = m10+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t10)>1.0) t10 =((t10>0.0)-(t10<0.0))*(1.0-fabs(t10))+t10;
// //........................................................................
// nn = ijk-strideZ-1; // neighbor index (get convention)
// m11 = Phi[nn]; // get neighbor for phi - 11
// t11 = m11+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t11)>1.0) t11 =((t11>0.0)-(t11<0.0))*(1.0-fabs(t11))+t11;
// //........................................................................
// nn = ijk+strideZ+1; // neighbor index (get convention)
// m12 = Phi[nn]; // get neighbor for phi - 12
// t12 = m12+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t12)>1.0) t12 =((t12>0.0)-(t12<0.0))*(1.0-fabs(t12))+t12;
// //........................................................................
// nn = ijk+strideZ-1; // neighbor index (get convention)
// m13 = Phi[nn]; // get neighbor for phi - 13
// t13 = m13+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t13)>1.0) t13 =((t13>0.0)-(t13<0.0))*(1.0-fabs(t13))+t13;
// //........................................................................
// nn = ijk-strideZ+1; // neighbor index (get convention)
// m14 = Phi[nn]; // get neighbor for phi - 14
// t14 = m14+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t14)>1.0) t14 =((t14>0.0)-(t14<0.0))*(1.0-fabs(t14))+t14;
// //........................................................................
// nn = ijk-strideZ-strideY; // neighbor index (get convention)
// m15 = Phi[nn]; // get neighbor for phi - 15
// t15 = m15+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t15)>1.0) t15 =((t15>0.0)-(t15<0.0))*(1.0-fabs(t15))+t15;
// //........................................................................
// nn = ijk+strideZ+strideY; // neighbor index (get convention)
// m16 = Phi[nn]; // get neighbor for phi - 16
// t16 = m16+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t16)>1.0) t16 =((t16>0.0)-(t16<0.0))*(1.0-fabs(t16))+t16;
// //........................................................................
// nn = ijk+strideZ-strideY; // neighbor index (get convention)
// m17 = Phi[nn]; // get neighbor for phi - 17
// t17 = m17+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t17)>1.0) t17 =((t17>0.0)-(t17<0.0))*(1.0-fabs(t17))+t17;
// //........................................................................
// nn = ijk-strideZ+strideY; // neighbor index (get convention)
// m18 = Phi[nn]; // get neighbor for phi - 18
// t18 = m18+(1.0-porosity)*GreySolidGrad[nn];
// if (fabs(t18)>1.0) t18 =((t18>0.0)-(t18<0.0))*(1.0-fabs(t18))+t18;
// //............Compute the Color Gradient...................................
// nx_phase = -(m1-m2+0.5*(m7-m8+m9-m10+m11-m12+m13-m14));
// ny_phase = -(m3-m4+0.5*(m7-m8-m9+m10+m15-m16+m17-m18));
// nz_phase = -(m5-m6+0.5*(m11-m12-m13+m14+m15-m16-m17+m18));
// C_phase = sqrt(nx_phase*nx_phase+ny_phase*ny_phase+nz_phase*nz_phase);
// //correct the normal color gradient by considering the effect of grey solid
// nx = -(t1-t2+0.5*(t7-t8+t9-t10+t11-t12+t13-t14));
// ny = -(t3-t4+0.5*(t7-t8-t9+t10+t15-t16+t17-t18));
// nz = -(t5-t6+0.5*(t11-t12-t13+t14+t15-t16-t17+t18));
//
// if (C_phase==0.0){
// nx = nx_phase;
// ny = ny_phase;
// nz = nz_phase;
// }
//
// //...........Normalize the Color Gradient.................................
// C = sqrt(nx*nx+ny*ny+nz*nz);
// double ColorMag = C;
// if (C==0.0) ColorMag=1.0;
// nx = nx/ColorMag;
// ny = ny/ColorMag;
// nz = nz/ColorMag;
//
// // q=0
// fq = dist[n];
// rho = fq;
// m1 = -30.0*fq;
// m2 = 12.0*fq;
//
// // q=1
// fq = dist[2*Np+n];
// rho += fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jx = fq;
// m4 = -4.0*fq;
// m9 = 2.0*fq;
// m10 = -4.0*fq;
//
// // f2 = dist[10*Np+n];
// fq = dist[1*Np+n];
// rho += fq;
// m1 -= 11.0*(fq);
// m2 -= 4.0*(fq);
// jx -= fq;
// m4 += 4.0*(fq);
// m9 += 2.0*(fq);
// m10 -= 4.0*(fq);
//
// // q=3
// fq = dist[4*Np+n];
// rho += fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jy = fq;
// m6 = -4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 = fq;
// m12 = -2.0*fq;
//
// // q = 4
// fq = dist[3*Np+n];
// rho+= fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jy -= fq;
// m6 += 4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 += fq;
// m12 -= 2.0*fq;
//
// // q=5
// fq = dist[6*Np+n];
// rho += fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jz = fq;
// m8 = -4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 -= fq;
// m12 += 2.0*fq;
//
// // q = 6
// fq = dist[5*Np+n];
// rho+= fq;
// m1 -= 11.0*fq;
// m2 -= 4.0*fq;
// jz -= fq;
// m8 += 4.0*fq;
// m9 -= fq;
// m10 += 2.0*fq;
// m11 -= fq;
// m12 += 2.0*fq;
//
// // q=7
// fq = dist[8*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jy += fq;
// m6 += fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 = fq;
// m16 = fq;
// m17 = -fq;
//
// // q = 8
// fq = dist[7*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jy -= fq;
// m6 -= fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 += fq;
// m16 -= fq;
// m17 += fq;
//
// // q=9
// fq = dist[10*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jy -= fq;
// m6 -= fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 -= fq;
// m16 += fq;
// m17 += fq;
//
// // q = 10
// fq = dist[9*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jy += fq;
// m6 += fq;
// m9 += fq;
// m10 += fq;
// m11 += fq;
// m12 += fq;
// m13 -= fq;
// m16 -= fq;
// m17 -= fq;
//
// // q=11
// fq = dist[12*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jz += fq;
// m8 += fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 = fq;
// m16 -= fq;
// m18 = fq;
//
// // q=12
// fq = dist[11*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jz -= fq;
// m8 -= fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 += fq;
// m16 += fq;
// m18 -= fq;
//
// // q=13
// fq = dist[14*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx += fq;
// m4 += fq;
// jz -= fq;
// m8 -= fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 -= fq;
// m16 -= fq;
// m18 -= fq;
//
// // q=14
// fq = dist[13*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jx -= fq;
// m4 -= fq;
// jz += fq;
// m8 += fq;
// m9 += fq;
// m10 += fq;
// m11 -= fq;
// m12 -= fq;
// m15 -= fq;
// m16 += fq;
// m18 += fq;
//
// // q=15
// fq = dist[16*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy += fq;
// m6 += fq;
// jz += fq;
// m8 += fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 = fq;
// m17 += fq;
// m18 -= fq;
//
// // q=16
// fq = dist[15*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy -= fq;
// m6 -= fq;
// jz -= fq;
// m8 -= fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 += fq;
// m17 -= fq;
// m18 += fq;
//
// // q=17
// fq = dist[18*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy += fq;
// m6 += fq;
// jz -= fq;
// m8 -= fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 -= fq;
// m17 += fq;
// m18 += fq;
//
// // q=18
// fq = dist[17*Np+n];
// rho += fq;
// m1 += 8.0*fq;
// m2 += fq;
// jy -= fq;
// m6 -= fq;
// jz += fq;
// m8 += fq;
// m9 -= 2.0*fq;
// m10 -= 2.0*fq;
// m14 -= fq;
// m17 -= fq;
// m18 -= fq;
//
// // Compute greyscale related parameters
// c0 = 0.5*(1.0+porosity*0.5*mu_eff/perm);
// if (porosity==1.0) c0 = 0.5;//i.e. apparent pore nodes
// //GeoFun = 1.75/sqrt(150.0*porosity*porosity*porosity);
// c1 = porosity*0.5*GeoFun/sqrt(perm);
// if (porosity==1.0) c1 = 0.0;//i.e. apparent pore nodes
//
// vx = jx/rho0+0.5*(porosity*Gx);
// vy = jy/rho0+0.5*(porosity*Gy);
// vz = jz/rho0+0.5*(porosity*Gz);
// v_mag=sqrt(vx*vx+vy*vy+vz*vz);
// ux = vx/(c0+sqrt(c0*c0+c1*v_mag));
// uy = vy/(c0+sqrt(c0*c0+c1*v_mag));
// uz = vz/(c0+sqrt(c0*c0+c1*v_mag));
// u_mag=sqrt(ux*ux+uy*uy+uz*uz);
//
// //Update the total force to include linear (Darcy) and nonlinear (Forchheimer) drags due to the porous medium
// Fx = rho0*(-porosity*mu_eff/perm*ux - porosity*GeoFun/sqrt(perm)*u_mag*ux + porosity*Gx);
// Fy = rho0*(-porosity*mu_eff/perm*uy - porosity*GeoFun/sqrt(perm)*u_mag*uy + porosity*Gy);
// Fz = rho0*(-porosity*mu_eff/perm*uz - porosity*GeoFun/sqrt(perm)*u_mag*uz + porosity*Gz);
// if (porosity==1.0){
// Fx=rho0*(Gx);
// Fy=rho0*(Gy);
// Fz=rho0*(Gz);
// }
//
// // write the velocity
// Velocity[n] = ux;
// Velocity[Np+n] = uy;
// Velocity[2*Np+n] = uz;
//
// //........................................................................
// //..............carry out relaxation process..............................
// //..........Toelke, Fruediger et. al. 2006................................
// if (C == 0.0) nx = ny = nz = 0.0;
// m1 = m1 + rlx_setA*((19*(ux*ux+uy*uy+uz*uz)*rho0/porosity - 11*rho) -19*alpha*C - m1);
// m2 = m2 + rlx_setA*((3*rho - 5.5*(ux*ux+uy*uy+uz*uz)*rho0/porosity)- m2);
// jx = jx + Fx;
// m4 = m4 + rlx_setB*((-0.6666666666666666*ux*rho0)- m4)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fx);
// jy = jy + Fy;
// m6 = m6 + rlx_setB*((-0.6666666666666666*uy*rho0)- m6)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fy);
// jz = jz + Fz;
// m8 = m8 + rlx_setB*((-0.6666666666666666*uz*rho0)- m8)
// + (1-0.5*rlx_setB)*(-0.6666666666666666*Fz);
// m9 = m9 + rlx_setA*(((2*ux*ux-uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(2*nx*nx-ny*ny-nz*nz) - m9);
// m10 = m10 + rlx_setA*( - m10);
// //m10 = m10 + rlx_setA*(-0.5*rho0*((2*ux*ux-uy*uy-uz*uz)/porosity)- m10);
// m11 = m11 + rlx_setA*(((uy*uy-uz*uz)*rho0/porosity) + 0.5*alpha*C*(ny*ny-nz*nz)- m11);
// m12 = m12 + rlx_setA*( - m12);
// //m12 = m12 + rlx_setA*(-0.5*(rho0*(uy*uy-uz*uz)/porosity)- m12);
// m13 = m13 + rlx_setA*( (ux*uy*rho0/porosity) + 0.5*alpha*C*nx*ny - m13);
// m14 = m14 + rlx_setA*( (uy*uz*rho0/porosity) + 0.5*alpha*C*ny*nz - m14);
// m15 = m15 + rlx_setA*( (ux*uz*rho0/porosity) + 0.5*alpha*C*nx*nz - m15);
// m16 = m16 + rlx_setB*( - m16);
// m17 = m17 + rlx_setB*( - m17);
// m18 = m18 + rlx_setB*( - m18);
//
// //.................inverse transformation......................................................
// // q=0
// fq = mrt_V1*rho-mrt_V2*m1+mrt_V3*m2;
// dist[n] = fq;
//
// // q = 1
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jx-m4)+mrt_V6*(m9-m10);
// dist[1*Np+n] = fq;
//
// // q=2
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m4-jx)+mrt_V6*(m9-m10);
// dist[2*Np+n] = fq;
//
// // q = 3
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jy-m6)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
// dist[3*Np+n] = fq;
//
// // q = 4
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m6-jy)+mrt_V7*(m10-m9)+mrt_V8*(m11-m12);
// dist[4*Np+n] = fq;
//
// // q = 5
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(jz-m8)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
// dist[5*Np+n] = fq;
//
// // q = 6
// fq = mrt_V1*rho-mrt_V4*m1-mrt_V5*m2+0.1*(m8-jz)+mrt_V7*(m10-m9)+mrt_V8*(m12-m11);
// dist[6*Np+n] = fq;
//
// // q = 7
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx+jy)+0.025*(m4+m6)+
// mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12+0.25*m13+0.125*(m16-m17);
// dist[7*Np+n] = fq;
//
//
// // q = 8
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jy)-0.025*(m4+m6) +mrt_V7*m9+mrt_V11*m10+mrt_V8*m11
// +mrt_V12*m12+0.25*m13+0.125*(m17-m16);
// dist[8*Np+n] = fq;
//
// // q = 9
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jx-jy)+0.025*(m4-m6)+
// mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13+0.125*(m16+m17);
// dist[9*Np+n] = fq;
//
// // q = 10
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2+0.1*(jy-jx)+0.025*(m6-m4)+
// mrt_V7*m9+mrt_V11*m10+mrt_V8*m11+mrt_V12*m12-0.25*m13-0.125*(m16+m17);
// dist[10*Np+n] = fq;
//
//
// // q = 11
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jx+jz)+0.025*(m4+m8)
// +mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
// -mrt_V12*m12+0.25*m15+0.125*(m18-m16);
// dist[11*Np+n] = fq;
//
// // q = 12
// fq = mrt_V1*rho+mrt_V9*m1+mrt_V10*m2-0.1*(jx+jz)-0.025*(m4+m8)+
// mrt_V7*m9+mrt_V11*m10-mrt_V8*m11-mrt_V12*m12+0.25*m15+0.125*(m16-m18);
// dist[12*Np+n] = fq;
//
// // q = 13
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jx-jz)+0.025*(m4-m8)
// +mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
// -mrt_V12*m12-0.25*m15-0.125*(m16+m18);
// dist[13*Np+n] = fq;
//
// // q= 14
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jz-jx)+0.025*(m8-m4)
// +mrt_V7*m9+mrt_V11*m10-mrt_V8*m11
// -mrt_V12*m12-0.25*m15+0.125*(m16+m18);
//
// dist[14*Np+n] = fq;
//
// // q = 15
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jy+jz)+0.025*(m6+m8)
// -mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m17-m18);
// dist[15*Np+n] = fq;
//
// // q = 16
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2-0.1*(jy+jz)-0.025*(m6+m8)
// -mrt_V6*m9-mrt_V7*m10+0.25*m14+0.125*(m18-m17);
// dist[16*Np+n] = fq;
//
//
// // q = 17
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jy-jz)+0.025*(m6-m8)
// -mrt_V6*m9-mrt_V7*m10-0.25*m14+0.125*(m17+m18);
// dist[17*Np+n] = fq;
//
// // q = 18
// fq = mrt_V1*rho+mrt_V9*m1
// +mrt_V10*m2+0.1*(jz-jy)+0.025*(m8-m6)
// -mrt_V6*m9-mrt_V7*m10-0.25*m14-0.125*(m17+m18);
// dist[18*Np+n] = fq;
// //........................................................................
//
// // Instantiate mass transport distributions
// // Stationary value - distribution 0
// nAB = 1.0/(nA+nB);
// Aq[n] = 0.3333333333333333*nA;
// Bq[n] = 0.3333333333333333*nB;
//
// //...............................................
// // q = 0,2,4
// // Cq = {1,0,0}, {0,1,0}, {0,0,1}
// delta = beta*nA*nB*nAB*0.1111111111111111*nx;
// if (!(nA*nB*nAB>0)) delta=0;
// a1 = nA*(0.1111111111111111*(1+4.5*ux))+delta;
// b1 = nB*(0.1111111111111111*(1+4.5*ux))-delta;
// a2 = nA*(0.1111111111111111*(1-4.5*ux))-delta;
// b2 = nB*(0.1111111111111111*(1-4.5*ux))+delta;
//
// Aq[1*Np+n] = a1;
// Bq[1*Np+n] = b1;
// Aq[2*Np+n] = a2;
// Bq[2*Np+n] = b2;
//
// //...............................................
// // q = 2
// // Cq = {0,1,0}
// delta = beta*nA*nB*nAB*0.1111111111111111*ny;
// if (!(nA*nB*nAB>0)) delta=0;
// a1 = nA*(0.1111111111111111*(1+4.5*uy))+delta;
// b1 = nB*(0.1111111111111111*(1+4.5*uy))-delta;
// a2 = nA*(0.1111111111111111*(1-4.5*uy))-delta;
// b2 = nB*(0.1111111111111111*(1-4.5*uy))+delta;
//
// Aq[3*Np+n] = a1;
// Bq[3*Np+n] = b1;
// Aq[4*Np+n] = a2;
// Bq[4*Np+n] = b2;
// //...............................................
// // q = 4
// // Cq = {0,0,1}
// delta = beta*nA*nB*nAB*0.1111111111111111*nz;
// if (!(nA*nB*nAB>0)) delta=0;
// a1 = nA*(0.1111111111111111*(1+4.5*uz))+delta;
// b1 = nB*(0.1111111111111111*(1+4.5*uz))-delta;
// a2 = nA*(0.1111111111111111*(1-4.5*uz))-delta;
// b2 = nB*(0.1111111111111111*(1-4.5*uz))+delta;
//
// Aq[5*Np+n] = a1;
// Bq[5*Np+n] = b1;
// Aq[6*Np+n] = a2;
// Bq[6*Np+n] = b2;
// //...............................................
//
// }
// }
//}
//__global__ void dvc_ScaLBL_D3Q19_GreyscaleColor_Init(double *dist, double *Porosity, int Np)
//{
// int n;
// int S = Np/NBLOCKS/NTHREADS + 1;
// double porosity;
// for (int s=0; s<S; s++){
// //........Get 1-D index for this thread....................
// n = S*blockIdx.x*blockDim.x + s*blockDim.x + threadIdx.x;
// if (n<Np ){
// porosity = Porosity[n];
// if (porosity==0.0) porosity=1.f;
// dist[n] = 0.3333333333333333/porosity;
// dist[Np+n] = 0.055555555555555555/porosity; //double(100*n)+1.f;
// dist[2*Np+n] = 0.055555555555555555/porosity; //double(100*n)+2.f;
// dist[3*Np+n] = 0.055555555555555555/porosity; //double(100*n)+3.f;
// dist[4*Np+n] = 0.055555555555555555/porosity; //double(100*n)+4.f;
// dist[5*Np+n] = 0.055555555555555555/porosity; //double(100*n)+5.f;
// dist[6*Np+n] = 0.055555555555555555/porosity; //double(100*n)+6.f;
// dist[7*Np+n] = 0.0277777777777778/porosity; //double(100*n)+7.f;
// dist[8*Np+n] = 0.0277777777777778/porosity; //double(100*n)+8.f;
// dist[9*Np+n] = 0.0277777777777778/porosity; //double(100*n)+9.f;
// dist[10*Np+n] = 0.0277777777777778/porosity; //double(100*n)+10.f;
// dist[11*Np+n] = 0.0277777777777778/porosity; //double(100*n)+11.f;
// dist[12*Np+n] = 0.0277777777777778/porosity; //double(100*n)+12.f;
// dist[13*Np+n] = 0.0277777777777778/porosity; //double(100*n)+13.f;
// dist[14*Np+n] = 0.0277777777777778/porosity; //double(100*n)+14.f;
// dist[15*Np+n] = 0.0277777777777778/porosity; //double(100*n)+15.f;
// dist[16*Np+n] = 0.0277777777777778/porosity; //double(100*n)+16.f;
// dist[17*Np+n] = 0.0277777777777778/porosity; //double(100*n)+17.f;
// dist[18*Np+n] = 0.0277777777777778/porosity; //double(100*n)+18.f;
// }
// }
//}
//extern "C" void ScaLBL_D3Q19_GreyscaleColor_Init(double *dist, double *Porosity, int Np){
// dvc_ScaLBL_D3Q19_GreyscaleColor_Init<<<NBLOCKS,NTHREADS >>>(dist,Porosity,Np);
// cudaError_t err = cudaGetLastError();
// if (cudaSuccess != err){
// printf("CUDA error in ScaLBL_D3Q19_GreyscaleColor_Init: %s \n",cudaGetErrorString(err));
// }
//}
//Model-1 & 4
extern "C" void ScaLBL_D3Q19_AAeven_GreyscaleColor(int *Map, double *dist, double *Aq, double *Bq, double *Den,
double *Phi,double *GreySolidGrad, double *Poros,double *Perm,double *Vel,
double rhoA, double rhoB, double tauA, double tauB,double tauA_eff,double tauB_eff, double alpha, double beta,
double Fx, double Fy, double Fz, int strideY, int strideZ, int start, int finish, int Np){
//cudaProfilerStart();
//cudaFuncSetCacheConfig(dvc_ScaLBL_D3Q19_AAeven_GreyscaleColor, cudaFuncCachePreferL1);
dvc_ScaLBL_D3Q19_AAeven_GreyscaleColor<<<NBLOCKS,NTHREADS >>>(Map, dist, Aq, Bq, Den, Phi, GreySolidGrad, Poros, Perm, Vel,
rhoA, rhoB, tauA, tauB, tauA_eff, tauB_eff, alpha, beta, Fx, Fy, Fz, strideY, strideZ, start, finish, Np);
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err){
printf("CUDA error in ScaLBL_D3Q19_AAeven_GreyscaleColor: %s \n",cudaGetErrorString(err));
}
//cudaProfilerStop();
}
//Model-1 & 4
extern "C" void ScaLBL_D3Q19_AAodd_GreyscaleColor(int *d_neighborList, int *Map, double *dist, double *Aq, double *Bq, double *Den,
double *Phi, double *GreySolidGrad, double *Poros,double *Perm,double *Vel,
double rhoA, double rhoB, double tauA, double tauB, double tauA_eff,double tauB_eff, double alpha, double beta,
double Fx, double Fy, double Fz, int strideY, int strideZ, int start, int finish, int Np){
//cudaProfilerStart();
//cudaFuncSetCacheConfig(dvc_ScaLBL_D3Q19_AAodd_GreyscaleColor, cudaFuncCachePreferL1);
dvc_ScaLBL_D3Q19_AAodd_GreyscaleColor<<<NBLOCKS,NTHREADS >>>(d_neighborList, Map, dist, Aq, Bq, Den, Phi, GreySolidGrad, Poros, Perm,Vel,
rhoA, rhoB, tauA, tauB, tauA_eff, tauB_eff,alpha, beta, Fx, Fy, Fz, strideY, strideZ, start, finish, Np);
cudaError_t err = cudaGetLastError();
if (cudaSuccess != err){
printf("CUDA error in ScaLBL_D3Q19_AAodd_GreyscaleColor: %s \n",cudaGetErrorString(err));
}
//cudaProfilerStop();
}
////Model-2&3
//extern "C" void ScaLBL_D3Q19_AAeven_GreyscaleColor(int *Map, double *dist, double *Aq, double *Bq, double *Den,
// double *Phi,double *GreySolidGrad, double *Poros,double *Perm,double *Vel,
// double rhoA, double rhoB, double tauA, double tauB,double tauA_eff,double tauB_eff, double alpha, double beta,
// double Fx, double Fy, double Fz, int strideY, int strideZ, int start, int finish, int Np){
//
// //cudaProfilerStart();
// //cudaFuncSetCacheConfig(dvc_ScaLBL_D3Q19_AAeven_GreyscaleColor, cudaFuncCachePreferL1);
//
// dvc_ScaLBL_D3Q19_AAeven_GreyscaleColor<<<NBLOCKS,NTHREADS >>>(Map, dist, Aq, Bq, Den, Phi, GreySolidGrad, Poros, Perm, Vel,
// rhoA, rhoB, tauA, tauB, tauA_eff, tauB_eff, alpha, beta, Fx, Fy, Fz, strideY, strideZ, start, finish, Np);
// cudaError_t err = cudaGetLastError();
// if (cudaSuccess != err){
// printf("CUDA error in ScaLBL_D3Q19_AAeven_GreyscaleColor: %s \n",cudaGetErrorString(err));
// }
// //cudaProfilerStop();
//
//}
//
////Model-2&3
//extern "C" void ScaLBL_D3Q19_AAodd_GreyscaleColor(int *d_neighborList, int *Map, double *dist, double *Aq, double *Bq, double *Den,
// double *Phi, double *GreySolidGrad, double *Poros,double *Perm,double *Vel,
// double rhoA, double rhoB, double tauA, double tauB, double tauA_eff,double tauB_eff, double alpha, double beta,
// double Fx, double Fy, double Fz, int strideY, int strideZ, int start, int finish, int Np){
//
// //cudaProfilerStart();
// //cudaFuncSetCacheConfig(dvc_ScaLBL_D3Q19_AAodd_GreyscaleColor, cudaFuncCachePreferL1);
//
// dvc_ScaLBL_D3Q19_AAodd_GreyscaleColor<<<NBLOCKS,NTHREADS >>>(d_neighborList, Map, dist, Aq, Bq, Den, Phi, GreySolidGrad, Poros, Perm,Vel,
// rhoA, rhoB, tauA, tauB, tauA_eff, tauB_eff,alpha, beta, Fx, Fy, Fz, strideY, strideZ, start, finish, Np);
//
// cudaError_t err = cudaGetLastError();
// if (cudaSuccess != err){
// printf("CUDA error in ScaLBL_D3Q19_AAodd_GreyscaleColor: %s \n",cudaGetErrorString(err));
// }
// //cudaProfilerStop();
//}
|
6,378 | #include "includes.h"
__global__ void LSTMGateGradientKernelBPTT( float *input, float *previousOutput, float *cellStates, float *inputGateDeltas, float *forgetGateDeltas, float *outputGateDeltas, float* outputGateWeightGradient, float* inputGateWeightGradient, float* forgetGateWeightGradient, int inputCount, int previousOutputCount, int cellsPerBlock )
{
int weightId = blockDim.x * blockIdx.y * gridDim.x //rows preceeding current row in grid
+ blockDim.x * blockIdx.x //blocks preceeding current block
+ threadIdx.x;
int weightsPerGate = inputCount + previousOutputCount + cellsPerBlock + 1;
if (weightId < weightsPerGate * previousOutputCount / cellsPerBlock)
{
int fromId = weightId % weightsPerGate;
int toId = weightId / weightsPerGate;
//calculate output gate weight gradient
int isFromInputUnit = fromId >= 0 && fromId < inputCount;
int isFromPreviousOutputUnit = (fromId >= inputCount) && (fromId < inputCount + previousOutputCount);
int isPeephole = (fromId >= inputCount + previousOutputCount) && (fromId < inputCount + previousOutputCount + cellsPerBlock);
int isFromBiasUnit = fromId == (inputCount + previousOutputCount + cellsPerBlock);
float inputFromWeight = isFromInputUnit * input[isFromInputUnit * fromId]
+ isFromPreviousOutputUnit * previousOutput[isFromPreviousOutputUnit * (fromId - inputCount)]
+ isPeephole * cellStates[isPeephole * (toId * cellsPerBlock + (fromId - inputCount - previousOutputCount))]
+ isFromBiasUnit * 1;
outputGateWeightGradient[weightId] = outputGateDeltas[toId] * inputFromWeight;
inputGateWeightGradient[weightId] = inputGateDeltas[toId] * inputFromWeight;
forgetGateWeightGradient[weightId] = forgetGateDeltas[toId] * inputFromWeight;
}
} |
6,379 | #include "includes.h"
__global__ void fillArray(int8_t *dest, int loop) {
const size_t i = blockDim.x * blockIdx.x + threadIdx.x;
const size_t k = blockDim.x * gridDim.x;
for (int n=0; n<loop; n++) {
dest[i+n*k] = sin((i+n*k)/(float)100.0)*30;
}
} |
6,380 | #include "DomainTransformKernels.cuh"
namespace mn {
// mark the boundary where the page # changes
__global__ void markPageBoundary(const int numParticle,
const uint64_t *_offsets,
int32_t *_marks) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= numParticle)
return;
if (!idx || (_offsets[idx] >> 12) != (_offsets[idx - 1] >> 12))
_marks[idx] = 1;
else
_marks[idx] = 0;
if (idx == 0)
_marks[numParticle] = 1;
}
// mark the boundary where the cell # changes
__global__ void markCellBoundary(const int numParticle,
const uint64_t *_offsets,
int32_t *_marks) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= numParticle)
return;
if (!idx || _offsets[idx] != _offsets[idx - 1])
_marks[idx] = 1;
else
_marks[idx] = 0;
if (idx == 0)
_marks[numParticle] = 1;
}
__global__ void markBlockOffset(const int numParticle,
int32_t *_blockMap,
int32_t *_particleOffsets) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= numParticle)
return;
int mapid = _blockMap[idx];
if (!idx || mapid != _blockMap[idx - 1]) {
_particleOffsets[mapid - 1] = idx;
}
/// mark sentinel
if (idx == numParticle - 1)
_particleOffsets[mapid] = numParticle;
}
__global__ void markPageSize(const int numPage,
const int32_t *_offsets,
int32_t *_marks) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= numPage)
return;
_marks[idx] = (_offsets[idx + 1] - _offsets[idx] + 511) / 512;
}
__global__ void markVirtualPageOffset(const int numPage,
const int32_t *_toVirtualOffsets,
int32_t *_marks) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= numPage)
return;
_marks[_toVirtualOffsets[idx]] = 1;
}
} // namespace mn
|
6,381 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <cuda.h>
#include <curand_kernel.h>
const int THREADS = 128; //threads per block
const int trial_number = 1024; //trial numbers per thread
const int BLOCKS = 16; //blocks per grid
const float PI = 3.1415926535;
__global__ void pi_estimation(float *pi, curandState *states)
{
unsigned int threadID = threadIdx.x + blockDim.x * blockIdx.x;
int count = 0;
float x,y,z;
curand_init(0, threadID, 1, &states[threadID]); //(seed, sequence number, offset, curandState)
for(int i = 0; i < trial_number; i++)
{
x = curand_uniform(&states[threadID]);//return sequence number of pseudorandom uniformly distributed.
y = curand_uniform(&states[threadID]);
z = (x*x + y*y);
if(z <= 1.0f) //if x,y in unit circle
{
count += 1;
}
}
pi[threadID] = 4.0f * count/(float)trial_number; //estimate PI value 4*count/number of trial
}
int main(int argc, char *argv[])
{
float host[THREADS * BLOCKS];
float *device;
curandState *deviceStates;
cudaMalloc((void **)&device, THREADS * BLOCKS * sizeof(float));
cudaMalloc((void **)&deviceStates, THREADS * BLOCKS * sizeof(curandState));
pi_estimation<<<BLOCKS,THREADS>>>(device,deviceStates); //call kernel
cudaMemcpy(host, device, THREADS * BLOCKS * sizeof(float), cudaMemcpyDeviceToHost); //copy estimated pi value from device to host
float pi = 0.0;
for(int i = 0; i < THREADS * BLOCKS; i++)
{
pi += host[i];
}
pi /= (THREADS * BLOCKS); //get average of each PI value from each thread.
printf("Monte Carlo PI estimation %d times\n", THREADS * BLOCKS * trial_number);
printf("PI estimation: %.10f\n",pi);
printf("Error: %.10\n\n",pi-PI);
cudaFree(device);
cudaFree(deviceStates);
return 0;
}
|
6,382 | #include <stdio.h>
/*
* Initialize array values on the host.
*/
void init(int *a, int N)
{
int i;
for (i = 0; i < N; ++i)
{
a[i] = i;
}
}
/*
* Double elements in parallel on the GPU.
*/
__global__
void doubleElements(int *a, int N)
{
int i;
i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N)
{
a[i] *= 2;
}
}
/*
* Check all elements have been doubled on the host.
*/
bool checkElementsAreDoubled(int *a, int N)
{
int i;
for (i = 0; i < N; ++i)
{
if (a[i] != i*2) return false;
}
return true;
}
int main()
{
int N = 100;
int *a;
size_t size = N * sizeof(int);
/*
* Refactor this memory allocation to provide a pointer
* `a` that can be used on both the host and the device.
*/
cudaMallocManaged(&a, size);
init(a, N);
size_t threads_per_block = 10;
size_t number_of_blocks = 10;
/*
* This launch will not work until the pointer `a` is also
* available to the device.
*/
doubleElements<<<number_of_blocks, threads_per_block>>>(a, N);
cudaDeviceSynchronize();
bool areDoubled = checkElementsAreDoubled(a, N);
printf("All elements were doubled? %s\n", areDoubled ? "TRUE" : "FALSE");
/*
* Refactor to free memory that has been allocated to be
* accessed by both the host and the device.
*/
cudaFree(a);
}
|
6,383 | #include <iostream>
#include<curand.h>
#include<curand_kernel.h>
#include <vector>
#include <ctime>
#include <cstdlib>
#include <fstream>
using namespace std;
#define PRECISION 10000
#define BLOCKS_NUBMER 4096
#define THREADS_NUMBER 1
__device__ float generate( curandState* globalState, int ind )
{
//int ind = threadIdx.x;
curandState localState = globalState[ind];
float RANDOM = curand_uniform( &localState );
globalState[ind] = localState;
return RANDOM;
}
// Szybkie Potegowanie modulo
//
// www.algorytm.org
// (c)2006 Tomasz Lubinski
//
__device__ int power_modulo_fast(long a, long b, long m)
{
long i;
long result = 1;
long x = a%m;
for (i=1; i<=b; i<<=1)
{
x %= m;
if ((b&i) != 0)
{
result *= x;
result %= m;
}
x *= x;
}
return result;
}
__global__ void setup_kernel ( curandState * state, unsigned long seed )
{
int id = blockIdx.x;
int sequence = id;
int offset = 0;
curand_init ( seed, sequence, offset, &state[id] );
}
__global__ void kernel(int p, bool* prime, curandState* globalState)
{
int i = blockIdx.x;
int a = 0;
while(*prime && i < PRECISION)
{
a = (generate(globalState, i%BLOCKS_NUBMER) * (p-2))+1;
if(power_modulo_fast(a, p-1, p) == 1)
{
i += BLOCKS_NUBMER;
}
else
{
*prime = false;
}
}
}
int main( int argc, char** argv )
{
if( argc != 2 )
{
cout<< "One argument is required!" <<endl;
cout<<"To run program type: ./primes_gpu file" << endl
<< "file - path to file with numbers" << endl;
return 1;
}
struct timespec start, finish;
double elapsed;
long tmp;
vector<unsigned long> numbers;
vector<bool> output;
ifstream file;
file.open(argv[1]);
if (!file)
{
cout << "\nError opening file.\n";
return 1;
}
//Reading numbers form file
while(file >> tmp)
{
numbers.push_back(tmp);
}
file.close();
output.resize(numbers.size());
int p = 15487317;
curandState* devStates;
cudaMalloc ( &devStates, BLOCKS_NUBMER*sizeof( curandState ) );
// setup seeds
setup_kernel <<< BLOCKS_NUBMER, THREADS_NUMBER >>> ( devStates,unsigned(time(NULL)) );
bool* prime;
bool init = true;
bool result = true;
cudaMalloc((void**) &prime, sizeof(bool));
clock_gettime(CLOCK_MONOTONIC, &start);
for (int i = 0; i < numbers.size(); i++)
{
p = numbers.at(i);
cudaMemcpy(prime, &init, sizeof(bool), cudaMemcpyHostToDevice);
kernel<<<BLOCKS_NUBMER,THREADS_NUMBER>>> (p, prime, devStates);
cudaMemcpy(&result, prime, sizeof(bool), cudaMemcpyDeviceToHost);
output.at(i)=result;
}
cudaDeviceSynchronize();
clock_gettime(CLOCK_MONOTONIC, &finish);
elapsed = (finish.tv_sec - start.tv_sec);
elapsed += (finish.tv_nsec - start.tv_nsec) / 1000000000.0;
cout<< "Time: " << elapsed * 1000 << "ms" << std::endl;
//Print the result
for (int i = 0; i < numbers.size(); i++)
{
if (output.at(i))
{
cout << numbers.at(i) << ": prime" << endl;
}
else
{
cout << numbers.at(i) << ": composite" << endl;
}
}
cudaFree(devStates);
cudaFree(prime);
return 0;
} |
6,384 | #include "includes.h"
__global__ void cmax(float *d_in, float *max, int len)
{
extern __shared__ float smax[];
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
smax[tid] = d_in[i]>d_in[i+len] ? d_in[i] : d_in[i+len];
__syncthreads();
if(blockDim.x > 512 && tid<512) {if(smax[tid] < smax[tid+512]) smax[tid] = smax[tid+512];} __syncthreads();
if(blockDim.x > 256 && tid<256) {if(smax[tid] < smax[tid+256]) smax[tid] = smax[tid+256];} __syncthreads();
if(blockDim.x > 128 && tid<128) {if(smax[tid] < smax[tid+128]) smax[tid] = smax[tid+128];} __syncthreads();
if(blockDim.x > 64 && tid<64) {if(smax[tid] < smax[tid+64]) smax[tid] = smax[tid+64];} __syncthreads();
if(tid<32) {
if(blockDim.x > 32 && smax[tid] < smax[tid+32]) smax[tid] = smax[tid+32];
if(blockDim.x > 16 && smax[tid] < smax[tid+16]) smax[tid] = smax[tid+16];
if(blockDim.x > 8 && smax[tid] < smax[tid+8]) smax[tid] = smax[tid+8];
if(blockDim.x > 4 && smax[tid] < smax[tid+4]) smax[tid] = smax[tid+4];
if(blockDim.x > 2 && smax[tid] < smax[tid+2]) smax[tid] = smax[tid+2];
if(smax[tid] < smax[tid+1]) smax[tid] = smax[tid+1];
__syncthreads();
}
if(tid == 0 )
{
max[blockIdx.x] = smax[0];
}
} |
6,385 | #include "includes.h"
__global__ void updhgF_SoA(float *f, float *z1, float *z2, float *g, float tf, float invlambda, int nx, int ny)
{
int px = blockIdx.x * blockDim.x + threadIdx.x;
int py = blockIdx.y * blockDim.y + threadIdx.y;
int idx = px + py*nx;
float DIVZ;
if (px<nx && py<ny)
{
// compute the divergence
DIVZ = 0;
if ((px<(nx - 1))) DIVZ += z1[idx];
if ((px>0)) DIVZ -= z1[idx - 1];
if ((py<(ny - 1))) DIVZ += z2[idx];
if ((py>0)) DIVZ -= z2[idx - nx];
// update f
f[idx] = (1 - tf) *f[idx] + tf * (g[idx] + invlambda*DIVZ);
}
} |
6,386 |
#define N 5
__global__ void MatAdd(float A[N][N], float B[N][N], float C[N][N])
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
if (i < N && j < N)
C[i][j] = A[i][j] + B[i][j];
}
|
6,387 | extern "C"
{
__global__ void DmeanSquareLoss_32(const int lengthx, const float pref, const float *gradc, const float *x,const float *y, float *gradn )
{
int i = threadIdx.x + blockIdx.x * blockDim.x;
if (i<lengthx)
{
gradn[i] += pref * gradc[0] * (x[i]-y[i]);
}
}
} |
6,388 | #include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <cuda.h>
#define SIZEOFINT sizeof(int)
#define BLOCK_DIM 64
#define TH_DIM 32
const int INF = ((1 << 30) - 1);
int n, m, padding_n, pitch_k, Dist_row_size_in_byte;
size_t pitch;
int up_part_size_in_block = 0, bottom_part_size_in_block = 0, up_part_height = 0, bottom_part_height = 0;
int *Dist, *Dist_s;
int *Dist_cuda, *Dist_cuda0, *Dist_cuda1;
void show_mat(int *start_p, int vertex_num){
for(int i = 0; i < vertex_num; i++){
for(int j = 0; j < vertex_num; j++){
if(start_p[i * vertex_num + j] == INF){
printf("INF\t ");
}else{
printf("%d\t ", start_p[i * vertex_num + j]);
}
}
printf("\n");
}
}
void show_mat_cuda(int *start_p, int vertex_num, int padding_n, size_t pitch, int device_id){
int *temp = (int*)malloc(SIZEOFINT * padding_n * padding_n);
cudaSetDevice(device_id);
// cudaMemcpy(temp, start_p, (SIZEOFINT * vertex_num * vertex_num), cudaMemcpyDeviceToHost);
cudaMemcpy2D(temp, SIZEOFINT * padding_n, start_p, pitch, SIZEOFINT * padding_n, padding_n, cudaMemcpyDeviceToHost);
printf("---\n");
for(int i = 0; i < vertex_num; i++){
for(int j = 0; j < vertex_num; j++){
if(temp[i * vertex_num + j] == INF){
printf("INF\t ");
}else{
printf("%d\t ", temp[i * vertex_num + j]);
}
}
printf("\n");
}
printf("---\n");
}
void malloc_Dist(){
cudaHostAlloc(&Dist, SIZEOFINT * padding_n * padding_n, cudaHostAllocPortable);
// Dist = (int*)malloc(SIZEOFINT * padding_n * padding_n);
Dist_s = (int*)malloc(SIZEOFINT * n * n);
}
int getDist(int i, int j){return Dist[i * padding_n + j];}
int *getDistAddr(int i, int j){return &(Dist[i * padding_n + j]);}
void setDist(int i, int j, int val){Dist[i * padding_n + j] = val;}
void setup_DistCuda(){
// cudaMalloc((void **)&Dist_cuda, SIZEOFINT * padding_n * padding_n);
// cudaMemcpy(Dist_cuda, Dist, (padding_n * padding_n * SIZEOFINT), cudaMemcpyHostToDevice);
// cudaMallocPitch(&Dist_cuda, &pitch, SIZEOFINT * padding_n, padding_n);
// cudaMemcpy2D(Dist_cuda, pitch, Dist, SIZEOFINT * padding_n, SIZEOFINT * padding_n, padding_n, cudaMemcpyHostToDevice);
// pitch_k = ((int)pitch) / SIZEOFINT;
cudaStream_t stream;
cudaStreamCreate(&stream);
cudaSetDevice(0);
cudaDeviceEnablePeerAccess(0, 0);
cudaMallocPitch(&Dist_cuda0, &pitch, SIZEOFINT * padding_n, padding_n);
cudaMemcpy2DAsync(Dist_cuda0, pitch, Dist, SIZEOFINT * padding_n, SIZEOFINT * padding_n, padding_n, cudaMemcpyHostToDevice, stream);
pitch_k = ((int)pitch) / SIZEOFINT;
cudaSetDevice(1);
cudaDeviceEnablePeerAccess(1, 0);
cudaMallocPitch(&Dist_cuda1, &pitch, SIZEOFINT * padding_n, padding_n);
cudaMemcpy2D(Dist_cuda1, pitch, Dist, SIZEOFINT * padding_n, SIZEOFINT * padding_n, padding_n, cudaMemcpyHostToDevice);
cudaStreamDestroy(stream);
}
void back_DistCuda(){
// cudaMemcpy(Dist, Dist_cuda, (padding_n * padding_n * SIZEOFINT), cudaMemcpyDeviceToHost);
// cudaMemcpy2D(Dist, SIZEOFINT * padding_n, Dist_cuda, pitch, SIZEOFINT * padding_n, padding_n, cudaMemcpyDeviceToHost);
cudaStream_t stream;
cudaStreamCreate(&stream);
cudaSetDevice(0);
cudaMemcpy2DAsync(Dist, SIZEOFINT * padding_n, Dist_cuda0, pitch, SIZEOFINT * padding_n, padding_n, cudaMemcpyDeviceToHost, stream);
cudaSetDevice(1);
cudaMemcpy2D(&(Dist[up_part_height * padding_n]), SIZEOFINT * padding_n, &(Dist_cuda1[up_part_height * pitch_k]), pitch, SIZEOFINT * padding_n, (bottom_part_height), cudaMemcpyDeviceToHost);
cudaStreamDestroy(stream);
}
void input(char* infile) {
FILE* file = fopen(infile, "rb");
fread(&n, sizeof(int), 1, file);
fread(&m, sizeof(int), 1, file);
padding_n = ((n + BLOCK_DIM - 1) / BLOCK_DIM) * BLOCK_DIM;
Dist_row_size_in_byte = SIZEOFINT * padding_n;
malloc_Dist();
for (int i = 0; i < padding_n; i++) {
for (int j = 0; j < padding_n; j++) {
if (i == j) {
setDist(i, j, 0);
// Dist[i][j] = 0;
} else {
setDist(i, j, INF);
// Dist[i][j] = INF;
}
}
}
int pair[3];
int *edges_buf = (int*)malloc(3 * m * SIZEOFINT);
fread(edges_buf, sizeof(int), 3 * m, file);
for (int i = 0; i < m; i++) {
// fread(pair, sizeof(int), 3, file);
setDist(edges_buf[3 * i], edges_buf[3 * i + 1], edges_buf[3 * i + 2]);
}
free(edges_buf);
fclose(file);
}
void output(char* outFileName) {
FILE* outfile = fopen(outFileName, "w");
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
// if (Dist[i][j] >= INF) Dist[i][j] = INF;
if (getDist(i, j) >= INF) setDist(i, j, INF);
Dist_s[i * n + j] = getDist(i, j);
}
// fwrite(Dist[i], sizeof(int), n, outfile);
// fwrite(getDistAddr(i, 0), SIZEOFINT, n, outfile);
}
fwrite(Dist_s, sizeof(int), n * n, outfile);
fclose(outfile);
}
__forceinline__
__device__ void block_calc(int* C, int* A, int* B, int bj, int bi) {
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = A[bi*BLOCK_DIM + k] + B[k*BLOCK_DIM + bj];
// int sum1 = A[(bi + TH_DIM)*BLOCK_DIM + k] + B[k*BLOCK_DIM + bj];
// int sum2 = A[bi*BLOCK_DIM + k] + B[k*BLOCK_DIM + (bj + TH_DIM)];
// int sum3 = A[(bi + TH_DIM)*BLOCK_DIM + k] + B[k*BLOCK_DIM + (bj + TH_DIM)];
C[bi*BLOCK_DIM + bj] = min(C[bi*BLOCK_DIM + bj], sum0);
// C[(bi + TH_DIM)*BLOCK_DIM + bj] = min(C[(bi + TH_DIM)*BLOCK_DIM + bj], sum1);
// C[bi*BLOCK_DIM + (bj + TH_DIM)] = min(C[bi*BLOCK_DIM + (bj + TH_DIM)], sum2);
// C[(bi + TH_DIM)*BLOCK_DIM + (bj + TH_DIM)] = min(C[(bi + TH_DIM)*BLOCK_DIM + (bj + TH_DIM)], sum3);
__syncthreads();
}
}
__forceinline__
__device__ void block_calc_rev_async(int* C, int* A, int* B, int bj, int bi) {
#pragma unroll
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = A[k*BLOCK_DIM + bi] + B[k*BLOCK_DIM + bj];
// int sum1 = A[k*BLOCK_DIM + (bi + TH_DIM)] + B[k*BLOCK_DIM + bj];
// int sum2 = A[k*BLOCK_DIM + bi] + B[k*BLOCK_DIM + (bj + TH_DIM)];
// int sum3 = A[k*BLOCK_DIM + (bi + TH_DIM)] + B[k*BLOCK_DIM + (bj + TH_DIM)];
C[bi*BLOCK_DIM + bj] = min(C[bi*BLOCK_DIM + bj], sum0);
// C[(bi + TH_DIM)*BLOCK_DIM + bj] = min(C[(bi + TH_DIM)*BLOCK_DIM + bj], sum1);
// C[bi*BLOCK_DIM + (bj + TH_DIM)] = min(C[bi*BLOCK_DIM + (bj + TH_DIM)], sum2);
// C[(bi + TH_DIM)*BLOCK_DIM + (bj + TH_DIM)] = min(C[(bi + TH_DIM)*BLOCK_DIM + (bj + TH_DIM)], sum3);
}
}
__global__ void floyd_warshall_block_kernel_phase1(int n, int k, int* graph) {
const unsigned int bi = threadIdx.y;
const unsigned int bj = threadIdx.x;
__shared__ int C[BLOCK_DIM][BLOCK_DIM];
// Transfer to temp shared arrays
C[bi][bj] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)];
C[(bi + TH_DIM)][bj] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)];
C[bi][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))];
C[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))];
__syncthreads();
// block_calc(C, C, C, bi, bj);
#pragma unroll
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = C[bi][k] + C[k][bj];
int sum1 = C[(bi + TH_DIM)][k] + C[k][bj];
int sum2 = C[bi][k] + C[k][(bj + TH_DIM)];
int sum3 = C[(bi + TH_DIM)][k] + C[k][(bj + TH_DIM)];
C[bi][bj] = min(C[bi][bj], sum0);
C[(bi + TH_DIM)][bj] = min(C[(bi + TH_DIM)][bj], sum1);
C[bi][(bj + TH_DIM)] = min(C[bi][(bj + TH_DIM)], sum2);
C[(bi + TH_DIM)][(bj + TH_DIM)] = min(C[(bi + TH_DIM)][(bj + TH_DIM)], sum3);
__syncthreads();
}
// Transfer back to graph
graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)] = C[bi][bj];
graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)] = C[(bi + TH_DIM)][bj];
graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))] = C[bi][(bj + TH_DIM)];
graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))] = C[(bi + TH_DIM)][(bj + TH_DIM)];
}
__global__ void floyd_warshall_block_kernel_phase2(int n, int k, int* graph) {
// BlockDim is one dimensional (Straight along diagonal)
// Blocks themselves are two dimensional
// Phase 2 1/2
const unsigned int i = blockIdx.x;
const unsigned int bi = threadIdx.y;
const unsigned int bj = threadIdx.x;
// __shared__ int A[BLOCK_DIM * BLOCK_DIM];
__shared__ int B[BLOCK_DIM][BLOCK_DIM];
__shared__ int C[BLOCK_DIM][BLOCK_DIM];
C[bi][bj] = graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)];
C[(bi + TH_DIM)][bj] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)];
C[bi][(bj + TH_DIM)] = graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))];
C[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))];
B[bi][bj] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)];
B[(bi + TH_DIM)][bj] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)];
B[bi][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))];
B[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))];
__syncthreads();
// block_calc(C, C, B, bi, bj);
#pragma unroll
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = C[bi][k] + B[k][bj];
int sum1 = C[(bi + TH_DIM)][k] + B[k][bj];
int sum2 = C[bi][k] + B[k][(bj + TH_DIM)];
int sum3 = C[(bi + TH_DIM)][k] + B[k][(bj + TH_DIM)];
C[bi][bj] = min(C[bi][bj], sum0);
C[(bi + TH_DIM)][bj] = min(C[(bi + TH_DIM)][bj], sum1);
C[bi][(bj + TH_DIM)] = min(C[bi][(bj + TH_DIM)], sum2);
C[(bi + TH_DIM)][(bj + TH_DIM)] = min(C[(bi + TH_DIM)][(bj + TH_DIM)], sum3);
__syncthreads();
}
graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)] = C[bi][bj];
graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)] = C[(bi + TH_DIM)][bj];
graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))] = C[bi][(bj + TH_DIM)];
graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))] = C[(bi + TH_DIM)][(bj + TH_DIM)];
// Phase 2 2/2
C[bi][bj] = graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + bj)];
C[(bi + TH_DIM)][bj] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + bj)];
C[bi][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + (bj + TH_DIM))];
C[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + (bj + TH_DIM))];
__syncthreads();
// block_calc(C, B, C, bi, bj);
#pragma unroll
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = B[bi][k] + C[k][bj];
int sum1 = B[(bi + TH_DIM)][k] + C[k][bj];
int sum2 = B[bi][k] + C[k][(bj + TH_DIM)];
int sum3 = B[(bi + TH_DIM)][k] + C[k][(bj + TH_DIM)];
C[bi][bj] = min(C[bi][bj], sum0);
C[(bi + TH_DIM)][bj] = min(C[(bi + TH_DIM)][bj], sum1);
C[bi][(bj + TH_DIM)] = min(C[bi][(bj + TH_DIM)], sum2);
C[(bi + TH_DIM)][(bj + TH_DIM)] = min(C[(bi + TH_DIM)][(bj + TH_DIM)], sum3);
__syncthreads();
}
// Block C is the only one that could be changed
graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + bj)] = C[bi][bj];
graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + bj)] = C[(bi + TH_DIM)][bj];
graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + (bj + TH_DIM))] = C[bi][(bj + TH_DIM)];
graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + (bj + TH_DIM))] = C[(bi + TH_DIM)][(bj + TH_DIM)];
}
__global__ void floyd_warshall_block_kernel_phase3(int n, int k, int* graph, int start_x, int start_y) {
// BlockDim is one dimensional (Straight along diagonal)
// Blocks themselves are two dimensional
const unsigned int j = start_x + blockIdx.x;
const unsigned int i = start_y + blockIdx.y;
const unsigned int bi = threadIdx.y;
const unsigned int bj = threadIdx.x;
__shared__ int A[BLOCK_DIM][BLOCK_DIM];
__shared__ int B[BLOCK_DIM][BLOCK_DIM];
__shared__ int C[BLOCK_DIM][BLOCK_DIM];
C[bi][bj] = graph[(i*BLOCK_DIM + bi)*n + (j*BLOCK_DIM + bj)];
C[(bi + TH_DIM)][bj] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (j*BLOCK_DIM + bj)];
C[bi][(bj + TH_DIM)] = graph[(i*BLOCK_DIM + bi)*n + (j*BLOCK_DIM + (bj + TH_DIM))];
C[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (j*BLOCK_DIM + (bj + TH_DIM))];
A[bj][bi] = graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)];
A[bj][(bi + TH_DIM)] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)];
A[(bj + TH_DIM)][bi] = graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))];
A[(bj + TH_DIM)][(bi + TH_DIM)] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))];
B[bi][bj] = graph[(k*BLOCK_DIM + bi)*n + (j*BLOCK_DIM + bj)];
B[(bi + TH_DIM)][bj] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (j*BLOCK_DIM + bj)];
B[bi][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + bi)*n + (j*BLOCK_DIM + (bj + TH_DIM))];
B[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (j*BLOCK_DIM + (bj + TH_DIM))];
__syncthreads();
// block_calc_rev_async(C, A, B, bi, bj);
#pragma unroll
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = A[k][bi] + B[k][bj];
int sum1 = A[k][(bi + TH_DIM)] + B[k][bj];
int sum2 = A[k][bi] + B[k][(bj + TH_DIM)];
int sum3 = A[k][(bi + TH_DIM)] + B[k][(bj + TH_DIM)];
C[bi][bj] = min(C[bi][bj], sum0);
C[(bi + TH_DIM)][bj] = min(C[(bi + TH_DIM)][bj], sum1);
C[bi][(bj + TH_DIM)] = min(C[bi][(bj + TH_DIM)], sum2);
C[(bi + TH_DIM)][(bj + TH_DIM)] = min(C[(bi + TH_DIM)][(bj + TH_DIM)], sum3);
}
// __syncthreads();
graph[(i*BLOCK_DIM + bi)*n + (j*BLOCK_DIM + bj)] = C[bi][bj];
graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (j*BLOCK_DIM + bj)] = C[(bi + TH_DIM)][bj];
graph[(i*BLOCK_DIM + bi)*n + (j*BLOCK_DIM + (bj + TH_DIM))] = C[bi][(bj + TH_DIM)];
graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (j*BLOCK_DIM + (bj + TH_DIM))] = C[(bi + TH_DIM)][(bj + TH_DIM)];
}
__global__ void floyd_warshall_block_kernel_phase21(int n, int k, int* graph, int start) {
// BlockDim is one dimensional (Straight along diagonal)
// Blocks themselves are two dimensional
// Phase 2 1/2, update column
// const unsigned int i = blockIdx.x;
const unsigned int i = start + blockIdx.x;
const unsigned int bi = threadIdx.y;
const unsigned int bj = threadIdx.x;
__shared__ int A[BLOCK_DIM][BLOCK_DIM];
__shared__ int B[BLOCK_DIM][BLOCK_DIM];
__shared__ int C[BLOCK_DIM][BLOCK_DIM];
C[bi][bj] = graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)];
C[(bi + TH_DIM)][bj] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)];
C[bi][(bj + TH_DIM)] = graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))];
C[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))];
B[bi][bj] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)];
B[(bi + TH_DIM)][bj] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)];
B[bi][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))];
B[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))];
__syncthreads();
// block_calc(C, C, B, bi, bj);
#pragma unroll
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = C[bi][k] + B[k][bj];
int sum1 = C[(bi + TH_DIM)][k] + B[k][bj];
int sum2 = C[bi][k] + B[k][(bj + TH_DIM)];
int sum3 = C[(bi + TH_DIM)][k] + B[k][(bj + TH_DIM)];
C[bi][bj] = min(C[bi][bj], sum0);
C[(bi + TH_DIM)][bj] = min(C[(bi + TH_DIM)][bj], sum1);
C[bi][(bj + TH_DIM)] = min(C[bi][(bj + TH_DIM)], sum2);
C[(bi + TH_DIM)][(bj + TH_DIM)] = min(C[(bi + TH_DIM)][(bj + TH_DIM)], sum3);
__syncthreads();
}
graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)] = C[bi][bj];
graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)] = C[(bi + TH_DIM)][bj];
graph[(i*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))] = C[bi][(bj + TH_DIM)];
graph[(i*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))] = C[(bi + TH_DIM)][(bj + TH_DIM)];
}
__global__ void floyd_warshall_block_kernel_phase22(int n, int k, int* graph, int start) {
// BlockDim is one dimensional (Straight along diagonal)
// Blocks themselves are two dimensional
// Phase 2 2/2, update row
// const unsigned int i = blockIdx.x;
const unsigned int i = start + blockIdx.x;
const unsigned int bi = threadIdx.y;
const unsigned int bj = threadIdx.x;
__shared__ int A[BLOCK_DIM][BLOCK_DIM];
__shared__ int B[BLOCK_DIM][BLOCK_DIM];
__shared__ int C[BLOCK_DIM][BLOCK_DIM];
B[bi][bj] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + bj)];
B[(bi + TH_DIM)][bj] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + bj)];
B[bi][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + bi)*n + (k*BLOCK_DIM + (bj + TH_DIM))];
B[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (k*BLOCK_DIM + (bj + TH_DIM))];
C[bi][bj] = graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + bj)];
C[(bi + TH_DIM)][bj] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + bj)];
C[bi][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + (bj + TH_DIM))];
C[(bi + TH_DIM)][(bj + TH_DIM)] = graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + (bj + TH_DIM))];
__syncthreads();
// block_calc(C, B, C, bi, bj);
#pragma unroll
for (int k = 0; k < BLOCK_DIM; k++) {
int sum0 = B[bi][k] + C[k][bj];
int sum1 = B[(bi + TH_DIM)][k] + C[k][bj];
int sum2 = B[bi][k] + C[k][(bj + TH_DIM)];
int sum3 = B[(bi + TH_DIM)][k] + C[k][(bj + TH_DIM)];
C[bi][bj] = min(C[bi][bj], sum0);
C[(bi + TH_DIM)][bj] = min(C[(bi + TH_DIM)][bj], sum1);
C[bi][(bj + TH_DIM)] = min(C[bi][(bj + TH_DIM)], sum2);
C[(bi + TH_DIM)][(bj + TH_DIM)] = min(C[(bi + TH_DIM)][(bj + TH_DIM)], sum3);
__syncthreads();
}
// Block C is the only one that could be changed
graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + bj)] = C[bi][bj];
graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + bj)] = C[(bi + TH_DIM)][bj];
graph[(k*BLOCK_DIM + bi)*n + (i*BLOCK_DIM + (bj + TH_DIM))] = C[bi][(bj + TH_DIM)];
graph[(k*BLOCK_DIM + (bi + TH_DIM))*n + (i*BLOCK_DIM + (bj + TH_DIM))] = C[(bi + TH_DIM)][(bj + TH_DIM)];
}
void block_FW_cuda() {
// int round = padding_n / B;
const int blocks = padding_n / BLOCK_DIM;
dim3 block_dim(TH_DIM, TH_DIM, 1);
dim3 phase3_grid(blocks, blocks, 1);
// for (int k = 0; k < blocks; k++) {
// floyd_warshall_block_kernel_phase1<<<1, block_dim>>>(pitch_k, k, Dist_cuda);
// floyd_warshall_block_kernel_phase2<<<blocks, block_dim>>>(pitch_k, k, Dist_cuda);
// floyd_warshall_block_kernel_phase3<<<phase3_grid, block_dim>>>(pitch_k, k, Dist_cuda, 0, 0);
// }
const int row_size_pitchk = BLOCK_DIM * pitch_k;
up_part_size_in_block = (blocks+1)/2;
bottom_part_size_in_block = blocks/2;
up_part_height = BLOCK_DIM * up_part_size_in_block;
bottom_part_height = BLOCK_DIM * bottom_part_size_in_block;
dim3 phase31_grid(blocks, up_part_size_in_block, 1);
dim3 phase32_grid(blocks, bottom_part_size_in_block, 1);
// printf("Up Blocks: %d, Bottom Blocks: %d\n", up_part_size_in_block, bottom_part_size_in_block);
for (int k = 0; k < blocks; k++) {
int next_k = k + 1;
// Phase 1
cudaSetDevice(0);
floyd_warshall_block_kernel_phase1<<<1, block_dim>>>(pitch_k, k, Dist_cuda0);
cudaSetDevice(1);
floyd_warshall_block_kernel_phase1<<<1, block_dim>>>(pitch_k, k, Dist_cuda1);
// Phase 2
cudaStream_t stream;
cudaStreamCreate(&stream);
cudaSetDevice(0);
// floyd_warshall_block_kernel_phase2<<<blocks, block_dim>>>(pitch_k, k, Dist_cuda0);
floyd_warshall_block_kernel_phase21<<<up_part_size_in_block, block_dim, 0>>>(pitch_k, k, Dist_cuda0, 0);
cudaSetDevice(1);
// floyd_warshall_block_kernel_phase2<<<blocks, block_dim>>>(pitch_k, k, Dist_cuda1);
floyd_warshall_block_kernel_phase21<<<bottom_part_size_in_block, block_dim, 0>>>(pitch_k, k, Dist_cuda1, up_part_size_in_block);
// Calculate rows of phase 2
if(k < up_part_size_in_block){
cudaSetDevice(0);
floyd_warshall_block_kernel_phase22<<<blocks, block_dim, 0, stream>>>(pitch_k, k, Dist_cuda0, 0);
}else{
cudaSetDevice(1);
floyd_warshall_block_kernel_phase22<<<blocks, block_dim, 0, stream>>>(pitch_k, k, Dist_cuda1, 0);
}
cudaStreamDestroy(stream);
// Phase 3
cudaSetDevice(0);
floyd_warshall_block_kernel_phase3<<<phase31_grid, block_dim>>>(pitch_k, k, Dist_cuda0, 0, 0);
cudaSetDevice(1);
floyd_warshall_block_kernel_phase3<<<phase32_grid, block_dim>>>(pitch_k, k, Dist_cuda1, 0, up_part_size_in_block);
// Transfer data to another GPU
if(next_k < up_part_size_in_block){
// printf("Up K: %d, Next_K: %d, Blocks: %d\n", k, next_k, blocks);
cudaMemcpyPeer(&(Dist_cuda1[next_k * row_size_pitchk]), 1, &(Dist_cuda0[next_k * row_size_pitchk]), 0, SIZEOFINT * row_size_pitchk);
}else if(next_k < blocks){
// printf("Down K: %d, Next_K: %d, Blocks: %d\n", k, next_k, blocks);
cudaMemcpyPeer(&(Dist_cuda0[next_k * row_size_pitchk]), 0, &(Dist_cuda1[next_k * row_size_pitchk]), 1, SIZEOFINT * row_size_pitchk);
}
}
}
int main(int argc, char* argv[]) {
input(argv[1]);
// show_mat(getDistAddr(0, 0), n);
setup_DistCuda();
// printf("Vertice: %d, Edge: %d, B: %d, Padding: %d\n", n, m, BLOCK_DIM, padding_n);
block_FW_cuda();
back_DistCuda();
// show_mat(getDistAddr(0, 0), n);
output(argv[2]);
// show_mat(getDistAddr(0, 0), n);
return 0;
} |
6,389 | /**
* Assignment 07 Program - moving_average.cu (edited from module 6 for 7)
* Sarah Helble
* 10/16/17
*
* Calculates the average of each index and its neighbors
*
* Usage ./a.out [-v] [-n num_elements] [-b threads_per_block] [-m max_int]
*/
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <cuda.h>
#include <unistd.h>
#define DEFAULT_NUM_ELEMENTS 512
#define DEFAULT_THREADS_PER_BLOCK 256
#define DEFAULT_MAX_INT 30
/**
* Kernel function that takes a moving average of the values in
* @list and puts the results in @averages
* Uses registers to store the calculations.
*/
__global__ void average_window(unsigned int *list, float *averages, int num_elements)
{
/* Calculate the current index */
const unsigned int idx = (blockIdx.x * blockDim.x) + threadIdx.x;
unsigned int sum;
unsigned int num;
if(idx == 0) {
sum = list[idx] + list[idx+1];
num = 2;
} else if(idx + 1 == num_elements) {
sum = list[idx] + list[idx-1];
num = 2;
} else {
sum = list[idx] + list[idx-1] + list[idx+1];
num = 3;
}
averages[idx] = (float) sum / num;
}
/**
* Fuction to handle the printing of results.
* @list is the original array
* @averages is the result
*/
void print_results(unsigned int *list, float *averages, int num_elements)
{
int i = 0;
printf("\n");
for(i = 0; i < num_elements; i++) {
printf("Original value at index [%d]: %d, average: %f\n", i, list[i], averages[i]);
}
printf("\n");
}
/**
* Function that sets up everything for the kernel function
*
* @verbosity is 1 if the function should print detailed results of averages
* verbosity of 0 will only print timing data
*/
void exec_kernel_sync(int verbosity, int num_elements, int threads_per_block, int max_int)
{
/* Calculate the size of the array */
int array_size_in_bytes = (sizeof(unsigned int) * (num_elements));
int float_array_size_in_bytes = (sizeof(float) * (num_elements));
int i = 0;
unsigned int *list, *d_list;
float *averages, *d_averages;
cudaEvent_t start, stop;
float duration;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaMalloc((void **)&d_list, array_size_in_bytes);
cudaMalloc((void **)&d_averages, float_array_size_in_bytes);
cudaMallocHost((void **)&list, array_size_in_bytes);
cudaMallocHost((void **)&averages, float_array_size_in_bytes);
// Fill array with random numbers between 0 and MAX_INT
for(i = 0; i < num_elements; i++) {
list[i] = (unsigned int) rand() % max_int;
}
/* Recording from copy to copy back */
cudaEventRecord(start, 0);
/* Copy the CPU memory to the GPU memory */
cudaMemcpy(d_list, list, array_size_in_bytes, cudaMemcpyHostToDevice);
/* Designate the number of blocks and threads */
const unsigned int num_blocks = num_elements/threads_per_block;
const unsigned int num_threads = num_elements/num_blocks;
/* Kernel call */
average_window<<<num_blocks, num_threads>>>(d_list, d_averages, num_elements);
/* Copy the changed GPU memory back to the CPU */
cudaMemcpy( averages, d_averages, float_array_size_in_bytes, cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&duration, start, stop);
printf("\tList size: %d, Duration: %fmsn\n", num_elements, duration);
if(verbosity) {
print_results(list, averages, num_elements);
}
/* Free the GPU memory */
cudaFree(d_list);
cudaFree(d_averages);
/* Free the pinned CPU memory */
cudaFreeHost(list);
cudaFreeHost(averages);
}
/**
* Function that sets up everything for the kernel function
*
* @verbosity is 1 if the function should print detailed results of averages
* verbosity of 0 will only print timing data
*/
void exec_kernel_async(int verbosity, int num_elements, int threads_per_block, int max_int)
{
/* Calculate the size of the array */
int array_size_in_bytes = (sizeof(unsigned int) * (num_elements));
int float_array_size_in_bytes = (sizeof(float) * (num_elements));
int i = 0;
unsigned int *list, *d_list;
float *averages, *d_averages;
cudaEvent_t start, stop;
float duration;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaStream_t stream;
cudaStreamCreate(&stream);
cudaMalloc((void **)&d_list, array_size_in_bytes);
cudaMalloc((void **)&d_averages, float_array_size_in_bytes);
cudaMallocHost((void **)&list, array_size_in_bytes);
cudaMallocHost((void **)&averages, float_array_size_in_bytes);
// Fill array with random numbers between 0 and MAX_INT
for(i = 0; i < num_elements; i++) {
list[i] = (unsigned int) rand() % max_int;
}
/* Recording from copy to copy back */
cudaEventRecord(start, 0);
/* Copy the CPU memory to the GPU memory asynchronously */
cudaMemcpyAsync(d_list, list, array_size_in_bytes, cudaMemcpyHostToDevice, stream);
/* Designate the number of blocks and threads */
const unsigned int num_blocks = num_elements/threads_per_block;
const unsigned int num_threads = num_elements/num_blocks;
/* Kernel call */
average_window<<<num_blocks, num_threads>>>(d_list, d_averages, num_elements);
/* Copy the changed GPU memory back to the CPU */
cudaMemcpyAsync( averages, d_averages, float_array_size_in_bytes, cudaMemcpyDeviceToHost, stream);
cudaStreamSynchronize(stream);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&duration, start, stop);
printf("\tList size: %d, Duration: %fmsn\n", num_elements, duration);
if(verbosity) {
print_results(list, averages, num_elements);
}
/* Free the GPU memory */
cudaFree(d_list);
cudaFree(d_averages);
/* Free the pinned CPU memory */
cudaFreeHost(list);
cudaFreeHost(averages);
}
/**
* Entry point for execution. Checks command line arguments
* then passes execution to subordinate function
*/
int main(int argc, char *argv[])
{
int verbosity = 0;
int num_elements = DEFAULT_NUM_ELEMENTS;
int threads_per_block = DEFAULT_THREADS_PER_BLOCK;
int max_int = DEFAULT_MAX_INT;
int c;
while((c = getopt(argc, argv, "vn:b:m:")) != -1) {
switch(c) {
case 'v':
verbosity = 1;
break;
case 'n':
num_elements = atoi(optarg);
break;
case 'b':
threads_per_block = atoi(optarg);
break;
case 'm':
max_int = atoi(optarg);
break;
default:
printf("Error: unrecognized option: %c\n", c);
printf("Usage: %s [-v] [-n num_elements] [-b threads_per_block] [-m max_int]", argv[0]);
exit(-1);
}
}
printf("verbosity: %d\tnum_elements: %d\tthreads_per_block: %d\tmax_int: %d\n",
verbosity, num_elements, threads_per_block, max_int);
/* Do the average with shared memory */
printf("\nFirst Run of Averages done synchronously");
exec_kernel_sync(verbosity, num_elements, threads_per_block, max_int);
printf("-----------------------------------------------------------------\n");
printf("Second Run of Averages done synchronously");
exec_kernel_sync(verbosity, num_elements, threads_per_block, max_int);
printf("-----------------------------------------------------------------\n");
/* Do the average with shared memory */
printf("\nFirst Run of Averages done asynchronously");
exec_kernel_async(verbosity, num_elements, threads_per_block, max_int);
printf("-----------------------------------------------------------------\n");
printf("Second Run of Averages done asynchronously");
exec_kernel_async(verbosity, num_elements, threads_per_block, max_int);
printf("-----------------------------------------------------------------\n");
return EXIT_SUCCESS;
}
|
6,390 | //#define REARRANGED_DOMAIN
__global__ void _balance_deep_and_shallow(
int N,
double H0,
double alpha_balance,
int tight_slope_limiters,
int use_centroid_velocities,
double* wc, // stage_centroid_values
double* zc, // elevation_centroid_values
double* wv, // stage_vertex_values
double* zv, // elevation_vertex_values
//double* hvbar, // Retire this
double* xmomc, // xmom_centroid_values
double* ymomc, // ymom_centroid_values
double* xmomv, // xmom_vertex_values
double* ymomv // ymom_vertex_values
)
{
const int k =
threadIdx.x+threadIdx.y*blockDim.x+
(blockIdx.x+blockIdx.y*gridDim.x)*blockDim.x*blockDim.y;
int i;
double dz, hmin, alpha, h_diff, hc_k;
double epsilon = 1.0e-6; // FIXME: Temporary measure
double hv[3]; // Depths at vertices
double uc, vc; // Centroid speeds
if (k >= N )
return;
// Compute linear combination between w-limited stages and
// h-limited stages close to the bed elevation.
//for (k = 0; k < N; k++) {
// Compute maximal variation in bed elevation
// This quantitiy is
// dz = max_i abs(z_i - z_c)
// and it is independent of dimension
// In the 1d case zc = (z0+z1)/2
// In the 2d case zc = (z0+z1+z2)/3
#ifndef REARRANGED_DOMAIN
int k3 = 3 * k;
#endif
hc_k = wc[k] - zc[k]; // Centroid value at triangle k
dz = 0.0;
if (tight_slope_limiters == 0) {
// FIXME: Try with this one precomputed
for (i = 0; i < 3; i++) {
#ifndef REARRANGED_DOMAIN
dz = max(dz, fabs(zv[k3 + i] - zc[k]));
#else
dz = max(dz, fabs(zv[k + i*N] -zc[k]));
#endif
}
}
// Calculate depth at vertices (possibly negative here!)
#ifndef REARRANGED_DOMAIN
hv[0] = wv[k3] - zv[k3];
hv[1] = wv[k3 + 1] - zv[k3 + 1];
hv[2] = wv[k3 + 2] - zv[k3 + 2];
#else
hv[0] = wv[k] - zv[k];
hv[1] = wv[k + N] - zv[k + N];
hv[2] = wv[k + 2*N] - zv[k + 2*N];
#endif
// Calculate minimal depth across all three vertices
hmin = min(hv[0], min(hv[1], hv[2]));
//if (hmin < 0.0 ) {
// printf("hmin = %f\n",hmin);
//}
// Create alpha in [0,1], where alpha==0 means using the h-limited
// stage and alpha==1 means using the w-limited stage as
// computed by the gradient limiter (both 1st or 2nd order)
if (tight_slope_limiters == 0) {
// If hmin > dz/alpha_balance then alpha = 1 and the bed will have no
// effect
// If hmin < 0 then alpha = 0 reverting to constant height above bed.
// The parameter alpha_balance==2 by default
if (dz > 0.0) {
alpha = max(min(alpha_balance * hmin / dz, 1.0), 0.0);
} else {
alpha = 1.0; // Flat bed
}
//printf("Using old style limiter\n");
} else {
// Tight Slope Limiter (2007)
// Make alpha as large as possible but still ensure that
// final depth is positive and that velocities at vertices
// are controlled
if (hmin < H0) {
alpha = 1.0;
for (i = 0; i < 3; i++) {
h_diff = hc_k - hv[i];
if (h_diff <= 0) {
// Deep water triangle is further away from bed than
// shallow water (hbar < h). Any alpha will do
} else {
// Denominator is positive which means that we need some of the
// h-limited stage.
alpha = min(alpha, (hc_k - H0) / h_diff);
}
}
// Ensure alpha in [0,1]
if (alpha > 1.0) alpha = 1.0;
if (alpha < 0.0) alpha = 0.0;
} else {
// Use w-limited stage exclusively in deeper water.
alpha = 1.0;
}
}
// Let
//
// wvi be the w-limited stage (wvi = zvi + hvi)
// wvi- be the h-limited state (wvi- = zvi + hvi-)
//
//
// where i=0,1,2 denotes the vertex ids
//
// Weighted balance between w-limited and h-limited stage is
//
// wvi := (1-alpha)*(zvi+hvi-) + alpha*(zvi+hvi)
//
// It follows that the updated wvi is
// wvi := zvi + (1-alpha)*hvi- + alpha*hvi
//
// Momentum is balanced between constant and limited
if (alpha < 1) {
for (i = 0; i < 3; i++) {
#ifndef REARRANGED_DOMAIN
wv[k3 + i] = zv[k3 + i] + (1 - alpha) * hc_k + alpha * hv[i];
#else
wv[k + i*N] = zv[k + i*N] + (1 - alpha) * hc_k + alpha * hv[i];
#endif
// Update momentum at vertices
if (use_centroid_velocities == 1) {
// This is a simple, efficient and robust option
// It uses first order approximation of velocities, but retains
// the order used by stage.
// Speeds at centroids
if (hc_k > epsilon) {
uc = xmomc[k] / hc_k;
vc = ymomc[k] / hc_k;
} else {
uc = 0.0;
vc = 0.0;
}
// controlled speed
// Recompute (balanced) vertex depth
#ifndef REARRANGED_DOMAIN
hv[i] = wv[k3 + i] - zv[k3 + i];
xmomv[k3 + i] = uc * hv[i];
ymomv[k3 + i] = vc * hv[i];
#else
hv[i] = wv[k + i*N] - zv[k + i*N];
xmomv[k + i*N] = uc * hv[i];
ymomv[k + i*N] = vc * hv[i];
#endif
} else {
// Update momentum as a linear combination of
// xmomc and ymomc (shallow) and momentum
// from extrapolator xmomv and ymomv (deep).
// This assumes that values from xmomv and ymomv have
// been established e.g. by the gradient limiter.
// FIXME (Ole): I think this should be used with vertex momenta
// computed above using centroid_velocities instead of xmomc
// and ymomc as they'll be more representative first order
// values.
#ifndef REARRANGED_DOMAIN
xmomv[k3 + i] = (1 - alpha) * xmomc[k] + alpha *xmomv[k3 + i];
ymomv[k3 + i] = (1 - alpha) * ymomc[k] + alpha *ymomv[k3 + i];
#else
xmomv[k + i*N] = (1-alpha) * xmomc[k] + alpha *xmomv[k + i*N];
ymomv[k + i*N] = (1-alpha) * ymomc[k] + alpha *ymomv[k + i*N];
#endif
}
}
}
//}
//return 0;
}
|
6,391 | #include<stdio.h>
__global__ void cuda_hello()
{
printf("Hello World from GPU!\n");
}
int main()
{
printf("Hello World from CPU!\n");
cuda_hello<<<2,3>>>();
cudaDeviceReset();
return 0;
}
|
6,392 | //#include <cuda_runtime_api.h>
#include <cuda.h>
#include <stdio.h>
#include <cstdlib>
#define SIZE 1024
/*
void VectorAdd(int *a, int *b, int *c,int n) {
int i = 0;
for(; i < n; ++i) {
c[i] = a[i] + b[i];
}
}
*/
__global__ void VectorAdd(int *a, int *b, int *c,int n) {
int i = threadIdx.x;
if( i < n)
c[i] = a[i] + b[i];
}
int main()
{
int *a, *b, *c;
//a = (int *) std::malloc(SIZE * sizeof(int));
cudaMallocManaged(&a, SIZE * sizeof(int));
//b = (int *) std::malloc(SIZE * sizeof(int));
cudaMallocManaged(&b, SIZE * sizeof(int));
//c = (int *) std::malloc(SIZE * sizeof(int));
cudaMallocManaged(&c, SIZE * sizeof(int));
for(int i = 0; i < SIZE; ++i) {
a[i] = i;
b[i] = i;
c[i] = 0;
}
//VectorAdd(a, b, c, SIZE);
VectorAdd <<<1, SIZE>>> (a, b, c, SIZE); // block and thread size
cudaDeviceSynchronize();
for(int i = 0; i < 10; ++i)
printf("C[%d] = %d \n", i, c[i]);
//free(a);
cudaFree(a);
//free(b);
cudaFree(b);
//free(c);
cudaFree(c);
return 0;
} |
6,393 | #include <iostream>
#include <cmath>
#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);\
}\
}
using namespace std;
__global__ void matrixInitByX(float *matrix) {
auto x = threadIdx.x + blockIdx.x * blockDim.x;
auto y = threadIdx.y + blockIdx.y * blockDim.y;
auto N = blockDim.x * gridDim.x;
matrix[x + y * N] = (float) (x + y * N);
}
__global__ void matrixInitByY(float matrix[]) {
auto y = threadIdx.x + blockIdx.x * blockDim.x;
auto x = threadIdx.y + blockIdx.y * blockDim.y;
auto N = blockDim.x * gridDim.x;
matrix[x + y * N] = (float) (x + y * N);
}
__global__ void matrixTranspose(const float storage_d[], float storage_d_t[]) {
auto i = threadIdx.x + blockIdx.x * blockDim.x;
auto j = threadIdx.y + blockIdx.y * blockDim.y;
auto N = blockDim.x * gridDim.x;
storage_d_t[j + i * N] = storage_d[i + j * N];
}
int main() {
auto N = 1u << 8u;
auto threads = 32;
auto blocks = N / threads;
float *matrix, *t_matrix;
CUDA_CHECK_RETURN(cudaMallocManaged(&matrix, N * sizeof(float)))
CUDA_CHECK_RETURN(cudaMallocManaged(&t_matrix, N * sizeof(float)))
matrixInitByY<<<dim3(blocks, blocks), dim3(threads, threads)>>>(matrix);
cudaDeviceSynchronize();
CUDA_CHECK_RETURN(cudaGetLastError())
// matrixTranspose<<<dim3(blocks, blocks), dim3(threads, threads)>>>(matrix, t_matrix);
// cudaDeviceSynchronize();
// CUDA_CHECK_RETURN(cudaGetLastError())
// int side = static_cast<int>(sqrt(N));
// for (int i = 0; i < side; i++) {
// for (int j = 0; j < side; j++) {
// cout << setw(3) << matrix[i * side + j] << ' ';
// }
// cout << '\n';
// }
// cout << '\n';
// for (int i = 0; i < side; i++) {
// for (int j = 0; j < side; j++) {
// cout << setw(3) << t_matrix[i * side + j] << ' ';
// }
// cout << '\n';
// }
} |
6,394 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <ctime>
#include <cstdio>
#include <cstdlib>
#include <complex>
#include <cuda.h>
#include <cufft.h>
#include <math.h>
#include <vector>
#include <stdio.h>
#include <iostream>
//#include <cuPrintf.cu>
#define M_PI 3.1415926
using namespace std;
typedef complex<long double> long_double_complex;
long_double_complex Il=long_double_complex(0.0,1.0);
const long double Cl_SI_HBAR = 1; //1.054571596e-34;
//const long double Cl_SI_H=1.054571596e-34*2.0*M_PI;
const long double Cl_SI_MASS_ELECTRON = 1; //9.10938188e-31;
const long double Cl_SI_CHARGE_ELECTRON=1;//1.602176462e-19;
//const long double Cl_SI_EPSILON0 =8.854187817e-12;
//const long double Cl_SI_KB = 1.3806503e-23;
#define BLOCK_DIM 16
#define BLOCK_SIZE 256
long double potential( long double x, long double y );
long double psiInit(long double x, long double y, long double a );
__global__ void GPUSummation_parallel_partial( const double2 *iA, double2* g_odata, int N );
__global__ void GPUSummation_serial( const double2 *iA, double2* oC, int N, int it );
__global__ void SetEtElement(double* xGrid, double* yGrid, double2* expEGrid,int NX, int NY, double dt,double Et,int flag);
__device__ void printet(double Et);
__global__ void GPUMatrixElementMultEt(double2* iA, double2* iB, double2* oC, int N, double scale);
__global__ void GPUMatrixElementMult(double2* iA, double2* iB, double2* oC, int N, double scale);
int main( void )
{
int NT,NX,NY,N,in,it;
double *xGrid,*yGrid,*kxGrid,*kyGrid,*kxGridShift,*kyGridShift;
long double x0,y0,x1,y1,DX,DY,dx,dy;
long double dt,h,m,a,meff;
int err;
FILE *fd, *fd3,*fde;
cufftHandle plan;
double2 *dev_psiPosGrid, *dev_psiPosInitGrid, *dev_psiMomGrid, *dev_expTGrid, *dev_expVGrid, *dev_act ,*dev_psiCor,*dev_g_odata; // device
double2 *psiPosGrid,*psiPosInitGrid,*psiMomGrid,*expTGrid,*expVGrid,*act,*psiCor;
double2 *dev_expEGrid,*expEGrid;
double *dev_xGrid,*dev_yGrid;
size_t sizeN,sizeNT;
clock_t c0,c1;
NX=1024/2; // grid points in x-direction
NY=1024/2; // grid points in y-direction
DX=400/2; //0.4E-6; // half-width of potential in x-direction
DY=400/2; //0.4E-6; // half-width of potential in y-direction
dx=2.0*DX/(double)(NX); // grid step size in x-direction
dy=2.0*DY/(double)(NY); // grid step size in y-direction
x0=-DX; // lower left corner x-coordinate
y0=-DY; // lower left corner y-coordinate
x1=x0+2.0*DX; // upper right corner x-coordinate
y1=y0+2.0*DY; // upper right corner y-coordinate
N=NX*NY; // total number of grid points
NT=1000; // number of time-propagtion steps
dt=0.1; //100.0E-15; // time step
meff=1.0; //0.067; // effective mass
a=1.E0; //80.0E-9; // gaussian width of initial wavepacket
h=Cl_SI_HBAR; // hbar
m=meff*Cl_SI_MASS_ELECTRON; // electron mass in kg
sizeN = N * sizeof(double2);
sizeNT = NT * sizeof(double2);
psiPosGrid = (double2*)malloc(sizeN);
psiPosInitGrid = (double2*)malloc(sizeN);
psiMomGrid = (double2*)malloc(sizeN);
psiCor = (double2*)malloc(sizeN);
expTGrid = (double2*)malloc(sizeN);
expVGrid = (double2*)malloc(sizeN);
expEGrid = (double2*)malloc(sizeN); //
act = (double2*)malloc(sizeNT);
cudaMalloc((void**)&dev_psiPosGrid,sizeN);
cudaMalloc((void**)&dev_psiPosInitGrid,sizeN);
cudaMalloc((void**)&dev_psiMomGrid,sizeN);
cudaMalloc((void**)&dev_psiCor,sizeN);
cudaMalloc((void**)&dev_expTGrid,sizeN);
cudaMalloc((void**)&dev_expVGrid,sizeN);
cudaMalloc((void**)&dev_expEGrid,sizeN); //
cudaMalloc((void**)&dev_xGrid,sizeof(double)*NX); // ------------------------------
cudaMalloc((void**)&dev_yGrid,sizeof(double)*NY); //
cudaMalloc((void**)&dev_act,sizeNT);
cudaMalloc((void**)&dev_g_odata,BLOCK_SIZE);
cufftPlan2d(&plan, NX, NY, CUFFT_Z2Z);
// initialize the position space grid
// initialize the momentum space grid and shift it
xGrid = (double*) malloc(sizeof(double)*NX);
kxGrid = (double*) malloc(sizeof(double)*NX);
kxGridShift= (double*) malloc(sizeof(double)*NX);
kyGrid = (double*) malloc(sizeof(double)*NY);
kyGridShift= (double*) malloc(sizeof(double)*NY);
yGrid = (double*) malloc(sizeof(double)*NY);
for(int ix=0;ix<NX;ix++)
{
xGrid[ix]=x0+ix*dx;
kxGrid[ix]=-M_PI/dx+double(ix)*2.0*M_PI/double(NX)/dx;
}
for(int ix=0;ix<NX/2;ix++)
{
kxGridShift[ix]=kxGrid[NX/2+ix];
}
for(int ix=NX/2;ix<NX;ix++)
{
kxGridShift[ix]=kxGrid[ix-NX/2];
}
for(int iy=0;iy<NY;iy++)
{
yGrid[iy]=y0+iy*dy;
kyGrid[iy]=-M_PI/dy+double(iy)*2.0*M_PI/double(NY)/dy;
}
for(int iy=0;iy<NY/2;iy++)
{
kyGridShift[iy]=kyGrid[NY/2+iy];
}
for(int iy=NY/2;iy<NY;iy++)
{
kyGridShift[iy]=kyGrid[iy-NY/2];
}
double Norm=0.0;
for(int iy=0;iy<NY;iy++)
{
for(int ix=0;ix<NX;ix++)
{
int in=ix*NY+iy;
// do all intermediate calculations in long double to avoid any out of range errors, which DO happen if one uses double for the exp()
long double V=potential(xGrid[ix],yGrid[iy]);
long_double_complex expV=exp(Il*(long double)(-(V)*dt));
long_double_complex expT=exp(Il*(long double)(-(kxGridShift[ix]*kxGridShift[ix]/(2.0l*m)+kyGridShift[iy]*kyGridShift[iy]/(2.0l*m))*dt));
long_double_complex psi=psiInit(xGrid[ix],yGrid[iy],a);
// demote long double results to double
expVGrid[in].x=expV.real();
expVGrid[in].y=expV.imag();
expTGrid[in].x=expT.real();
expTGrid[in].y=expT.imag();
psiPosGrid[in].x=(double)psi.real();
psiPosGrid[in].y=(double)psi.imag();
psiPosInitGrid[in].x=(double)psi.real();
psiPosInitGrid[in].y=(double)psi.imag();
//Norm += psiPosGrid[in].x * psiPosGrid[in].x + psiPosGrid[in].y * psiPosGrid[in].y;
Norm+=psi.real()*psi.real()+psi.imag()*psi.imag();
}
}
Norm=sqrt(Norm*dx*dy);
std::cout<<"Norm is "<<Norm<<endl;
//Normalized
for(int iy=0;iy<NY;iy++)
{
for(int ix=0;ix<NX;ix++)
{
psiPosGrid[in].x/=Norm;
psiPosGrid[in].y/=Norm;
}
}
Norm=0.0;
for(int iy=0;iy<NY;iy++)
{
for(int ix=0;ix<NX;ix++)
{
int in=ix*NY+iy;
Norm += psiPosGrid[in].x * psiPosGrid[in].x + psiPosGrid[in].y * psiPosGrid[in].y;
}
}
Norm=sqrt(Norm*dx*dy);
cout<<"Norm is "<<Norm<<" dx:"<<dx<<" dy:"<<dy<<endl;
long double E0=0.1;
long double omega = 0.057;
long double times,t0=0,phi=0.0;
times=t0;
long double Et =E0*sin(omega*times-phi);
for(int iy=0;iy<NY;iy++)
{
for(int ix=0;ix<NX;ix++)
{
int in=ix*NY+iy;
long double Ve= -Et*xGrid[ix];
long_double_complex expE=exp(Il*(long double)(-(Ve)*dt));
expEGrid[in].x = expE.real();
expEGrid[in].y = expE.imag();
}
}
for(int it=0;it<NT;it++) // initialize act[].x/.y
{
act[it].x=0.0;
act[it].y=0.0;
}
cudaMemcpy(dev_psiPosGrid,psiPosGrid,sizeN,cudaMemcpyHostToDevice);
cudaMemcpy(dev_psiMomGrid,psiMomGrid,sizeN,cudaMemcpyHostToDevice);
cudaMemcpy(dev_psiPosInitGrid,psiPosInitGrid,sizeN,cudaMemcpyHostToDevice);
cudaMemcpy(dev_expTGrid,expTGrid,sizeN,cudaMemcpyHostToDevice);
cudaMemcpy(dev_expVGrid,expVGrid,sizeN,cudaMemcpyHostToDevice);
cudaMemcpy(dev_expEGrid,expEGrid,sizeN,cudaMemcpyHostToDevice);
cudaMemcpy(dev_xGrid,xGrid,sizeof(double)*NX,cudaMemcpyHostToDevice);
cudaMemcpy(dev_yGrid,yGrid,sizeof(double)*NY,cudaMemcpyHostToDevice);
cudaMemcpy(dev_act,act,sizeNT,cudaMemcpyHostToDevice);
fd=fopen("result_ini.dat","w");
for(in=0;in<N;in+=100)
{
fprintf(fd,"GPU psiPosInitGrid[%i]=(%e,%e)\n",in,(double)psiPosInitGrid[in].x,(double)psiPosInitGrid[in].y);
}
fclose(fd);
fprintf(stderr,"Initializing finished. Starting timer ...\n");
c0=clock();
fde=fopen("efield.dat","w");
fd=fopen("result_outd.dat","w");
for(it=0;it<=NT*10;it++) //NT<->5 ------------------------------PROPAGATION------------------------------
{
times=dt*it;
Et=E0*sin(omega*times+phi);//*sin(M_PI*times/100)*sin(M_PI*times/100);
fprintf(fde,"%e %e\n",times,Et);
/* for(int iy=0;iy<NY;iy++)
{
for(int ix=0;ix<NX;ix++)
{
int in=ix*NY+iy;
long double Ve= -Et*xGrid[ix];
long_double_complex expE=exp(Il*(long double)(-(Ve)*dt));
expEGrid[in].x = expE.real();
expEGrid[in].y = expE.imag();
}
}
err=cudaMemcpy(dev_expEGrid,expEGrid,sizeN,cudaMemcpyHostToDevice);
printf("%d %d ",it,err);*/
SetEtElement<<<N/256,256>>>(dev_xGrid, dev_yGrid, dev_expEGrid, NX, NY, dt, Et, 0);
cudaThreadSynchronize();
GPUMatrixElementMult<<<N/256,256>>>(dev_expEGrid,dev_psiPosGrid,dev_psiPosGrid,N,1.0);
cudaThreadSynchronize();
GPUMatrixElementMult<<<N/256,256>>>(dev_expVGrid,dev_psiPosGrid,dev_psiPosGrid,N,1.0);
cudaThreadSynchronize();
cufftExecZ2Z(plan, dev_psiPosGrid, dev_psiMomGrid, CUFFT_INVERSE);
cudaThreadSynchronize();
GPUMatrixElementMult<<<N/256,256>>>(dev_expTGrid,dev_psiMomGrid,dev_psiMomGrid,N,1.0/(double)N);
cudaThreadSynchronize();
cufftExecZ2Z(plan, dev_psiMomGrid, dev_psiPosGrid, CUFFT_FORWARD);
cudaThreadSynchronize();
#if 1
GPUMatrixElementMult<<<N/256,256>>>(dev_psiPosGrid,dev_psiPosInitGrid,dev_psiCor,N,1.0);
cudaThreadSynchronize();
GPUSummation_parallel_partial<<<BLOCK_SIZE,BLOCK_SIZE>>>(dev_psiCor,dev_g_odata,(unsigned int)N);
cudaThreadSynchronize();
GPUSummation_serial<<<1,1>>>(dev_g_odata,dev_act,BLOCK_SIZE,it);
cudaThreadSynchronize();
#endif
#if 1
if(it%100==0){
printf("%d %f\n",it,times);
cudaMemcpy(psiPosGrid,dev_psiPosGrid,sizeN,cudaMemcpyDeviceToHost);
for(int idy=0;idy<NY;idy+=2)
{
for(int idx=0;idx<NX;idx+=2){
in=idy*NX+idx;
fprintf(fd,"%f ",log10(psiPosGrid[in].x*psiPosGrid[in].x+psiPosGrid[in].y*psiPosGrid[in].y));
}
}
}
#endif
}
fclose(fd);
c1=clock();
fclose(fde);
fprintf(stderr,"Propagation took %.2f s\n",(double)(c1-c0)/(double)CLOCKS_PER_SEC);
cudaMemcpy(act,dev_act,sizeNT,cudaMemcpyDeviceToHost);
cudaMemcpy(psiPosGrid,dev_psiPosGrid,sizeN,cudaMemcpyDeviceToHost);
fd=fopen("result_gpu_act_dp.dat","w");
// write recorded autocorrelation function at each time-step
for(it=0;it<NT;it++)
{
fprintf(fd,"%e %e %e\n",(double)(it*dt),(double)(act[it].x*dx*dy),(double)(act[it].y*dx*dy));
}
fclose(fd);
fd=fopen("result_out.dat","w");
fd3=fopen("result_outxy.dat","w");
/* for(in=0;in<N;in+=1)
{
int idx=in-NX*((in)/NX);
int idy=(in)/NX;
//fprintf(fd,"%e %e %e %e\n",(double)xGrid[idx],(double)yGrid[idy],(double)psiPosGrid[in].x,(double)psiPosGrid[in].y);
//fprintf(fd,"%e %e\n",(double)psiPosGrid[in].x,(double)psiPosGrid[in].y);
fprintf(fd,"%e ",psiPosGrid[in].x*psiPosGrid[in].x+psiPosGrid[in].y*psiPosGrid[in].y);
}*/
for(int idy=0;idy<NY;idy+=2)
{
for(int idx=0;idx<NX;idx+=2){
in=idy*NX+idx;
fprintf(fd,"%f ",(psiPosGrid[in].x*psiPosGrid[in].x+psiPosGrid[in].y*psiPosGrid[in].y));
fprintf(fd3,"%f %f %f\n ",(double)xGrid[idx],(double)yGrid[idy],log10(psiPosGrid[in].x*psiPosGrid[in].x+psiPosGrid[in].y*psiPosGrid[in].y));
}
//fprintf(fd,"\n");
//fprintf(fd3,"\n");
}
fclose(fd); fclose(fd3);
// all memory frees are missing ...
return 0;
}
__global__ void GPUMatrixElementMult(double2* iA, double2* iB, double2* oC, int N, double scale)
{
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
double2 z;
if (idx<N)
{
z.x = iA[idx].x * iB[idx].x - iA[idx].y * iB[idx].y;
z.y = iA[idx].x * iB[idx].y + iA[idx].y * iB[idx].x;
oC[idx].x = z.x *scale;
oC[idx].y = z.y *scale;
}
}
__global__ void GPUMatrixElementMultEt(double2* iA, double2* iB, double2* oC, int N, double scale)
{
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
double2 z;
if (idx<N)
{
z.x = iA[idx].x * iB[idx].x - iA[idx].y * iB[idx].y;
z.y = iA[idx].x * iB[idx].y + iA[idx].y * iB[idx].x;
oC[idx].x = z.x *scale;
oC[idx].y = z.y *scale;
}
}
__device__ void printet(double Et)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
printf("Et= %f %d\n",Et,idx);
}
__global__ void SetEtElement(double* xGrid, double* yGrid, double2* expEGrid,int NX, int NY, double dt,double Et,int flag)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
//double2 z;
int N=NX*NY;
//printet(Et);
// printf("Et= %f %d %d %d %d\n",Et,idx,blockIdx.x,NX,NY);
//if(idx == 0) {printf("%f, DONE!\n",Et);}
if (idx<N)
{ //printf("%d,%d,%d\n", idx,NX,NY);
//int idx=ix*NY+iy;
int ix=idx/NY; //flag=0 x-direction; 1 y-direction;2 cicular (not implemented yet)
double Ve= -Et*xGrid[ix];
//double2 expE;
//expE.x=cos( (Ve*dt)); //exp(Il*(-(Ve)*dt));
//expE.y=- sin(Ve*dt);
expEGrid[idx].x = cos(Ve*dt);// expE.x;
expEGrid[idx].y = - sin(Ve*dt);// expE.y;
}
}
/* GPU Correlation requires to adapt the "reduction" example from the SDK,
since we have to avoid memory synchronization problems when we write the results
from all threads in a single double */
/* slow serial kernel */
__global__ void GPUSummation_serial( const double2 *iA, double2* oC, int N, int it )
{
if( threadIdx.x == 0 )
{
oC[it].x = 0.0;
oC[it].y = 0.0;
for(int idx = 0; idx < N; idx++)
{
oC[it].x += iA[idx].x;
oC[it].y += iA[idx].y;
}
}
}
__global__ void GPUSummation_parallel_partial( const double2 *iA, double2* g_odata, int N )
{
__shared__ double2 sdata[BLOCK_SIZE];
unsigned int tid = threadIdx.x;
unsigned int idx = blockIdx.x * BLOCK_SIZE + threadIdx.x;
unsigned int gridSize = BLOCK_SIZE * gridDim.x;
double2 accum;
accum.x = iA[idx].x;
accum.y = iA[idx].y;
idx+=gridSize;
while (idx < N)
{
accum.x += iA[idx].x;
accum.y += iA[idx].y;
idx += gridSize;
}
sdata[tid].x=accum.x;
sdata[tid].y=accum.y;
__syncthreads();
if (BLOCK_SIZE >= 512) { if (tid < 256)
{ sdata[tid].x += sdata[tid + 256].x; sdata[tid].y += sdata[tid + 256].y; } __syncthreads(); }
if (BLOCK_SIZE >= 256) { if (tid < 128)
{ sdata[tid].x += sdata[tid + 128].x; sdata[tid].y += sdata[tid + 128].y; } __syncthreads(); }
if (BLOCK_SIZE >= 128) { if (tid < 64)
{ sdata[tid].x += sdata[tid + 64].x; sdata[tid].y += sdata[tid + 64].y; } __syncthreads(); }
if (tid < 32)
{
if (BLOCK_SIZE >= 64) { sdata[tid].x += sdata[tid + 32].x; sdata[tid].y += sdata[tid + 32].y; __syncthreads(); }
if (BLOCK_SIZE >= 32) { sdata[tid].x += sdata[tid + 16].x; sdata[tid].y += sdata[tid + 16].y; __syncthreads(); }
if (BLOCK_SIZE >= 16) { sdata[tid].x += sdata[tid + 8].x; sdata[tid].y += sdata[tid + 8].y; __syncthreads(); }
if (BLOCK_SIZE >= 8) { sdata[tid].x += sdata[tid + 4].x; sdata[tid].y += sdata[tid + 4].y; __syncthreads(); }
if (BLOCK_SIZE >= 4) { sdata[tid].x += sdata[tid + 2].x; sdata[tid].y += sdata[tid + 2].y; __syncthreads(); }
if (BLOCK_SIZE >= 2) { sdata[tid].x += sdata[tid + 1].x; sdata[tid].y += sdata[tid + 1].y; __syncthreads(); }
}
// write result for this block to global mem
if (tid == 0)
{
g_odata[blockIdx.x].x = sdata[0].x;
g_odata[blockIdx.x].y = sdata[0].y;
}
}
long double psiInit(long double x, long double y, long double a )
{
return exp(-(x*x+y*y)/(2.0*a*a))/(a*sqrt(M_PI));
}
long double potential( long double x, long double y )
{
return -1.0/(x*x+y*y+1);
//return 10000.0*Cl_SI_CHARGE_ELECTRON*x;
}
|
6,395 | #include "includes.h"
__global__ void kernel_push_stochastic1( int *g_push_reser, int *s_push_reser, int *g_count_blocks, bool *g_finish, int *g_block_num, int width1)
{
int x = __umul24( blockIdx.x, blockDim.x ) + threadIdx.x ;
int y = __umul24( blockIdx.y , blockDim.y ) + threadIdx.y ;
int thid = __umul24( y , width1 ) + x ;
s_push_reser[thid] = g_push_reser[thid] ;
if( thid == 0 )
{
if((*g_count_blocks) == 0 )
(*g_finish) = false ;
}
} |
6,396 | #include <stdio.h>
#include <string.h>
#include <math.h>
#include <cuda_runtime.h>
#include <sys/time.h>
#include <time.h>
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 256
#endif
#ifndef N_ELEMS
#define N_ELEMS 753411
#endif
// Src: Lab1-CudaIntro. Get time difference
int timeval_subtract(
struct timeval *result,
struct timeval *t2,
struct timeval *t1)
{
unsigned int resolution = 1000000;
long int diff = (t2->tv_usec + resolution * t2->tv_sec) -
(t1->tv_usec + resolution * t2->tv_sec);
result->tv_sec = diff / resolution; result->tv_usec = diff % resolution;
return (diff<0);
}
__global__ void kernel(float *d_in, float *d_out, int N){
const unsigned int lid = threadIdx.x; // Local id inside a block
const unsigned int gid = blockIdx.x*blockDim.x + lid; // global id
if (gid < N){
d_out[gid] = powf(d_in[gid]/(d_in[gid]-2.3), 3);
}
}
void gpu_run(float* inp, float* out, int N)
{
// Most of this code is stolened from the lab1 slides
// Time tracking vars
unsigned long int elapsed;
struct timeval t_start, t_end, t_diff;
// Block distr vars
unsigned int block_size = BLOCK_SIZE;
unsigned int num_blocks = ((N + (block_size - 1)) / block_size);
// Memory assignment
unsigned int mem_size = N*sizeof(float);
float* d_in;
float* d_out;
cudaMalloc((void**)&d_in, mem_size);
cudaMalloc((void**)&d_out, mem_size);
// Copy host mem to device
cudaMemcpy(d_in, inp, mem_size, cudaMemcpyHostToDevice);
// Exec kernel(with timetrack)
gettimeofday(&t_start, NULL);
kernel<<<num_blocks, block_size>>>(d_in, d_out, N);
gettimeofday(&t_end, NULL);
// Copy result from device to host
cudaMemcpy(out, d_out, mem_size, cudaMemcpyDeviceToHost);
cudaFree(d_in); cudaFree(d_out);
// Calculate and print time
timeval_subtract(&t_diff, &t_end, &t_start);
elapsed = (t_diff.tv_sec*1e6+t_diff.tv_usec);
printf("GPU Run took %d microseconds (%.2fms)\n", elapsed, elapsed / 1000.0);
}
void seq_run(float* inp, float* out, int N){
unsigned long int elapsed;
struct timeval t_start, t_end, t_diff;
gettimeofday(&t_start, NULL);
for(unsigned int i = 0; i < N; ++i){
out[i] = powf(inp[i]/(inp[i]-2.3), 3);
}
gettimeofday(&t_end, NULL);
timeval_subtract(&t_diff, &t_end, &t_start);
elapsed = (t_diff.tv_sec*1e6+t_diff.tv_usec);
printf("CPU Run took %d microseconds (%.2fms)\n", elapsed, elapsed / 1000.0);
}
int main( int argc, char** argv){
unsigned int N = N_ELEMS;
unsigned int mem_size = N*sizeof(float);
// Init memory arrays
float* in = (float*) malloc(mem_size);
float* gpu_out = (float*) malloc(mem_size);
float* seq_out = (float*) malloc(mem_size);
// And init the input array
for (unsigned int i=0; i<N; ++i) in[i] = (float)i;
// Run the code on the CPU
seq_run(in, seq_out, N);
// Run the code on the GPU
gpu_run(in, gpu_out, N);
// Now validate results:
int passed = 0;
int invalid = 0;
for (int i = 0; i < N; ++i) {
if (fabs(seq_out[i] - gpu_out[i]) < 0.0001)
passed++;
else invalid++;
}
printf("Passed: %06d, Invalid: %06d\n", passed, invalid);
//DEBUG: Print the first 10 and last 10 values to 10p of precision
// for(int i = 0; i < 10; i++) printf("%6d:\t%.10f\t%.10f\n", i, seq_out[i], gpu_out[i]);
// for(int i = 0; i < 10; i++) printf("%6d:\t%.10f\t%.10f\n", N-i, seq_out[N-i], gpu_out[N-i]);
// Free outpus databases
free(in); free(gpu_out); free(seq_out);
return 0;
}
|
6,397 | #include "includes.h"
__global__ void fm_order2_dgrad_kernel(const float* in, const float* top_grad, float* dgrad, int batch_size, int slot_num, int emb_vec_size) {
int tid = threadIdx.x;
int bid = blockIdx.x;
if (tid < emb_vec_size && bid < batch_size) {
float emb_sum = 0.0f;
int offset = bid * slot_num * emb_vec_size + tid;
for (int i = 0; i < slot_num; i++) {
int index = offset + i * emb_vec_size;
emb_sum += in[index];
}
float tgrad = top_grad[bid * emb_vec_size + tid];
for (int i = 0; i < slot_num; i++) {
int index = offset + i * emb_vec_size;
dgrad[index] = tgrad * (emb_sum - in[index]);
}
}
} |
6,398 |
extern "C"
__global__ void reverseVec(int n, float *a, float *b)
{
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i<n)
{
b[n-1-i] = a[i];
}
}
|
6,399 | #include <iostream>
#include <cstdlib>
int main() {
int* vetor; //Declares a integer pointer
int* vetor_d; //Declares a integer pointer
int N = 10; //Declares and initializes N to 10
int buffer_size = sizeof(int)*N; //Number of bytes in our array
vetor = (int*) malloc (buffer_size); //Allocates the host vector
cudaMalloc((void**) &vetor_d, buffer_size); //Allocates the device vector
for (int i=0; i<N; i++) {
vetor[i] = N - i;
}
//Copies from the host to the device
cudaMemcpy( vetor_d, vetor, buffer_size, cudaMemcpyHostToDevice );
//Sets the values of the device vector to zero
cudaMemset( vetor_d, 0, buffer_size);
//Copies the vector back from the device to the host
cudaMemcpy( vetor, vetor_d, buffer_size, cudaMemcpyDeviceToHost );
for (int i=0; i<N; i++) {
std::cout << "vetor em " << i << ": " << vetor[i] << std::endl;
}
return 0;
}
|
6,400 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <iostream>
#include <time.h>
#include <cstdlib>
using namespace std;
#define N 2048
#define Iteration 100
__global__ void matrixTranspose(int *a, int *b)
{
int i = blockIdx.y * blockDim.y + threadIdx.y; // row
int j = blockIdx.x * blockDim.x + threadIdx.x; // col
int index_in = i*N+j; // (i,j) from matrix A
int index_out = j*N+i; // transposed index
b[index_out] = a[index_in];
}
int main(){
int *a, *b;
int *d_a, *d_b;
int size = N*N*sizeof(int);
clock_t start, end;
// Alloc space for device copies of a, b
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
// Alloc space for host copies of a, b, and setup input values
a = (int*)malloc(size);
b = (int*)malloc(size);
for(int i = 0; i < N; i++)
for(int j = 0; j < N; j++)
a[i*N+j] = j;
dim3 grid(64, 64);
dim3 block(32, 32);
start = clock();
// Copy inputs to device
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
//cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
//Launch kernel
for(int i = 0; i < Iteration; i++)
matrixTranspose<<<grid, block>>>(d_a, d_b);
// Copy result back to host
cudaMemcpy(b, d_b, size, cudaMemcpyDeviceToHost);
end = clock();
/* for(int i = 0; i < 10; i++){
for(int j = 0; j < 10; j++)
cout<<b[i*N+j]<<" ";
cout<<endl;
}*/
//Cleanup
free(a); free(b);
cudaFree(d_a); cudaFree(d_b);
cout << "Totle Time : " <<(double)(end - start)<< "ms" << endl;
return 0;
}
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