serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
22,301 | #include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <curand_kernel.h>
#include <math_constants.h>
extern "C"
{
__global__ void
rtruncnorm_kernel(
float *x, // Vector to contain returned samples
int n, // Number of samples to return
float *mu, // Vector of mu's
float *sigma, // Vector of sigma's
float *a, // Vector of lower-truncation values
float *b, // Vector of upper-truncation values
int rng_a, // RNG seed constant
int rng_b, // RNG seed constant
int rng_c, // RNG seed constant
int maxtries)
{
// Usual block/thread indexing...
int myblock = blockIdx.x + blockIdx.y * gridDim.x;
int blocksize = blockDim.x * blockDim.y * blockDim.z;
int subthread = threadIdx.z*(blockDim.x * blockDim.y) + threadIdx.y*blockDim.x + threadIdx.x;
int idx = myblock * blocksize + subthread;
// Initialize RNG
curandState rng; // random number generator
curand_init(rng_a+idx*rng_b, rng_c, 0, &rng); // rng_c sequence number (related to iteration number in MCMC)
// initialize different seed for different index
// Sample the truncated normal:
// mu for this index is mu[idx]
// sigma for this index is sigma[idx]
// a for this index is a[idx]
// b for this index is b[idx]
// X_i ~ Truncated-Normal(mu_i, sigma_i; [a_i, b_i])
// Sample N(mu, sigma^2):
if (idx < n) {
int accept = 0;
int numtries = 0;
while(!accept && numtries < maxtries)
{
float temp = mu[idx] +sigma[idx]*curand_normal(&rng);
numtries = numtries+1;
if (temp>=a[idx] && temp<=b[idx]) {
accept = 1;
x[idx] = temp;
}
}
if (numtries >= maxtries) {
printf("Reach Maximum Number of Tries. \n");
}
}
return;
}
} // END extern "C" |
22,302 | #include "includes.h"
#define HISTOGRAM_LENGTH 256
__global__ void histo_kernel(unsigned char * buffer, unsigned int * histo, long size)
{
// compute histogram with a private version in each block
__shared__ unsigned int histo_private[HISTOGRAM_LENGTH];
int bx = blockIdx.x;
int tx = threadIdx.x;
// index of current pixel
int index = tx+bx*blockDim.x;
// set initial values of histogram to zero
if (tx < HISTOGRAM_LENGTH) histo_private[tx] = 0;
__syncthreads();
int stride = blockDim.x*gridDim.x;
//iterate to add values
while (index < stride)
{
atomicAdd(&(histo_private[buffer[index]]), 1);
index += stride;
}
__syncthreads();
//copy private histogram to device histogram
if(tx<256)
{
atomicAdd(&(histo[tx]), histo_private[tx]);
}
} |
22,303 | #include <stdio.h>
#define N 2048 * 2048 // Number of elements in each vector
__global__ void saxpy(float scalar, float * x, float * y)
{
// Determine our unique global thread ID, so we know which element to process
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if ( tid < N ) // Make sure we don't do more work than we have data!
y[tid] = scalar * x[tid] + y[tid];
}
int main()
{
float *x, *y;
// CUDA types for error statuses
cudaError_t ierrSync, ierrAsync;
int size = N * sizeof (float); // The total number of bytes per vector
// Allocate memory
cudaMallocManaged(&x, size);
cudaMallocManaged(&y, size);
// Initialize memory
for( int i = 0; i < N; ++i )
{
x[i] = 1.0f;
y[i] = 2.0f;
}
int threads_per_block = 256;
int number_of_blocks = (N / threads_per_block) + 1;
saxpy <<< number_of_blocks, threads_per_block >>> ( 2.0f, x, y );
/* TODO: get last error to see if kernel launch failed
* check is == cudaSuccess
*/
/* TODO: get error AFTER kernel finished (first sync CPU and device)
* check is == cudaSuccess
*/
// Print out our Max Error
float maxError = 0;
for( int i = 0; i < N; ++i )
if (abs(4-y[i]) > maxError) { maxError = abs(4-y[i]); }
printf("Max Error: %.5f", maxError);
// Free all our allocated memory
cudaFree( x ); cudaFree( y );
}
|
22,304 | #include <stdio.h>
#include <stdlib.h>
#include <float.h>
#include <math.h>
#include <cuda.h>
#include <curand.h>
// Type for points
typedef struct{
float x; // x coordinate
float y; // y coordinate
int cluster; // cluster this point belongs to
} Point;
// Type for centroids
typedef struct{
float x; // x coordinate
float y; // y coordinate
int nPoints; // number of points in this cluster
} Centroid;
// Global variables
int nPoints; // Number of points
int nClusters; // Number of clusters/centroids
Point* points; // Array containig all points
Centroid* centroids; // Array containing all centroids
// Reading command line arguments
void parse_args(int argc, char** argv){
if(argc != 3){
printf("Useage: kmeans nClusters nPoints\n");
exit(-1);
}
nClusters = atoi(argv[1]);
nPoints = atoi(argv[2]);
}
// Create random point
Point create_random_point(){
Point p;
p.x = ((float)rand() / (float)RAND_MAX) * 1000.0 - 500.0;
p.y = ((float)rand() / (float)RAND_MAX) * 1000.0 - 500.0;
p.cluster = rand() % nClusters;
return p;
}
// Create random centroid
Centroid create_random_centroid(){
Centroid p;
p.x = ((float)rand() / (float)RAND_MAX) * 1000.0 - 500.0;
p.y = ((float)rand() / (float)RAND_MAX) * 1000.0 - 500.0;
p.nPoints = 0;
return p;
}
// Initialize random data
// Points will be uniformly distributed
void init_data(){
points = (Point*)malloc(sizeof(Point)*nPoints);
for(int i = 0; i < nPoints; i++){
points[i] = create_random_point();
if(i < nClusters){
points[i].cluster = i;
}
}
centroids = (Centroid*)malloc(sizeof(Centroid)*nClusters);
for(int i = 0; i < nClusters; i++){
centroids[i] = create_random_centroid();
}
}
// Initialize random data
// Points will be placed in circular clusters
void init_clustered_data(){
float diameter = 500.0/sqrt(nClusters);
centroids = (Centroid*)malloc(sizeof(Centroid)*nClusters);
for(int i = 0; i < nClusters; i++){
centroids[i] = create_random_centroid();
}
points = (Point*)malloc(sizeof(Point)*nPoints);
for(int i = 0; i < nPoints; i++){
points[i] = create_random_point();
if(i < nClusters){
points[i].cluster = i;
}
}
for(int i = 0; i < nPoints; i++){
int c = points[i].cluster;
points[i].x = centroids[c].x + ((float)rand() / (float)RAND_MAX) * diameter - (diameter/2);
points[i].y = centroids[c].y + ((float)rand() / (float)RAND_MAX) * diameter - (diameter/2);
points[i].cluster = rand() % nClusters;
}
for(int i = 0; i < nClusters; i++){
centroids[i] = create_random_centroid();
}
}
// Print all points and centroids to standard output
void print_data(){
for(int i = 0; i < nPoints; i++){
printf("%f\t%f\t%d\t\n", points[i].x, points[i].y, points[i].cluster);
}
printf("\n\n");
for(int i = 0; i < nClusters; i++){
printf("%f\t%f\t%d\t\n", centroids[i].x, centroids[i].y, i);
}
}
// Print all points and centroids to a file
// File name will be based on input argument
// Can be used to print result after each iteration
void print_data_to_file(int i){
char filename[15];
sprintf(filename, "%04d.dat", i);
FILE* f = fopen(filename, "w+");
for(int i = 0; i < nPoints; i++){
fprintf(f, "%f\t%f\t%d\t\n", points[i].x, points[i].y, points[i].cluster);
}
fprintf(f,"\n\n");
for(int i = 0; i < nClusters; i++){
fprintf(f,"%f\t%f\t%d\t\n", centroids[i].x, centroids[i].y, i);
}
fclose(f);
}
// Computing distance between point and centroid
float distance(Point a, Centroid b){
float dx = a.x - b.x;
float dy = a.y - b.y;
return sqrt(dx*dx + dy*dy);
}
//Kernel to compute new centroid positions. One thread is created for each cluster.
__global__ void new_centroids(Point* points, Centroid* centroids, int nPoints, int nClusters, float rand_x, float rand_y) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
//Check that the amount of threads don't exceed the amount of points
if (i < nClusters) {
//Reset centroid position
centroids[i].x = 0.0;
centroids[i].y = 0.0;
centroids[i].nPoints= 0;
for(int j = 0; j < nPoints; j++){
if (i == points[j].cluster) {
centroids[i].x += points[j].x;
centroids[i].y += points[j].y;
centroids[i].nPoints++;
}
}
//If a cluster lost all of its points, give it a new position
if(centroids[i].nPoints == 0){
Centroid p;
p.x = rand_x;
p.y = rand_y;
p.nPoints = 0;
centroids[i] = p;
}
else{
//Divide the coordinates of each cluster by its amount of points.
centroids[i].x /= centroids[i].nPoints;
centroids[i].y /= centroids[i].nPoints;
}
}
}
//Kernel to reassign points to the correct clusters. One thread is created for each point.
__global__ void reassign_points(Point* points, Centroid* centroids, int nPoints, int nClusters, int* updated) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
//Check that the amount of threads don't exceed the amount of points
if (i < nPoints) {
float bestDistance = DBL_MAX;
int bestCluster = -1;
//For each cluster j...
for(int j = 0; j < nClusters; j++){
//Check distance between point i and cluster j
float dx = points[i].x - centroids[j].x;
float dy = points[i].y - centroids[j].y;
float d = sqrt(dx*dx + dy*dy);
//If distance to cluster j is smaller than distance measured so far to all other clusters
if(d < bestDistance){
bestDistance = d;
bestCluster = j;
}
}
// If one point got reassigned to a new cluster, we have to do another iteration
if(bestCluster != points[i].cluster){
points[i].cluster = bestCluster;
*updated = 1;
}
points[i].cluster = bestCluster;
}
}
int main(int argc, char** argv){
parse_args(argc, argv);
// Create random data, either function can be used.
//init_clustered_data();
init_data();
//Allocate memory on device for points
Point* points_device;
cudaMalloc((Point**)&points_device, sizeof(Point)*nPoints);
//Allocate memory on device for centroids
Centroid* centroids_device;
cudaMalloc((Centroid**)¢roids_device, sizeof(Centroid)*nClusters);
//Allocate memory on device for "updated" variable
int* updated_device;
cudaMalloc((int**)&updated_device, sizeof(int));
//Copy points and centroids to device
cudaMemcpy(points_device, points, sizeof(Point)*nPoints, cudaMemcpyHostToDevice);
cudaMemcpy(centroids_device, centroids, sizeof(Centroid)*nClusters, cudaMemcpyHostToDevice);
// Iterate until no points are updated
int updated = 1;
while(updated){
updated = 0;
float rand_x = ((float)rand() / (float)RAND_MAX) * 1000.0 - 500.0;
float rand_y = ((float)rand() / (float)RAND_MAX) * 1000.0 - 500.0;
int block_size = 256;
//Calculate new centroid positions
int grid_size_step_one = (nClusters/block_size) + 1; //This addition of 1 means I don't assume input as powers of 2
new_centroids<<<grid_size_step_one, block_size>>>(points_device, centroids_device, nPoints, nClusters, rand_x, rand_y);
//Reassign points to closest centroid
int grid_size_step_two = (nPoints/block_size) + 1; //This addition of 1 means I don't assume input as powers of 2.
//Updated variable is copied to device before each iteration of reassign points...
cudaMemcpy(updated_device, &updated, sizeof(int), cudaMemcpyHostToDevice);
reassign_points<<<grid_size_step_two, block_size>>>(points_device, centroids_device, nPoints, nClusters, updated_device);
//...And updated is copied back to host after each iteration
cudaMemcpy(&updated, updated_device, sizeof(int), cudaMemcpyDeviceToHost);
}
//Copy points and centroids back to host
cudaMemcpy(points, points_device, sizeof(Point)*nPoints, cudaMemcpyDeviceToHost);
cudaMemcpy(centroids, centroids_device, sizeof(Centroid)*nClusters, cudaMemcpyDeviceToHost);
// print_data();
}
|
22,305 | /*
Finds: TLB misses
For the Tesla V100-SXM2-16GB's TLB
Soure code based on paper https://arxiv.org/pdf/1509.02308.pdf
*/
#include <stdio.h>
#include <stdint.h>
#include "cuda_runtime.h"
#define LEN 256
__global__ void global_latency(unsigned int* my_array, int N, int iterations, unsigned int* duration, unsigned int* index) {
// data access latencies array
__shared__ unsigned int s_tvalue[LEN];
// accessed data indices array
__shared__ unsigned int s_index[LEN];
// initialize arrays
for (int k = 0; k < LEN; k++) {
s_index[k] = 0;
s_tvalue[k] = 0;
}
// warm up the TLB
unsigned int j = 0;
for (int k = 0; k < LEN*iterations; k++)
j = my_array[j];
// ready to begin benchmarking
unsigned int start_time, end_time;
for (int k = 0; k < iterations*LEN; k++) {
start_time = clock();
// traverse array with elements initialized as indices of next memory access
j = my_array[j];
// handles ILP with this data dependency
s_index[k]= j;
end_time = clock();
s_tvalue[k] = end_time - start_time;
}
my_array[N] = j;
my_array[N+1] = my_array[j];
for(int k = 0; k < LEN; k++){
index[k] = s_index[k];
duration[k] = s_tvalue[k];
}
}
void parametric_measure_global(int N, int iterations, int stride) {
// destroy context
cudaDeviceReset();
cudaError_t error_id;
// host (CPU) array
unsigned int * h_a;
h_a = (unsigned int*) malloc((N+2) * sizeof(unsigned int));
for (int i = 0; i < N; i++) {
h_a[i] = (i+stride) % N;
}
h_a[N] = 0;
h_a[N+1] = 0;
// device (GPU) array
unsigned int * d_a;
error_id = cudaMalloc((void **) &d_a, (N+2) * sizeof(unsigned int));
if (error_id != cudaSuccess) {
printf("Error from allocating device array is %s\n", cudaGetErrorString(error_id));
}
error_id = cudaMemcpy(d_a, h_a, sizeof(unsigned int) * N, cudaMemcpyHostToDevice);
if (error_id != cudaSuccess) {
printf("Error from copying over host array is %s\n", cudaGetErrorString(error_id));
}
// accessed data indices array on host (CPU)
unsigned int *h_index = (unsigned int*) malloc(LEN*sizeof(unsigned int));
// accessed data indices array on device (GPU)
unsigned int *d_index;
error_id = cudaMalloc((void **) &d_index, sizeof(unsigned int)*LEN );
if (error_id != cudaSuccess) {
printf("Error from allocating indices array is %s\n", cudaGetErrorString(error_id));
}
// data access latencies array on host (CPU)
unsigned int *h_duration = (unsigned int*) malloc(LEN*sizeof(unsigned int));
// data access latencies array on device (GPU)
unsigned int *d_duration;
error_id = cudaMalloc ((void**) &d_duration, LEN*sizeof(unsigned int));
if (error_id != cudaSuccess) {
printf("Error from allocating latencies array is %s\n", cudaGetErrorString(error_id));
}
// blocks until the device has completed all preceding requested tasks
cudaThreadSynchronize();
// 1 x 1 block of threads
dim3 Db = dim3(1);
// 1 x 1 x 1 block of threads
dim3 Dg = dim3(1,1,1);
// launch kernel
global_latency<<<Dg, Db>>>(d_a, N, iterations, d_duration, d_index);
cudaThreadSynchronize();
error_id = cudaGetLastError();
if (error_id != cudaSuccess) {
printf("Error from kernel is %s\n", cudaGetErrorString(error_id));
}
cudaThreadSynchronize();
// copy results from GPU to CPU
error_id = cudaMemcpy((void*) h_duration, (void*) d_duration, LEN*sizeof(unsigned int), cudaMemcpyDeviceToHost);
if (error_id != cudaSuccess) {
printf("Error 2.0 is %s\n", cudaGetErrorString(error_id));
}
error_id = cudaMemcpy((void*) h_index, (void*) d_index, LEN*sizeof(unsigned int), cudaMemcpyDeviceToHost);
if (error_id != cudaSuccess) {
printf("Error 2.1 is %s\n", cudaGetErrorString(error_id));
}
cudaThreadSynchronize();
for(int i = 0; i < LEN; i++) {
printf("%d\t %d\n", h_index[i], h_duration[i]);
}
// free memory on GPU
cudaFree(d_a);
cudaFree(d_index);
cudaFree(d_duration);
// free memory on CPU
free(h_a);
free(h_index);
free(h_duration);
// destroy context
cudaDeviceReset();
}
void measure_global() {
int iterations = 1;
// 2 MB stride
int stride = 2*1024*1024/sizeof(unsigned int);
//1. The L1 TLB has 16 entries. Test with N_min=28 *1024*256, N_max>32*1024*256
//2. The L2 TLB has 65 entries. Test with N_min=128*1024*256, N_max=160*1024*256
for (int N = 28*1024*256; N <= 46*1024*256; N+=stride) {
printf("\n=====%3.1f MB array, warm TLB, read 256 element====\n", sizeof(unsigned int)*(float)N/1024/1024);
printf("Stride = %d element, %d MB\n", stride, stride * sizeof(unsigned int)/1024/1024);
parametric_measure_global(N, iterations, stride);
printf("===============================================\n\n");
}
}
int main() {
// current device
cudaSetDevice(0);
measure_global();
// destroy context
cudaDeviceReset();
return 0;
}
|
22,306 | #include <inttypes.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
/*
Kernel for use with ManagedCuda
*/
__global__ void kernel(uint16_t *c, int xsize, double y_scale, double y_base, double x_scale, double x_base, int max)
{
int idx_x = blockDim.x * blockIdx.x + threadIdx.x;
int idx_y = blockDim.y * blockIdx.y + threadIdx.y;
int idx = xsize * idx_y + idx_x;
double x0 = (x_base + ((double)idx_x * x_scale));
double y0 = (y_base + ((double)idx_y * y_scale));
double x = 0;
double y = 0;
uint16_t ii = 0;
double temp = 0;
while (ii < max && x * x + y * y < 4)
{
temp = x * x - y * y + x0;
y = x * 2 * y + y0;
x = temp;
ii++;
}
c[idx] = ii;
}
int main()
{
return 0;
} |
22,307 | /*********************************************************************
* Copyright © 2011-2012,
* Marwan Abdellah: <abdellah.marwan@gmail.com>
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation.
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,
* MA 02110-1301, USA.
********************************************************************/
#define MAGIC_NO 1.12345
/*!
* CUDA : This kernel fills an input vector - or 1D array - with a
* sequence of numbers of length N.
*
* @param devArray
* Input device vector - or 1D array - to the kernel.
*
* @param N
* Vector length.
*
* @param fillOrder
* Ascending if <0> or descending order <1>.
*
* @author
* Marwan Abdellah <abdellah.marwan@gmail.com>
*
* @date
* Created: June, 2011.
* @date
* Last Update: October, 2012.
*
* @note
* Minimum CUDA version 3.2.
* @note
* Minimum Device Compute Capability 1.0.
*/
template <typename T>
__global__
void Fill_1D_Array_Kernel(T* devArray, int N, int fillOrder)
{
// Thread index @X
int x_threadIdx = threadIdx.x;
// Block size @X
int x_blockDim = blockDim.x;
// Block index @X
int x_blockIdx = blockIdx.x;
// Thread flat index
int index = ((x_blockIdx * x_blockDim) + x_threadIdx);
#ifdef VEC_CHECK
if (index < N)
{
if (fillOrder == 0)
devArray[index] = (T) index;
else
devArray[index] = (T) (N - index);
}
#else
if (fillOrder == 0)
devArray[index] = (T) index;
else
devArray[index] = (T) (N - index);
#endif
}
|
22,308 |
/* Program to demonstrate time taken by function fun() */
#include <stdio.h>
#include <time.h>
// A function that terminates when enter key is pressed
void fun()
{
printf("fun() starts \n");
printf("Press enter to stop fun \n");
while(1)
{
if (getchar())
break;
}
printf("fun() ends \n");
}
// The main program calls fun() and measures time taken by fun()
int main()
{
// Calculate the time taken by fun()
clock_t t;
t = clock();
fun();
t = clock() - t;
double time_taken = ((double)t)/CLOCKS_PER_SEC; // in seconds
printf("fun() took %f seconds to execute \n", time_taken);
return 0;
}
|
22,309 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
namespace cudakernel {
__global__ void stepLIF(float* voltages,
float* spike_trains,
int* refractory_buffer,
float* in_currents,
float* weights_in,
float* weights_rec,
int n_in,
int n_rec,
int weights_in_size,
int weights_rec_size,
int buffer_len,
int ref_period,
float volt_coeff,
float threshold,
int start,
int steps) {
int post = blockIdx.x*blockDim.x + threadIdx.x;
//compute new voltages and spikes
if(post < n_rec) {
//printf("%d", sizeof(spike_trains)/sizeof(float));
int end = start + steps;
for(int t = start; t < end; ++t) {
int last_t = t == 0 ? buffer_len - 1 : t - 1;
int tm = t%buffer_len;
int index = n_rec*tm + post;
int last_index = n_rec*last_t + post;
//if a spike occurred in the last step, or we are in the refractory period, clamp the voltage and spike trains to 0
if(spike_trains[last_index] > 0.5 || refractory_buffer[last_index] > 0) {
//printf("spike\n");
voltages[index] = 0.0;
spike_trains[index] = 0.0;
refractory_buffer[index] = (1 + refractory_buffer[last_index])%ref_period;
}
//otherwise sum the synaptic potentials and possibly generate a spike
else {
/*
if(t < 20) {
printf("%d %f\n", post, voltages[index]);
}
*/
voltages[index] = volt_coeff*voltages[last_index];
//recurrent
for(size_t pre_ = 0; pre_ < n_rec; ++pre_) {
voltages[index] += weights_rec[n_rec*pre_ + post]*spike_trains[n_rec*last_t + pre_];
}
//input
for(size_t pre_ = 0; pre_ < n_in; ++pre_) {
voltages[index] += weights_in[n_rec*pre_ + post]*in_currents[n_in*last_t + pre_];
}
spike_trains[index] = voltages[index] > threshold ? 1.0 : 0.0;
}
__syncthreads();
}
}
}
}
|
22,310 | #include <iostream>
#include <string.h>
#include <cstdlib>
#include <stdlib.h>
#include <stdio.h>
#include <iomanip>
#include <fstream>
#include <sstream>
#include <list>
#include <utility>
#include <math.h>
#include <limits>
#include <ctime>
#define N 20000
using namespace std;
__global__ void reduce(int *g_idata, int* g_odata, int* sdata)
{
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*blockDim.x + threadIdx.x;
if(tid<544){
for(int j=0; j<20 ; j++){
sdata[tid] = sdata[tid] + g_idata[i+1024*j];
}
}else{
for(int j=0; j<19 ; j++){
sdata[tid] = sdata[tid] + g_idata[i+1024*j];
}
}
__syncthreads();
// do reduction in shared mem
for(unsigned int s=1; s < blockDim.x; s *= 2) {
if ((tid % (2*s)) == 0){
sdata[tid] += sdata[tid + s];
}
__syncthreads();
}
// write result for this block to global mem
if (tid == 0){
g_odata[blockIdx.x] = sdata[tid];
}
}
int* load_data1(){
int* input_data = new int[N];
for(int i=0;i<N;i++){
input_data[i] = i;
}
return(input_data);
}
int* load_data(const char* input_file){
int* answer = new int[N];
FILE* fp = fopen(input_file,"r");
int element;
int i = 0;
while( fscanf(fp, "%d", &element) != EOF )
{
answer[i] = element;
i++;
}
fclose(fp);
return(answer);
}
int main(){
cudaEvent_t start, stop;
float time;
cudaEventCreate(&start);
cudaEventCreate(&stop);
int* h_idata;
int* h_odata; /* host data*/
int* d_idata;
int* d_odata; /* device data*/
int* subh_xdata;
int* subd_xdata;
int numThreadsperBlock = 1024;
int numBlocks = 1;
int size1 = N * sizeof( int );
int size2 = numBlocks * sizeof(int);
int size3 = numThreadsperBlock*sizeof(int);
cudaMalloc( (void **) &d_idata, size1 );
cudaMalloc( (void **) &d_odata, size2);
cudaMalloc( (void **) &subd_xdata, size3);
h_idata = (int *)malloc( size1 );
h_odata = (int *)malloc( size2);
subh_xdata = (int *)malloc( size3);
const char* name = "reduce_data.txt";
h_idata = load_data(name);
for(int i=0; i<1024; i++){
subh_xdata[i] = 0;
}
/* copying inputs to device memory */
cudaMemcpy(d_idata, h_idata, size1, cudaMemcpyHostToDevice) ;
cudaMemcpy(d_odata, h_odata, size2, cudaMemcpyHostToDevice) ;
cudaMemcpy(subd_xdata, subh_xdata, size3, cudaMemcpyHostToDevice) ;
dim3 dimBlock(numThreadsperBlock, 1, 1); dim3 dimGrid(numBlocks, 1, 1);
cudaEventRecord(start, 0);
reduce<<< dimGrid, dimBlock >>>(d_idata, d_odata, subd_xdata);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
int result;
cudaMemcpy( h_odata, d_odata, size2, cudaMemcpyDeviceToHost );
cudaMemcpy( &result, d_odata, size2, cudaMemcpyDeviceToHost );
cout << "sum is " << result << endl;
cudaEventElapsedTime(&time, start, stop);
printf ("Time for the kernel: %f ms\n", time);
free(h_idata);
free(h_odata);
free(subh_xdata);
cudaFree(d_idata);
cudaFree(d_odata);
cudaFree(subd_xdata);
} |
22,311 | __global__ void update_e( int Ntot, int Nz, int Nyz, int c1, int c2, float *Ex, float *Ey, float *Ez, float *Hx, float *Hy, float *Hz, float *CEx, float *CEy, float *CEz ) {
int tk = threadIdx.x;
int idx = blockIdx.x*blockDim.x + tk;
int fidx = idx + idx/c1*Nz + c2;
extern __shared__ float hs[];
float* hx = (float*) hs;
float* hy = (float*) &hx[blockDim.x+1];
float* hz = (float*) &hy[blockDim.x+1];
if ( idx < Ntot ) {
hx[tk] = Hx[fidx];
hy[tk] = Hy[fidx];
hz[tk] = Hz[fidx];
if ( tk==blockDim.x-1 ) {
hx[tk+1] = Hx[fidx+1];
hy[tk+1] = Hy[fidx+1];
}
}
__syncthreads();
if ( fidx < Ntot ) {
Ex[fidx] += CEx[fidx]*( Hz[fidx+Nz] - hz[tk] - hy[tk+1] + hy[tk] );
Ey[fidx] += CEy[fidx]*( hx[tk+1] - hx[tk] - Hz[fidx+Nyz] + hz[tk] );
Ez[fidx] += CEz[fidx]*( Hy[fidx+Nyz] - hy[tk] - Hx[fidx+Nz] + hx[tk] );
}
}
__global__ void update_h( int Ntot, int Nz, int Nyz, int c1, int c2, float *Ex, float *Ey, float *Ez, float *Hx, float *Hy, float *Hz ) {
int tk = threadIdx.x;
int idx = blockIdx.x*blockDim.x + tk;
int fidx = idx + idx/c1*Nz + c2;
extern __shared__ float es[];
float* ex = (float*) es;
float* ey = (float*) &ex[blockDim.x+1];
float* ez = (float*) &ey[blockDim.x+1];
if ( fidx < Ntot ) {
ex[tk+1] = Ex[fidx];
ey[tk+1] = Ey[fidx];
ez[tk] = Ez[fidx];
if ( tk==0 ) {
ex[0] = Ex[fidx-1];
ey[0] = Ey[fidx-1];
}
}
__syncthreads();
if ( fidx < Ntot ) {
Hx[fidx] -= 0.5*( ez[tk] - Ez[fidx-Nz] - ey[tk+1] + ey[tk] );
Hy[fidx] -= 0.5*( ex[tk+1] - ex[tk] - ez[tk] + Ez[fidx-Nyz] );
Hz[fidx] -= 0.5*( ey[tk] - Ey[fidx-Nyz] - ex[tk] + Ex[fidx-Nz] );
}
}
|
22,312 | #include "includes.h"
__global__ void multi(int *a, int *b, int *c,int n) {
int suma = 0;
int row = blockIdx.y * blockDim.y + threadIdx.y ;
int col = blockIdx.x * blockDim.x + threadIdx.x ;
if (row <n && col<n){
for(int i=0;i<N;++i){
suma+= a[row*n+i] * b[i*n+col];
}
}
c[row*n+col] = suma;
} |
22,313 | #include "cuda.h"
typedef long long int64;
__global__ void Init(double *out, const double *in, int size){
int i = blockIdx.x*blockDim.x + threadIdx.x;
if(i<size) out[i] = in[i];
}
__global__ void Zero(double *out, int size){
int i = blockIdx.x*blockDim.x + threadIdx.x;
if(i<size) out[i] = 0.0;
}
__global__ void AddSource(double *out, const int64 *srci, const int64* srcj, const double *srcv, int nsrc, int64 NY){
int i = blockIdx.x*blockDim.x + threadIdx.x;
if(i<nsrc){
// printf("source: %d, %d, (%d, %d), %f\n", srci[i], srcj[i], NX, NY, srcv[i]);
int idx = (srci[i]-1)*(NY+2)+srcj[i]-1;
out[idx] += srcv[i];
}
}
__global__ void AddSourceGrad(double *grad_src, const int64 *srci, const int64* srcj, const double *grad_out, int nsrc, int64 NY){
int i = blockIdx.x*blockDim.x + threadIdx.x;
if(i<nsrc){
int idx = (srci[i]-1)*(NY+2)+srcj[i]-1;
grad_src[i] += grad_out[idx];
}
}
void forwardGPU(double *out, const double *in, const int64 *srci, const int64* srcj,
const double *srcv, int nsrc,const int64 *nx_tensor, const int64 *ny_tensor){
int64 NX, NY;
cudaMemcpy(&NX, nx_tensor, sizeof(int64), cudaMemcpyDeviceToHost);
cudaMemcpy(&NY, ny_tensor, sizeof(int64), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
Init<<<((NX+2)*(NY+2)+255)/256, 256>>>(out, in, (NX+2)*(NY+2));
AddSource<<<(nsrc+255)/256, 256>>>(out, srci, srcj, srcv, nsrc, NY);
}
void backwardGPU(double *grad_src, double *grad_in, const double *grad_out, const double *in, const int64 *srci,
const int64* srcj, const double *srcv, int nsrc, const int64 *nx_tensor, const int64 *ny_tensor){
int64 NX, NY;
cudaMemcpy(&NX, nx_tensor, sizeof(long long), cudaMemcpyDeviceToHost);
cudaMemcpy(&NY, ny_tensor, sizeof(long long), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
Init<<<((NX+2)*(NY+2)+255)/256, 256>>>(grad_in, grad_out, (NX+2)*(NY+2));
Zero<<<(nsrc+255)/256, 256>>>(grad_src, nsrc);
AddSourceGrad<<<(nsrc+255)/256, 256>>>(grad_src, srci, srcj, grad_out, nsrc, NY);
} |
22,314 | #include "includes.h"
__global__ void SigmoidBackKernel(float* Z, float* dZ, int size){
int id = blockIdx.x * blockDim.x + threadIdx.x;
if(id < size){
float t = Z[id];
dZ[id] = dZ[id] * t * (1-t) ;
}
} |
22,315 | #include "includes.h"
__global__ void __fillToInds3D(double A, double *B, int ldb, int rdb, int *I, int nrows, int *J, int ncols, int *K, int nk) {
int ii = threadIdx.x + blockDim.x * blockIdx.x;
int jj = threadIdx.y + blockDim.y * blockIdx.y;
int kk = threadIdx.z + blockDim.z * blockIdx.z;
int i, j, k, mapi, mapj, mapk;
for (k = kk; k < nk; k += blockDim.z * gridDim.z) {
mapk = k;
if (K != NULL) mapk = K[k];
for (j = jj; j < ncols; j += blockDim.y * gridDim.y) {
mapj = j;
if (J != NULL) mapj = J[j];
if (I != NULL) {
for (i = ii; i < nrows; i += blockDim.x * gridDim.x) {
mapi = I[i];
B[mapi + ldb * (mapj + rdb * mapk)] = A;
}
} else {
for (i = ii; i < nrows; i += blockDim.x * gridDim.x) {
mapi = i;
B[mapi + ldb * (mapj + rdb * mapk)] = A;
}
}
}
}
} |
22,316 | #include <stdio.h>
#include <stdlib.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#define BLOCK_DIM 16
__global__ void transpose(float *odata, float *idata, int width, int height)
{
__shared__ float block[BLOCK_DIM][BLOCK_DIM+1];
unsigned int xIndex = blockIdx.x * BLOCK_DIM + threadIdx.x;
unsigned int yIndex = blockIdx.y * BLOCK_DIM + threadIdx.y;
if ((xIndex < width) && (yIndex < height))
{
unsigned int index_in = yIndex * width + xIndex;
block[threadIdx.y][threadIdx.x] = idata[index_in];
}
__syncthreads();
xIndex = blockIdx.y * BLOCK_DIM + threadIdx.x;
yIndex = blockIdx.x * BLOCK_DIM + threadIdx.y;
if ((xIndex < height) && (yIndex < width))
{
unsigned int index_out = yIndex * height + xIndex;
odata[index_out] = block[threadIdx.x][threadIdx.y];
}
}
int main()
{
cudaSetDevice(0);
float *cpu_A;
//float *cpu_B;
float *gpu_A;
float *gpu_B;
int width = 4096;
int height = 16384;
size_t pitch;
int r, c;
cpu_A = (float*)malloc(sizeof(float)*width*height);
cudaMallocPitch((void**)&gpu_A, &pitch, sizeof(float)*width, height);
printf("The pitch is: %d\n", pitch);
for ( r = 0; r < height; ++r){
for (c = 0; c < width; ++c){
cpu_A[r*width+c] = r*c;
}
}
cudaMemcpy2D(gpu_A, pitch, cpu_A, sizeof(float)*width, sizeof(float)*width, height, cudaMemcpyHostToDevice);
cudaMemcpy2D(gpu_B, pitch, cpu_A, sizeof(float)*width, sizeof(float)*width, height, cudaMemcpyHostToDevice);
dim3 Dg(1, 2, 1);
dim3 Db(width, 1, 1);
transpose<<<Dg, Db, 0>>>(gpu_B, gpu_A, width, height);
cudaMemcpy2D(cpu_A, sizeof(float)*width, gpu_B, pitch, sizeof(float)*width, height, cudaMemcpyDeviceToHost);
//print result
/*
printf("After transpose, CPU_B DATA:\n");
for (r = 0; r < height; ++r){
for (c = 0; c < width; ++c){
printf("%f/t", cpu_A[r*width+c]);
}
printf("\n");
}
*/
free(cpu_A);
cudaFree(gpu_A);
return 0;
}
|
22,317 | //Nathanael Grix
//Hw5 Cuda Core Benchmark:
//Purpose: Compare cpu and gpu execution time to sum large arrays of numbers on
// Penn State's computing cluster. I will compare the exectution time from
// sets of 1 to 200,000,000.
#include <stdio.h>
#include <stdlib.h>
#include <sys/time.h>
#define B 1024
//unused implementation
__global__ void scan(float *g_odata, float *g_idata, unsigned long long int n);
//first part of sacning a large array
__global__ void prescan(float *g_odata, float *g_idata, unsigned long long int n, float* blkSum, unsigned long int N);
//last part of scaning a large array
__global__ void postscan(float *g_odata, unsigned long long int n, float* blkSum);
//cpu impementation of a simple scan
void scanCPU(float *f_out, float *f_in, unsigned long long int i_n);
//function for getting the time difference
double myDiffTime(struct timeval &start, struct timeval &end)
{
double d_start, d_end;
d_start = (double)(start.tv_sec + start.tv_usec/1000000.0);
d_end = (double)(end.tv_sec + end.tv_usec/1000000.0);
return (d_end - d_start);
}
int main(int argc,char** argv) {
//number of values and threads
unsigned long int N;
//default val or not?
if(argc==2){
N=atol(argv[1]);
}
else{
N=100000000;
}
printf("N=%d, B=%d\n\n",N,B);
//calculate the number of blocks
unsigned long long int blkNum =(unsigned long long int)ceil((double)N/((double)(B*2)));
//the values used to calculate the scans
float *a, *c, *g, *blkSumin, *blkOut;
//values used to time
timeval start, end;
//variables used to talk to memory from cuda
float *dev_a, *dev_g, *dev_blkSum;
//sizes of an array of N and the sum of the blocks
unsigned long long int size = N * sizeof(float);
unsigned long long int blkSumSize = blkNum * sizeof(float);
//time vars
double d_gpuTime, d_cpuTime, d_gpuLoadT, d_gpuPreT, d_gpuScaT, d_gpuPostT;
double load[4];
//the serial final result
c=(float*)malloc(size);
//cuda optomized malloc for vars that cuda will need
cudaMallocHost((void **) &a, size);
cudaMallocHost((void **) &g, size);
cudaMallocHost((void **) &blkSumin, blkSumSize);
cudaMallocHost((void **) &blkOut, blkSumSize);
// initialize matrices a
for (unsigned long long int i = 0; i < N; i++)
{
// fill a with rand floats
a[i] = (float)(rand() % 1000000) / 1000.0;
}
// initialize a and b matrices here
cudaMalloc((void **) &dev_a, size);
cudaMalloc((void **) &dev_g, size);
cudaMalloc((void **) &dev_blkSum, blkSumSize);
// START TIME
gettimeofday(&start, NULL);
// COPY 1 TIME
// fill the graphics card's memory with a
cudaMemcpy(dev_a, a, size, cudaMemcpyHostToDevice);
gettimeofday(&end, NULL);
load[0] = myDiffTime(start, end);
// PRE TIME
// have small scans done on the memory in blocks
gettimeofday(&start, NULL);
prescan<<<blkNum,B,2*B*sizeof(float)>>>(dev_g, dev_a, B*2, dev_blkSum, N);
cudaDeviceSynchronize();
gettimeofday(&end, NULL);
d_gpuPreT = myDiffTime(start, end);
// COPY 2 TIME
// get the blocks' sums out of the graphics cards so we can do a small serial
// scan on them.
gettimeofday(&start, NULL);
cudaMemcpy(blkSumin, dev_blkSum, blkSumSize, cudaMemcpyDeviceToHost);
gettimeofday(&end, NULL);
load[1] += myDiffTime(start, end);
// SCAN TIME
// do a small serial scan on the blocks' sums.
gettimeofday(&start, NULL);
scanCPU(blkOut, blkSumin, blkNum);
gettimeofday(&end, NULL);
d_gpuScaT = myDiffTime(start, end);
// COPY 3 TIME
// give the blocks back to the graphics cards for post.
gettimeofday(&start, NULL);
cudaMemcpy(dev_blkSum, blkOut, blkSumSize, cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
gettimeofday(&end, NULL);
load[2] += myDiffTime(start, end);
// POST TIME
// add the new scanned sum to each block to get the scan of the whole array
gettimeofday(&start, NULL);
postscan<<<(unsigned long long int)ceil((double)N/(double)(B)),B>>>(dev_g, N, dev_blkSum);
gettimeofday(&end, NULL);
d_gpuPostT = myDiffTime(start, end);
// COPY 4 TIME
// finally get the result into normal memory
gettimeofday(&start, NULL);
cudaMemcpy(g, dev_g, size, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
// END TIME
gettimeofday(&end, NULL);
load[3] += myDiffTime(start, end);
// get the total time for the loads
d_gpuLoadT = load[0] + load[1] + load[2] +load[3];
// get the total time for my implementation
d_gpuTime = d_gpuLoadT + d_gpuPreT + d_gpuScaT + d_gpuPostT;
//do a serial scan on the whole array
gettimeofday(&start, NULL);
scanCPU(c, a, N);
gettimeofday(&end, NULL);
d_cpuTime = myDiffTime(start, end);
// free the memory for the graphics card
cudaFree(dev_a); cudaFree(dev_g); cudaFree(dev_blkSum);
// display at least 50 sums and compare the error percent of the serial
// vs cuda implentation.
for (unsigned long long int i = 0; i < N; i++)
{ if(N>50){
if(i%(N/50)==0 || i == N-1){
printf("c[%i] = %0.3f, g[%i] = %0.3f\n", i, c[i], i, g[i]);
printf("error percent = %f%\n",fabs((g[i]-c[i])/c[i])*100.0);
}
}
else{
printf("c[%i] = %0.3f, g[%i] = %0.3f\n", i, c[i], i, g[i]);
printf("error percent = %f%\n",fabs((g[i]-c[i])/c[i])*100.0);
}
}
// print out the time for the cpu and the gpu
printf("CPU Time for scan size %i: %f\n", N, d_cpuTime);
printf("GPU Time for scan size %i: %f\n", N, d_gpuTime);
// print out the times of the components of the gpu implementation
printf("\tgpu Load=%f, Pre=%f, Sca=%f, Post=%f\n",d_gpuLoadT, d_gpuPreT, d_gpuScaT, d_gpuPostT);
// print out the various loading times for the implementation
printf("\t\tgpu load times={%f,%f,%f,%f}\n", load[0],load[1],load[2],load[3]);
// free c from memory
free(c);
// free the rest of the variables
cudaFreeHost(a);
cudaFreeHost(g);
cudaFreeHost(blkSumin);
cudaFreeHost(blkOut);
}
// old unused verson of scan that was included in the naivescan
__global__ void scan(float *g_odata, float *g_idata, unsigned long long int n)
{
extern __shared__ float temp[]; // allocated on invocation
unsigned long long int thid = threadIdx.x;
unsigned long long int pout = 0, pin = 1;
// Load input into shared memory.
// This is exclusive scan, so shift right by one
// and set first element to 0
temp[pout*n + thid] = (thid > 0) ? g_idata[thid-1] : 0;
__syncthreads();
for (unsigned long long int offset = 1; offset < n; offset *= 2)
{
pout = 1 - pout; // swap double buffer indices
pin = 1 - pout;
if (thid >= offset)
temp[pout*n+thid] += temp[pin*n+thid - offset];
else
temp[pout*n+thid] = temp[pin*n+thid];
__syncthreads();
}
g_odata[thid] = temp[pout*n+thid]; // write output
}
// function that adds the new blocks' sums back into the scanned blocks
__global__ void postscan(float *g_odata, unsigned long long int n, float* blkSum){
unsigned long long int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n)
{ // prescan does two vals per block but this one does 1 so
// adjust the block ID to reflect that.
g_odata[i] += blkSum[blockIdx.x/2];
}
}
// scans a block of the data
__global__ void prescan(float *g_odata, float *g_idata, unsigned long long int n, float* blkSum, unsigned long int N)
{
// name the newly allocated memory as temp.
extern __shared__ float temp[];
//get the begining of the array for this block
unsigned long long int bldata = blockIdx.x*(blockDim.x);
// get the thread's id for the block
unsigned long long int thid = threadIdx.x;
// initialize the offset to 1
unsigned long long int offset = 1;
// fill the temp array with the input data because the math can be executed
// much faster than other forms of meory
temp[2*thid] = 0.0;
temp[2*thid+1] = 0.0;
if(2*(thid+bldata)<N){
temp[2*thid] = g_idata[2*(thid+bldata)];
}
if(2*(thid+bldata)+1<N){
temp[2*thid+1] = g_idata[2*(thid+bldata)+1];
}
//log for loop
for (unsigned long long int d = n>>1; d > 0; d >>= 1)
// build sum in place up the tree
{
__syncthreads();
if (thid < d){
unsigned long long int ai = offset*(2*thid+1)-1;
unsigned long long int bi = ai+offset;
temp[bi] += temp[ai];
}
// exponential offset
offset <<= 1;
}
// set the lst value and get the total sum
if (thid == 0){
blkSum[blockIdx.x] = temp[n-1];
temp[n-1] = 0;
}
for (unsigned long long int d = 1; d < n; d <<= 1)
// traverse down tree & build scan
{
offset >>= 1;
__syncthreads();
if (thid < d){
unsigned long long int ai = offset*(2*thid+1)-1;
unsigned long long int bi = ai+offset;
float t = temp[ai];
temp[ai] = temp[bi];
temp[bi] += t;
}
}
__syncthreads();
// write results to device memory
if(2*(thid+bldata)<N){
g_odata[2*(thid+bldata)] = temp[2*thid];
}
if(2*(thid+bldata)+1<N){
g_odata[2*(thid+bldata)+1] = temp[2*thid+1];
}
}
void scanCPU(float *f_out, float *f_in, unsigned long long int i_n)
{
f_out[0] = 0;
for (unsigned long long int i = 1; i < i_n; i++)
f_out[i] = f_out[i-1] + f_in[i-1];
}
|
22,318 | #include <cuda_runtime.h>
#include <stdio.h>
/***
__shared__ 通过共享内存来完成线程间的通信
这一段代码 通过共享内存
***/
cudaError_t addWithCuda(int *c, const int *a, size_t size);
__global__ void addKernel(int *c, const int *a){
int i = threadIdx.x;
extern __shared__ int seme []; //声明一个全局的 共享内存的变量
seme[i] = a[i];
__syncthreads(); //同一个块的线程同步 等待seme将所有数据加载进来
if(i==0){ //第一个线程进行二次方
c[0] = 0;
for (int d=0; d<5; d++){
printf("seme[d] * seme [d] %d \n", d);
c[0] += seme[d] * seme [d];
}
printf("给 seme 赋值 %d ", i);
seme[i] = 0;
}
if(i==1){
c[1] = 0;
for (int d=0; d<5; d++){
printf("c[1] += seme[d] %d \n", d);
c[1] += seme[d];
}
printf("给 seme 赋值 %d ", i);
seme[i] = 0;
}
if(i==2){
c[2] = 1;
for(int d=0; d<5; d++){
printf("c[2] *= seme[d] %d \n", d);
c[2] *= seme[d];
}
printf("给 seme 赋值 %d ", i);
seme[i] = 0;
}
}
int main(){
const int arraySize = 5;
const int a[arraySize] = {1, 2, 3, 4, 5};
int c[arraySize] = {0};
cudaError_t cudaStatus = addWithCuda(c, a, arraySize);
if (cudaStatus != cudaSuccess){
fprintf(stderr, "addWithCuda 失败");
return 1;
}
printf("\t1+2+3+4+5 = %d\n\t1^2+2^2+3^2+4^2+5^2 = %d\n\t1*2*3*4*5 = %d\n\n\n\n\n\n", c[1], c[0], c[2]);
cudaStatus = cudaThreadExit();
if (cudaStatus != cudaSuccess){
fprintf(stderr, "cudaThreadExit 失败");
return 1;
}
return 0;
}
cudaError_t addWithCuda(int *c,const int *a, size_t size){
int *dev_a = 0;
int *dev_c = 0;
cudaError_t cudaStatus;
cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));
if (cudaStatus != cudaSuccess){
fprintf(stderr, "cuda 分配内存失败");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));
if (cudaStatus != cudaSuccess){
fprintf(stderr, "cuda 分配内存失败");
goto Error;
}
cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess){
fprintf(stderr, "从Device向Hostcopy数据失败");
goto Error;
}
addKernel<<<1, size, size * sizeof(int), 0>>>(dev_c, dev_a);
cudaStatus = cudaThreadSynchronize();
if (cudaStatus != cudaSuccess){
fprintf(stderr, "cuda线程同步异常");
goto Error;
}
cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess){
fprintf(stderr, "从Device向Hostcopy数据失败");
goto Error;
}
Error:
cudaFree(dev_c);
cudaFree(dev_a);
return cudaStatus;
} |
22,319 | /*
** Projeto de Algoritmos Paralelos
** Ola Mundo
*/
#include <limits.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <cuda_profiler_api.h>
__global__ void cuda_olamundo(){
printf("Ola Mundo direto da GPU!\n");
}
int main(int argc, const char * argv[]){
int count;
cudaError_t erro;
cudaDeviceProp deviceProp;
cudaProfilerStart();
erro = cudaGetDeviceCount(&count);
if (erro != cudaSuccess) {
printf("Erro: %s\n", cudaGetErrorString(erro));
exit(-1);
}
printf("Numero de dispositivos: %d\n", count);
erro = cudaGetDeviceProperties(&deviceProp, 0);
if (erro != cudaSuccess) {
printf("Erro: %s\n", cudaGetErrorString(erro));
exit(-1);
}
printf("Dispositivo %d tem a capacidade computacional %d.%d.\n\n", 0, deviceProp.major, deviceProp.minor);
cuda_olamundo<<<1,1>>>();
cudaDeviceReset();
exit(EXIT_SUCCESS);
} |
22,320 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#define BLOCK_SIZE 512 //
__global__ void findmax(float * input, float * output, int len) {
__shared__ float partialMax[BLOCK_SIZE];
unsigned int t = threadIdx.x;
unsigned int start = blockIdx.x * blockDim.x;
partialMax[t] = (start + t < len) ? input[start + t] : 0;
__syncthreads();
for(unsigned int stride = blockDim.x/4; stride >= 1; stride >>= 1) {
if(t < stride){
partialMax[t] = (partialMax[t] < partialMax[t + stride]) ? partialMax[t + stride]:partialMax[t];
partialMax[t] = (partialMax[t] < partialMax[t + 2*stride]) ? partialMax[t + 2*stride]:partialMax[t];
partialMax[t] = (partialMax[t] < partialMax[t + 3*stride]) ? partialMax[t + 3*stride]:partialMax[t];
}
__syncthreads();
}
if(t == 0) {
output[blockIdx.x + t] = partialMax[t];
}
}
int main(int argc, char ** argv) {
int ii;
float * hostInput; // The input 1D array
float * hostOutput; // The output array
float * deviceInput;
float * deviceOutput;
int numInputElements = 4099; // number of elements in the input array
int numOutputElements; // number of elements in the output array
hostInput = (float*) malloc(numInputElements * sizeof(float));
for(int i = 0; i < numInputElements; i++) {
hostInput[i] = (i-2000)*(i-2000);
}
numOutputElements = numInputElements / (BLOCK_SIZE);
if (numInputElements % (BLOCK_SIZE)) {
numOutputElements++;
}
hostOutput = (float*) malloc(numOutputElements * sizeof(float));
cudaMalloc((void **) &deviceInput, numInputElements * sizeof(float));
cudaMalloc((void **) &deviceOutput, numOutputElements * sizeof(float));
cudaMemcpy(deviceInput, hostInput, numInputElements * sizeof(float), cudaMemcpyHostToDevice);
dim3 DimGrid((numInputElements - 1)/BLOCK_SIZE + 1, 1, 1);
dim3 DimBlock(BLOCK_SIZE, 1, 1);
findmax<<<DimGrid, DimBlock>>>(deviceInput, deviceOutput, numInputElements);
cudaDeviceSynchronize();
cudaMemcpy(hostOutput, deviceOutput, numOutputElements * sizeof(float), cudaMemcpyDeviceToHost);
for (ii = 1; ii < numOutputElements; ii++) {
hostOutput[0] = (hostOutput[0]<hostOutput[ii]) ? hostOutput[ii]:hostOutput[0];
}
printf("%f\n", hostOutput[0]);
cudaFree(deviceInput);
cudaFree(deviceOutput);
free(hostInput);
free(hostOutput);
return 0;
}
|
22,321 |
#include <iostream>
#define numBlocks 512
#define numThreads 1024
using namespace std;
__global__
void addVectors(int blockSize, int totalSize, int * a, int * b, int * dest) {
int idxBlock = blockIdx.x;
int idx = blockIdx.x * blockSize + threadIdx.x;
while (idx < (idxBlock+1)*blockSize && idx < totalSize) {
dest[idx] = a[idx] + b[idx];
idx+=numThreads;
}
}
int main() {
int N = 100000000;
int * a = new int[N]; // declared on heap so no segfault
int * b = new int[N];
int * sum = new int[N];
for (int i = 0; i < N; ++i) {
a[i] = i*i;
b[i] = i*i;
}
int *d_a, *d_b, *dest;
cudaMalloc((void**)&d_a, N*sizeof(int));
cudaMalloc((void**)&d_b, N*sizeof(int));
cudaMalloc((void**)&dest, N*sizeof(int));
cudaMemcpy(d_a, a, N*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, N*sizeof(int), cudaMemcpyHostToDevice);
int blockSize = (N + numBlocks - 1)/numBlocks;
addVectors<<<numBlocks, numThreads>>>(blockSize, N, d_a, d_b, dest);
cudaMemcpy(sum, dest, N * sizeof(int), cudaMemcpyDeviceToHost);
if (sum[10] == 200) {
return 0;
}
else {
cout << "SEMANTIC ERROR" << endl;
}
} |
22,322 | #include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<cuda.h>
#include<cuda_runtime.h>
namespace placeholder
{
class Data
{
public:
int InputSize(void)
{
FILE *input = NULL;
input = fopen("input.txt", "r");
char line[30];
int N = 0;
while(fgets(line, 30, input) != NULL)
N++;
fclose(input);
return N;
}
void ReadFromFile(double *x, double *y, double *z, bool *b, int N)
{
FILE *input = NULL;
input = fopen("input.txt", "r");
char line[30];
for(int i = 0; i < N; i++)
{
fgets(line, 30, input);
sscanf(line, "%lf %lf %lf", &x[i], &y[i], &z[i]);
b[i] = true;
}
fclose(input);
printf("Data imported from input.txt successfully!\n");
}
void WriteToFile(double *x, double *y, double *z, bool *b, int N)
{
FILE *output = NULL;
output = fopen("output.txt", "w");
for(int i = 0; i < N; i++)
{
if(b[i] == true)
fprintf(output, "%.1lf %.1lf %.1lf\n", x[i], y[i], z[i]);
}
fclose(output);
printf("Data exported to output.txt successfully!\n");
}
};
class NeighbourSearch
{
public:
void CopyFromHostToDevice(double *x1, double *x2, double *y1, double *y2, double *z1, double *z2, bool *b1, bool *b2, int N1, int *N2)
{
cudaMemcpy(N2, &N1, sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(x2, x1, sizeof(double)*N1, cudaMemcpyHostToDevice);
cudaMemcpy(y2, y1, sizeof(double)*N1, cudaMemcpyHostToDevice);
cudaMemcpy(z2, z1, sizeof(double)*N1, cudaMemcpyHostToDevice);
cudaMemcpy(b2, b1, sizeof(bool)*N1, cudaMemcpyHostToDevice);
}
void CopyFromDeviceToHost(bool *b1, bool *b2, int N)
{
cudaMemcpy(b2, b1, sizeof(bool)*N, cudaMemcpyDeviceToHost);
}
};
__global__ void search(double *d_xx, double *d_yy, double *d_zz, bool *d_bb, int *d_N, double *x, double *y, double *z, double *r)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
if(index < *d_N)
{
if((pow((*x)-d_xx[index], 2) + pow((*y)-d_yy[index], 2) + pow((*z)-d_zz[index], 2)) > pow(*r, 2))
d_bb[index] = false;
}
}
}
int main()
{
placeholder::Data f1 = placeholder::Data();
double x, y, z;
double r;
double *d_x, *d_y, *d_z;
double *d_r;
double *xx, *yy, *zz;
bool *bb;
int N = f1.InputSize();
double *d_xx, *d_yy, *d_zz;
bool *d_bb;
int *d_N;
int grid_size, block_size = 256;
xx = (double *)malloc(sizeof(double)*N);
yy = (double *)malloc(sizeof(double)*N);
zz = (double *)malloc(sizeof(double)*N);
bb = (bool *)malloc(sizeof(bool)*N);
f1.ReadFromFile(xx, yy, zz, bb, N);
cudaMalloc((void **)&d_xx, sizeof(double) * N);
cudaMalloc((void **)&d_yy, sizeof(double) * N);
cudaMalloc((void **)&d_zz, sizeof(double) * N);
cudaMalloc((void **)&d_bb, sizeof(bool) * N);
cudaMalloc((void **)&d_N, sizeof(int));
cudaMalloc((void **)&d_x, sizeof(double));
cudaMalloc((void **)&d_y, sizeof(double));
cudaMalloc((void **)&d_z, sizeof(double));
cudaMalloc((void **)&d_r, sizeof(double));
placeholder::NeighbourSearch ns;
ns.CopyFromHostToDevice(xx, d_xx, yy, d_yy, zz, d_zz, bb, d_bb, N, d_N);
printf("To end the program, type a number which is not positive in the search distance field.\n");
while(1)
{
printf("Enter the x, y and z coordinates of the point and the search distance:\t");
scanf("%lf %lf %lf %lf", &x, &y, &z, &r);
if(r <= 0)
break;
else
{
cudaMemcpy(d_x, &x, sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, &y, sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_z, &z, sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy(d_r, &r, sizeof(double), cudaMemcpyHostToDevice);
grid_size = (N + block_size) / block_size;
placeholder::search<<<grid_size,block_size>>>(d_xx, d_yy, d_zz, d_bb, d_N, d_x, d_y, d_z, d_r);
ns.CopyFromDeviceToHost(d_bb, bb, N);
f1.WriteToFile(xx, yy, zz, bb, N);
}
}
free(xx);
free(yy);
free(zz);
free(bb);
cudaFree(d_xx);
cudaFree(d_yy);
cudaFree(d_zz);
cudaFree(d_bb);
cudaFree(d_N);
cudaFree(d_x);
cudaFree(d_y);
cudaFree(d_z);
cudaFree(d_r);
printf("Program terminated.\n");
return 0;
}
|
22,323 | #include <iostream>
using namespace std;
long layer_sizes[] = {56l * 56 * 96, 28l * 28 * 96, 27l * 27 * 256,
13l * 13 * 256, 13l * 12 * 384, 13l * 12 * 384,
13l * 13 * 256, 6l * 6 * 256};
int num_layers = 8;
int main() {
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
// long size_to_alloc = layer_sizes[0];
for (int j = 0; j < num_layers; j++) {
long size_to_alloc = layer_sizes[j];
int num_pieces = 8;
void *p[num_pieces];
for (int i = 0; i < num_pieces; i++) {
cudaMallocHost(&p[i], size_to_alloc / num_pieces);
}
for (int i = 0; i < num_pieces; i++) {
cudaFreeHost(p[i]);
}
}
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milli;
cudaEventElapsedTime(&milli, start, stop);
cout << "allocating and freeing pieces(ms): " << milli << endl;
void *p_bulk;
cudaEventRecord(start);
for (int j = 0; j < num_layers; j++) {
long size_to_alloc = layer_sizes[j];
cudaMallocHost(&p_bulk, size_to_alloc);
cudaFreeHost(p_bulk);
}
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&milli, start, stop);
cout << "allocating and freeing bulk(ms): " << milli << endl;
} |
22,324 | #include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/sort.h>
#include <thrust/random.h>
#include <iostream>
#include <iomanip>
#include <ctime>
#include <fstream>
#include <string>
void print(const thrust::device_vector<int>& v)
{
for(size_t i = 0; i < v.size(); i++)
std::cout << " " << v[i];
std::cout << "\n";
}
// user-defined comparison operator that acts like less<int>,
// except even numbers are considered to be smaller than odd numbers
struct evens_before_odds
{
__host__ __device__
bool operator()(int x, int y)
{
if (x % 2 == y % 2)
return x < y;
else if (x % 2)
return false;
else
return true;
}
};
/* Sorting Ascending Order */
void sortingFuncAsc(std::vector<int> host_array)
{
thrust::device_vector<int> device_vecArray(host_array);
std::cout << "Initiating the Sorting Process\n";
{
std::string PID;
std::string csvfileLoc;
std::ifstream infile;
/* Open pid_Process file and copy the PID into PID variable */
infile.open ("/tmp/pid_Process.txt");
getline(infile, PID); // Saves the line in STRING.
infile.close();
/* Store file location in fileLoc */
csvfileLoc = "/tmp/";
csvfileLoc = csvfileLoc + PID;
csvfileLoc = csvfileLoc + ".csv";
/* Start the Sorter clock */
std::clock_t start_sort;
double duration_sort;
start_sort = std::clock();
/* Sort the Array */
thrust::sort(device_vecArray.begin(), device_vecArray.end());
/* Calculate the duration and print it */
duration_sort = ( std::clock() - start_sort ) / (double) CLOCKS_PER_SEC;
std::cout<<"Sorting time: "<< duration_sort <<'\n';
/* Start the Copy clock */
std::clock_t start_copy;
double duration_copy;
start_copy = std::clock();
/* Copy Vectory array to the pglog csv file */
std::ofstream output_file(csvfileLoc.c_str());
std::ostream_iterator<int> output_iterator(output_file, "\n");
thrust::copy(device_vecArray.begin(), device_vecArray.end(), output_iterator);
/* Calculate the Copying duration and print it */
duration_copy = ( std::clock() - start_copy ) / (double) CLOCKS_PER_SEC;
std::cout<<"Copying time: "<< duration_copy <<'\n';
}
}
/*Sorting Descending Order */
void sortingFuncDesc(std::vector<int> host_array)
{
thrust::device_vector<int> device_vecArray(host_array);
std::cout << "Initiating the Sorting Process\n";
{
std::string PID;
std::string csvfileLoc;
std::ifstream infile;
/* Open pid_Process file and copy the PID into PID variable */
infile.open ("/tmp/pid_Process.txt");
getline(infile, PID); // Saves the line in STRING.
infile.close();
/* Store file location in fileLoc */
csvfileLoc = "/tmp/";
csvfileLoc = csvfileLoc + PID;
csvfileLoc = csvfileLoc + ".csv";
/* Start the Sorter clock */
std::clock_t start_sort;
double duration_sort;
start_sort = std::clock();
/* Sort the Array */
thrust::sort(device_vecArray.begin(), device_vecArray.end(), thrust::greater<int>());
/* Calculate the duration and print it */
duration_sort = ( std::clock() - start_sort ) / (double) CLOCKS_PER_SEC;
std::cout<<"Sorting time: "<< duration_sort <<'\n';
/* Start the Copy clock */
std::clock_t start_copy;
double duration_copy;
start_copy = std::clock();
/* Copy Vectory array to the pglog csv file */
std::ofstream output_file(csvfileLoc.c_str());
std::ostream_iterator<int> output_iterator(output_file, "\n");
thrust::copy(device_vecArray.begin(), device_vecArray.end(), output_iterator);
/* Calculate the Copying duration and print it */
duration_copy = ( std::clock() - start_copy ) / (double) CLOCKS_PER_SEC;
std::cout<<"Copying time: "<< duration_copy <<'\n';
}
}
|
22,325 | /**
* Copyright 1993-2012 NVIDIA Corporation. All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*/
#include <stdio.h>
#include <stdlib.h>
static const int VECTOR_SIZE = 32;
static const int WORK_SIZE = 16;
/**
* This macro checks return value of the CUDA runtime call and exits
* the application if the call failed.
*/
#define CUDA_CHECK_RETURN(value) { \
cudaError_t _m_cudaStat = value; \
if (_m_cudaStat != cudaSuccess) { \
fprintf(stderr, "Error %s at line %d in file %s\n", \
cudaGetErrorString(_m_cudaStat), __LINE__, __FILE__); \
exit(1); \
} }
__device__ int scan_warp(int *ptr, const unsigned int idx = threadIdx.x) {
const unsigned int aux = idx & 31;
if(aux >= 1) ptr[idx] = ptr[idx - 1] + ptr[idx];
if(aux >= 2) ptr[idx] = ptr[idx - 2] + ptr[idx];
if(aux >= 4) ptr[idx] = ptr[idx - 4] + ptr[idx];
if(aux >= 8) ptr[idx] = ptr[idx - 8] + ptr[idx];
if(aux >= 16) ptr[idx] = ptr[idx - 16] + ptr[idx];
return ptr[idx];
}
__device__ int scan_block(int *ptr, const unsigned int idx = threadIdx.x) {
const unsigned int aux = idx & 31;
const unsigned int warp_id = idx >> 5;
__shared__ int temp[32];
int val = scan_warp(ptr, idx);
__syncthreads();
if(aux == 31) temp[warp_id] = ptr[idx];
__syncthreads();
if(warp_id == 0) scan_warp(temp, idx);
__syncthreads();
if(warp_id > 0) val = temp[warp_id - 1] + val;
__syncthreads();
ptr[idx] = val;
__syncthreads();
return val;
}
__global__ void scan_global(int *ptr) {
const unsigned int idx = threadIdx.x;
const unsigned int aux = idx & blockDim.x;
const unsigned int bi = blockIdx.x;
extern __shared__ int temp[];
int val = scan_block(ptr, idx);
__syncthreads();
if(aux == blockDim.x) temp[bi] = ptr[idx];
__syncthreads();
if(bi == 0) scan_block(temp, idx);
__syncthreads();
if(bi > 0) val = temp[bi - 1] + val;
__syncthreads();
ptr[idx] = val;
__syncthreads();
return;
}
/**
* Host function that prepares data array and passes it to the CUDA kernel.
*/
int main(void) {
int *d = NULL;
int i;
unsigned int GRID_SIZE = (1 + VECTOR_SIZE) / WORK_SIZE;
unsigned int idata[VECTOR_SIZE], odata[VECTOR_SIZE];
for (i = 0; i < VECTOR_SIZE; i++)
idata[i] = (unsigned int) i;
CUDA_CHECK_RETURN(cudaMalloc((void**) &d, sizeof(int) * VECTOR_SIZE));
CUDA_CHECK_RETURN(
cudaMemcpy(d, idata, sizeof(int) * VECTOR_SIZE, cudaMemcpyHostToDevice));
scan_global<<<GRID_SIZE, WORK_SIZE, WORK_SIZE * sizeof(int)>>>(d);
CUDA_CHECK_RETURN(cudaDeviceSynchronize()); // Wait for the GPU launched work to complete
CUDA_CHECK_RETURN(cudaGetLastError());
CUDA_CHECK_RETURN(cudaMemcpy(odata, d, sizeof(int) * VECTOR_SIZE, cudaMemcpyDeviceToHost));
unsigned int sum = idata[0];
for (i = 0; i < VECTOR_SIZE - 1; i++) {
if(odata[i] != sum) {
printf("Erro !!!");
}
if((i + 1) < VECTOR_SIZE) {
sum += idata[i+1];
}
printf("Input value: %u, device output: %u\n", idata[i], odata[i]);
}
if(odata[VECTOR_SIZE - 1] != sum) {
printf("Erro !!!");
}
printf("Input value: %u, device output: %u\n", idata[i], odata[VECTOR_SIZE - 1]);
CUDA_CHECK_RETURN(cudaFree((void*) d));
CUDA_CHECK_RETURN(cudaDeviceReset());
return 0;
}
|
22,326 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdlib.h>
#include <stdio.h>
#define DEBUG
void initializeMatrix(int *matrix, int rows, int cols);
void printMatrix(int *matrix, int rows, int cols);
__global__ void matrixSumGpu(int *a, int *b, int *c, int rows, int cols);
int main()
{
int rows, cols;
int matrixDim;
int *aHost, *bHost,*cHost;
int *aDevice, *bDevice,*cDevice;
int size;
dim3 gridDim, blockDim;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
printf("Inserire le dimensioni della matrice NxN(rows cols):");
fflush(stdout);
scanf("%d %d", &rows, &cols);
matrixDim = rows * cols;
size = matrixDim * sizeof(int);
blockDim.x = 4; blockDim.y = 8; //blocchi di 32 thread
gridDim.x = cols / blockDim.x + ((cols % blockDim.x) == 0 ? 0 : 1);
gridDim.y = rows / blockDim.y + ((rows % blockDim.y) == 0 ? 0 : 1);
#ifdef DEBUG
printf("Numero di blocchi lungo asse x: %d Numero di blocchi lungo asse y:%d\n", gridDim.x, gridDim.y);
printf("Numero di elementi della matrice : %d\n", matrixDim);
printf("Numero di thread lungo asse x: %d Numero di thread lungo asse y:%d\n", blockDim.x,blockDim.y);
#endif
aHost = (int*)malloc(size);
bHost = (int*)malloc(size);
cHost = (int*)calloc(matrixDim, sizeof(int));
cudaMalloc((void**)&aDevice, size);
cudaMalloc((void**)&bDevice, size);
cudaMalloc((void**)&cDevice, size);
initializeMatrix(aHost, rows, cols);
initializeMatrix(bHost, rows, cols);
cudaMemset(cDevice, 0, size);
#ifdef DEBUG
printMatrix(aHost, rows, cols);
printMatrix(bHost, rows, cols);
#endif
cudaMemcpy(aDevice, aHost, size, cudaMemcpyHostToDevice);
cudaMemcpy(bDevice, bHost, size, cudaMemcpyHostToDevice);
cudaEventRecord(start,0);
matrixSumGpu <<<gridDim, blockDim >>> (aDevice, bDevice, cDevice, rows, cols);
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
float elasped;
cudaEventElapsedTime(&elasped, start, stop);
printf("Tempo per la somma di matrici :%f ms\n",elasped);
cudaMemcpy(cHost, cDevice, size, cudaMemcpyDeviceToHost);
#ifdef DEBUG
printMatrix(cHost, rows, cols);
#endif
return 0;
}
void initializeMatrix(int *matrix, int rows, int cols)
{
int i, j;
for (i = 0; i < rows; i++)
{
for (j = 0; j < cols; j++)
{
matrix[(i*cols) + j] = (i*rows)+j;
}
}
}
void printMatrix(int *matrix, int rows, int cols)
{
int i, j;
if (cols < 10 && rows < 10)
{
for (i = 0; i < rows; i++)
{
for (j = 0; j < cols; j++)
{
printf("%d ", matrix[(i*cols) + j]);
}
printf("\n");
}
printf("\n");
}
}
/*
*a: primo elemento della somma
*b: secondo elemento della somma
*c: array contenete il risultato della somma
cols: numero della colonne
rows: numero delle righe
*/
__global__ void matrixSumGpu(int *a, int *b, int *c, int rows, int cols)
{
int i, j;
//definire id per il controllo
int idx = (blockDim.x*blockIdx.x) + threadIdx.x;
int idy = (blockDim.y*blockIdx.y) + threadIdx.y;
for (i = 0; i < rows; i++)
{
for (j = 0; j < cols; j++)
{
if (idx < cols && idy < rows)
{
c[(i*cols) + j] = a[(i*cols) + j] + b[(i*cols) + j];
}
}
}
}
|
22,327 | #include "includes.h"
__global__ void extractValues(void* fb, int* voxels, int num_voxels, int* values) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < num_voxels) {
//TODO: Make this support other storage_type's besides int32
float* tile = (float*)fb;
values[index] = __float_as_int(tile[voxels[index]]);
}
} |
22,328 | #include "kernel.cuh"
#include <stdio.h>
__global__ void firstKernel(void)
{
printf("Hello I'm thread in block: %d\n", blockIdx.x);
}
void runKernel()
{
getDeviceProperties();
firstKernel << <16, 1 >> > ();
}
void getDeviceProperties()
{
int device_count = 0;
cudaGetDeviceCount(&device_count);
if (device_count == 0)
{
printf("There are no available devices that support CUDA");
}
else
{
printf("Detected %d CUDA capable device(s)\n", device_count);
printDeviceName(0);
printDriverRuntimeVersion();
printMemoryData(0);
printCpuData(0);
}
}
void printDeviceName(int const deviceIndex)
{
cudaDeviceProp device_property;
cudaGetDeviceProperties(&device_property, deviceIndex);
printf("\nDevice %d: \"%s\" \n", 0, device_property.name);
}
void printDriverRuntimeVersion()
{
int driver_version, runtime_version;
cudaDriverGetVersion(&driver_version);
cudaRuntimeGetVersion(&runtime_version);
printf(" CUDA Driver Version / Runtime Version: %d.%d / %d.%d\n", driver_version / 1000, (driver_version % 100) / 10, runtime_version / 1000, (runtime_version % 100) / 10);
}
void printMemoryData(int const deviceIndex)
{
cudaDeviceProp device_property;
cudaGetDeviceProperties(&device_property, deviceIndex);
printf(" Total amount of global memeory %.0f MBytes (%llu bytes)\n", (float)device_property.totalGlobalMem / 1048576.0f, (unsigned long long) device_property.totalGlobalMem);
printf(" Memory Clock rate: %.0f Mhz\n", device_property.memoryClockRate);
if (device_property.l2CacheSize)
{
printf(" L2 Cache Size: %d bytes\n", device_property.l2CacheSize);
}
printf(" Total amount of constant memory: %lu bytes\n", device_property.totalConstMem);
printf(" Total amount of shared memeory per block: %lu bytes\n", device_property.sharedMemPerBlock);
printf(" Total amount of regisers available per block: %d\n", device_property.regsPerBlock);
}
void printCpuData(int const deviceIndex)
{
cudaDeviceProp device_property;
cudaGetDeviceProperties(&device_property, deviceIndex);
printf(" (%2d) Multiprocessors", device_property.multiProcessorCount);
printf(" GPU Max Clock rate: %.0f MHz (%0.2f GHz)\n", device_property.clockRate * 1e-3f, device_property.clockRate * 1e-6f);
printf(" Maximum number of threads per multiprocessor: %d\n", device_property.maxThreadsPerMultiProcessor);
printf(" Maximum number of threads per block: %d\n", device_property.maxThreadsPerBlock);
printf(" Max dimension size of a thread block (x,y,z): (%d, %d, %d)\n", device_property.maxThreadsDim[0], device_property.maxThreadsDim[1], device_property.maxThreadsDim[2]);
printf(" Max dimension size of a grid size (x, y, z): (%d, %d, %d)\n", device_property.maxGridSize[0], device_property.maxGridSize[1], device_property.maxGridSize[2]);
}
|
22,329 | #include <stdio.h>
#include <iostream>
#define M 32
#define N 64
#define BLOCK_DIM 32
__global__ void matrixTranspose(int *d_a, int *d_out, int nRows, int nCols){
// Mapping from 2D block grid to absolute 2D locations on C matrix
int row = blockDim.y * blockIdx.y + threadIdx.y;
int col = blockDim.x * blockIdx.x + threadIdx.x;
// Transpose
if (row < nRows && col < nCols){
d_out[row * nCols + col] = d_a[col * nRows + row];
}
}
int main(){
// Declare 2D matrices on host
int h_a[M][N], h_out[N][M];
// Declare device/GPU memory pointers
int *d_a, *d_out;
// Memory size
int size = M * N * sizeof(int);
// Initialize matrices on host
for (int i=0; i<M; i++){
for (int j=0; j<N; j++){
h_a[i][j] = i * j; // Matrix A
}
}
// Allocate GPU memory
cudaMalloc((void **) &d_a, size);
cudaMalloc((void **) &d_out, size);
// Transfer input matrices from host to device
cudaMemcpy(d_a, h_a, size, cudaMemcpyHostToDevice);
// Define grid blocks dimensions
dim3 blockSize(BLOCK_DIM, BLOCK_DIM);
dim3 gridSize((int)ceil(M/blockSize.x), (int)ceil(N/blockSize.y));
// Launch the kernel
matrixTranspose<<<gridSize, blockSize>>>(d_a, d_out, M, N);
// Copy the result from device to the host
cudaMemcpy(h_out, d_out, size, cudaMemcpyDeviceToHost);
// Print out the original array
for (int i=0; i<M; i++){
for (int j=0; j<N; j++){
printf("A[%d,%d]: %d\t", i, j, h_a[i][j]);
printf("\n");
}
}
// Print out the transposed array
for (int i=0; i<N; i++){
for (int j=0; j<M; j++){
printf("AT[%d,%d]: %d", i, j, h_out[i][j]);
printf("\n");
}
}
// Free GPU memory allocation
cudaFree(d_a);
cudaFree(d_out);
return 0;
}
|
22,330 | #include <stdio.h>
#include <cuda.h>
#define ARRAY_SIZE 64
__global__ void SquareKernel(float *d_out, float *d_in)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
float val = d_in[idx];
d_out[idx] = val * val;
}
int main()
{
const int ARRAY_BYTES = ARRAY_SIZE * sizeof(float);
// Allocate the array on the host
float *h_in, *h_out;
h_in = (float *) malloc(ARRAY_BYTES);
h_out = (float *) malloc(ARRAY_BYTES);
for (int i = 0; i < ARRAY_SIZE; i++)
{
h_in[i] = (float) i;
}
// Allocate arrays onto the device
float *d_in, *d_out;
cudaMalloc((void **) &d_in, ARRAY_BYTES);
cudaMalloc((void **) &d_out, ARRAY_BYTES);
cudaMemcpy(d_in, h_in, ARRAY_BYTES, cudaMemcpyHostToDevice);
// Launch the kernel
dim3 gridDim(ARRAY_SIZE / 2, 1, 1);
dim3 blockDim(ARRAY_SIZE / 2, 1, 1);
SquareKernel<<<gridDim, blockDim>>> (d_out, d_in);
// Copy back the results
cudaMemcpy(h_out, d_out, ARRAY_BYTES, cudaMemcpyDeviceToHost);
// Print the results
for (int i = 0; i < ARRAY_SIZE; i++)
{
printf ("%f", h_out[i]);
printf ((i % 4 == 3)? "\n" : "\t");
}
free(h_in);
free(h_out);
cudaFree(d_in);
cudaFree(d_out);
return 0;
} |
22,331 | #include <algorithm>
namespace kernels {
__global__
void diffusion_interior(double *x0, double *x1, int nx, int ny, double dt) {
auto i = threadIdx.x + blockDim.x*blockIdx.x;
auto j = threadIdx.y + blockDim.y*blockIdx.y+1;
if(i<nx && j<ny-1) {
auto pos = (j+1)*(nx+2) + i + 1;
x1[pos] = x0[pos] + dt* (-4.*x0[pos]
+ x0[pos-nx-2] + x0[pos+nx+2]
+ x0[pos-1] + x0[pos+1]);
}
}
__global__
void diffusion_boundary(double *x0, double *x1, int nx, double dt) {
auto i = threadIdx.x + blockDim.x*blockIdx.x;
if(i<nx) {
auto pos = 0;
x1[pos] = x0[pos] + dt* (-4.*x0[pos]
+ x0[pos-nx-2] + x0[pos+nx+2]
+ x0[pos-1] + x0[pos+1]);
}
}
template <typename T>
__global__
void fill(T *v, T value, int n) {
int tid = threadIdx.x + blockDim.x*blockIdx.x;
if(tid<n) {
v[tid] = value;
}
}
}
void diffusion_interior(double *x0, double *x1, int nx, int ny, double dt, cudaStream_t s=NULL) {
dim3 b_dim(16, 16);
dim3 g_dim(nx/b_dim.x, (ny-2)/b_dim.y);
kernels::diffusion_interior <<<g_dim, b_dim, 0, s>>> (x0, x1, nx, ny, dt);
}
void diffusion_boundary(double *x0, double *x1, int nx, double dt, cudaStream_t s=NULL) {
kernels::diffusion_boundary <<<128, nx/128 + 1, 0, s>>> (x0, x1, nx, dt);
}
template <typename T>
void fill_gpu(T *v, T value, int n) {
auto block_dim = 128ul;
auto grid_dim = n/block_dim + (n%block_dim ? 1 : 0);
kernels::fill<T><<<grid_dim, block_dim>>>(v, value, n);
}
void fill_gpu(double *v, double value, int n) {
fill_gpu<double>(v, value, n);
}
|
22,332 | #include "includes.h"
__global__ void reduceNeighboredLess(int *g_idata, int *g_odata, unsigned int n){
unsigned int tid = threadIdx.x;
unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
int *idata = g_idata + blockIdx.x * blockDim.x;
if(idx >= n) return;
for(int stride = 1; stride < blockDim.x; stride <<= 1){
int index = 2 * stride * tid;
if(index < blockDim.x / 2)
idata[tid] += idata[tid + stride];
__syncthreads();
}
if(tid == 0) g_odata[blockIdx.x] = idata[0];
} |
22,333 | /*
We consider 1024 map, which are initially constructed with (at most)size = 1024 key, value pairs, for which keys are integers and values are float point numbers.
Then each of them processes 262,144 operations.
The operations include:
'i': insert a key-value pair, or modify the original value. (if the map is full, do nothing)
'r': remove a key.
'g': get the value for a key. If that key dose not exist, return 0
*/
#include <bits/stdc++.h>
#include <cassert>
#include <thrust/device_vector.h>
#include <thrust/copy.h>
#include <thrust/sequence.h>
#include <thrust/find.h>
#include <thrust/execution_policy.h>
#define to_ptr(x) thrust::raw_pointer_cast(&x[0])
#define gpu_copy(x, y) thrust::copy((x).begin(), (x).end(), (y).begin())
#define gpu_copy_to(x, y, pos) thrust::copy((x).begin(), (x).end(), (y).begin() + (pos))
#define gpu_seq(x) thrust::sequence((x).begin(), (x).end())
#define host_find(x, n, key) thrust::find((x), (x)+n, key);
#define device_find(x, n, key) thrust::find(thrust::device, (x), (x)+n, key)
#define def_dvec(t) thrust::device_vector<t>
using namespace std;
const int BLOCK_SIZE = 512;
const int NUM_INSTANCE = 1024;
const int NUM_OPERATION = 262144;
const int MOD = 10000;
const int MAX_SIZE = 31;
__device__ int gpuIndex(int size, int *keys, int key){
return (int)(device_find(keys, size, key) - keys);
}
__global__ void procKernel(int n_ins, int *sizes, int *keys, float *values,
int n_ops, char *ops, int *input_keys, float *input_values, float *ans){
int b_id = blockIdx.x, b_sz = blockDim.x, t_id = threadIdx.x;
int global_idx = b_id*b_sz + t_id;
int sz = sizes[global_idx];
int start = global_idx*MAX_SIZE;
for(int i=global_idx*n_ops; i<(global_idx+1)*n_ops; ++i){
int key = input_keys[i];
char op = ops[i];
float value = input_values[i];
int idx = gpuIndex(sz, keys + start, key);
if(op == 'g'){
ans[i] = (idx == sz? 0.:values[start+idx]);
}
else if(op == 'r'){
sz -= (idx != sz);
keys[start+idx] = keys[start+sz];
values[start+idx] = values[start+sz];
}
else{
keys[start + idx] = key;
keys[start + idx] = value;
sz += (idx==sz && sz < MAX_SIZE);
}
}
sizes[global_idx] = sz;
}
class GPUMapTest{
int N_ins, N_ops;
def_dvec(int) dkeys, dinkeys, dsizes;
def_dvec(float) dvalues, dinvalues;
def_dvec(char) dops;
public:
GPUMapTest(int num_ins): N_ins(num_ins){
dkeys.resize(num_ins * MAX_SIZE);
dvalues.resize(num_ins * MAX_SIZE);
dsizes.assign(num_ins, 0);
}
void loadOps(const vector<char> &ops, const vector<int> &inkeys,const vector<float> &invals, int n_ops){
N_ops = n_ops;
assert((int)ops.size() == N_ops * N_ins);
dinkeys.resize(N_ops * N_ins);
dinvalues.resize(N_ops * N_ins);
dops.resize(N_ops * N_ins);
gpu_copy(ops, dops);
gpu_copy(inkeys, dinkeys);
gpu_copy(invals, dinvalues);
}
void procOps(vector<float> &ans){
ans.resize(N_ins * N_ops);
def_dvec(float) dans(N_ins * N_ops);
int n_block = (N_ins+BLOCK_SIZE-1)/BLOCK_SIZE;
procKernel<<<n_block, BLOCK_SIZE>>>(N_ins, to_ptr(dsizes), to_ptr(dkeys), to_ptr(dvalues), N_ops,
to_ptr(dops), to_ptr(dinkeys), to_ptr(dinvalues), to_ptr(dans));
gpu_copy(dans, ans);
return ;
}
};
int main(int argc, char *argv[]){
srand(0);
int num_ins = NUM_INSTANCE, num_ops = NUM_OPERATION;
if(argc > 1) num_ins = stoi(argv[1]);
if(argc > 2) num_ops = stoi(argv[2]);
/* using cudaEvent to evaluate time */
cudaEvent_t start, stop;
float cuda_time;
cudaEventCreate(&start); // creating the event 1
cudaEventCreate(&stop); // creating the event 2
/* Generating data*/
cudaEventRecord(start, 0);
string ref;
ref += string(1500, 'g') + string(300, 'i') + string(200, 'r');
vector<char> ops(num_ins * num_ops);
vector<int> input_keys(num_ins * num_ops);
vector<float> input_values(num_ins * num_ops);
generate(input_keys.begin(), input_keys.end(), [](){return rand()%MOD;});
generate(input_values.begin(), input_values.end(), [](){return float(rand())/RAND_MAX;});
generate(ops.begin(), ops.end(), [&ref](){return ref[rand()%(int)ref.size()];});
cudaEventRecord(stop, 0); // Stop time measuring
cudaEventSynchronize(stop);
cudaEventElapsedTime(&cuda_time, start, stop); // Saving the time measured
cout<<"Time Usage for generating random data is: "<<cuda_time/1000<<"s"<<endl;
cudaEventRecord(start, 0);
GPUMapTest gpu_test(num_ins);
gpu_test.loadOps(ops, input_keys, input_values, num_ops);
cudaEventRecord(stop, 0); // Stop time measuring
cudaEventSynchronize(stop);
cudaEventElapsedTime(&cuda_time, start, stop); // Saving the time measured
cout<<"Time Usage for preparing maps is: "<<cuda_time/1000<<"s"<<endl;
cudaEventRecord(start, 0);
vector<float> ans;
gpu_test.procOps(ans);
cudaEventRecord(stop, 0); // Stop time measuring
cudaEventSynchronize(stop);
cudaEventElapsedTime(&cuda_time, start, stop); // Saving the time measured
cout<<"Time Usage for processing operations is: "<<cuda_time/1000<<"s"<<endl;
cout<<"Showing GPU code answers:"<<endl;
for(int i=0;i<num_ins*num_ops ; i+=num_ins*num_ops/25 + 1) cout<<ans[i]<<' ';
cout << endl;
cout<<"DONE!"<<endl;
return 0;
} |
22,334 | float h_A[]= {
0.6231129915441065, 0.6159796536213568, 0.7516223300466565, 0.5614399174950864, 0.7806484910812693, 0.6231672425083965, 0.9403363360562412, 0.6721354421249044, 0.9066093680806764, 0.8975730352955702, 0.5148014596757533, 0.7417318092438872, 0.7395976420984723, 0.7838657534425599, 0.7958196527715926, 0.9375050923134054, 0.8925208888533525, 0.9182557013015487, 0.7745808923236535, 0.8083106383229153, 0.6827379301611063, 0.7757183931732181, 0.6277217423914624, 0.9584034790959745, 0.5386046765163974, 0.5353430803782013, 0.5459403184326601, 0.8212653861444605, 0.6110477850671165, 0.8689436423980099, 0.8462802727011302, 0.5115158775423836, 0.854267800810526, 0.5401720830282366, 0.5489186938344522, 0.631719961603628, 0.6607827994601548, 0.7434779337661432, 0.8082060954841934, 0.6489736635388637, 0.7154711251206838, 0.8642656107268478, 0.8659626680894827, 0.9498259714562574, 0.8384446230232696, 0.6631239631499953, 0.9040393642032084, 0.6349754120196889, 0.8011538140809963, 0.6006921546597147, 0.6374219346747811, 0.7233058506355274, 0.8207923396424412, 0.5088873655394561, 0.9900742116034964, 0.9266812371695384, 0.8367248863675525, 0.9526369590172841, 0.7322410991662831, 0.8786529394998712, 0.5437779463522676, 0.5380024376868351, 0.5734568167815381, 0.6910514571287729, 0.8544264536935693, 0.9318651407577285, 0.8591376391571255, 0.6156888717580042, 0.8014405849173016, 0.9277805086053746, 0.8784878132450062, 0.798362565885458, 0.649093181842976, 0.9834114914139269, 0.5008324392102079, 0.9612254530848625, 0.912095210579595, 0.940520147390048, 0.8248652530071363, 0.8306116274446957, 0.9551910442225091, 0.7006083425860362, 0.8073798191743724, 0.7654246239545331, 0.7335094060071523, 0.9566657212856664, 0.7134826179794267, 0.9537858036804708, 0.617057065367487, 0.8619508800347451, 0.6843935305503841, 0.5207910741411301, 0.9785500292291038, 0.5070104662087561, 0.6249875591972298, 0.8994998996467646, 0.6259984816008164, 0.8747435062294515, 0.938836018654275, 0.7721455900197995, 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0.5857254635738498, 0.7790757786327085, 0.8823912782279965, 0.9214154491597883, 0.7283615184695518, 0.572591885575124, 0.6648706254943351, 0.9439095672733605, 0.5253407672500108, 0.7863690515129997, 0.5455443278861344, 0.6438136344667222, 0.9374785353493906, 0.8972977047308985, 0.6004819835522256, 0.6395590259775534, 0.8987983499976934, 0.9675072056319514, 0.5633308293726884, 0.8883649203832633, 0.5829654476253352, 0.5013164107323628, 0.7002038083946834, 0.8027518369398386, 0.7215795913003811, 0.5859748694978305, 0.7470136911412459, 0.5582081240957555, 0.799261501548656, 0.6686134702916434, 0.5208705942370586, 0.7449911940535414, 0.8950199102343919, 0.7134236429211035, 0.5779958710026889, 0.9368402820634836, 0.8502208813441919, 0.8491445101319519, 0.7513150657356016, 0.7172601775233629, 0.547086084104076, 0.8632288276386928, 0.7585948127655777, 0.891218077092473, 0.6311271173774031, 0.5241552109128934, 0.5803068018549067, 0.7238705053994479, 0.9661773491980747, 0.8517562205993452, 0.711786301185078, 0.8296536054881389, 0.8558790217822125, 0.5323903899360951, 0.8262630994857088, 0.6903907295961049, 0.7469083552118995, 0.8342707665813881, 0.5462122200305988, 0.8672879710074772, 0.5758876419313687, 0.869605157463223, 0.6592178551205133, 0.6973622229466734, 0.5444106541748334, 0.9909327421621097, 0.8996499604197771, 0.768465382292338, 0.5776504464538238, 0.8114383726714889, 0.7593553266364711, 0.6314834660656488, 0.6918555032946907, 0.700083151240229, 0.9681912256954939, 0.7846189051407151, 0.516513730913807, 0.7023207431215169, 0.7321651052498201, 0.7940881390388713, 0.9372859755510003, 0.9023663558219934, 0.7555736982410377, 0.8905900598157874, 0.8865461570653275, 0.7337637966758268, 0.6816429747871868, 0.5452709612913318, 0.7197302428770824, 0.9438830610381373, 0.9291262762784609, 0.7302872935553663, 0.7929056794151729, 0.5013611390274417, 0.5372735738466381, 0.9153212080882209, 0.9583464807849685, 0.7037102267809356, 0.5598576553428354, 0.8863800357403175, 0.9724423318814701, 0.9564224447330456, 0.8677905698760117, 0.9003301494165303, 0.5657129447860059, 0.798467791704494, 0.9401498438014178, 0.6823359766613204, 0.5169539393374736, 0.7342695165754785, 0.795095758702276, 0.9614107155586284, 0.8670781239460646, 0.8906947141397663, 0.6614170704904325, 0.859461661479137, 0.6665389064922671, 0.9693794334437136, 0.6139655511434061, 0.9288914488787332, 0.5396670982855992, 0.5624244361579509, 0.5469663494910185, 0.5236062110442327, 0.5152344446349895, 0.5296556394241256, 0.8443916034898653, 0.797509423693785, 0.7713180227232399, 0.8402495278829447, 0.8336262728326702, 0.8370090884910961, 0.9281733845509879, 0.7863816078493793, 0.8846075440145162, 0.8428802350410953, 0.9899069857483777, 0.676146913458611, 0.5069454353593996, 0.5037838546347488, 0.6610588010800987, 0.8708305864786401, 0.5229473107472871, 0.7186536996502952, 0.9543379527341599, 0.5244920026126567, 0.7766618825933342, 0.7608328615256252, 0.8831102953474137, 0.6940810220216176, 0.5333676146512109, 0.9761363101399565, 0.6378405154218351, 0.6082935140314678, 0.981202186455274, 0.8245352685285013, 0.6856205466565846, 0.7403575396246995, 0.6906158501879436, 0.7205168960064088, 0.8098689935826404, 0.982614484586956, 0.5883568660391013, 0.9222524734307183, 0.5446886284894845, 0.7929076054618344, 0.7941175390618026, 0.9832317130076127, 0.5240559639585616, 0.5991277846742225, 0.5378314021393389, 0.7833053173354318, 0.5155962746491738, 0.6578837701459107, 0.7338072069374738, 0.6148064664736551, 0.8511326317298731, 0.650017395123197, 0.84438605166194, 0.8804958327061956, 0.965081814330646, 0.6435326947874628, 0.6859080412011882, 0.7422444962935304, 0.5269780803535288, 0.6027197927652561, 0.5527556681128968, 0.8256191392220095, 0.852553880327363, 0.8575192571670468, 0.9097911376931316, 0.7782762919068716, 0.9968082332412347, 0.9881974708156538, 0.9995806799412106, 0.767991853175288, 0.7444533876419199, 0.8394174196445332, 0.8078678404281556, 0.9068389036251978, 0.8477362376140594, 0.5284009390700173, 0.5781693972964606, 0.8041474480683733, 0.5680171668448175, 0.819848096520603, 0.9545895895616169, 0.5533396127084483, 0.5834213793694355, 0.5051049214741941, 0.7664986609305161, 0.8430453215822035, 0.6094766426536279, 0.6831079862056135, 0.6259620571407525, 0.5474972135490375, 0.7127406197928766, 0.861550668606208, 0.8523766113835715, 0.8521930711225006, 0.9790451983107569, 0.7385221557574319, 0.5234069651293946, 0.7205098947606349, 0.8249448851673404, 0.7659185192129242, 0.8303847036490912, 0.5622555130292365, 0.9308027611777011, 0.5616539888537022, 0.7319383197954372, 0.5061269943029434, 0.9426735312079031, 0.7826005559391673, 0.9197263623481077, 0.7100242324565307, 0.7560394523468977, 0.8550501242571193, 0.796814928254245, 0.785994766090303, 0.9248928556152485, 0.8179112362832484, 0.6449497842652434, 0.6089001159333592, 0.5420393736598408, 0.6826536958815741, 0.8801436649474956, 0.6654322893816281, 0.8953558094437972, 0.8015779510584211, 0.7640023620144297, 0.8091059867026058, 0.5991964845073501, 0.9382272364814858, 0.6143011320040478, 0.8730869795622367, 0.5150515353100726, 0.6393414364501309, 0.7734783804714509, 0.7046863886661519, 0.9052940937732548, 0.7796985060243291, 0.6074647520555484, 0.6365357790335463, 0.8385112000984151, 0.8732143714642794, 0.7049051078727842, 0.9572401676467395, 0.9618408715772635, 0.8415257754437142, 0.9554688475078221, 0.5285860774285429, 0.962776106411459, 0.8031072972268947, 0.7945445291217914, 0.6977790422882477, 0.6762561163923575, 0.8314563335555931, 0.600706334214631, 0.8154947812910812, 0.5981870835882845, 0.8753222927491456, 0.5908008990640313, 0.8580794515403676, 0.6413665017230687, 0.6986033114615935, 0.8483254093575165, 0.6723660076982181, 0.8154656465110323, 0.7987938607059342, 0.770209206438285, 0.7464233964515289, 0.8561073756584063, 0.649902106066766, 0.9831066661186172, 0.8800623782776971, 0.617635958547702, 0.7130346521069819, 0.7619438378163561, 0.6250207113316084, 0.7638354012065066, 0.823852852952252, 0.7735471961279943, 0.5369002481090861, 0.6211808505561002, 0.8982083556787313, 0.6718427710895418, 0.9280130600617931, 0.8140280142061238, 0.9679206396033802, 0.8838050367726409, 0.7205677020170126, 0.9425354222404121, 0.7020785062668413, 0.747832714913678, 0.5640599535520416, 0.6199108087754157, 0.660880798016565, 0.6446472508705119, 0.9341524751004308, 0.6246645876994303, 0.8511955223915197, 0.9131765111097774, 0.9674797758822187, 0.8358580718405626, 0.6503799016783753, 0.9156610509271208, 0.6719442005216296, 0.9168411041564094, 0.9823782423757811, 0.7943192397570101, 0.8002030534718996, 0.6138005830285533, 0.9966532879246821, 0.9218142202060416, 0.6111047832272472, 0.5107886273384015, 0.7066643591009043, 0.5324804976936317, 0.782254768451019, 0.6926906483442623, 0.7019715312380815, 0.9571238459454776, 0.9418624921079136, 0.903331917647665, 0.8965127748591281, 0.9928844887655248, 0.7509821302376921, 0.9470496430460422, 0.7400790151957862, 0.5589455059997835, 0.9112044751261599, 0.8732069319444539, 0.8852361901058599, 0.8612065823880574, 0.5962290262854784, 0.6946276316775621, 0.6097538003846591, 0.8005141272637994, 0.5823798928930293, 0.6260994393552735, 0.5832317825084814, 0.6472333666934127, 0.8132164869195939, 0.9510345406422426, 0.525910425617248, 0.8729234904567376, 0.5338493268761877, 0.9957678897732622, 0.76522317293978, 0.5954152993485142, 0.5753687577102655, 0.8076171778553736, 0.7243259231349843, 0.8664092857301835, 0.997002745565357, 0.7548616581381258, 0.926386621110858, 0.5613188842612256, 0.9274671419506799, 0.8423049882893523, 0.7130138885216316, 0.6479984036035871, 0.7491863792955296, 0.5923775471672915, 0.8015792819504799, 0.5154502153370468, 0.5514992046960526, 0.7312015066162371, 0.7892285051959617, 0.9738067271452171, 0.93761537396627, 0.5888392215891104, 0.812357962293455, 0.8895170377953473, 0.9384563892587261, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
0, 2, 4, 6, 8, 10, 12, 14, 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, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 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, 250, 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, 476, 478, 480, 482, 484, 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, 642, 644, 646, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 680, 682, 684, 686, 688, 690, 692, 694, 696, 698, 700, 702, 704, 706, 708, 710, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 768, 770, 772, 774, 776, 778, 780, 782, 784, 786, 788, 790, 792, 794, 796, 798, 800, 802, 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, 824, 826, 828, 830, 832, 834, 836, 838, 840, 842, 844, 846, 848, 850, 852, 854, 856, 858, 860, 862, 864, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 892, 894, 896, 898, 900, 902, 904, 906, 908, 910, 912, 914, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 946, 948, 950, 952, 954, 956, 958, 960, 962, 964, 966, 968, 970, 972, 974, 976, 978, 980, 982, 984, 986, 988, 990, 992, 994, 996, 998, 1000, 1002, 1004, 1006, 1008, 1010, 1012, 1014, 1016, 1018, 1020, 1022, 1024, 1026, 1028, 1030, 1032, 1034, 1036, 1038, 1040, 1042, 1044, 1046, 1048, 1050, 1052, 1054, 1056, 1058, 1060, 1062, 1064, 1066, 1068, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1090, 1092, 1094, 1096, 1098, 1100, 1102, 1104, 1106, 1108, 1110, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1132, 1134, 1136, 1138, 1140, 1142, 1144, 1146, 1148, 1150, 1152, 1154, 1156, 1158, 1160, 1162, 1164, 1166, 1168, 1170, 1172, 1174, 1176, 1178, 1180, 1182, 1184, 1186, 1188, 1190, 1192, 1194, 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, 1288, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1326, 1328, 1330, 1332, 1334, 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, 1388, 1390, 1392, 1394, 1396, 1398, 1400, 1402, 1404, 1406, 1408, 1410, 1412, 1414, 1416, 1418, 1420, 1422, 1424, 1426, 1428, 1430, 1432, 1434, 1436, 1438, 1440, 1442, 1444, 1446, 1448, 1450, 1452, 1454, 1456, 1458, 1460, 1462, 1464, 1466, 1468, 1470, 1472, 1474, 1476, 1478, 1480, 1482, 1484, 1486, 1488, 1490, 1492, 1494, 1496, 1498, 1500, 1502, 1504, 1506, 1508, 1510, 1512, 1514, 1516, 1518, 1520, 1522, 1524, 1526, 1528, 1530, 1532, 1534, 1536, 1538, 1540, 1542, 1544, 1546, 1548, 1550, 1552, 1554, 1556, 1558, 1560, 1562, 1564, 1566, 1568, 1570, 1572, 1574, 1576, 1578, 1580, 1582, 1584, 1586, 1588, 1590, 1592, 1594, 1596, 1598, 1600, 1602, 1604, 1606, 1608, 1610, 1612, 1614, 1616, 1618, 1620, 1622, 1624, 1626, 1628, 1630, 1632, 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, 1738, 1740, 1742, 1744, 1746, 1748, 1750, 1752, 1754, 1756, 1758, 1760, 1762, 1764, 1766, 1768, 1770, 1772, 1774, 1776, 1778, 1780, 1782, 1784, 1786, 1788, 1790, 1792, 1794, 1796, 1798, 1800, 1802, 1804, 1806, 1808, 1810, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1826, 1828, 1830, 1832, 1834, 1836, 1838, 1840, 1842, 1844, 1846, 1848, 1850, 1852, 1854, 1856, 1858, 1860, 1862, 1864, 1866, 1868, 1870, 1872, 1874, 1876, 1878, 1880, 1882, 1884, 1886, 1888, 1890, 1892, 1894, 1896, 1898, 1900, 1902, 1904, 1906, 1908, 1910, 1912, 1914, 1916, 1918, 1920, 1922, 1924, 1926, 1928, 1930, 1932, 1934, 1936, 1938, 1940, 1942, 1944, 1946, 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, 2013, 2015, 2017, 2019, 2021, 2023, 2025, 2027, 2029, 2031, 2033, 2035, 2037, 2039, 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, 2108, 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, 2174, 2176, 2178, 2180, 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34056, 36148, 34060, 34971, 34066, 34067, 36152, 36153, 36154, 36156, 36158, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 36175, 36176, 36177, 36178, 36179, 36180, 36181, 36183, 36184, 36185, 36186, 36187, 36189, 36190, 36191, 36192, 36193, 36194, 36195, 36196, 36197, 36198, 36199, 36200, 36019, 36202, 36203, 36204, 36205, 36206, 36207, 36208, 36209, 36021, 36211, 36212, 36213, 36214, 36215, 36217, 36218, 36219, 36220, 36221, 36222, 36223, 36225, 35134, 36226, 36227, 35145, 36228, 36229, 36230, 35590, 35588, 36231, 36232, 36233, 35151, 36235, 36236, 36238, 36239, 36240, 35166, 36242, 35177, 36243, 36244, 36245, 36246, 36248, 36249, 36250, 36252, 36254, 36256, 36257, 36035, 36259, 36260, 36037, 36262, 36263, 36264, 36265, 36266, 36268, 36269, 36270, 36271, 36272, 36274, 35419, 36275, 36276, 36277, 35929, 36279, 36280, 36282, 36283, 36284, 36286, 36287, 36288, 36289, 36046, 36291, 36293, 36295, 36296, 36050, 36298, 36300, 36302, 36055, 36305, 36306, 36307, 36308, 36309, 36311, 36312, 36313, 36314, 36315, 36316, 36317, 36319, 36320, 36321, 36322, 36323, 36324, 36325, 36326, 36327, 36329, 36330, 36331, 36332, 36333, 36334, 36335, 36336, 36337, 35590, 35588, 36339, 36340, 36341, 35574, 36343, 35601, 36344, 36345, 36346, 35611, 36347, 36348, 36349, 36350, 35230, 36352, 36353, 36355, 36356, 36357, 36359, 36360, 36361, 36363, 36069, 36366, 36367, 36368, 36369, 36071, 36371, 36372, 36373, 36374, 36375, 36377, 36379, 36381, 36383, 36385, 36386, 36387, 36388, 36389, 36391, 35929, 36393, 36394, 36396, 36397, 36398, 35320, 36399, 36400, 36401, 36403, 36404, 36405, 36407, 36408, 36409, 36410, 36087, 36412, 36413, 36414, 36415, 36416, 36418, 36419, 36090, 36421, 36422, 36423, 36424, 36425, 36427, 36428, 36429, 36430, 36431, 36433, 36435, 36437, 36439, 35419, 36440, 36441, 36442, 36445, 36446, 36447, 35929, 36449, 36450, 36451, 36452, 36453, 36454, 36455, 36456, 36457, 36458, 36461, 36462, 36463, 36464, 36465, 36466, 36467, 36469, 36470, 36109, 36472, 36473, 36474, 36475, 36476, 36478, 36480, 36482, 36116, 36485, 36487, 36489, 36490, 36120, 36492, 36493, 36494, 36495, 36496, 36497, 36498, 36499, 36500, 36502, 36504, 36505, 36506, 35574, 36508, 36509, 35581, 36511, 35590, 35588, 36512, 36513, 35601, 36514, 36515, 36516, 35611, 36517, 36518, 36519, 36520, 35617, 36522, 36523, 36525, 36526, 36535, 36536, 36543, 36545, 36546, 36547, 36548, 36582, 36621, 36623, 36624, 36626, 36628, 36630, 36631, 36632, 36635, 36234, 36637, 36638, 36641, 36241, 36643, 36645, 36647, 36648, 36663, 36668, 36670, 36671, 36674, 36278, 36676, 36677, 36679, 36680, 36714, 36723, 36724, 36725, 36726, 36729, 36342, 36731, 36733, 36735, 36737, 36740, 36351, 36742, 36743, 36745, 36746, 36760, 36769, 36771, 36392, 36773, 36774, 36777, 36778, 36780, 36781, 36783, 36784, 36793, 36801, 36806, 36811, 36812, 36814, 36815, 36818, 36448, 36828, 36861, 36862, 36863, 36866, 36507, 36869, 36510, 36871, 36872, 36873, 36875, 36877, 36879, 36881, 36884, 36521, 36886, 36887, 36581, 36579, 36577, 36586, 36584, 35631, 36591, 36589, 36599, 36595, 36597, 36593, 35633, 36608, 36606, 36604, 36602, 35635, 36613, 36611, 35636, 36620, 36618, 36616, 36253, 36251, 36034, 36654, 35646, 36657, 35648, 36662, 36660, 36667, 36665, 36683, 34927, 36688, 36294, 36292, 35659, 36303, 36301, 36299, 34929, 36697, 36695, 35661, 36704, 36702, 36700, 35662, 36713, 36711, 36709, 36707, 36722, 36720, 36718, 36716, 36364, 36362, 35669, 36754, 36752, 35671, 36759, 36757, 36382, 36380, 36378, 36079, 36768, 36766, 36787, 35682, 36792, 36790, 36795, 35685, 36800, 36798, 36805, 36803, 36436, 36434, 36098, 36827, 36825, 36823, 36821, 36834, 36832, 36830, 35697, 36837, 34978, 36842, 36840, 35699, 36483, 36481, 36479, 34980, 36851, 36488, 36486, 35702, 36860, 36858, 36856, 36854, 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, 36622, 36900, 36902, 36634, 36640, 36911, 36669, 36673, 36927, 36728, 36932, 36934, 36739, 36770, 36776, 36810, 36959, 36817, 36963, 36865, 36868, 36971, 36974, 36976, 36883, 36982, 36983, 36984, 34893, 36985, 36986, 36987, 36988, 36989, 36990, 36991, 36992, 36993, 36994, 36995, 36996, 36997, 36998, 36999, 37000, 37001, 37002, 37003, 37004, 37005, 34898, 36908, 36914, 37006, 37007, 37008, 37009, 37010, 37011, 37012, 37013, 37014, 35649, 37015, 37016, 35650, 36922, 36924, 37017, 37018, 37019, 37020, 37021, 37022, 37023, 37024, 37025, 37026, 37027, 37028, 37029, 37030, 37031, 37032, 37033, 37034, 37035, 37036, 37037, 35663, 37038, 37039, 37040, 37041, 35664, 36939, 36941, 37042, 37043, 37044, 37045, 37046, 37047, 37048, 37049, 35672, 37050, 37051, 37052, 37053, 37054, 37055, 35675, 36947, 36951, 36953, 37056, 37057, 37058, 37059, 35683, 37060, 37061, 37062, 37063, 35686, 37064, 37065, 35687, 37066, 37067, 37068, 37069, 37070, 37071, 37072, 37073, 37074, 37075, 37076, 37077, 37078, 37079, 37080, 37081, 37082, 37083, 37084, 37085, 37086, 37087, 37088, 37089, 37090, 37091, 37092, 37093, 35703, 36966, 36981, 37145, 37148, 37149, 37152, 37154, 37156, 37159, 37161, 37164, 37167, 37170, 36899, 36901, 36904, 36906, 37171, 36910, 36912, 37172, 37173, 37180, 37182, 37183, 37185, 36918, 36920, 37186, 37187, 37190, 37194, 37198, 37201, 37205, 37207, 37209, 37210, 37212, 37214, 36929, 36931, 36933, 36935, 36937, 37215, 37216, 37217, 37220, 37223, 37225, 37226, 37230, 37232, 36945, 37233, 36949, 37234, 37235, 37238, 37240, 37243, 37245, 37246, 37248, 37249, 36958, 36960, 36962, 37252, 37254, 35696, 37256, 37262, 37265, 37269, 37273, 37275, 37277, 37278, 36968, 36970, 36973, 36975, 36977, 36979, 37279, 37179, 37177, 37189, 37242, 37237, 37261, 27, 28, 29, 30, 31, 37280, 37283, 37286, 37289, 37291, 37292, 37293, 37294, 37296, 37297, 37304, 37305, 37308, 37309, 37311, 37312, 37315, 37318, 37319, 37320, 37321, 37322, 37329, 37332, 37334, 37344, 37345, 37346, 37347, 37349, 37350, 37352, 37353, 37354, 37358, 37359, 37360, 37361, 37362, 37363, 37166, 37151, 36531, 36534, 37175, 37303, 37365, 37301, 37366, 36538, 36537, 37367, 37200, 36147, 36146, 37328, 37331, 37222, 37219, 36150, 36149, 36544, 37340, 37368, 37251, 37369, 37338, 37342, 37370, 37264, 36159, 36151, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37377, 37163, 37416, 37417, 37281, 37290, 37418, 36530, 36533, 36528, 36527, 36529, 37419, 36532, 37420, 37421, 37423, 37425, 37426, 36890, 36889, 37197, 37204, 37193, 37314, 37317, 37428, 36541, 36540, 37429, 36539, 37430, 36142, 36542, 37229, 37431, 37432, 37433, 37434, 37435, 37436, 37437, 36891, 36892, 37438, 37440, 37442, 37443, 36895, 36894, 36893, 37405, 37259, 37272, 37445, 37356, 37268, 36155, 37446, 36551, 36549, 36552, 37447, 36550, 36553, 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, 37473, 37476, 37477, 37158, 37479, 37480, 37481, 37482, 37483, 37485, 37486, 37422, 37491, 37492, 37489, 37493, 37494, 37495, 37496, 37497, 37499, 37500, 37502, 37504, 37505, 37506, 37508, 37514, 37515, 37511, 37516, 37517, 37518, 37520, 37521, 37522, 37523, 37524, 37525, 37527, 37528, 37529, 37531, 37532, 37533, 37535, 37536, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37571, 37568, 37475, 37478, 37573, 37575, 37484, 37578, 37580, 37583, 37585, 37427, 37588, 37501, 37503, 37593, 37513, 37598, 37601, 37604, 37606, 37526, 37609, 37610, 37612, 37613, 26, 27, 28, 29, 30, 31, 37570, 37633, 37635, 37637, 37639, 37582, 37641, 37643, 37644, 37646, 37647, 37597, 37649, 37650, 37651, 37653, 37654, 37656, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37665, 37666, 37670, 37672, 37674, 37675, 37678, 37680, 37669, 37677, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37701, 37704, 37699, 37703, 37697, 37705, 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, 37728, 37730, 37731, 37732, 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, 37760, 37761, 37763, 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, 37792, 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, 37824, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
int h_C[]= {
1, 3, 5, 7, 9, 11, 13, 15, 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, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 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, 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, 475, 477, 479, 481, 483, 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, 641, 643, 645, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 681, 683, 685, 687, 689, 691, 693, 695, 697, 699, 701, 703, 705, 707, 709, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 767, 769, 771, 773, 775, 777, 779, 781, 783, 785, 787, 789, 791, 793, 795, 797, 799, 801, 803, 805, 807, 809, 811, 813, 815, 817, 819, 821, 823, 825, 827, 829, 831, 833, 835, 837, 839, 841, 843, 845, 847, 849, 851, 853, 855, 857, 859, 861, 863, 865, 867, 869, 871, 873, 875, 877, 879, 881, 883, 885, 887, 889, 891, 893, 895, 897, 899, 901, 903, 905, 907, 909, 911, 913, 915, 917, 919, 921, 923, 925, 927, 929, 931, 933, 935, 937, 939, 941, 943, 945, 947, 949, 951, 953, 955, 957, 959, 961, 963, 965, 967, 969, 971, 973, 975, 977, 979, 981, 983, 985, 987, 989, 991, 993, 995, 997, 999, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, 1019, 1021, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1039, 1041, 1043, 1045, 1047, 1049, 1051, 1053, 1055, 1057, 1059, 1061, 1063, 1065, 1067, 1069, 1071, 1073, 1075, 1077, 1079, 1081, 1083, 1085, 1087, 1089, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1111, 1113, 1115, 1117, 1119, 1121, 1123, 1125, 1127, 1129, 1131, 1133, 1135, 1137, 1139, 1141, 1143, 1145, 1147, 1149, 1151, 1153, 1155, 1157, 1159, 1161, 1163, 1165, 1167, 1169, 1171, 1173, 1175, 1177, 1179, 1181, 1183, 1185, 1187, 1189, 1191, 1193, 1195, 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, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1325, 1327, 1329, 1331, 1333, 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, 1387, 1389, 1391, 1393, 1395, 1397, 1399, 1401, 1403, 1405, 1407, 1409, 1411, 1413, 1415, 1417, 1419, 1421, 1423, 1425, 1427, 1429, 1431, 1433, 1435, 1437, 1439, 1441, 1443, 1445, 1447, 1449, 1451, 1453, 1455, 1457, 1459, 1461, 1463, 1465, 1467, 1469, 1471, 1473, 1475, 1477, 1479, 1481, 1483, 1485, 1487, 1489, 1491, 1493, 1495, 1497, 1499, 1501, 1503, 1505, 1507, 1509, 1511, 1513, 1515, 1517, 1519, 1521, 1523, 1525, 1527, 1529, 1531, 1533, 1535, 1537, 1539, 1541, 1543, 1545, 1547, 1549, 1551, 1553, 1555, 1557, 1559, 1561, 1563, 1565, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 1581, 1583, 1585, 1587, 1589, 1591, 1593, 1595, 1597, 1599, 1601, 1603, 1605, 1607, 1609, 1611, 1613, 1615, 1617, 1619, 1621, 1623, 1625, 1627, 1629, 1631, 1633, 1635, 1637, 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, 1737, 1739, 1741, 1743, 1745, 1747, 1749, 1751, 1753, 1755, 1757, 1759, 1761, 1763, 1765, 1767, 1769, 1771, 1773, 1775, 1777, 1779, 1781, 1783, 1785, 1787, 1789, 1791, 1793, 1795, 1797, 1799, 1801, 1803, 1805, 1807, 1809, 1811, 1813, 1815, 1817, 1819, 1821, 1823, 1825, 1827, 1829, 1831, 1833, 1835, 1837, 1839, 1841, 1843, 1845, 1847, 1849, 1851, 1853, 1855, 1857, 1859, 1861, 1863, 1865, 1867, 1869, 1871, 1873, 1875, 1877, 1879, 1881, 1883, 1885, 1887, 1889, 1891, 1893, 1895, 1897, 1899, 1901, 1903, 1905, 1907, 1909, 1911, 1913, 1915, 1917, 1919, 1921, 1923, 1925, 1927, 1929, 1931, 1933, 1935, 1937, 1939, 1941, 1943, 1945, 1947, 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, 2014, 2016, 2018, 2020, 2022, 2024, 2026, 2028, 2030, 2032, 2034, 2036, 2038, 2040, 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, 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, 2173, 2175, 2177, 2179, 2181, 2183, 2185, 2187, 2189, 2191, 2193, 2195, 2197, 2199, 2201, 2203, 2205, 2207, 2209, 2211, 2213, 2215, 2217, 2219, 2221, 2223, 2225, 2227, 2229, 2231, 2233, 2235, 2237, 2239, 2241, 2243, 2245, 2247, 2249, 2251, 2253, 2255, 2257, 2259, 2261, 2263, 2265, 2267, 2269, 2272, 2274, 2276, 2278, 2281, 2283, 2285, 2287, 2289, 2291, 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, 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, 2561, 2563, 2565, 2567, 2569, 2571, 2573, 2575, 2577, 2579, 2581, 2583, 2585, 2587, 2589, 2591, 2593, 2595, 2597, 2599, 2601, 2603, 2605, 2607, 2609, 2611, 2613, 2615, 2617, 2619, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2643, 2645, 2647, 2649, 2651, 2653, 2655, 2657, 2659, 2661, 2663, 2665, 2667, 2669, 2671, 2673, 2675, 2677, 2679, 2681, 2683, 2685, 2687, 2689, 2691, 2693, 2695, 2697, 2699, 2701, 2703, 2705, 2707, 2709, 2711, 2713, 2715, 2717, 2719, 2721, 2723, 2725, 2727, 2729, 2731, 2733, 2735, 2737, 2739, 2741, 2743, 2745, 2747, 2749, 2751, 2753, 2755, 2757, 2759, 2761, 2763, 2765, 2767, 2769, 2771, 2773, 2775, 2777, 2779, 2781, 2783, 2785, 2787, 2789, 2791, 2793, 2795, 2797, 2799, 2801, 2803, 2805, 2807, 2809, 2811, 2813, 2815, 2817, 2819, 2821, 2823, 2825, 2827, 2829, 2831, 2833, 2835, 2837, 2839, 2841, 2843, 2845, 2847, 2849, 2851, 2853, 2855, 2857, 2859, 2861, 2863, 2865, 2867, 2869, 2871, 2873, 2875, 2877, 2879, 2881, 2883, 2885, 2887, 2889, 2891, 2893, 2895, 2897, 2899, 2901, 2903, 2905, 2907, 2909, 2911, 2913, 2915, 2917, 2919, 2921, 2923, 2925, 2927, 2929, 2931, 2933, 2935, 2937, 2939, 2941, 2943, 2945, 2947, 2949, 2951, 2953, 2955, 2957, 2959, 2961, 2963, 2965, 2967, 2969, 2971, 2973, 2975, 2977, 2979, 2981, 2983, 2985, 2987, 2989, 2991, 2993, 2995, 2997, 2999, 3001, 3003, 3005, 3007, 3009, 3011, 3013, 3015, 3017, 3019, 3021, 3023, 3025, 3027, 3029, 3031, 3033, 3035, 3037, 3039, 3041, 3043, 3045, 3047, 3049, 3051, 3053, 3055, 3057, 3059, 3061, 3063, 3065, 3067, 3069, 3071, 3073, 3075, 3077, 3079, 3081, 3083, 3085, 3087, 3089, 3091, 3093, 3095, 3097, 3099, 3101, 3103, 3105, 3107, 3109, 3111, 3113, 3115, 3117, 3119, 3121, 3123, 3125, 3127, 3129, 3131, 3133, 3135, 3137, 3139, 3141, 3143, 3145, 3147, 3149, 3151, 3153, 3155, 3157, 3159, 3161, 3163, 3165, 3167, 3169, 3171, 3173, 3175, 3177, 3179, 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37239, 14457, 37244, 14462, 37247, 14465, 37250, 37135, 37136, 37137, 37253, 37255, 37138, 37257, 37263, 37266, 37270, 37274, 37276, 14501, 14502, 37139, 37140, 37141, 37142, 37143, 37144, 14517, 37178, 37176, 37188, 37241, 37236, 37260, 27, 28, 29, 30, 31, 37147, 37284, 37287, 37169, 14346, 14348, 14351, 14353, 14357, 14359, 14377, 14379, 37192, 37196, 37203, 37313, 37316, 14412, 14414, 14416, 14418, 14421, 37228, 14443, 14447, 14469, 14471, 14473, 37348, 14479, 37258, 37267, 37271, 37355, 14504, 14506, 14508, 14510, 14512, 14515, 37288, 37282, 37295, 37298, 37299, 37302, 14535, 37300, 14537, 37307, 37306, 14546, 37310, 37324, 37323, 37327, 37330, 37326, 37325, 37336, 37335, 37333, 37339, 14567, 37343, 14569, 37337, 37341, 14578, 37351, 37364, 37357, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37285, 37378, 14520, 14521, 37376, 37379, 14525, 37383, 37385, 37381, 37380, 37382, 14531, 37384, 14533, 14534, 14536, 14538, 14539, 37387, 37386, 37389, 37390, 37388, 37391, 37392, 14548, 37396, 37394, 14551, 37393, 14553, 37395, 37397, 37398, 14557, 14558, 14559, 14560, 14561, 14562, 14563, 37399, 37400, 14566, 14568, 14570, 14571, 37403, 37402, 37401, 37404, 37406, 37408, 14579, 37409, 37407, 37413, 14583, 37412, 37410, 37414, 14587, 37411, 37415, 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, 14519, 14522, 14523, 37472, 14526, 14527, 14528, 14529, 14530, 14532, 37487, 37488, 14540, 14541, 37490, 14542, 14543, 14544, 14545, 14547, 14549, 14550, 14552, 14554, 14555, 14556, 37509, 14564, 14565, 37512, 37439, 37441, 37519, 14572, 14573, 14574, 14575, 14576, 14577, 14580, 14581, 14582, 14584, 14585, 14586, 14588, 14589, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 14524, 37474, 37569, 37572, 37574, 37576, 37577, 37579, 37581, 37584, 37586, 37587, 37589, 37590, 37591, 37507, 37595, 37599, 37602, 37605, 37444, 37607, 37530, 37611, 37534, 37614, 26, 27, 28, 29, 30, 31, 37632, 37634, 37636, 37638, 37424, 37640, 37642, 37498, 37645, 37592, 37594, 37648, 37600, 37603, 37652, 37608, 37655, 37657, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37664, 37667, 37671, 37673, 37510, 37596, 37679, 37681, 37668, 37676, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 37700, 14591, 37698, 37702, 37696, 14595, 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, 14590, 14592, 14593, 14594, 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, 37729, 37762, 37733, 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, 37793, 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, 37794, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
bool h_Op[]= {
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 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, 0, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 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, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
#define THREADS_PER_BLOCK 32
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 14624
#define SIZE_OF_AC 23264
__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[1184*THREADS_PER_BLOCK];
const int t= THREADS_PER_BLOCK;
__shared__ float final;
final=0;
R[i + 0*t] = A[i + 0*t];
R[i + 1*t] = A[i + 1*t];
R[i + 2*t] = A[i + 2*t];
R[i + 3*t] = A[i + 3*t];
R[i + 4*t] = A[i + 4*t];
R[i + 5*t] = A[i + 5*t];
R[i + 6*t] = A[i + 6*t];
R[i + 7*t] = A[i + 7*t];
R[i + 8*t] = A[i + 8*t];
R[i + 9*t] = A[i + 9*t];
R[i + 10*t] = A[i + 10*t];
R[i + 11*t] = A[i + 11*t];
R[i + 12*t] = A[i + 12*t];
R[i + 13*t] = A[i + 13*t];
R[i + 14*t] = A[i + 14*t];
R[i + 15*t] = A[i + 15*t];
R[i + 16*t] = A[i + 16*t];
R[i + 17*t] = A[i + 17*t];
R[i + 18*t] = A[i + 18*t];
R[i + 19*t] = A[i + 19*t];
R[i + 20*t] = A[i + 20*t];
R[i + 21*t] = A[i + 21*t];
R[i + 22*t] = A[i + 22*t];
R[i + 23*t] = A[i + 23*t];
R[i + 24*t] = A[i + 24*t];
R[i + 25*t] = A[i + 25*t];
R[i + 26*t] = A[i + 26*t];
R[i + 27*t] = A[i + 27*t];
R[i + 28*t] = A[i + 28*t];
R[i + 29*t] = A[i + 29*t];
R[i + 30*t] = A[i + 30*t];
R[i + 31*t] = A[i + 31*t];
R[i + 32*t] = A[i + 32*t];
R[i + 33*t] = A[i + 33*t];
R[i + 34*t] = A[i + 34*t];
R[i + 35*t] = A[i + 35*t];
R[i + 36*t] = A[i + 36*t];
R[i + 37*t] = A[i + 37*t];
R[i + 38*t] = A[i + 38*t];
R[i + 39*t] = A[i + 39*t];
R[i + 40*t] = A[i + 40*t];
R[i + 41*t] = A[i + 41*t];
R[i + 42*t] = A[i + 42*t];
R[i + 43*t] = A[i + 43*t];
R[i + 44*t] = A[i + 44*t];
R[i + 45*t] = A[i + 45*t];
R[i + 46*t] = A[i + 46*t];
R[i + 47*t] = A[i + 47*t];
R[i + 48*t] = A[i + 48*t];
R[i + 49*t] = A[i + 49*t];
R[i + 50*t] = A[i + 50*t];
R[i + 51*t] = A[i + 51*t];
R[i + 52*t] = A[i + 52*t];
R[i + 53*t] = A[i + 53*t];
R[i + 54*t] = A[i + 54*t];
R[i + 55*t] = A[i + 55*t];
R[i + 56*t] = A[i + 56*t];
R[i + 57*t] = A[i + 57*t];
R[i + 58*t] = A[i + 58*t];
R[i + 59*t] = A[i + 59*t];
R[i + 60*t] = A[i + 60*t];
R[i + 61*t] = A[i + 61*t];
R[i + 62*t] = A[i + 62*t];
R[i + 63*t] = A[i + 63*t];
R[i + 64*t] = A[i + 64*t];
R[i + 65*t] = A[i + 65*t];
R[i + 66*t] = A[i + 66*t];
R[i + 67*t] = A[i + 67*t];
R[i + 68*t] = A[i + 68*t];
R[i + 69*t] = A[i + 69*t];
R[i + 70*t] = A[i + 70*t];
R[i + 71*t] = A[i + 71*t];
R[i + 72*t] = A[i + 72*t];
R[i + 73*t] = A[i + 73*t];
R[i + 74*t] = A[i + 74*t];
R[i + 75*t] = A[i + 75*t];
R[i + 76*t] = A[i + 76*t];
R[i + 77*t] = A[i + 77*t];
R[i + 78*t] = A[i + 78*t];
R[i + 79*t] = A[i + 79*t];
R[i + 80*t] = A[i + 80*t];
R[i + 81*t] = A[i + 81*t];
R[i + 82*t] = A[i + 82*t];
R[i + 83*t] = A[i + 83*t];
R[i + 84*t] = A[i + 84*t];
R[i + 85*t] = A[i + 85*t];
R[i + 86*t] = A[i + 86*t];
R[i + 87*t] = A[i + 87*t];
R[i + 88*t] = A[i + 88*t];
R[i + 89*t] = A[i + 89*t];
R[i + 90*t] = A[i + 90*t];
R[i + 91*t] = A[i + 91*t];
R[i + 92*t] = A[i + 92*t];
R[i + 93*t] = A[i + 93*t];
R[i + 94*t] = A[i + 94*t];
R[i + 95*t] = A[i + 95*t];
R[i + 96*t] = A[i + 96*t];
R[i + 97*t] = A[i + 97*t];
R[i + 98*t] = A[i + 98*t];
R[i + 99*t] = A[i + 99*t];
R[i + 100*t] = A[i + 100*t];
R[i + 101*t] = A[i + 101*t];
R[i + 102*t] = A[i + 102*t];
R[i + 103*t] = A[i + 103*t];
R[i + 104*t] = A[i + 104*t];
R[i + 105*t] = A[i + 105*t];
R[i + 106*t] = A[i + 106*t];
R[i + 107*t] = A[i + 107*t];
R[i + 108*t] = A[i + 108*t];
R[i + 109*t] = A[i + 109*t];
R[i + 110*t] = A[i + 110*t];
R[i + 111*t] = A[i + 111*t];
R[i + 112*t] = A[i + 112*t];
R[i + 113*t] = A[i + 113*t];
R[i + 114*t] = A[i + 114*t];
R[i + 115*t] = A[i + 115*t];
R[i + 116*t] = A[i + 116*t];
R[i + 117*t] = A[i + 117*t];
R[i + 118*t] = A[i + 118*t];
R[i + 119*t] = A[i + 119*t];
R[i + 120*t] = A[i + 120*t];
R[i + 121*t] = A[i + 121*t];
R[i + 122*t] = A[i + 122*t];
R[i + 123*t] = A[i + 123*t];
R[i + 124*t] = A[i + 124*t];
R[i + 125*t] = A[i + 125*t];
R[i + 126*t] = A[i + 126*t];
R[i + 127*t] = A[i + 127*t];
R[i + 128*t] = A[i + 128*t];
R[i + 129*t] = A[i + 129*t];
R[i + 130*t] = A[i + 130*t];
R[i + 131*t] = A[i + 131*t];
R[i + 132*t] = A[i + 132*t];
R[i + 133*t] = A[i + 133*t];
R[i + 134*t] = A[i + 134*t];
R[i + 135*t] = A[i + 135*t];
R[i + 136*t] = A[i + 136*t];
R[i + 137*t] = A[i + 137*t];
R[i + 138*t] = A[i + 138*t];
R[i + 139*t] = A[i + 139*t];
R[i + 140*t] = A[i + 140*t];
R[i + 141*t] = A[i + 141*t];
R[i + 142*t] = A[i + 142*t];
R[i + 143*t] = A[i + 143*t];
R[i + 144*t] = A[i + 144*t];
R[i + 145*t] = A[i + 145*t];
R[i + 146*t] = A[i + 146*t];
R[i + 147*t] = A[i + 147*t];
R[i + 148*t] = A[i + 148*t];
R[i + 149*t] = A[i + 149*t];
R[i + 150*t] = A[i + 150*t];
R[i + 151*t] = A[i + 151*t];
R[i + 152*t] = A[i + 152*t];
R[i + 153*t] = A[i + 153*t];
R[i + 154*t] = A[i + 154*t];
R[i + 155*t] = A[i + 155*t];
R[i + 156*t] = A[i + 156*t];
R[i + 157*t] = A[i + 157*t];
R[i + 158*t] = A[i + 158*t];
R[i + 159*t] = A[i + 159*t];
R[i + 160*t] = A[i + 160*t];
R[i + 161*t] = A[i + 161*t];
R[i + 162*t] = A[i + 162*t];
R[i + 163*t] = A[i + 163*t];
R[i + 164*t] = A[i + 164*t];
R[i + 165*t] = A[i + 165*t];
R[i + 166*t] = A[i + 166*t];
R[i + 167*t] = A[i + 167*t];
R[i + 168*t] = A[i + 168*t];
R[i + 169*t] = A[i + 169*t];
R[i + 170*t] = A[i + 170*t];
R[i + 171*t] = A[i + 171*t];
R[i + 172*t] = A[i + 172*t];
R[i + 173*t] = A[i + 173*t];
R[i + 174*t] = A[i + 174*t];
R[i + 175*t] = A[i + 175*t];
R[i + 176*t] = A[i + 176*t];
R[i + 177*t] = A[i + 177*t];
R[i + 178*t] = A[i + 178*t];
R[i + 179*t] = A[i + 179*t];
R[i + 180*t] = A[i + 180*t];
R[i + 181*t] = A[i + 181*t];
R[i + 182*t] = A[i + 182*t];
R[i + 183*t] = A[i + 183*t];
R[i + 184*t] = A[i + 184*t];
R[i + 185*t] = A[i + 185*t];
R[i + 186*t] = A[i + 186*t];
R[i + 187*t] = A[i + 187*t];
R[i + 188*t] = A[i + 188*t];
R[i + 189*t] = A[i + 189*t];
R[i + 190*t] = A[i + 190*t];
R[i + 191*t] = A[i + 191*t];
R[i + 192*t] = A[i + 192*t];
R[i + 193*t] = A[i + 193*t];
R[i + 194*t] = A[i + 194*t];
R[i + 195*t] = A[i + 195*t];
R[i + 196*t] = A[i + 196*t];
R[i + 197*t] = A[i + 197*t];
R[i + 198*t] = A[i + 198*t];
R[i + 199*t] = A[i + 199*t];
R[i + 200*t] = A[i + 200*t];
R[i + 201*t] = A[i + 201*t];
R[i + 202*t] = A[i + 202*t];
R[i + 203*t] = A[i + 203*t];
R[i + 204*t] = A[i + 204*t];
R[i + 205*t] = A[i + 205*t];
R[i + 206*t] = A[i + 206*t];
R[i + 207*t] = A[i + 207*t];
R[i + 208*t] = A[i + 208*t];
R[i + 209*t] = A[i + 209*t];
R[i + 210*t] = A[i + 210*t];
R[i + 211*t] = A[i + 211*t];
R[i + 212*t] = A[i + 212*t];
R[i + 213*t] = A[i + 213*t];
R[i + 214*t] = A[i + 214*t];
R[i + 215*t] = A[i + 215*t];
R[i + 216*t] = A[i + 216*t];
R[i + 217*t] = A[i + 217*t];
R[i + 218*t] = A[i + 218*t];
R[i + 219*t] = A[i + 219*t];
R[i + 220*t] = A[i + 220*t];
R[i + 221*t] = A[i + 221*t];
R[i + 222*t] = A[i + 222*t];
R[i + 223*t] = A[i + 223*t];
R[i + 224*t] = A[i + 224*t];
R[i + 225*t] = A[i + 225*t];
R[i + 226*t] = A[i + 226*t];
R[i + 227*t] = A[i + 227*t];
R[i + 228*t] = A[i + 228*t];
R[i + 229*t] = A[i + 229*t];
R[i + 230*t] = A[i + 230*t];
R[i + 231*t] = A[i + 231*t];
R[i + 232*t] = A[i + 232*t];
R[i + 233*t] = A[i + 233*t];
R[i + 234*t] = A[i + 234*t];
R[i + 235*t] = A[i + 235*t];
R[i + 236*t] = A[i + 236*t];
R[i + 237*t] = A[i + 237*t];
R[i + 238*t] = A[i + 238*t];
R[i + 239*t] = A[i + 239*t];
R[i + 240*t] = A[i + 240*t];
R[i + 241*t] = A[i + 241*t];
R[i + 242*t] = A[i + 242*t];
R[i + 243*t] = A[i + 243*t];
R[i + 244*t] = A[i + 244*t];
R[i + 245*t] = A[i + 245*t];
R[i + 246*t] = A[i + 246*t];
R[i + 247*t] = A[i + 247*t];
R[i + 248*t] = A[i + 248*t];
R[i + 249*t] = A[i + 249*t];
R[i + 250*t] = A[i + 250*t];
R[i + 251*t] = A[i + 251*t];
R[i + 252*t] = A[i + 252*t];
R[i + 253*t] = A[i + 253*t];
R[i + 254*t] = A[i + 254*t];
R[i + 255*t] = A[i + 255*t];
R[i + 256*t] = A[i + 256*t];
R[i + 257*t] = A[i + 257*t];
R[i + 258*t] = A[i + 258*t];
R[i + 259*t] = A[i + 259*t];
R[i + 260*t] = A[i + 260*t];
R[i + 261*t] = A[i + 261*t];
R[i + 262*t] = A[i + 262*t];
R[i + 263*t] = A[i + 263*t];
R[i + 264*t] = A[i + 264*t];
R[i + 265*t] = A[i + 265*t];
R[i + 266*t] = A[i + 266*t];
R[i + 267*t] = A[i + 267*t];
R[i + 268*t] = A[i + 268*t];
R[i + 269*t] = A[i + 269*t];
R[i + 270*t] = A[i + 270*t];
R[i + 271*t] = A[i + 271*t];
R[i + 272*t] = A[i + 272*t];
R[i + 273*t] = A[i + 273*t];
R[i + 274*t] = A[i + 274*t];
R[i + 275*t] = A[i + 275*t];
R[i + 276*t] = A[i + 276*t];
R[i + 277*t] = A[i + 277*t];
R[i + 278*t] = A[i + 278*t];
R[i + 279*t] = A[i + 279*t];
R[i + 280*t] = A[i + 280*t];
R[i + 281*t] = A[i + 281*t];
R[i + 282*t] = A[i + 282*t];
R[i + 283*t] = A[i + 283*t];
R[i + 284*t] = A[i + 284*t];
R[i + 285*t] = A[i + 285*t];
R[i + 286*t] = A[i + 286*t];
R[i + 287*t] = A[i + 287*t];
R[i + 288*t] = A[i + 288*t];
R[i + 289*t] = A[i + 289*t];
R[i + 290*t] = A[i + 290*t];
R[i + 291*t] = A[i + 291*t];
R[i + 292*t] = A[i + 292*t];
R[i + 293*t] = A[i + 293*t];
R[i + 294*t] = A[i + 294*t];
R[i + 295*t] = A[i + 295*t];
R[i + 296*t] = A[i + 296*t];
R[i + 297*t] = A[i + 297*t];
R[i + 298*t] = A[i + 298*t];
R[i + 299*t] = A[i + 299*t];
R[i + 300*t] = A[i + 300*t];
R[i + 301*t] = A[i + 301*t];
R[i + 302*t] = A[i + 302*t];
R[i + 303*t] = A[i + 303*t];
R[i + 304*t] = A[i + 304*t];
R[i + 305*t] = A[i + 305*t];
R[i + 306*t] = A[i + 306*t];
R[i + 307*t] = A[i + 307*t];
R[i + 308*t] = A[i + 308*t];
R[i + 309*t] = A[i + 309*t];
R[i + 310*t] = A[i + 310*t];
R[i + 311*t] = A[i + 311*t];
R[i + 312*t] = A[i + 312*t];
R[i + 313*t] = A[i + 313*t];
R[i + 314*t] = A[i + 314*t];
R[i + 315*t] = A[i + 315*t];
R[i + 316*t] = A[i + 316*t];
R[i + 317*t] = A[i + 317*t];
R[i + 318*t] = A[i + 318*t];
R[i + 319*t] = A[i + 319*t];
R[i + 320*t] = A[i + 320*t];
R[i + 321*t] = A[i + 321*t];
R[i + 322*t] = A[i + 322*t];
R[i + 323*t] = A[i + 323*t];
R[i + 324*t] = A[i + 324*t];
R[i + 325*t] = A[i + 325*t];
R[i + 326*t] = A[i + 326*t];
R[i + 327*t] = A[i + 327*t];
R[i + 328*t] = A[i + 328*t];
R[i + 329*t] = A[i + 329*t];
R[i + 330*t] = A[i + 330*t];
R[i + 331*t] = A[i + 331*t];
R[i + 332*t] = A[i + 332*t];
R[i + 333*t] = A[i + 333*t];
R[i + 334*t] = A[i + 334*t];
R[i + 335*t] = A[i + 335*t];
R[i + 336*t] = A[i + 336*t];
R[i + 337*t] = A[i + 337*t];
R[i + 338*t] = A[i + 338*t];
R[i + 339*t] = A[i + 339*t];
R[i + 340*t] = A[i + 340*t];
R[i + 341*t] = A[i + 341*t];
R[i + 342*t] = A[i + 342*t];
R[i + 343*t] = A[i + 343*t];
R[i + 344*t] = A[i + 344*t];
R[i + 345*t] = A[i + 345*t];
R[i + 346*t] = A[i + 346*t];
R[i + 347*t] = A[i + 347*t];
R[i + 348*t] = A[i + 348*t];
R[i + 349*t] = A[i + 349*t];
R[i + 350*t] = A[i + 350*t];
R[i + 351*t] = A[i + 351*t];
R[i + 352*t] = A[i + 352*t];
R[i + 353*t] = A[i + 353*t];
R[i + 354*t] = A[i + 354*t];
R[i + 355*t] = A[i + 355*t];
R[i + 356*t] = A[i + 356*t];
R[i + 357*t] = A[i + 357*t];
R[i + 358*t] = A[i + 358*t];
R[i + 359*t] = A[i + 359*t];
R[i + 360*t] = A[i + 360*t];
R[i + 361*t] = A[i + 361*t];
R[i + 362*t] = A[i + 362*t];
R[i + 363*t] = A[i + 363*t];
R[i + 364*t] = A[i + 364*t];
R[i + 365*t] = A[i + 365*t];
R[i + 366*t] = A[i + 366*t];
R[i + 367*t] = A[i + 367*t];
R[i + 368*t] = A[i + 368*t];
R[i + 369*t] = A[i + 369*t];
R[i + 370*t] = A[i + 370*t];
R[i + 371*t] = A[i + 371*t];
R[i + 372*t] = A[i + 372*t];
R[i + 373*t] = A[i + 373*t];
R[i + 374*t] = A[i + 374*t];
R[i + 375*t] = A[i + 375*t];
R[i + 376*t] = A[i + 376*t];
R[i + 377*t] = A[i + 377*t];
R[i + 378*t] = A[i + 378*t];
R[i + 379*t] = A[i + 379*t];
R[i + 380*t] = A[i + 380*t];
R[i + 381*t] = A[i + 381*t];
R[i + 382*t] = A[i + 382*t];
R[i + 383*t] = A[i + 383*t];
R[i + 384*t] = A[i + 384*t];
R[i + 385*t] = A[i + 385*t];
R[i + 386*t] = A[i + 386*t];
R[i + 387*t] = A[i + 387*t];
R[i + 388*t] = A[i + 388*t];
R[i + 389*t] = A[i + 389*t];
R[i + 390*t] = A[i + 390*t];
R[i + 391*t] = A[i + 391*t];
R[i + 392*t] = A[i + 392*t];
R[i + 393*t] = A[i + 393*t];
R[i + 394*t] = A[i + 394*t];
R[i + 395*t] = A[i + 395*t];
R[i + 396*t] = A[i + 396*t];
R[i + 397*t] = A[i + 397*t];
R[i + 398*t] = A[i + 398*t];
R[i + 399*t] = A[i + 399*t];
R[i + 400*t] = A[i + 400*t];
R[i + 401*t] = A[i + 401*t];
R[i + 402*t] = A[i + 402*t];
R[i + 403*t] = A[i + 403*t];
R[i + 404*t] = A[i + 404*t];
R[i + 405*t] = A[i + 405*t];
R[i + 406*t] = A[i + 406*t];
R[i + 407*t] = A[i + 407*t];
R[i + 408*t] = A[i + 408*t];
R[i + 409*t] = A[i + 409*t];
R[i + 410*t] = A[i + 410*t];
R[i + 411*t] = A[i + 411*t];
R[i + 412*t] = A[i + 412*t];
R[i + 413*t] = A[i + 413*t];
R[i + 414*t] = A[i + 414*t];
R[i + 415*t] = A[i + 415*t];
R[i + 416*t] = A[i + 416*t];
R[i + 417*t] = A[i + 417*t];
R[i + 418*t] = A[i + 418*t];
R[i + 419*t] = A[i + 419*t];
R[i + 420*t] = A[i + 420*t];
R[i + 421*t] = A[i + 421*t];
R[i + 422*t] = A[i + 422*t];
R[i + 423*t] = A[i + 423*t];
R[i + 424*t] = A[i + 424*t];
R[i + 425*t] = A[i + 425*t];
R[i + 426*t] = A[i + 426*t];
R[i + 427*t] = A[i + 427*t];
R[i + 428*t] = A[i + 428*t];
R[i + 429*t] = A[i + 429*t];
R[i + 430*t] = A[i + 430*t];
R[i + 431*t] = A[i + 431*t];
R[i + 432*t] = A[i + 432*t];
R[i + 433*t] = A[i + 433*t];
R[i + 434*t] = A[i + 434*t];
R[i + 435*t] = A[i + 435*t];
R[i + 436*t] = A[i + 436*t];
R[i + 437*t] = A[i + 437*t];
R[i + 438*t] = A[i + 438*t];
R[i + 439*t] = A[i + 439*t];
R[i + 440*t] = A[i + 440*t];
R[i + 441*t] = A[i + 441*t];
R[i + 442*t] = A[i + 442*t];
R[i + 443*t] = A[i + 443*t];
R[i + 444*t] = A[i + 444*t];
R[i + 445*t] = A[i + 445*t];
R[i + 446*t] = A[i + 446*t];
R[i + 447*t] = A[i + 447*t];
R[i + 448*t] = A[i + 448*t];
R[i + 449*t] = A[i + 449*t];
R[i + 450*t] = A[i + 450*t];
R[i + 451*t] = A[i + 451*t];
R[i + 452*t] = A[i + 452*t];
R[i + 453*t] = A[i + 453*t];
R[i + 454*t] = A[i + 454*t];
R[i + 455*t] = A[i + 455*t];
R[i + 456*t] = A[i + 456*t];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 457*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 + 458*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 + 459*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 + 460*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 + 461*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 + 462*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 + 463*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 + 464*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 + 465*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 + 466*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 + 467*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 + 468*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 + 469*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 + 470*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 + 471*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 + 472*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 + 473*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 + 474*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 + 475*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 + 476*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 + 477*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 + 478*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 + 479*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 + 480*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 + 481*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 + 482*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 + 483*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 + 484*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 + 485*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 + 486*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 + 487*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 + 488*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 + 489*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 + 490*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 + 491*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 + 492*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 + 493*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 + 494*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 + 495*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 + 496*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 + 497*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 + 498*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 + 499*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 + 500*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 + 501*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 + 502*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 + 503*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 + 504*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 + 505*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 + 506*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 + 507*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 + 508*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 + 509*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 + 510*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 + 511*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 + 512*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 + 513*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 + 514*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 + 515*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 + 516*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 + 517*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 + 518*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 + 519*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 + 520*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 + 521*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 + 522*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 + 523*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 + 524*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 + 525*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 + 526*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 + 527*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 + 528*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 + 529*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 + 530*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 + 531*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 + 532*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 + 533*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 + 534*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 + 535*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 + 536*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 + 537*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 + 538*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 + 539*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 + 540*t] = Op[i + 83*t] ? R[B[i + 83*t]] * R[C[i + 83*t]] : R[B[i + 83*t]] + R[C[i + 83*t]];
R[i + 541*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 + 542*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 + 543*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 + 544*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 + 545*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 + 546*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 + 547*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 + 548*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 + 549*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 + 550*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 + 551*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 + 552*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 + 553*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 + 554*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 + 555*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 + 556*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 + 557*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 + 558*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 + 559*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 + 560*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 + 561*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 + 562*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 + 563*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 + 564*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 + 565*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 + 566*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 + 567*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 + 568*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 + 569*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 + 570*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 + 571*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 + 572*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 + 573*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 + 574*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 + 575*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 + 576*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 + 577*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 + 578*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 + 579*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 + 580*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 + 581*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 + 582*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 + 583*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 + 584*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 + 585*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 + 586*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 + 587*t] = Op[i + 130*t] ? R[B[i + 130*t]] * R[C[i + 130*t]] : R[B[i + 130*t]] + R[C[i + 130*t]];
R[i + 588*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 + 589*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 + 590*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 + 591*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 + 592*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 + 593*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 + 594*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 + 595*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 + 596*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 + 597*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]];
__syncthreads();
R[i + 598*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 + 599*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 + 600*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 + 601*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 + 602*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 + 603*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 + 604*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 + 605*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 + 606*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 + 607*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 + 608*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 + 609*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 + 610*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 + 611*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 + 612*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 + 613*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 + 614*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 + 615*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 + 616*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 + 617*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 + 618*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 + 619*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 + 620*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 + 621*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 + 622*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 + 623*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 + 624*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 + 625*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 + 626*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 + 627*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 + 628*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 + 629*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 + 630*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 + 631*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 + 632*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 + 633*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 + 634*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 + 635*t] = Op[i + 178*t] ? R[B[i + 178*t]] * R[C[i + 178*t]] : R[B[i + 178*t]] + R[C[i + 178*t]];
R[i + 636*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 + 637*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 + 638*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 + 639*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 + 640*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 + 641*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 + 642*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 + 643*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 + 644*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 + 645*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 + 646*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 + 647*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 + 648*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 + 649*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 + 650*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 + 651*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 + 652*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 + 653*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 + 654*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 + 655*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 + 656*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 + 657*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 + 658*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 + 659*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 + 660*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 + 661*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 + 662*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 + 663*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 + 664*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 + 665*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 + 666*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 + 667*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 + 668*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 + 669*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 + 670*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 + 671*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 + 672*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 + 673*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 + 674*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 + 675*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 + 676*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 + 677*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 + 678*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 + 679*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 + 680*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 + 681*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 + 682*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 + 683*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 + 684*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 + 685*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 + 686*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 + 687*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 + 688*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 + 689*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 + 690*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 + 691*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 + 692*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 + 693*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 + 694*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 + 695*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 + 696*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 + 697*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 + 698*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 + 699*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 + 700*t] = Op[i + 243*t] ? R[B[i + 243*t]] * R[C[i + 243*t]] : R[B[i + 243*t]] + R[C[i + 243*t]];
R[i + 701*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 + 702*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 + 703*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 + 704*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 + 705*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 + 706*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 + 707*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 + 708*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 + 709*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 + 710*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 + 711*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 + 712*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 + 713*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 + 714*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 + 715*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 + 716*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 + 717*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 + 718*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 + 719*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 + 720*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 + 721*t] = Op[i + 264*t] ? R[B[i + 264*t]] * R[C[i + 264*t]] : R[B[i + 264*t]] + R[C[i + 264*t]];
R[i + 722*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 + 723*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 + 724*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 + 725*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 + 726*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 + 727*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 + 728*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 + 729*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 + 730*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 + 731*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 + 732*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 + 733*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 + 734*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 + 735*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 + 736*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 + 737*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 + 738*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 + 739*t] = Op[i + 282*t] ? R[B[i + 282*t]] * R[C[i + 282*t]] : R[B[i + 282*t]] + R[C[i + 282*t]];
R[i + 740*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 + 741*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 + 742*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 + 743*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 + 744*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 + 745*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 + 746*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 + 747*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 + 748*t] = Op[i + 291*t] ? R[B[i + 291*t]] * R[C[i + 291*t]] : R[B[i + 291*t]] + R[C[i + 291*t]];
R[i + 749*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 + 750*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 + 751*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]];
__syncthreads();
R[i + 752*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 + 753*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 + 754*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 + 755*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 + 756*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 + 757*t] = Op[i + 300*t] ? R[B[i + 300*t]] * R[C[i + 300*t]] : R[B[i + 300*t]] + R[C[i + 300*t]];
R[i + 758*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 + 759*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 + 760*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 + 761*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 + 762*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 + 763*t] = Op[i + 306*t] ? R[B[i + 306*t]] * R[C[i + 306*t]] : R[B[i + 306*t]] + R[C[i + 306*t]];
R[i + 764*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 + 765*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 + 766*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 + 767*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 + 768*t] = Op[i + 311*t] ? R[B[i + 311*t]] * R[C[i + 311*t]] : R[B[i + 311*t]] + R[C[i + 311*t]];
R[i + 769*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 + 770*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 + 771*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 + 772*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 + 773*t] = Op[i + 316*t] ? R[B[i + 316*t]] * R[C[i + 316*t]] : R[B[i + 316*t]] + R[C[i + 316*t]];
R[i + 774*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 + 775*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 + 776*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 + 777*t] = Op[i + 320*t] ? R[B[i + 320*t]] * R[C[i + 320*t]] : R[B[i + 320*t]] + R[C[i + 320*t]];
R[i + 778*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 + 779*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 + 780*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 + 781*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 + 782*t] = Op[i + 325*t] ? R[B[i + 325*t]] * R[C[i + 325*t]] : R[B[i + 325*t]] + R[C[i + 325*t]];
R[i + 783*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 + 784*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 + 785*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 + 786*t] = Op[i + 329*t] ? R[B[i + 329*t]] * R[C[i + 329*t]] : R[B[i + 329*t]] + R[C[i + 329*t]];
R[i + 787*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 + 788*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 + 789*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 + 790*t] = Op[i + 333*t] ? R[B[i + 333*t]] * R[C[i + 333*t]] : R[B[i + 333*t]] + R[C[i + 333*t]];
R[i + 791*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 + 792*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 + 793*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 + 794*t] = Op[i + 337*t] ? R[B[i + 337*t]] * R[C[i + 337*t]] : R[B[i + 337*t]] + R[C[i + 337*t]];
R[i + 795*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 + 796*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 + 797*t] = Op[i + 340*t] ? R[B[i + 340*t]] * R[C[i + 340*t]] : R[B[i + 340*t]] + R[C[i + 340*t]];
R[i + 798*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 + 799*t] = Op[i + 342*t] ? R[B[i + 342*t]] * R[C[i + 342*t]] : R[B[i + 342*t]] + R[C[i + 342*t]];
R[i + 800*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 + 801*t] = Op[i + 344*t] ? R[B[i + 344*t]] * R[C[i + 344*t]] : R[B[i + 344*t]] + R[C[i + 344*t]];
R[i + 802*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 + 803*t] = Op[i + 346*t] ? R[B[i + 346*t]] * R[C[i + 346*t]] : R[B[i + 346*t]] + R[C[i + 346*t]];
R[i + 804*t] = Op[i + 347*t] ? R[B[i + 347*t]] * R[C[i + 347*t]] : R[B[i + 347*t]] + R[C[i + 347*t]];
R[i + 805*t] = Op[i + 348*t] ? R[B[i + 348*t]] * R[C[i + 348*t]] : R[B[i + 348*t]] + R[C[i + 348*t]];
R[i + 806*t] = Op[i + 349*t] ? R[B[i + 349*t]] * R[C[i + 349*t]] : R[B[i + 349*t]] + R[C[i + 349*t]];
R[i + 807*t] = Op[i + 350*t] ? R[B[i + 350*t]] * R[C[i + 350*t]] : R[B[i + 350*t]] + R[C[i + 350*t]];
R[i + 808*t] = Op[i + 351*t] ? R[B[i + 351*t]] * R[C[i + 351*t]] : R[B[i + 351*t]] + R[C[i + 351*t]];
R[i + 809*t] = Op[i + 352*t] ? R[B[i + 352*t]] * R[C[i + 352*t]] : R[B[i + 352*t]] + R[C[i + 352*t]];
R[i + 810*t] = Op[i + 353*t] ? R[B[i + 353*t]] * R[C[i + 353*t]] : R[B[i + 353*t]] + R[C[i + 353*t]];
R[i + 811*t] = Op[i + 354*t] ? R[B[i + 354*t]] * R[C[i + 354*t]] : R[B[i + 354*t]] + R[C[i + 354*t]];
R[i + 812*t] = Op[i + 355*t] ? R[B[i + 355*t]] * R[C[i + 355*t]] : R[B[i + 355*t]] + R[C[i + 355*t]];
R[i + 813*t] = Op[i + 356*t] ? R[B[i + 356*t]] * R[C[i + 356*t]] : R[B[i + 356*t]] + R[C[i + 356*t]];
R[i + 814*t] = Op[i + 357*t] ? R[B[i + 357*t]] * R[C[i + 357*t]] : R[B[i + 357*t]] + R[C[i + 357*t]];
R[i + 815*t] = Op[i + 358*t] ? R[B[i + 358*t]] * R[C[i + 358*t]] : R[B[i + 358*t]] + R[C[i + 358*t]];
R[i + 816*t] = Op[i + 359*t] ? R[B[i + 359*t]] * R[C[i + 359*t]] : R[B[i + 359*t]] + R[C[i + 359*t]];
R[i + 817*t] = Op[i + 360*t] ? R[B[i + 360*t]] * R[C[i + 360*t]] : R[B[i + 360*t]] + R[C[i + 360*t]];
R[i + 818*t] = Op[i + 361*t] ? R[B[i + 361*t]] * R[C[i + 361*t]] : R[B[i + 361*t]] + R[C[i + 361*t]];
R[i + 819*t] = Op[i + 362*t] ? R[B[i + 362*t]] * R[C[i + 362*t]] : R[B[i + 362*t]] + R[C[i + 362*t]];
R[i + 820*t] = Op[i + 363*t] ? R[B[i + 363*t]] * R[C[i + 363*t]] : R[B[i + 363*t]] + R[C[i + 363*t]];
R[i + 821*t] = Op[i + 364*t] ? R[B[i + 364*t]] * R[C[i + 364*t]] : R[B[i + 364*t]] + R[C[i + 364*t]];
R[i + 822*t] = Op[i + 365*t] ? R[B[i + 365*t]] * R[C[i + 365*t]] : R[B[i + 365*t]] + R[C[i + 365*t]];
R[i + 823*t] = Op[i + 366*t] ? R[B[i + 366*t]] * R[C[i + 366*t]] : R[B[i + 366*t]] + R[C[i + 366*t]];
__syncthreads();
R[i + 824*t] = Op[i + 367*t] ? R[B[i + 367*t]] * R[C[i + 367*t]] : R[B[i + 367*t]] + R[C[i + 367*t]];
R[i + 825*t] = Op[i + 368*t] ? R[B[i + 368*t]] * R[C[i + 368*t]] : R[B[i + 368*t]] + R[C[i + 368*t]];
R[i + 826*t] = Op[i + 369*t] ? R[B[i + 369*t]] * R[C[i + 369*t]] : R[B[i + 369*t]] + R[C[i + 369*t]];
R[i + 827*t] = Op[i + 370*t] ? R[B[i + 370*t]] * R[C[i + 370*t]] : R[B[i + 370*t]] + R[C[i + 370*t]];
R[i + 828*t] = Op[i + 371*t] ? R[B[i + 371*t]] * R[C[i + 371*t]] : R[B[i + 371*t]] + R[C[i + 371*t]];
R[i + 829*t] = Op[i + 372*t] ? R[B[i + 372*t]] * R[C[i + 372*t]] : R[B[i + 372*t]] + R[C[i + 372*t]];
R[i + 830*t] = Op[i + 373*t] ? R[B[i + 373*t]] * R[C[i + 373*t]] : R[B[i + 373*t]] + R[C[i + 373*t]];
R[i + 831*t] = Op[i + 374*t] ? R[B[i + 374*t]] * R[C[i + 374*t]] : R[B[i + 374*t]] + R[C[i + 374*t]];
R[i + 832*t] = Op[i + 375*t] ? R[B[i + 375*t]] * R[C[i + 375*t]] : R[B[i + 375*t]] + R[C[i + 375*t]];
R[i + 833*t] = Op[i + 376*t] ? R[B[i + 376*t]] * R[C[i + 376*t]] : R[B[i + 376*t]] + R[C[i + 376*t]];
R[i + 834*t] = Op[i + 377*t] ? R[B[i + 377*t]] * R[C[i + 377*t]] : R[B[i + 377*t]] + R[C[i + 377*t]];
R[i + 835*t] = Op[i + 378*t] ? R[B[i + 378*t]] * R[C[i + 378*t]] : R[B[i + 378*t]] + R[C[i + 378*t]];
R[i + 836*t] = Op[i + 379*t] ? R[B[i + 379*t]] * R[C[i + 379*t]] : R[B[i + 379*t]] + R[C[i + 379*t]];
R[i + 837*t] = Op[i + 380*t] ? R[B[i + 380*t]] * R[C[i + 380*t]] : R[B[i + 380*t]] + R[C[i + 380*t]];
R[i + 838*t] = Op[i + 381*t] ? R[B[i + 381*t]] * R[C[i + 381*t]] : R[B[i + 381*t]] + R[C[i + 381*t]];
R[i + 839*t] = Op[i + 382*t] ? R[B[i + 382*t]] * R[C[i + 382*t]] : R[B[i + 382*t]] + R[C[i + 382*t]];
R[i + 840*t] = Op[i + 383*t] ? R[B[i + 383*t]] * R[C[i + 383*t]] : R[B[i + 383*t]] + R[C[i + 383*t]];
R[i + 841*t] = Op[i + 384*t] ? R[B[i + 384*t]] * R[C[i + 384*t]] : R[B[i + 384*t]] + R[C[i + 384*t]];
R[i + 842*t] = Op[i + 385*t] ? R[B[i + 385*t]] * R[C[i + 385*t]] : R[B[i + 385*t]] + R[C[i + 385*t]];
R[i + 843*t] = Op[i + 386*t] ? R[B[i + 386*t]] * R[C[i + 386*t]] : R[B[i + 386*t]] + R[C[i + 386*t]];
R[i + 844*t] = Op[i + 387*t] ? R[B[i + 387*t]] * R[C[i + 387*t]] : R[B[i + 387*t]] + R[C[i + 387*t]];
R[i + 845*t] = Op[i + 388*t] ? R[B[i + 388*t]] * R[C[i + 388*t]] : R[B[i + 388*t]] + R[C[i + 388*t]];
R[i + 846*t] = Op[i + 389*t] ? R[B[i + 389*t]] * R[C[i + 389*t]] : R[B[i + 389*t]] + R[C[i + 389*t]];
R[i + 847*t] = Op[i + 390*t] ? R[B[i + 390*t]] * R[C[i + 390*t]] : R[B[i + 390*t]] + R[C[i + 390*t]];
R[i + 848*t] = Op[i + 391*t] ? R[B[i + 391*t]] * R[C[i + 391*t]] : R[B[i + 391*t]] + R[C[i + 391*t]];
R[i + 849*t] = Op[i + 392*t] ? R[B[i + 392*t]] * R[C[i + 392*t]] : R[B[i + 392*t]] + R[C[i + 392*t]];
R[i + 850*t] = Op[i + 393*t] ? R[B[i + 393*t]] * R[C[i + 393*t]] : R[B[i + 393*t]] + R[C[i + 393*t]];
R[i + 851*t] = Op[i + 394*t] ? R[B[i + 394*t]] * R[C[i + 394*t]] : R[B[i + 394*t]] + R[C[i + 394*t]];
R[i + 852*t] = Op[i + 395*t] ? R[B[i + 395*t]] * R[C[i + 395*t]] : R[B[i + 395*t]] + R[C[i + 395*t]];
R[i + 853*t] = Op[i + 396*t] ? R[B[i + 396*t]] * R[C[i + 396*t]] : R[B[i + 396*t]] + R[C[i + 396*t]];
R[i + 854*t] = Op[i + 397*t] ? R[B[i + 397*t]] * R[C[i + 397*t]] : R[B[i + 397*t]] + R[C[i + 397*t]];
R[i + 855*t] = Op[i + 398*t] ? R[B[i + 398*t]] * R[C[i + 398*t]] : R[B[i + 398*t]] + R[C[i + 398*t]];
R[i + 856*t] = Op[i + 399*t] ? R[B[i + 399*t]] * R[C[i + 399*t]] : R[B[i + 399*t]] + R[C[i + 399*t]];
R[i + 857*t] = Op[i + 400*t] ? R[B[i + 400*t]] * R[C[i + 400*t]] : R[B[i + 400*t]] + R[C[i + 400*t]];
R[i + 858*t] = Op[i + 401*t] ? R[B[i + 401*t]] * R[C[i + 401*t]] : R[B[i + 401*t]] + R[C[i + 401*t]];
R[i + 859*t] = Op[i + 402*t] ? R[B[i + 402*t]] * R[C[i + 402*t]] : R[B[i + 402*t]] + R[C[i + 402*t]];
R[i + 860*t] = Op[i + 403*t] ? R[B[i + 403*t]] * R[C[i + 403*t]] : R[B[i + 403*t]] + R[C[i + 403*t]];
R[i + 861*t] = Op[i + 404*t] ? R[B[i + 404*t]] * R[C[i + 404*t]] : R[B[i + 404*t]] + R[C[i + 404*t]];
R[i + 862*t] = Op[i + 405*t] ? R[B[i + 405*t]] * R[C[i + 405*t]] : R[B[i + 405*t]] + R[C[i + 405*t]];
R[i + 863*t] = Op[i + 406*t] ? R[B[i + 406*t]] * R[C[i + 406*t]] : R[B[i + 406*t]] + R[C[i + 406*t]];
R[i + 864*t] = Op[i + 407*t] ? R[B[i + 407*t]] * R[C[i + 407*t]] : R[B[i + 407*t]] + R[C[i + 407*t]];
R[i + 865*t] = Op[i + 408*t] ? R[B[i + 408*t]] * R[C[i + 408*t]] : R[B[i + 408*t]] + R[C[i + 408*t]];
R[i + 866*t] = Op[i + 409*t] ? R[B[i + 409*t]] * R[C[i + 409*t]] : R[B[i + 409*t]] + R[C[i + 409*t]];
R[i + 867*t] = Op[i + 410*t] ? R[B[i + 410*t]] * R[C[i + 410*t]] : R[B[i + 410*t]] + R[C[i + 410*t]];
R[i + 868*t] = Op[i + 411*t] ? R[B[i + 411*t]] * R[C[i + 411*t]] : R[B[i + 411*t]] + R[C[i + 411*t]];
R[i + 869*t] = Op[i + 412*t] ? R[B[i + 412*t]] * R[C[i + 412*t]] : R[B[i + 412*t]] + R[C[i + 412*t]];
__syncthreads();
R[i + 870*t] = Op[i + 413*t] ? R[B[i + 413*t]] * R[C[i + 413*t]] : R[B[i + 413*t]] + R[C[i + 413*t]];
R[i + 871*t] = Op[i + 414*t] ? R[B[i + 414*t]] * R[C[i + 414*t]] : R[B[i + 414*t]] + R[C[i + 414*t]];
R[i + 872*t] = Op[i + 415*t] ? R[B[i + 415*t]] * R[C[i + 415*t]] : R[B[i + 415*t]] + R[C[i + 415*t]];
R[i + 873*t] = Op[i + 416*t] ? R[B[i + 416*t]] * R[C[i + 416*t]] : R[B[i + 416*t]] + R[C[i + 416*t]];
R[i + 874*t] = Op[i + 417*t] ? R[B[i + 417*t]] * R[C[i + 417*t]] : R[B[i + 417*t]] + R[C[i + 417*t]];
R[i + 875*t] = Op[i + 418*t] ? R[B[i + 418*t]] * R[C[i + 418*t]] : R[B[i + 418*t]] + R[C[i + 418*t]];
R[i + 876*t] = Op[i + 419*t] ? R[B[i + 419*t]] * R[C[i + 419*t]] : R[B[i + 419*t]] + R[C[i + 419*t]];
R[i + 877*t] = Op[i + 420*t] ? R[B[i + 420*t]] * R[C[i + 420*t]] : R[B[i + 420*t]] + R[C[i + 420*t]];
R[i + 878*t] = Op[i + 421*t] ? R[B[i + 421*t]] * R[C[i + 421*t]] : R[B[i + 421*t]] + R[C[i + 421*t]];
R[i + 879*t] = Op[i + 422*t] ? R[B[i + 422*t]] * R[C[i + 422*t]] : R[B[i + 422*t]] + R[C[i + 422*t]];
R[i + 880*t] = Op[i + 423*t] ? R[B[i + 423*t]] * R[C[i + 423*t]] : R[B[i + 423*t]] + R[C[i + 423*t]];
R[i + 881*t] = Op[i + 424*t] ? R[B[i + 424*t]] * R[C[i + 424*t]] : R[B[i + 424*t]] + R[C[i + 424*t]];
R[i + 882*t] = Op[i + 425*t] ? R[B[i + 425*t]] * R[C[i + 425*t]] : R[B[i + 425*t]] + R[C[i + 425*t]];
R[i + 883*t] = Op[i + 426*t] ? R[B[i + 426*t]] * R[C[i + 426*t]] : R[B[i + 426*t]] + R[C[i + 426*t]];
R[i + 884*t] = Op[i + 427*t] ? R[B[i + 427*t]] * R[C[i + 427*t]] : R[B[i + 427*t]] + R[C[i + 427*t]];
R[i + 885*t] = Op[i + 428*t] ? R[B[i + 428*t]] * R[C[i + 428*t]] : R[B[i + 428*t]] + R[C[i + 428*t]];
R[i + 886*t] = Op[i + 429*t] ? R[B[i + 429*t]] * R[C[i + 429*t]] : R[B[i + 429*t]] + R[C[i + 429*t]];
R[i + 887*t] = Op[i + 430*t] ? R[B[i + 430*t]] * R[C[i + 430*t]] : R[B[i + 430*t]] + R[C[i + 430*t]];
R[i + 888*t] = Op[i + 431*t] ? R[B[i + 431*t]] * R[C[i + 431*t]] : R[B[i + 431*t]] + R[C[i + 431*t]];
R[i + 889*t] = Op[i + 432*t] ? R[B[i + 432*t]] * R[C[i + 432*t]] : R[B[i + 432*t]] + R[C[i + 432*t]];
R[i + 890*t] = Op[i + 433*t] ? R[B[i + 433*t]] * R[C[i + 433*t]] : R[B[i + 433*t]] + R[C[i + 433*t]];
R[i + 891*t] = Op[i + 434*t] ? R[B[i + 434*t]] * R[C[i + 434*t]] : R[B[i + 434*t]] + R[C[i + 434*t]];
R[i + 892*t] = Op[i + 435*t] ? R[B[i + 435*t]] * R[C[i + 435*t]] : R[B[i + 435*t]] + R[C[i + 435*t]];
R[i + 893*t] = Op[i + 436*t] ? R[B[i + 436*t]] * R[C[i + 436*t]] : R[B[i + 436*t]] + R[C[i + 436*t]];
R[i + 894*t] = Op[i + 437*t] ? R[B[i + 437*t]] * R[C[i + 437*t]] : R[B[i + 437*t]] + R[C[i + 437*t]];
R[i + 895*t] = Op[i + 438*t] ? R[B[i + 438*t]] * R[C[i + 438*t]] : R[B[i + 438*t]] + R[C[i + 438*t]];
R[i + 896*t] = Op[i + 439*t] ? R[B[i + 439*t]] * R[C[i + 439*t]] : R[B[i + 439*t]] + R[C[i + 439*t]];
R[i + 897*t] = Op[i + 440*t] ? R[B[i + 440*t]] * R[C[i + 440*t]] : R[B[i + 440*t]] + R[C[i + 440*t]];
R[i + 898*t] = Op[i + 441*t] ? R[B[i + 441*t]] * R[C[i + 441*t]] : R[B[i + 441*t]] + R[C[i + 441*t]];
R[i + 899*t] = Op[i + 442*t] ? R[B[i + 442*t]] * R[C[i + 442*t]] : R[B[i + 442*t]] + R[C[i + 442*t]];
R[i + 900*t] = Op[i + 443*t] ? R[B[i + 443*t]] * R[C[i + 443*t]] : R[B[i + 443*t]] + R[C[i + 443*t]];
R[i + 901*t] = Op[i + 444*t] ? R[B[i + 444*t]] * R[C[i + 444*t]] : R[B[i + 444*t]] + R[C[i + 444*t]];
R[i + 902*t] = Op[i + 445*t] ? R[B[i + 445*t]] * R[C[i + 445*t]] : R[B[i + 445*t]] + R[C[i + 445*t]];
R[i + 903*t] = Op[i + 446*t] ? R[B[i + 446*t]] * R[C[i + 446*t]] : R[B[i + 446*t]] + R[C[i + 446*t]];
R[i + 904*t] = Op[i + 447*t] ? R[B[i + 447*t]] * R[C[i + 447*t]] : R[B[i + 447*t]] + R[C[i + 447*t]];
R[i + 905*t] = Op[i + 448*t] ? R[B[i + 448*t]] * R[C[i + 448*t]] : R[B[i + 448*t]] + R[C[i + 448*t]];
R[i + 906*t] = Op[i + 449*t] ? R[B[i + 449*t]] * R[C[i + 449*t]] : R[B[i + 449*t]] + R[C[i + 449*t]];
R[i + 907*t] = Op[i + 450*t] ? R[B[i + 450*t]] * R[C[i + 450*t]] : R[B[i + 450*t]] + R[C[i + 450*t]];
R[i + 908*t] = Op[i + 451*t] ? R[B[i + 451*t]] * R[C[i + 451*t]] : R[B[i + 451*t]] + R[C[i + 451*t]];
R[i + 909*t] = Op[i + 452*t] ? R[B[i + 452*t]] * R[C[i + 452*t]] : R[B[i + 452*t]] + R[C[i + 452*t]];
R[i + 910*t] = Op[i + 453*t] ? R[B[i + 453*t]] * R[C[i + 453*t]] : R[B[i + 453*t]] + R[C[i + 453*t]];
R[i + 911*t] = Op[i + 454*t] ? R[B[i + 454*t]] * R[C[i + 454*t]] : R[B[i + 454*t]] + R[C[i + 454*t]];
R[i + 912*t] = Op[i + 455*t] ? R[B[i + 455*t]] * R[C[i + 455*t]] : R[B[i + 455*t]] + R[C[i + 455*t]];
R[i + 913*t] = Op[i + 456*t] ? R[B[i + 456*t]] * R[C[i + 456*t]] : R[B[i + 456*t]] + R[C[i + 456*t]];
R[i + 914*t] = Op[i + 457*t] ? R[B[i + 457*t]] * R[C[i + 457*t]] : R[B[i + 457*t]] + R[C[i + 457*t]];
R[i + 915*t] = Op[i + 458*t] ? R[B[i + 458*t]] * R[C[i + 458*t]] : R[B[i + 458*t]] + R[C[i + 458*t]];
R[i + 916*t] = Op[i + 459*t] ? R[B[i + 459*t]] * R[C[i + 459*t]] : R[B[i + 459*t]] + R[C[i + 459*t]];
R[i + 917*t] = Op[i + 460*t] ? R[B[i + 460*t]] * R[C[i + 460*t]] : R[B[i + 460*t]] + R[C[i + 460*t]];
R[i + 918*t] = Op[i + 461*t] ? R[B[i + 461*t]] * R[C[i + 461*t]] : R[B[i + 461*t]] + R[C[i + 461*t]];
R[i + 919*t] = Op[i + 462*t] ? R[B[i + 462*t]] * R[C[i + 462*t]] : R[B[i + 462*t]] + R[C[i + 462*t]];
R[i + 920*t] = Op[i + 463*t] ? R[B[i + 463*t]] * R[C[i + 463*t]] : R[B[i + 463*t]] + R[C[i + 463*t]];
R[i + 921*t] = Op[i + 464*t] ? R[B[i + 464*t]] * R[C[i + 464*t]] : R[B[i + 464*t]] + R[C[i + 464*t]];
R[i + 922*t] = Op[i + 465*t] ? R[B[i + 465*t]] * R[C[i + 465*t]] : R[B[i + 465*t]] + R[C[i + 465*t]];
R[i + 923*t] = Op[i + 466*t] ? R[B[i + 466*t]] * R[C[i + 466*t]] : R[B[i + 466*t]] + R[C[i + 466*t]];
R[i + 924*t] = Op[i + 467*t] ? R[B[i + 467*t]] * R[C[i + 467*t]] : R[B[i + 467*t]] + R[C[i + 467*t]];
R[i + 925*t] = Op[i + 468*t] ? R[B[i + 468*t]] * R[C[i + 468*t]] : R[B[i + 468*t]] + R[C[i + 468*t]];
R[i + 926*t] = Op[i + 469*t] ? R[B[i + 469*t]] * R[C[i + 469*t]] : R[B[i + 469*t]] + R[C[i + 469*t]];
R[i + 927*t] = Op[i + 470*t] ? R[B[i + 470*t]] * R[C[i + 470*t]] : R[B[i + 470*t]] + R[C[i + 470*t]];
R[i + 928*t] = Op[i + 471*t] ? R[B[i + 471*t]] * R[C[i + 471*t]] : R[B[i + 471*t]] + R[C[i + 471*t]];
R[i + 929*t] = Op[i + 472*t] ? R[B[i + 472*t]] * R[C[i + 472*t]] : R[B[i + 472*t]] + R[C[i + 472*t]];
R[i + 930*t] = Op[i + 473*t] ? R[B[i + 473*t]] * R[C[i + 473*t]] : R[B[i + 473*t]] + R[C[i + 473*t]];
R[i + 931*t] = Op[i + 474*t] ? R[B[i + 474*t]] * R[C[i + 474*t]] : R[B[i + 474*t]] + R[C[i + 474*t]];
R[i + 932*t] = Op[i + 475*t] ? R[B[i + 475*t]] * R[C[i + 475*t]] : R[B[i + 475*t]] + R[C[i + 475*t]];
R[i + 933*t] = Op[i + 476*t] ? R[B[i + 476*t]] * R[C[i + 476*t]] : R[B[i + 476*t]] + R[C[i + 476*t]];
R[i + 934*t] = Op[i + 477*t] ? R[B[i + 477*t]] * R[C[i + 477*t]] : R[B[i + 477*t]] + R[C[i + 477*t]];
R[i + 935*t] = Op[i + 478*t] ? R[B[i + 478*t]] * R[C[i + 478*t]] : R[B[i + 478*t]] + R[C[i + 478*t]];
R[i + 936*t] = Op[i + 479*t] ? R[B[i + 479*t]] * R[C[i + 479*t]] : R[B[i + 479*t]] + R[C[i + 479*t]];
R[i + 937*t] = Op[i + 480*t] ? R[B[i + 480*t]] * R[C[i + 480*t]] : R[B[i + 480*t]] + R[C[i + 480*t]];
R[i + 938*t] = Op[i + 481*t] ? R[B[i + 481*t]] * R[C[i + 481*t]] : R[B[i + 481*t]] + R[C[i + 481*t]];
R[i + 939*t] = Op[i + 482*t] ? R[B[i + 482*t]] * R[C[i + 482*t]] : R[B[i + 482*t]] + R[C[i + 482*t]];
R[i + 940*t] = Op[i + 483*t] ? R[B[i + 483*t]] * R[C[i + 483*t]] : R[B[i + 483*t]] + R[C[i + 483*t]];
R[i + 941*t] = Op[i + 484*t] ? R[B[i + 484*t]] * R[C[i + 484*t]] : R[B[i + 484*t]] + R[C[i + 484*t]];
R[i + 942*t] = Op[i + 485*t] ? R[B[i + 485*t]] * R[C[i + 485*t]] : R[B[i + 485*t]] + R[C[i + 485*t]];
R[i + 943*t] = Op[i + 486*t] ? R[B[i + 486*t]] * R[C[i + 486*t]] : R[B[i + 486*t]] + R[C[i + 486*t]];
R[i + 944*t] = Op[i + 487*t] ? R[B[i + 487*t]] * R[C[i + 487*t]] : R[B[i + 487*t]] + R[C[i + 487*t]];
R[i + 945*t] = Op[i + 488*t] ? R[B[i + 488*t]] * R[C[i + 488*t]] : R[B[i + 488*t]] + R[C[i + 488*t]];
__syncthreads();
R[i + 946*t] = Op[i + 489*t] ? R[B[i + 489*t]] * R[C[i + 489*t]] : R[B[i + 489*t]] + R[C[i + 489*t]];
R[i + 947*t] = Op[i + 490*t] ? R[B[i + 490*t]] * R[C[i + 490*t]] : R[B[i + 490*t]] + R[C[i + 490*t]];
R[i + 948*t] = Op[i + 491*t] ? R[B[i + 491*t]] * R[C[i + 491*t]] : R[B[i + 491*t]] + R[C[i + 491*t]];
R[i + 949*t] = Op[i + 492*t] ? R[B[i + 492*t]] * R[C[i + 492*t]] : R[B[i + 492*t]] + R[C[i + 492*t]];
R[i + 950*t] = Op[i + 493*t] ? R[B[i + 493*t]] * R[C[i + 493*t]] : R[B[i + 493*t]] + R[C[i + 493*t]];
R[i + 951*t] = Op[i + 494*t] ? R[B[i + 494*t]] * R[C[i + 494*t]] : R[B[i + 494*t]] + R[C[i + 494*t]];
R[i + 952*t] = Op[i + 495*t] ? R[B[i + 495*t]] * R[C[i + 495*t]] : R[B[i + 495*t]] + R[C[i + 495*t]];
R[i + 953*t] = Op[i + 496*t] ? R[B[i + 496*t]] * R[C[i + 496*t]] : R[B[i + 496*t]] + R[C[i + 496*t]];
R[i + 954*t] = Op[i + 497*t] ? R[B[i + 497*t]] * R[C[i + 497*t]] : R[B[i + 497*t]] + R[C[i + 497*t]];
R[i + 955*t] = Op[i + 498*t] ? R[B[i + 498*t]] * R[C[i + 498*t]] : R[B[i + 498*t]] + R[C[i + 498*t]];
R[i + 956*t] = Op[i + 499*t] ? R[B[i + 499*t]] * R[C[i + 499*t]] : R[B[i + 499*t]] + R[C[i + 499*t]];
R[i + 957*t] = Op[i + 500*t] ? R[B[i + 500*t]] * R[C[i + 500*t]] : R[B[i + 500*t]] + R[C[i + 500*t]];
R[i + 958*t] = Op[i + 501*t] ? R[B[i + 501*t]] * R[C[i + 501*t]] : R[B[i + 501*t]] + R[C[i + 501*t]];
R[i + 959*t] = Op[i + 502*t] ? R[B[i + 502*t]] * R[C[i + 502*t]] : R[B[i + 502*t]] + R[C[i + 502*t]];
R[i + 960*t] = Op[i + 503*t] ? R[B[i + 503*t]] * R[C[i + 503*t]] : R[B[i + 503*t]] + R[C[i + 503*t]];
R[i + 961*t] = Op[i + 504*t] ? R[B[i + 504*t]] * R[C[i + 504*t]] : R[B[i + 504*t]] + R[C[i + 504*t]];
R[i + 962*t] = Op[i + 505*t] ? R[B[i + 505*t]] * R[C[i + 505*t]] : R[B[i + 505*t]] + R[C[i + 505*t]];
R[i + 963*t] = Op[i + 506*t] ? R[B[i + 506*t]] * R[C[i + 506*t]] : R[B[i + 506*t]] + R[C[i + 506*t]];
R[i + 964*t] = Op[i + 507*t] ? R[B[i + 507*t]] * R[C[i + 507*t]] : R[B[i + 507*t]] + R[C[i + 507*t]];
R[i + 965*t] = Op[i + 508*t] ? R[B[i + 508*t]] * R[C[i + 508*t]] : R[B[i + 508*t]] + R[C[i + 508*t]];
R[i + 966*t] = Op[i + 509*t] ? R[B[i + 509*t]] * R[C[i + 509*t]] : R[B[i + 509*t]] + R[C[i + 509*t]];
R[i + 967*t] = Op[i + 510*t] ? R[B[i + 510*t]] * R[C[i + 510*t]] : R[B[i + 510*t]] + R[C[i + 510*t]];
R[i + 968*t] = Op[i + 511*t] ? R[B[i + 511*t]] * R[C[i + 511*t]] : R[B[i + 511*t]] + R[C[i + 511*t]];
R[i + 969*t] = Op[i + 512*t] ? R[B[i + 512*t]] * R[C[i + 512*t]] : R[B[i + 512*t]] + R[C[i + 512*t]];
R[i + 970*t] = Op[i + 513*t] ? R[B[i + 513*t]] * R[C[i + 513*t]] : R[B[i + 513*t]] + R[C[i + 513*t]];
R[i + 971*t] = Op[i + 514*t] ? R[B[i + 514*t]] * R[C[i + 514*t]] : R[B[i + 514*t]] + R[C[i + 514*t]];
R[i + 972*t] = Op[i + 515*t] ? R[B[i + 515*t]] * R[C[i + 515*t]] : R[B[i + 515*t]] + R[C[i + 515*t]];
R[i + 973*t] = Op[i + 516*t] ? R[B[i + 516*t]] * R[C[i + 516*t]] : R[B[i + 516*t]] + R[C[i + 516*t]];
R[i + 974*t] = Op[i + 517*t] ? R[B[i + 517*t]] * R[C[i + 517*t]] : R[B[i + 517*t]] + R[C[i + 517*t]];
R[i + 975*t] = Op[i + 518*t] ? R[B[i + 518*t]] * R[C[i + 518*t]] : R[B[i + 518*t]] + R[C[i + 518*t]];
R[i + 976*t] = Op[i + 519*t] ? R[B[i + 519*t]] * R[C[i + 519*t]] : R[B[i + 519*t]] + R[C[i + 519*t]];
R[i + 977*t] = Op[i + 520*t] ? R[B[i + 520*t]] * R[C[i + 520*t]] : R[B[i + 520*t]] + R[C[i + 520*t]];
R[i + 978*t] = Op[i + 521*t] ? R[B[i + 521*t]] * R[C[i + 521*t]] : R[B[i + 521*t]] + R[C[i + 521*t]];
R[i + 979*t] = Op[i + 522*t] ? R[B[i + 522*t]] * R[C[i + 522*t]] : R[B[i + 522*t]] + R[C[i + 522*t]];
R[i + 980*t] = Op[i + 523*t] ? R[B[i + 523*t]] * R[C[i + 523*t]] : R[B[i + 523*t]] + R[C[i + 523*t]];
R[i + 981*t] = Op[i + 524*t] ? R[B[i + 524*t]] * R[C[i + 524*t]] : R[B[i + 524*t]] + R[C[i + 524*t]];
R[i + 982*t] = Op[i + 525*t] ? R[B[i + 525*t]] * R[C[i + 525*t]] : R[B[i + 525*t]] + R[C[i + 525*t]];
R[i + 983*t] = Op[i + 526*t] ? R[B[i + 526*t]] * R[C[i + 526*t]] : R[B[i + 526*t]] + R[C[i + 526*t]];
R[i + 984*t] = Op[i + 527*t] ? R[B[i + 527*t]] * R[C[i + 527*t]] : R[B[i + 527*t]] + R[C[i + 527*t]];
R[i + 985*t] = Op[i + 528*t] ? R[B[i + 528*t]] * R[C[i + 528*t]] : R[B[i + 528*t]] + R[C[i + 528*t]];
R[i + 986*t] = Op[i + 529*t] ? R[B[i + 529*t]] * R[C[i + 529*t]] : R[B[i + 529*t]] + R[C[i + 529*t]];
R[i + 987*t] = Op[i + 530*t] ? R[B[i + 530*t]] * R[C[i + 530*t]] : R[B[i + 530*t]] + R[C[i + 530*t]];
R[i + 988*t] = Op[i + 531*t] ? R[B[i + 531*t]] * R[C[i + 531*t]] : R[B[i + 531*t]] + R[C[i + 531*t]];
R[i + 989*t] = Op[i + 532*t] ? R[B[i + 532*t]] * R[C[i + 532*t]] : R[B[i + 532*t]] + R[C[i + 532*t]];
R[i + 990*t] = Op[i + 533*t] ? R[B[i + 533*t]] * R[C[i + 533*t]] : R[B[i + 533*t]] + R[C[i + 533*t]];
R[i + 991*t] = Op[i + 534*t] ? R[B[i + 534*t]] * R[C[i + 534*t]] : R[B[i + 534*t]] + R[C[i + 534*t]];
R[i + 992*t] = Op[i + 535*t] ? R[B[i + 535*t]] * R[C[i + 535*t]] : R[B[i + 535*t]] + R[C[i + 535*t]];
R[i + 993*t] = Op[i + 536*t] ? R[B[i + 536*t]] * R[C[i + 536*t]] : R[B[i + 536*t]] + R[C[i + 536*t]];
R[i + 994*t] = Op[i + 537*t] ? R[B[i + 537*t]] * R[C[i + 537*t]] : R[B[i + 537*t]] + R[C[i + 537*t]];
R[i + 995*t] = Op[i + 538*t] ? R[B[i + 538*t]] * R[C[i + 538*t]] : R[B[i + 538*t]] + R[C[i + 538*t]];
R[i + 996*t] = Op[i + 539*t] ? R[B[i + 539*t]] * R[C[i + 539*t]] : R[B[i + 539*t]] + R[C[i + 539*t]];
R[i + 997*t] = Op[i + 540*t] ? R[B[i + 540*t]] * R[C[i + 540*t]] : R[B[i + 540*t]] + R[C[i + 540*t]];
R[i + 998*t] = Op[i + 541*t] ? R[B[i + 541*t]] * R[C[i + 541*t]] : R[B[i + 541*t]] + R[C[i + 541*t]];
R[i + 999*t] = Op[i + 542*t] ? R[B[i + 542*t]] * R[C[i + 542*t]] : R[B[i + 542*t]] + R[C[i + 542*t]];
R[i + 1000*t] = Op[i + 543*t] ? R[B[i + 543*t]] * R[C[i + 543*t]] : R[B[i + 543*t]] + R[C[i + 543*t]];
R[i + 1001*t] = Op[i + 544*t] ? R[B[i + 544*t]] * R[C[i + 544*t]] : R[B[i + 544*t]] + R[C[i + 544*t]];
R[i + 1002*t] = Op[i + 545*t] ? R[B[i + 545*t]] * R[C[i + 545*t]] : R[B[i + 545*t]] + R[C[i + 545*t]];
R[i + 1003*t] = Op[i + 546*t] ? R[B[i + 546*t]] * R[C[i + 546*t]] : R[B[i + 546*t]] + R[C[i + 546*t]];
R[i + 1004*t] = Op[i + 547*t] ? R[B[i + 547*t]] * R[C[i + 547*t]] : R[B[i + 547*t]] + R[C[i + 547*t]];
R[i + 1005*t] = Op[i + 548*t] ? R[B[i + 548*t]] * R[C[i + 548*t]] : R[B[i + 548*t]] + R[C[i + 548*t]];
R[i + 1006*t] = Op[i + 549*t] ? R[B[i + 549*t]] * R[C[i + 549*t]] : R[B[i + 549*t]] + R[C[i + 549*t]];
R[i + 1007*t] = Op[i + 550*t] ? R[B[i + 550*t]] * R[C[i + 550*t]] : R[B[i + 550*t]] + R[C[i + 550*t]];
R[i + 1008*t] = Op[i + 551*t] ? R[B[i + 551*t]] * R[C[i + 551*t]] : R[B[i + 551*t]] + R[C[i + 551*t]];
R[i + 1009*t] = Op[i + 552*t] ? R[B[i + 552*t]] * R[C[i + 552*t]] : R[B[i + 552*t]] + R[C[i + 552*t]];
R[i + 1010*t] = Op[i + 553*t] ? R[B[i + 553*t]] * R[C[i + 553*t]] : R[B[i + 553*t]] + R[C[i + 553*t]];
R[i + 1011*t] = Op[i + 554*t] ? R[B[i + 554*t]] * R[C[i + 554*t]] : R[B[i + 554*t]] + R[C[i + 554*t]];
R[i + 1012*t] = Op[i + 555*t] ? R[B[i + 555*t]] * R[C[i + 555*t]] : R[B[i + 555*t]] + R[C[i + 555*t]];
R[i + 1013*t] = Op[i + 556*t] ? R[B[i + 556*t]] * R[C[i + 556*t]] : R[B[i + 556*t]] + R[C[i + 556*t]];
__syncthreads();
R[i + 1014*t] = Op[i + 557*t] ? R[B[i + 557*t]] * R[C[i + 557*t]] : R[B[i + 557*t]] + R[C[i + 557*t]];
R[i + 1015*t] = Op[i + 558*t] ? R[B[i + 558*t]] * R[C[i + 558*t]] : R[B[i + 558*t]] + R[C[i + 558*t]];
R[i + 1016*t] = Op[i + 559*t] ? R[B[i + 559*t]] * R[C[i + 559*t]] : R[B[i + 559*t]] + R[C[i + 559*t]];
R[i + 1017*t] = Op[i + 560*t] ? R[B[i + 560*t]] * R[C[i + 560*t]] : R[B[i + 560*t]] + R[C[i + 560*t]];
R[i + 1018*t] = Op[i + 561*t] ? R[B[i + 561*t]] * R[C[i + 561*t]] : R[B[i + 561*t]] + R[C[i + 561*t]];
R[i + 1019*t] = Op[i + 562*t] ? R[B[i + 562*t]] * R[C[i + 562*t]] : R[B[i + 562*t]] + R[C[i + 562*t]];
R[i + 1020*t] = Op[i + 563*t] ? R[B[i + 563*t]] * R[C[i + 563*t]] : R[B[i + 563*t]] + R[C[i + 563*t]];
R[i + 1021*t] = Op[i + 564*t] ? R[B[i + 564*t]] * R[C[i + 564*t]] : R[B[i + 564*t]] + R[C[i + 564*t]];
R[i + 1022*t] = Op[i + 565*t] ? R[B[i + 565*t]] * R[C[i + 565*t]] : R[B[i + 565*t]] + R[C[i + 565*t]];
R[i + 1023*t] = Op[i + 566*t] ? R[B[i + 566*t]] * R[C[i + 566*t]] : R[B[i + 566*t]] + R[C[i + 566*t]];
R[i + 1024*t] = Op[i + 567*t] ? R[B[i + 567*t]] * R[C[i + 567*t]] : R[B[i + 567*t]] + R[C[i + 567*t]];
R[i + 1025*t] = Op[i + 568*t] ? R[B[i + 568*t]] * R[C[i + 568*t]] : R[B[i + 568*t]] + R[C[i + 568*t]];
R[i + 1026*t] = Op[i + 569*t] ? R[B[i + 569*t]] * R[C[i + 569*t]] : R[B[i + 569*t]] + R[C[i + 569*t]];
R[i + 1027*t] = Op[i + 570*t] ? R[B[i + 570*t]] * R[C[i + 570*t]] : R[B[i + 570*t]] + R[C[i + 570*t]];
R[i + 1028*t] = Op[i + 571*t] ? R[B[i + 571*t]] * R[C[i + 571*t]] : R[B[i + 571*t]] + R[C[i + 571*t]];
R[i + 1029*t] = Op[i + 572*t] ? R[B[i + 572*t]] * R[C[i + 572*t]] : R[B[i + 572*t]] + R[C[i + 572*t]];
R[i + 1030*t] = Op[i + 573*t] ? R[B[i + 573*t]] * R[C[i + 573*t]] : R[B[i + 573*t]] + R[C[i + 573*t]];
R[i + 1031*t] = Op[i + 574*t] ? R[B[i + 574*t]] * R[C[i + 574*t]] : R[B[i + 574*t]] + R[C[i + 574*t]];
R[i + 1032*t] = Op[i + 575*t] ? R[B[i + 575*t]] * R[C[i + 575*t]] : R[B[i + 575*t]] + R[C[i + 575*t]];
R[i + 1033*t] = Op[i + 576*t] ? R[B[i + 576*t]] * R[C[i + 576*t]] : R[B[i + 576*t]] + R[C[i + 576*t]];
R[i + 1034*t] = Op[i + 577*t] ? R[B[i + 577*t]] * R[C[i + 577*t]] : R[B[i + 577*t]] + R[C[i + 577*t]];
R[i + 1035*t] = Op[i + 578*t] ? R[B[i + 578*t]] * R[C[i + 578*t]] : R[B[i + 578*t]] + R[C[i + 578*t]];
R[i + 1036*t] = Op[i + 579*t] ? R[B[i + 579*t]] * R[C[i + 579*t]] : R[B[i + 579*t]] + R[C[i + 579*t]];
R[i + 1037*t] = Op[i + 580*t] ? R[B[i + 580*t]] * R[C[i + 580*t]] : R[B[i + 580*t]] + R[C[i + 580*t]];
R[i + 1038*t] = Op[i + 581*t] ? R[B[i + 581*t]] * R[C[i + 581*t]] : R[B[i + 581*t]] + R[C[i + 581*t]];
R[i + 1039*t] = Op[i + 582*t] ? R[B[i + 582*t]] * R[C[i + 582*t]] : R[B[i + 582*t]] + R[C[i + 582*t]];
R[i + 1040*t] = Op[i + 583*t] ? R[B[i + 583*t]] * R[C[i + 583*t]] : R[B[i + 583*t]] + R[C[i + 583*t]];
R[i + 1041*t] = Op[i + 584*t] ? R[B[i + 584*t]] * R[C[i + 584*t]] : R[B[i + 584*t]] + R[C[i + 584*t]];
R[i + 1042*t] = Op[i + 585*t] ? R[B[i + 585*t]] * R[C[i + 585*t]] : R[B[i + 585*t]] + R[C[i + 585*t]];
R[i + 1043*t] = Op[i + 586*t] ? R[B[i + 586*t]] * R[C[i + 586*t]] : R[B[i + 586*t]] + R[C[i + 586*t]];
R[i + 1044*t] = Op[i + 587*t] ? R[B[i + 587*t]] * R[C[i + 587*t]] : R[B[i + 587*t]] + R[C[i + 587*t]];
__syncthreads();
R[i + 1045*t] = Op[i + 588*t] ? R[B[i + 588*t]] * R[C[i + 588*t]] : R[B[i + 588*t]] + R[C[i + 588*t]];
R[i + 1046*t] = Op[i + 589*t] ? R[B[i + 589*t]] * R[C[i + 589*t]] : R[B[i + 589*t]] + R[C[i + 589*t]];
R[i + 1047*t] = Op[i + 590*t] ? R[B[i + 590*t]] * R[C[i + 590*t]] : R[B[i + 590*t]] + R[C[i + 590*t]];
R[i + 1048*t] = Op[i + 591*t] ? R[B[i + 591*t]] * R[C[i + 591*t]] : R[B[i + 591*t]] + R[C[i + 591*t]];
R[i + 1049*t] = Op[i + 592*t] ? R[B[i + 592*t]] * R[C[i + 592*t]] : R[B[i + 592*t]] + R[C[i + 592*t]];
R[i + 1050*t] = Op[i + 593*t] ? R[B[i + 593*t]] * R[C[i + 593*t]] : R[B[i + 593*t]] + R[C[i + 593*t]];
R[i + 1051*t] = Op[i + 594*t] ? R[B[i + 594*t]] * R[C[i + 594*t]] : R[B[i + 594*t]] + R[C[i + 594*t]];
R[i + 1052*t] = Op[i + 595*t] ? R[B[i + 595*t]] * R[C[i + 595*t]] : R[B[i + 595*t]] + R[C[i + 595*t]];
R[i + 1053*t] = Op[i + 596*t] ? R[B[i + 596*t]] * R[C[i + 596*t]] : R[B[i + 596*t]] + R[C[i + 596*t]];
R[i + 1054*t] = Op[i + 597*t] ? R[B[i + 597*t]] * R[C[i + 597*t]] : R[B[i + 597*t]] + R[C[i + 597*t]];
R[i + 1055*t] = Op[i + 598*t] ? R[B[i + 598*t]] * R[C[i + 598*t]] : R[B[i + 598*t]] + R[C[i + 598*t]];
R[i + 1056*t] = Op[i + 599*t] ? R[B[i + 599*t]] * R[C[i + 599*t]] : R[B[i + 599*t]] + R[C[i + 599*t]];
R[i + 1057*t] = Op[i + 600*t] ? R[B[i + 600*t]] * R[C[i + 600*t]] : R[B[i + 600*t]] + R[C[i + 600*t]];
R[i + 1058*t] = Op[i + 601*t] ? R[B[i + 601*t]] * R[C[i + 601*t]] : R[B[i + 601*t]] + R[C[i + 601*t]];
R[i + 1059*t] = Op[i + 602*t] ? R[B[i + 602*t]] * R[C[i + 602*t]] : R[B[i + 602*t]] + R[C[i + 602*t]];
R[i + 1060*t] = Op[i + 603*t] ? R[B[i + 603*t]] * R[C[i + 603*t]] : R[B[i + 603*t]] + R[C[i + 603*t]];
R[i + 1061*t] = Op[i + 604*t] ? R[B[i + 604*t]] * R[C[i + 604*t]] : R[B[i + 604*t]] + R[C[i + 604*t]];
R[i + 1062*t] = Op[i + 605*t] ? R[B[i + 605*t]] * R[C[i + 605*t]] : R[B[i + 605*t]] + R[C[i + 605*t]];
R[i + 1063*t] = Op[i + 606*t] ? R[B[i + 606*t]] * R[C[i + 606*t]] : R[B[i + 606*t]] + R[C[i + 606*t]];
R[i + 1064*t] = Op[i + 607*t] ? R[B[i + 607*t]] * R[C[i + 607*t]] : R[B[i + 607*t]] + R[C[i + 607*t]];
R[i + 1065*t] = Op[i + 608*t] ? R[B[i + 608*t]] * R[C[i + 608*t]] : R[B[i + 608*t]] + R[C[i + 608*t]];
__syncthreads();
R[i + 1066*t] = Op[i + 609*t] ? R[B[i + 609*t]] * R[C[i + 609*t]] : R[B[i + 609*t]] + R[C[i + 609*t]];
R[i + 1067*t] = Op[i + 610*t] ? R[B[i + 610*t]] * R[C[i + 610*t]] : R[B[i + 610*t]] + R[C[i + 610*t]];
R[i + 1068*t] = Op[i + 611*t] ? R[B[i + 611*t]] * R[C[i + 611*t]] : R[B[i + 611*t]] + R[C[i + 611*t]];
R[i + 1069*t] = Op[i + 612*t] ? R[B[i + 612*t]] * R[C[i + 612*t]] : R[B[i + 612*t]] + R[C[i + 612*t]];
R[i + 1070*t] = Op[i + 613*t] ? R[B[i + 613*t]] * R[C[i + 613*t]] : R[B[i + 613*t]] + R[C[i + 613*t]];
R[i + 1071*t] = Op[i + 614*t] ? R[B[i + 614*t]] * R[C[i + 614*t]] : R[B[i + 614*t]] + R[C[i + 614*t]];
R[i + 1072*t] = Op[i + 615*t] ? R[B[i + 615*t]] * R[C[i + 615*t]] : R[B[i + 615*t]] + R[C[i + 615*t]];
R[i + 1073*t] = Op[i + 616*t] ? R[B[i + 616*t]] * R[C[i + 616*t]] : R[B[i + 616*t]] + R[C[i + 616*t]];
R[i + 1074*t] = Op[i + 617*t] ? R[B[i + 617*t]] * R[C[i + 617*t]] : R[B[i + 617*t]] + R[C[i + 617*t]];
R[i + 1075*t] = Op[i + 618*t] ? R[B[i + 618*t]] * R[C[i + 618*t]] : R[B[i + 618*t]] + R[C[i + 618*t]];
R[i + 1076*t] = Op[i + 619*t] ? R[B[i + 619*t]] * R[C[i + 619*t]] : R[B[i + 619*t]] + R[C[i + 619*t]];
R[i + 1077*t] = Op[i + 620*t] ? R[B[i + 620*t]] * R[C[i + 620*t]] : R[B[i + 620*t]] + R[C[i + 620*t]];
R[i + 1078*t] = Op[i + 621*t] ? R[B[i + 621*t]] * R[C[i + 621*t]] : R[B[i + 621*t]] + R[C[i + 621*t]];
R[i + 1079*t] = Op[i + 622*t] ? R[B[i + 622*t]] * R[C[i + 622*t]] : R[B[i + 622*t]] + R[C[i + 622*t]];
R[i + 1080*t] = Op[i + 623*t] ? R[B[i + 623*t]] * R[C[i + 623*t]] : R[B[i + 623*t]] + R[C[i + 623*t]];
R[i + 1081*t] = Op[i + 624*t] ? R[B[i + 624*t]] * R[C[i + 624*t]] : R[B[i + 624*t]] + R[C[i + 624*t]];
R[i + 1082*t] = Op[i + 625*t] ? R[B[i + 625*t]] * R[C[i + 625*t]] : R[B[i + 625*t]] + R[C[i + 625*t]];
R[i + 1083*t] = Op[i + 626*t] ? R[B[i + 626*t]] * R[C[i + 626*t]] : R[B[i + 626*t]] + R[C[i + 626*t]];
R[i + 1084*t] = Op[i + 627*t] ? R[B[i + 627*t]] * R[C[i + 627*t]] : R[B[i + 627*t]] + R[C[i + 627*t]];
R[i + 1085*t] = Op[i + 628*t] ? R[B[i + 628*t]] * R[C[i + 628*t]] : R[B[i + 628*t]] + R[C[i + 628*t]];
R[i + 1086*t] = Op[i + 629*t] ? R[B[i + 629*t]] * R[C[i + 629*t]] : R[B[i + 629*t]] + R[C[i + 629*t]];
R[i + 1087*t] = Op[i + 630*t] ? R[B[i + 630*t]] * R[C[i + 630*t]] : R[B[i + 630*t]] + R[C[i + 630*t]];
R[i + 1088*t] = Op[i + 631*t] ? R[B[i + 631*t]] * R[C[i + 631*t]] : R[B[i + 631*t]] + R[C[i + 631*t]];
R[i + 1089*t] = Op[i + 632*t] ? R[B[i + 632*t]] * R[C[i + 632*t]] : R[B[i + 632*t]] + R[C[i + 632*t]];
R[i + 1090*t] = Op[i + 633*t] ? R[B[i + 633*t]] * R[C[i + 633*t]] : R[B[i + 633*t]] + R[C[i + 633*t]];
R[i + 1091*t] = Op[i + 634*t] ? R[B[i + 634*t]] * R[C[i + 634*t]] : R[B[i + 634*t]] + R[C[i + 634*t]];
R[i + 1092*t] = Op[i + 635*t] ? R[B[i + 635*t]] * R[C[i + 635*t]] : R[B[i + 635*t]] + R[C[i + 635*t]];
R[i + 1093*t] = Op[i + 636*t] ? R[B[i + 636*t]] * R[C[i + 636*t]] : R[B[i + 636*t]] + R[C[i + 636*t]];
__syncthreads();
R[i + 1094*t] = Op[i + 637*t] ? R[B[i + 637*t]] * R[C[i + 637*t]] : R[B[i + 637*t]] + R[C[i + 637*t]];
R[i + 1095*t] = Op[i + 638*t] ? R[B[i + 638*t]] * R[C[i + 638*t]] : R[B[i + 638*t]] + R[C[i + 638*t]];
R[i + 1096*t] = Op[i + 639*t] ? R[B[i + 639*t]] * R[C[i + 639*t]] : R[B[i + 639*t]] + R[C[i + 639*t]];
R[i + 1097*t] = Op[i + 640*t] ? R[B[i + 640*t]] * R[C[i + 640*t]] : R[B[i + 640*t]] + R[C[i + 640*t]];
R[i + 1098*t] = Op[i + 641*t] ? R[B[i + 641*t]] * R[C[i + 641*t]] : R[B[i + 641*t]] + R[C[i + 641*t]];
R[i + 1099*t] = Op[i + 642*t] ? R[B[i + 642*t]] * R[C[i + 642*t]] : R[B[i + 642*t]] + R[C[i + 642*t]];
R[i + 1100*t] = Op[i + 643*t] ? R[B[i + 643*t]] * R[C[i + 643*t]] : R[B[i + 643*t]] + R[C[i + 643*t]];
R[i + 1101*t] = Op[i + 644*t] ? R[B[i + 644*t]] * R[C[i + 644*t]] : R[B[i + 644*t]] + R[C[i + 644*t]];
R[i + 1102*t] = Op[i + 645*t] ? R[B[i + 645*t]] * R[C[i + 645*t]] : R[B[i + 645*t]] + R[C[i + 645*t]];
R[i + 1103*t] = Op[i + 646*t] ? R[B[i + 646*t]] * R[C[i + 646*t]] : R[B[i + 646*t]] + R[C[i + 646*t]];
R[i + 1104*t] = Op[i + 647*t] ? R[B[i + 647*t]] * R[C[i + 647*t]] : R[B[i + 647*t]] + R[C[i + 647*t]];
R[i + 1105*t] = Op[i + 648*t] ? R[B[i + 648*t]] * R[C[i + 648*t]] : R[B[i + 648*t]] + R[C[i + 648*t]];
R[i + 1106*t] = Op[i + 649*t] ? R[B[i + 649*t]] * R[C[i + 649*t]] : R[B[i + 649*t]] + R[C[i + 649*t]];
R[i + 1107*t] = Op[i + 650*t] ? R[B[i + 650*t]] * R[C[i + 650*t]] : R[B[i + 650*t]] + R[C[i + 650*t]];
R[i + 1108*t] = Op[i + 651*t] ? R[B[i + 651*t]] * R[C[i + 651*t]] : R[B[i + 651*t]] + R[C[i + 651*t]];
R[i + 1109*t] = Op[i + 652*t] ? R[B[i + 652*t]] * R[C[i + 652*t]] : R[B[i + 652*t]] + R[C[i + 652*t]];
R[i + 1110*t] = Op[i + 653*t] ? R[B[i + 653*t]] * R[C[i + 653*t]] : R[B[i + 653*t]] + R[C[i + 653*t]];
R[i + 1111*t] = Op[i + 654*t] ? R[B[i + 654*t]] * R[C[i + 654*t]] : R[B[i + 654*t]] + R[C[i + 654*t]];
R[i + 1112*t] = Op[i + 655*t] ? R[B[i + 655*t]] * R[C[i + 655*t]] : R[B[i + 655*t]] + R[C[i + 655*t]];
R[i + 1113*t] = Op[i + 656*t] ? R[B[i + 656*t]] * R[C[i + 656*t]] : R[B[i + 656*t]] + R[C[i + 656*t]];
R[i + 1114*t] = Op[i + 657*t] ? R[B[i + 657*t]] * R[C[i + 657*t]] : R[B[i + 657*t]] + R[C[i + 657*t]];
R[i + 1115*t] = Op[i + 658*t] ? R[B[i + 658*t]] * R[C[i + 658*t]] : R[B[i + 658*t]] + R[C[i + 658*t]];
R[i + 1116*t] = Op[i + 659*t] ? R[B[i + 659*t]] * R[C[i + 659*t]] : R[B[i + 659*t]] + R[C[i + 659*t]];
__syncthreads();
R[i + 1117*t] = Op[i + 660*t] ? R[B[i + 660*t]] * R[C[i + 660*t]] : R[B[i + 660*t]] + R[C[i + 660*t]];
R[i + 1118*t] = Op[i + 661*t] ? R[B[i + 661*t]] * R[C[i + 661*t]] : R[B[i + 661*t]] + R[C[i + 661*t]];
R[i + 1119*t] = Op[i + 662*t] ? R[B[i + 662*t]] * R[C[i + 662*t]] : R[B[i + 662*t]] + R[C[i + 662*t]];
R[i + 1120*t] = Op[i + 663*t] ? R[B[i + 663*t]] * R[C[i + 663*t]] : R[B[i + 663*t]] + R[C[i + 663*t]];
R[i + 1121*t] = Op[i + 664*t] ? R[B[i + 664*t]] * R[C[i + 664*t]] : R[B[i + 664*t]] + R[C[i + 664*t]];
R[i + 1122*t] = Op[i + 665*t] ? R[B[i + 665*t]] * R[C[i + 665*t]] : R[B[i + 665*t]] + R[C[i + 665*t]];
R[i + 1123*t] = Op[i + 666*t] ? R[B[i + 666*t]] * R[C[i + 666*t]] : R[B[i + 666*t]] + R[C[i + 666*t]];
R[i + 1124*t] = Op[i + 667*t] ? R[B[i + 667*t]] * R[C[i + 667*t]] : R[B[i + 667*t]] + R[C[i + 667*t]];
R[i + 1125*t] = Op[i + 668*t] ? R[B[i + 668*t]] * R[C[i + 668*t]] : R[B[i + 668*t]] + R[C[i + 668*t]];
R[i + 1126*t] = Op[i + 669*t] ? R[B[i + 669*t]] * R[C[i + 669*t]] : R[B[i + 669*t]] + R[C[i + 669*t]];
R[i + 1127*t] = Op[i + 670*t] ? R[B[i + 670*t]] * R[C[i + 670*t]] : R[B[i + 670*t]] + R[C[i + 670*t]];
R[i + 1128*t] = Op[i + 671*t] ? R[B[i + 671*t]] * R[C[i + 671*t]] : R[B[i + 671*t]] + R[C[i + 671*t]];
R[i + 1129*t] = Op[i + 672*t] ? R[B[i + 672*t]] * R[C[i + 672*t]] : R[B[i + 672*t]] + R[C[i + 672*t]];
__syncthreads();
R[i + 1130*t] = Op[i + 673*t] ? R[B[i + 673*t]] * R[C[i + 673*t]] : R[B[i + 673*t]] + R[C[i + 673*t]];
R[i + 1131*t] = Op[i + 674*t] ? R[B[i + 674*t]] * R[C[i + 674*t]] : R[B[i + 674*t]] + R[C[i + 674*t]];
R[i + 1132*t] = Op[i + 675*t] ? R[B[i + 675*t]] * R[C[i + 675*t]] : R[B[i + 675*t]] + R[C[i + 675*t]];
R[i + 1133*t] = Op[i + 676*t] ? R[B[i + 676*t]] * R[C[i + 676*t]] : R[B[i + 676*t]] + R[C[i + 676*t]];
R[i + 1134*t] = Op[i + 677*t] ? R[B[i + 677*t]] * R[C[i + 677*t]] : R[B[i + 677*t]] + R[C[i + 677*t]];
R[i + 1135*t] = Op[i + 678*t] ? R[B[i + 678*t]] * R[C[i + 678*t]] : R[B[i + 678*t]] + R[C[i + 678*t]];
R[i + 1136*t] = Op[i + 679*t] ? R[B[i + 679*t]] * R[C[i + 679*t]] : R[B[i + 679*t]] + R[C[i + 679*t]];
R[i + 1137*t] = Op[i + 680*t] ? R[B[i + 680*t]] * R[C[i + 680*t]] : R[B[i + 680*t]] + R[C[i + 680*t]];
R[i + 1138*t] = Op[i + 681*t] ? R[B[i + 681*t]] * R[C[i + 681*t]] : R[B[i + 681*t]] + R[C[i + 681*t]];
R[i + 1139*t] = Op[i + 682*t] ? R[B[i + 682*t]] * R[C[i + 682*t]] : R[B[i + 682*t]] + R[C[i + 682*t]];
R[i + 1140*t] = Op[i + 683*t] ? R[B[i + 683*t]] * R[C[i + 683*t]] : R[B[i + 683*t]] + R[C[i + 683*t]];
R[i + 1141*t] = Op[i + 684*t] ? R[B[i + 684*t]] * R[C[i + 684*t]] : R[B[i + 684*t]] + R[C[i + 684*t]];
R[i + 1142*t] = Op[i + 685*t] ? R[B[i + 685*t]] * R[C[i + 685*t]] : R[B[i + 685*t]] + R[C[i + 685*t]];
__syncthreads();
R[i + 1143*t] = Op[i + 686*t] ? R[B[i + 686*t]] * R[C[i + 686*t]] : R[B[i + 686*t]] + R[C[i + 686*t]];
R[i + 1144*t] = Op[i + 687*t] ? R[B[i + 687*t]] * R[C[i + 687*t]] : R[B[i + 687*t]] + R[C[i + 687*t]];
R[i + 1145*t] = Op[i + 688*t] ? R[B[i + 688*t]] * R[C[i + 688*t]] : R[B[i + 688*t]] + R[C[i + 688*t]];
R[i + 1146*t] = Op[i + 689*t] ? R[B[i + 689*t]] * R[C[i + 689*t]] : R[B[i + 689*t]] + R[C[i + 689*t]];
R[i + 1147*t] = Op[i + 690*t] ? R[B[i + 690*t]] * R[C[i + 690*t]] : R[B[i + 690*t]] + R[C[i + 690*t]];
R[i + 1148*t] = Op[i + 691*t] ? R[B[i + 691*t]] * R[C[i + 691*t]] : R[B[i + 691*t]] + R[C[i + 691*t]];
R[i + 1149*t] = Op[i + 692*t] ? R[B[i + 692*t]] * R[C[i + 692*t]] : R[B[i + 692*t]] + R[C[i + 692*t]];
R[i + 1150*t] = Op[i + 693*t] ? R[B[i + 693*t]] * R[C[i + 693*t]] : R[B[i + 693*t]] + R[C[i + 693*t]];
R[i + 1151*t] = Op[i + 694*t] ? R[B[i + 694*t]] * R[C[i + 694*t]] : R[B[i + 694*t]] + R[C[i + 694*t]];
R[i + 1152*t] = Op[i + 695*t] ? R[B[i + 695*t]] * R[C[i + 695*t]] : R[B[i + 695*t]] + R[C[i + 695*t]];
__syncthreads();
R[i + 1153*t] = Op[i + 696*t] ? R[B[i + 696*t]] * R[C[i + 696*t]] : R[B[i + 696*t]] + R[C[i + 696*t]];
R[i + 1154*t] = Op[i + 697*t] ? R[B[i + 697*t]] * R[C[i + 697*t]] : R[B[i + 697*t]] + R[C[i + 697*t]];
R[i + 1155*t] = Op[i + 698*t] ? R[B[i + 698*t]] * R[C[i + 698*t]] : R[B[i + 698*t]] + R[C[i + 698*t]];
R[i + 1156*t] = Op[i + 699*t] ? R[B[i + 699*t]] * R[C[i + 699*t]] : R[B[i + 699*t]] + R[C[i + 699*t]];
R[i + 1157*t] = Op[i + 700*t] ? R[B[i + 700*t]] * R[C[i + 700*t]] : R[B[i + 700*t]] + R[C[i + 700*t]];
R[i + 1158*t] = Op[i + 701*t] ? R[B[i + 701*t]] * R[C[i + 701*t]] : R[B[i + 701*t]] + R[C[i + 701*t]];
R[i + 1159*t] = Op[i + 702*t] ? R[B[i + 702*t]] * R[C[i + 702*t]] : R[B[i + 702*t]] + R[C[i + 702*t]];
__syncthreads();
R[i + 1160*t] = Op[i + 703*t] ? R[B[i + 703*t]] * R[C[i + 703*t]] : R[B[i + 703*t]] + R[C[i + 703*t]];
R[i + 1161*t] = Op[i + 704*t] ? R[B[i + 704*t]] * R[C[i + 704*t]] : R[B[i + 704*t]] + R[C[i + 704*t]];
R[i + 1162*t] = Op[i + 705*t] ? R[B[i + 705*t]] * R[C[i + 705*t]] : R[B[i + 705*t]] + R[C[i + 705*t]];
R[i + 1163*t] = Op[i + 706*t] ? R[B[i + 706*t]] * R[C[i + 706*t]] : R[B[i + 706*t]] + R[C[i + 706*t]];
R[i + 1164*t] = Op[i + 707*t] ? R[B[i + 707*t]] * R[C[i + 707*t]] : R[B[i + 707*t]] + R[C[i + 707*t]];
__syncthreads();
R[i + 1165*t] = Op[i + 708*t] ? R[B[i + 708*t]] * R[C[i + 708*t]] : R[B[i + 708*t]] + R[C[i + 708*t]];
R[i + 1166*t] = Op[i + 709*t] ? R[B[i + 709*t]] * R[C[i + 709*t]] : R[B[i + 709*t]] + R[C[i + 709*t]];
R[i + 1167*t] = Op[i + 710*t] ? R[B[i + 710*t]] * R[C[i + 710*t]] : R[B[i + 710*t]] + R[C[i + 710*t]];
__syncthreads();
R[i + 1168*t] = Op[i + 711*t] ? R[B[i + 711*t]] * R[C[i + 711*t]] : R[B[i + 711*t]] + R[C[i + 711*t]];
R[i + 1169*t] = Op[i + 712*t] ? R[B[i + 712*t]] * R[C[i + 712*t]] : R[B[i + 712*t]] + R[C[i + 712*t]];
R[i + 1170*t] = Op[i + 713*t] ? R[B[i + 713*t]] * R[C[i + 713*t]] : R[B[i + 713*t]] + R[C[i + 713*t]];
__syncthreads();
R[i + 1171*t] = Op[i + 714*t] ? R[B[i + 714*t]] * R[C[i + 714*t]] : R[B[i + 714*t]] + R[C[i + 714*t]];
R[i + 1172*t] = Op[i + 715*t] ? R[B[i + 715*t]] * R[C[i + 715*t]] : R[B[i + 715*t]] + R[C[i + 715*t]];
R[i + 1173*t] = Op[i + 716*t] ? R[B[i + 716*t]] * R[C[i + 716*t]] : R[B[i + 716*t]] + R[C[i + 716*t]];
__syncthreads();
R[i + 1174*t] = Op[i + 717*t] ? R[B[i + 717*t]] * R[C[i + 717*t]] : R[B[i + 717*t]] + R[C[i + 717*t]];
R[i + 1175*t] = Op[i + 718*t] ? R[B[i + 718*t]] * R[C[i + 718*t]] : R[B[i + 718*t]] + R[C[i + 718*t]];
__syncthreads();
R[i + 1176*t] = Op[i + 719*t] ? R[B[i + 719*t]] * R[C[i + 719*t]] : R[B[i + 719*t]] + R[C[i + 719*t]];
__syncthreads();
R[i + 1177*t] = Op[i + 720*t] ? R[B[i + 720*t]] * R[C[i + 720*t]] : R[B[i + 720*t]] + R[C[i + 720*t]];
__syncthreads();
R[i + 1178*t] = Op[i + 721*t] ? R[B[i + 721*t]] * R[C[i + 721*t]] : R[B[i + 721*t]] + R[C[i + 721*t]];
__syncthreads();
R[i + 1179*t] = Op[i + 722*t] ? R[B[i + 722*t]] * R[C[i + 722*t]] : R[B[i + 722*t]] + R[C[i + 722*t]];
__syncthreads();
R[i + 1180*t] = Op[i + 723*t] ? R[B[i + 723*t]] * R[C[i + 723*t]] : R[B[i + 723*t]] + R[C[i + 723*t]];
__syncthreads();
R[i + 1181*t] = Op[i + 724*t] ? R[B[i + 724*t]] * R[C[i + 724*t]] : R[B[i + 724*t]] + R[C[i + 724*t]];
__syncthreads();
R[i + 1182*t] = Op[i + 725*t] ? R[B[i + 725*t]] * R[C[i + 725*t]] : R[B[i + 725*t]] + R[C[i + 725*t]];
__syncthreads();
R[i + 1183*t] = Op[i + 726*t] ? R[B[i + 726*t]] * R[C[i + 726*t]] : R[B[i + 726*t]] + R[C[i + 726*t]];
if (i==0) { final += R[1183*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
22,335 |
#include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
// We have to include cuFFT library
#include <cufft.h>
#define NX 256
#define BATCH 1
// cuFFT can do 1D, 2D and 3D Fourier transformations using fast Fourier transform (FFT) algorithm.
// cuFFT is also very flexible regarding input data format. This can be captured together with type
// of FFT transformation we are interested in by cuFFT plan. The cuFFT plan could be created by many
// different functions provided by NVIDIA, where the most advance is 'cufftPlanMany'. In this
// assignment we will use much simpler function. This is 'cufftPlan1d' which is only for one dimensional
// ffts.
void Do_FFT_C2C_forward(cufftComplex *d_fft_output, cufftComplex *d_input_data, int size_of_one_fft, int number_of_ffts){
// We first declare cuFFT plan, which we must create before executing any FFT. We also
// declare variable 'error' which is used to catch errors.
cufftHandle plan;
cufftResult error;
// Here we are creating cuFFT plan, where we state what kind of transform we want.
// In this case we are doing transformation from complex numbers to complex numbers.
// To tell this to cuFFT we need to pass CUFFT_C2C parameter to the cufftPlan1d.
// Parameters we pass to the 'cufftPlan1d' are:
// 'plan' - is a variable where cufft stores parameters about fft we want to perform.
// 'size_of_one_fft' - is size of one FFT we want to perform
// 'type' - we are using CUFFT_C2C, but there are other options. CUFFT_C2R or CUFFT_R2C where R mean real and C means complex numbers
// 'number_of_ffts' - tells cuFFT how many fft of size 'size_of_one_fft' we want to calculate.
//
// This plan is independent on any data, provided that data size does not change, we can create one plan
// and use it many times
error = cufftPlan1d(&plan, size_of_one_fft, CUFFT_C2C, number_of_ffts);
if (CUFFT_SUCCESS != error){
printf("CUFFT error: Plan creation failed");
return;
}
// This calls a function which performs fft on GPU. It is a C wrapper around
// GPU kernel. This way user does not need to decide what number of threads
// and blocks should be used, cuFFT does it on its own.
//
// cufftExecC2C is also configurable. We can ask it to perform a forward ffts
// by passing parameter CUFFT_FORWARD. This means we are transforming our series
// from time-domain to frequency-domain. If we want to perform inverse transformation
// (from frequency-domain to time-domain) we need to pass CUFFT_INVERSE.
//
// cuFFT can also calculate fft in-place, meaning that input array is used
// for output as well.
cufftExecC2C(plan, d_input_data, d_fft_output, CUFFT_FORWARD);
// This deallocate resources taken by the cuFFT plan
cufftDestroy(plan);
}
//------------------------------------------------
// TASK: write a function which will perform 1D FFT using cuFFT, as in function
// 'Do_FFT_C2C_forward', which will calculate inverse fft and it will calculate
// it in-place.
void Do_FFT_C2C_inverse_inplace(cufftComplex *d_fft_in_out, int size_of_one_fft, int number_of_ffts){
//write your function here
// We first declare cuFFT plan, which we must create before executing any FFT. We also
// declare variable 'error' which is used to catch errors.
cufftHandle plan;
cufftResult error;
// Here we are creating cuFFT plan, where we state what kind of transform we want.
// In this case we are doing transformation from complex numbers to complex numbers.
// To tell this to cuFFT we need to pass CUFFT_C2C parameter to the cufftPlan1d.
// Parameters we pass to the 'cufftPlan1d' are:
// 'plan' - is a variable where cufft stores parameters about fft we want to perform.
// 'size_of_one_fft' - is size of one FFT we want to perform
// 'type' - we are using CUFFT_C2C, but there are other options. CUFFT_C2R or CUFFT_R2C where R mean real and C means complex numbe$
// 'number_of_ffts' - tells cuFFT how many fft of size 'size_of_one_fft' we want to calculate.
//
// This plan is independent on any data, provided that data size does not change, we can create one plan
// and use it many times
error = cufftPlan1d(&plan, size_of_one_fft, CUFFT_C2C, number_of_ffts);
if (CUFFT_SUCCESS != error){
printf("CUFFT error: Plan creation failed");
return;
}
// This calls a function which performs fft on GPU. It is a C wrapper around
// GPU kernel. This way user does not need to decide what number of threads
// and blocks should be used, cuFFT does it on its own.
//
// cufftExecC2C is also configurable. We can ask it to perform a forward ffts
// by passing parameter CUFFT_FORWARD. This means we are transforming our series
// from time-domain to frequency-domain. If we want to perform inverse transformation
// (from frequency-domain to time-domain) we need to pass CUFFT_INVERSE.
//
// cuFFT can also calculate fft in-place, meaning that input array is used
// for output as well.
cufftExecC2C(plan, d_fft_in_out, d_fft_in_out, CUFFT_INVERSE);
// This deallocate resources taken by the cuFFT plan
cufftDestroy(plan);
}
int main(void) {
int size_of_one_fft = 16;
int number_of_ffts = 1;
size_t size = size_of_one_fft*number_of_ffts;
// device initialization
int deviceid = 0;
int devCount;
cudaGetDeviceCount(&devCount);
if(deviceid<devCount) cudaSetDevice(deviceid);
else return(1);
// declare, allocate and initiate host
cufftComplex *h_input_data, *h_fft_output;
h_input_data = (cufftComplex *) malloc(size*sizeof(*h_input_data));
h_fft_output = (cufftComplex *) malloc(size*sizeof(*h_fft_output));
for(int f=0; f<size_of_one_fft; f++){
h_input_data[f].x = sin(4.0*31.25*((float) f));
h_input_data[f].y = 0;
}
// data type used by cuFFT library.
// it is in fact float2 which means { {float,float} , {float,float} , ... }
cufftComplex *d_input_data, *d_fft_output;
cudaMalloc((void**)&d_input_data, size*sizeof(cufftComplex));
cudaMalloc((void**)&d_fft_output, size*sizeof(cufftComplex));
// if you have data in a float2 array you can do (cufftComplex *) your_array
// transfer data to the device
cudaMemcpy(d_input_data, h_input_data, size*sizeof(cufftComplex), cudaMemcpyHostToDevice);
// call function which will do FFT
Do_FFT_C2C_forward(d_fft_output, d_input_data, size_of_one_fft, number_of_ffts);
// transfer transformed series back to host
cudaMemcpy(h_fft_output, d_fft_output, size*sizeof(cufftComplex), cudaMemcpyDeviceToHost);
printf("Result of FFT transform:\n");
for(int f=0; f<size_of_one_fft; f++){
printf("[%0.3f] ",h_fft_output[f].x*h_fft_output[f].x + h_fft_output[f].y+h_fft_output[f].y);
}
printf("\n--------------\n");
// Let's perform inverse transformation of the result from previous transformation.
// Furthermore, we can do fft inplace. This means that fft can write results of them
// transformation into input array.
Do_FFT_C2C_inverse_inplace(d_fft_output, size_of_one_fft, number_of_ffts);
cudaMemcpy(h_fft_output, d_fft_output, size*sizeof(cufftComplex), cudaMemcpyDeviceToHost);
printf("Difference between initial input and current output:\n");
for(int f=0; f<size_of_one_fft; f++){
// cuFFT calculates non-normalized fft, this means that ifft(fft(X))=N*X, where N is fft size
// this is why we need to divide cuFFT results by size_of_one_fft
printf("[%0.3f,%0.3f] ", h_fft_output[f].x/16.0 - h_input_data[f].x, h_fft_output[f].y/16.0 - h_input_data[f].y);
}
printf("\n--------------\n");
// device deallocations
cudaFree(d_input_data);
cudaFree(d_fft_output);
// host deallocation
free(h_input_data);
free(h_fft_output);
cudaDeviceReset();
return(0);
}
|
22,336 | #include <stdlib.h>
#include <sys/time.h>
#include <stdio.h>
#include <string.h>
#include <time.h>
#include <math.h>
#include <cuda.h>
__device__ int endLoop=0;
__device__ __host__ int get_keypair(char* tab, int length, int first_char, int last_char){
int sum=0;
int pow=1;
int i=0;
for(i=0; i<length; i++){
sum+=tab[i]*pow;
pow*=(last_char - first_char);
}
return sum;
}
__host__ __device__ int check_keypairs(int crypt, int test)
{
if(crypt == test){
return 1;
}
else{
return 0;
}
}
__global__ void kernel( int *crypted, int length, int first_char, int last_char, double max_iter){
int loop_size = last_char - first_char;
int i =blockIdx.x * blockDim.x + threadIdx.x;
int total = (blockDim.x * gridDim.x);
char *tab = (char*)malloc(sizeof(char)*(length+1));
tab[length]='\0';
int j;
for(j=0; j<length; j++) tab[j] = first_char;
int current_keypair;
int pow=0;
for(int j=i; j<max_iter; j+=total){
pow=1;
for(int x=0; x<length; x++){
tab[x] = ((j/pow) % loop_size) + first_char;
pow*=loop_size;
}
current_keypair = get_keypair(tab, length, first_char, last_char);
if( check_keypairs(*crypted, current_keypair) ) {
printf( "password found: %s\n", tab );
endLoop=1;
}
if(endLoop==1){
j=max_iter;
}
}
}
int main( int argc, char** argv ) {
char* password;
int first_char, last_char;
float t1, t2;
//unsigned long cmp;
if( argc == 1 ) {
password = "A$4c";
first_char = 32;
last_char = 126;
/* ---ASCII values---
* special characters: 32 to 47
* numbers: 48 to 57
* special characters: 58 to 64
* letters uppercase: 65 to 90
* special characters: 91 to 96
* letters lowercase: 97 to 122
* special characters: 123 to 126
* */
} else if( argc == 4 ) {
password = argv[1];
first_char = atoi( argv[2] );
last_char = atoi( argv[3] );
} else {
printf("usage: breaker <password> <first_ch> <last_ch>\n");
printf("default: breaker A$4c 32 126\n");
printf("exemple to break the binary password 1101000:\n");
printf( "breaker 1101000 48 49\n" );
exit( 0 );
}
int length = strlen(password);
int sz_in_bytes = sizeof(int);
int *h_crypted = (int *)malloc(sizeof(int));
int crypted_to_break= get_keypair(password, length, first_char, last_char);
h_crypted = &crypted_to_break;
int *d_crypted=(int *)malloc(sizeof(int));
printf( "*running parameters*\n" );
printf( " -password length:\t%lu digits\n", strlen(password) );
printf( " -password length:\t%s digits\n", password);
printf( " -digits:\t\tfrom -%c- to -%c-\n", first_char, last_char );
printf( " -crypted to break:\t%d\n", crypted_to_break);
t1 = clock();
cudaMalloc((void**)&d_crypted, sz_in_bytes);
cudaMemcpy(d_crypted, h_crypted, sz_in_bytes, cudaMemcpyHostToDevice);
dim3 nBlocks;
dim3 nThperBlock;
nBlocks.x = 16;
nThperBlock.x = 1024;
int loop_size = last_char - first_char;
double max_iter = powl(loop_size, length);
kernel<<< nBlocks , nThperBlock >>>(d_crypted, length, first_char, last_char, max_iter);
cudaDeviceSynchronize();
t2 = clock();
cudaMemcpy(h_crypted, d_crypted, sz_in_bytes, cudaMemcpyDeviceToHost);
cudaFree(d_crypted);
float period = (t2-t1)/CLOCKS_PER_SEC;
if( period < 60 ){
printf( "time: %.1fs \n", period );
}else{
printf( "time: %.1fmin \n", period/60 );
}
return EXIT_SUCCESS;
}
|
22,337 | #include <iostream>
#include <math.h>
#include <time.h>
#include <stdlib.h>
#include <random>
#include <vector>
#include <chrono>
#include <deque>
#include <algorithm>
#include <iterator>
#include <curand.h>
#include <curand_kernel.h>
#define BLOCK_SIZE 1024
__global__ void partition(int *arr, int *bit_arr, int *l, int *r, const int pivot, const int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
if (bit_arr[i] == 1) {
if (arr[i] < pivot) {
l[i] = 1;
r[i] = 0;
}
else {
l[i] = 0;
r[i] = 1;
}
}
else {
l[i] = 0;
r[i] = 0;
}
}
}
void partition_array(int *arr, int *bit_arr, int *l, int *r, const int pivot, const int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
partition<<<(n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE>>>(arr, bit_arr, l, r, pivot, n);
cudaDeviceSynchronize();
cudaStreamDestroy(stream);
}
__global__ void copy_arr(int *in_arr, int *out_arr, const int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
out_arr[i] = in_arr[i];
}
}
void copy_array(int *arr1, int *arr2, const int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
copy_arr<<<(n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE, 0, stream>>>(arr1, arr2, n);
cudaDeviceSynchronize();
cudaStreamDestroy(stream);
}
__global__ void init_arr(int *arr, int val, const int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
arr[i] = val;
}
}
void init_array(int *arr, int val, const int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
init_arr<<<(n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE, 0, stream>>>(arr, val, n);
cudaDeviceSynchronize();
cudaStreamDestroy(stream);
}
__global__ void max_reduce(int *arr, const int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
int j = n-i-1;
int x = arr[i];
int y = arr[j];
arr[i] = x > y ? x:y;
}
}
__global__ void min_reduce(int *arr, const int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
int j = n-i-1;
int x = arr[i];
int y = arr[j];
arr[i] = x < y ? x:y;
}
}
__global__ void cnt_reduce(int *arr, const int n, const int m) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < m) {
int j = n-i-1;
if (i != j) {
arr[i] = arr[i] + arr[j];
}
}
}
__global__ void init_min_arr(int *arr, int *bit_arr, int *min_arr, const int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
if (bit_arr[i] == 1) {
min_arr[i] = arr[i];
}
else {
min_arr[i] = 1 << 30;
}
}
}
void init_min_array(int *arr, int *bit_arr, int *min_arr, const int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
init_min_arr<<<(n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE, 0, stream>>>(arr, bit_arr, min_arr, n);
cudaDeviceSynchronize();
cudaStreamDestroy(stream);
}
__global__ void init_max_arr(int *arr, int *bit_arr, int *max_arr, const int n) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n) {
if (bit_arr[i] == 1) {
max_arr[i] = arr[i];
}
else {
max_arr[i] = -(1 << 30);
}
}
}
void init_max_array(int *arr, int *bit_arr, int *max_arr, const int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
init_max_arr<<<(n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE, 0, stream>>>(arr, bit_arr, max_arr, n);
cudaDeviceSynchronize();
cudaStreamDestroy(stream);
}
int count_size(int *cnt_arr, int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
int *temp_arr;
cudaMallocManaged(&temp_arr, n*sizeof(int));
copy_array(cnt_arr, temp_arr, n);
int m = (n+1)/2;
while (n > 1) {
cnt_reduce<<<(m + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE, 0, stream>>>(temp_arr, n, m);
n = m;
m = (n+1)/2;
}
cudaDeviceSynchronize();
int out = temp_arr[0];
cudaFree(temp_arr);
cudaStreamDestroy(stream);
return out;
}
int get_max_val(int *max_arr, int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
while (n > 1) {
max_reduce<<<(n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE, 0, stream>>>(max_arr, n);
n = (n+1)/2;
}
cudaDeviceSynchronize();
cudaStreamDestroy(stream);
return max_arr[0];
}
int get_min_val(int *min_arr, int n) {
cudaStream_t stream;
cudaStreamCreate(&stream);
while (n > 1) {
min_reduce<<<(n + BLOCK_SIZE - 1)/BLOCK_SIZE, BLOCK_SIZE, 0, stream>>>(min_arr, n);
n = (n+1)/2;
}
cudaDeviceSynchronize();
cudaStreamDestroy(stream);
return min_arr[0];
}
void random_vector(int *arr, const int n, const int min_val=0.0, const int max_val=1000.0) {
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 < n; i++) {
arr[i] = dist(mte);
}
}
int quickselect(int *arr, int k, int n) {
int *l, *r, *bit_arr, *min_arr, *max_arr;
cudaMallocManaged(&l, n*sizeof(int));
cudaMallocManaged(&r, n*sizeof(int));
cudaMallocManaged(&bit_arr, n*sizeof(int));
cudaMallocManaged(&min_arr, n*sizeof(int));
cudaMallocManaged(&max_arr, n*sizeof(int));
init_array(bit_arr, 1, n);
init_array(l, 0, n);
init_array(r, 0, n);
int out = -1;
while (k > 0 && n > 0) {
init_min_array(arr, bit_arr, min_arr, n);
init_max_array(arr, bit_arr, max_arr, n);
int a = get_min_val(min_arr, n);
int b = get_max_val(max_arr, n);
if (a == b) {
out = a;
break;
}
int pivot = a+1 + rand() % static_cast<int>(b-a);
partition_array(arr, bit_arr, l, r, pivot, n);
int p = count_size(l, n);
int q = count_size(r, n);
if (p == k-1) {
init_min_array(arr, r, min_arr, n);
out = get_min_val(min_arr, n);
break;
}
if (p == k) {
init_max_array(arr, l, max_arr, n);
out = get_max_val(max_arr, n);
break;
}
if (p > k) {
copy_array(l, bit_arr, n);
}
else {
copy_array(r, bit_arr, n);
k -= p;
}
}
cudaFree(l);
cudaFree(r);
cudaFree(bit_arr);
cudaFree(min_arr);
cudaFree(max_arr);
return out;
}
bool check_correctness(int *arr, int pred, int k, int n) {
std::sort(arr, arr+n);
return pred == arr[k-1];
}
int main(void) {
int n = 1 << 20;
int k = 1 << 19;
int *arr, *temp;
cudaMallocManaged(&arr, n*sizeof(int));
random_vector(arr, n, 0, 10000);
temp = new int[n];
std::copy(arr, arr+n, temp);
auto t1 = std::chrono::high_resolution_clock::now();
int pred = quickselect(arr, k, n);
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;
t1 = std::chrono::high_resolution_clock::now();
std::cout << check_correctness(temp, pred, k, n) << std::endl;
t2 = std::chrono::high_resolution_clock::now();
duration = std::chrono::duration_cast<std::chrono::milliseconds>( t2 - t1 ).count();
std::cout << duration << std::endl;
cudaFree(arr);
return 0;
}
|
22,338 | #include <stdlib.h>
#include <stdio.h>
#include <cuda_runtime.h>
#define DATATYPE int
#define MEMSIZE 1024
#define REP 128
#define conflictnum 1
__constant__ int d_array_m1[MEMSIZE];
__constant__ int d_array_m2[MEMSIZE];
__global__ void constant_broadcast(double *time,DATATYPE *out,int its)
{
DATATYPE p,q=(threadIdx.x/conflictnum);
double time_tmp=0.0;
unsigned int start_time=0,stop_time=0;
unsigned int i,j;
for (i=0;i<its;i++)
{
__syncthreads();
start_time=clock();
#pragma unroll
for (j=0;j<REP;j++)
{
p=d_array_m1[q];
q=d_array_m2[p];
}
stop_time=clock();
time_tmp+=(stop_time-start_time);
}
time_tmp=time_tmp/REP/its;
out[blockDim.x*blockIdx.x+threadIdx.x] = p+q;
time[blockDim.x*blockIdx.x+threadIdx.x] = time_tmp;
}
int main_test(int blocks,int threads,DATATYPE *h_in1,DATATYPE *h_in2)
{
int its=30;
cudaMemcpyToSymbol(d_array_m1,h_in1,MEMSIZE*sizeof(int),0,cudaMemcpyHostToDevice);
cudaMemcpyToSymbol(d_array_m2,h_in2,MEMSIZE*sizeof(int),0,cudaMemcpyHostToDevice);
double *h_time,*d_time;
DATATYPE *d_out;
h_time=(double*)malloc(sizeof(double)*blocks*threads);
cudaMalloc((void**)&d_time,sizeof(double)*blocks*threads);
cudaMalloc((void**)&d_out,sizeof(DATATYPE)*blocks*threads);
constant_broadcast<<<blocks,threads>>>(d_time,d_out,its);
cudaMemcpy(h_time,d_time,sizeof(double)*blocks*threads,cudaMemcpyDeviceToHost);
double avert=0.0,maxt=0.0,mint=99999.9;
int nn=0;
for (int i=0;i<blocks;i++)
{
for (int j=0;j<threads;j+=32)
{
avert+=h_time[i*threads+j];
nn++;
if (maxt<h_time[i*threads+j])
{
maxt=h_time[i*threads+j];
}
if (mint>h_time[i*threads+j])
{
mint=h_time[i*threads+j];
}
}
}
avert/=nn;
printf("%d\t%d\t\t%f\t%f\t%f\n", blocks,threads,avert,mint,maxt);
cudaFree(d_time);
cudaFree(d_out);
free(h_time);
return 0;
}
void init_order(DATATYPE *a,int n)
{
for (int i=0;i<n;i++)
{
a[i]=i;
}
}
int main(int argc, char* argv[])
{
if (argc != 2) {
printf("%s <thread> \n", argv[0]);
} else {
int value = atoi(argv[1]);
DATATYPE * h_in1;
h_in1 = (DATATYPE *) malloc(sizeof(DATATYPE) * MEMSIZE);
init_order(h_in1, MEMSIZE);
printf("blocks\t threads\t aver \t min \t max \t(clocks)\n");
for (int i = 0; i <= 1024; i += 32) {
int blocks = (i == 0 ? 1 : i);
int threads = value;
main_test(blocks, threads, h_in1, h_in1);
}
free(h_in1);
}
return 0;
} |
22,339 | #include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdbool.h>
#include <sys/time.h>
double getTimeStamp() {
struct timeval tv;
gettimeofday( &tv, NULL );
return (double) tv.tv_usec/1000000 + tv.tv_sec;
}
void h_addmat(float*A, float*B, float*C, int nx, int ny)
{
int total = nx*ny;
int count = 0;
int i;
for(i=0; i<total; i++)
{
C[count] = A[count] + B[count];
count++;
}
return;
}
__global__ void f_addmat( float*A, float*B, float*C, int nx, int ny) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
while (idx < (nx*ny)) {
C[idx] = A[idx] + B[idx];
idx += blockDim.x * gridDim.x;
}
}
int main( int argc, char *argv[] ) {
if (argc != 3)
{
printf("Error: wrong number\n");
exit(0);
}
int nx = atoi ( argv[1] );
int ny = atoi (argv[2] );
if (nx <= 0 || ny <= 0)
{
printf("invalid inputs\n");
exit(0);
}
int noElems = nx*ny;
int bytes = noElems * sizeof(float);
int i,j, count;
count = 0;
float *h_A = (float *) malloc ( bytes );
float* h_B = (float *) malloc ( bytes );
float *h_hC = (float *) malloc ( bytes );
float *h_dC = (float *) malloc ( bytes );
for (i=0; i<nx; i++)
for (j=0; j<ny; j++)
{
h_A[count] = (float)(i+j)/3.0;
count++;
}
count = 0;
for (i=0; i<nx; i++)
for (j=0; j<ny; j++)
{
h_B[count]= (float)3.14*(i+j);
count++;
}
float *d_A, *d_B, *d_C ;
cudaMalloc( (void **) &d_A, bytes);
cudaMalloc( (void **) &d_B, bytes);
cudaMalloc( (void **) &d_C, bytes);
double timeStampA = getTimeStamp();
cudaMemcpy( d_A, h_A, bytes, cudaMemcpyHostToDevice);
cudaMemcpy( d_B, h_B, bytes, cudaMemcpyHostToDevice);
double timeStampB = getTimeStamp();
//dim3 block(32, 32);
//dim3 grid((nx + block.x-1)/block.x, (ny+block.y-1)/block.y);
f_addmat<<<512, 512>>>( d_A, d_B, d_C, nx, ny);
cudaDeviceSynchronize();
double timeStampC = getTimeStamp();
cudaMemcpy(h_dC, d_C, bytes, cudaMemcpyDeviceToHost );
double timeStampD = getTimeStamp();
cudaFree( d_A ); cudaFree( d_B); cudaFree( d_C);
cudaDeviceReset();
h_addmat(h_A, h_B, h_hC, nx, ny);
count = 0;
bool s = true;
for(i=0; i<noElems; i++)
{
if( h_hC[i] != h_dC[i] )
{
s = false;
printf("%d \n", i);
break;
}
}
if(s)
{
printf("total time is %.6f, CPU GPU transfer time is %.6f, kernel time is %.6f, GPU CPU transfer time is %.6f\n ", timeStampD-timeStampA, timeStampB - timeStampA, timeStampC- timeStampB, timeStampD - timeStampC);
exit(0);
}
printf("finished");
return 0;
}
|
22,340 | #include "includes.h"
__global__ void myhistKernel(unsigned char * buffer,unsigned int * histo)
{
__shared__ unsigned int temp[256];
int x = blockIdx.x * blockDim.x + threadIdx.x;
int y = blockIdx.y * blockDim.y + threadIdx.y;
int offset = x + y * blockDim.x * gridDim.x;
temp[threadIdx.x]=0;
__syncthreads();
atomicAdd( &temp[buffer[offset]], 1 );
__syncthreads();
atomicAdd( &(histo[threadIdx.x]), temp[threadIdx.x] );
} |
22,341 | #include "includes.h"
__global__ void calculoAlgoritmoTroca(float *dev_matrizSuperior, int linhaPerm, int colunaPerm, int totalColunas, int totalLinhas)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
float fatorAnulador = 0.0;
//evitar operação em endereço invalido
//se for indice da linha permissivel, desconsiderar
if (i > totalLinhas || i == linhaPerm)
return;
//computar fator anulador da respectiva linha
fatorAnulador = dev_matrizSuperior[i * totalColunas + colunaPerm] * (-1);
//calcular os valores dos elementos da linha usando o fator anulador coletado
for (int coluna = 0; coluna < totalColunas; coluna++){
if (i * totalColunas + coluna > totalLinhas * totalColunas)
return;
//o valor da coluna permissivel sera 0
if (coluna == colunaPerm)
dev_matrizSuperior[i * totalColunas + coluna] = 0;
else
//os demais valores devem respeitar a equacao
//Valor = FatorAnulador * ValorRefLinhaPerm + LinhaAtual;
dev_matrizSuperior[i * totalColunas + coluna] = fatorAnulador
* dev_matrizSuperior[linhaPerm * totalColunas + coluna]
+ dev_matrizSuperior[i * totalColunas + coluna];
}
} |
22,342 | #include <iostream>
#define THREAD_PER_BLOCK 1024.0
#define CUDAMALLOC_ERROR(_err) \
do { \
if (_err != cudaSuccess) { \
printf("%s in %s at line %d\n", cudaGetErrorString(_err),__FILE__,__LINE__); \
exit(EXIT_FAILURE); \
} \
}while(0)
void init_array(float *array, long size, float value) {
for (size_t i = 0; i < size; ++i) {
array[i] = value;
}
}
__global__
void vecAddKernel(float *A, float *B, float *C, long n) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < n) {
C[i] = A[i] + B[i];
}
}
void vecAdd(float *h_A, float *h_B, float *h_C, long n) {
float *d_A, *d_B, *d_C;
long size = n * sizeof (float);
cudaError_t err = cudaMalloc((void **) &d_A, size);
CUDAMALLOC_ERROR(err);
err = cudaMalloc((void **) &d_B, size);
CUDAMALLOC_ERROR(err);
err = cudaMalloc((void **) &d_C, size);
CUDAMALLOC_ERROR(err);
cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_C, h_C, size, cudaMemcpyHostToDevice);
dim3 dimGrid{(uint) ceil(n/THREAD_PER_BLOCK),1,1};
dim3 dimBlock = {(uint) THREAD_PER_BLOCK,1,1};
// KERNEL LAUNCH
vecAddKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C, n);
cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
int main() {
clock_t begin = clock();
long nbElements = 10e7;
long size = nbElements * sizeof(float);
float *h_A, *h_B, *h_C;
h_A = (float *) malloc(size);
h_B = (float *) malloc(size);
h_C = (float *) malloc(size);
init_array(h_A, nbElements, 1);
init_array(h_B, nbElements, 2);
init_array(h_C, nbElements, 0);
vecAdd(h_A, h_B, h_C, nbElements);
for (int i = 10e6; i < 10e7; ++i) {
std::cout << h_C[i] << std::endl;
}
free(h_A); free(h_B); free(h_C);
clock_t end = clock();
double elapsed_time = (double)(end - begin) / CLOCKS_PER_SEC;
std::cout << elapsed_time << "s" << std::endl;
return 0;
}
|
22,343 | //
// Created by gxl on 2021/3/29.
//
#include "TimeRecord.cuh"
|
22,344 | /**
* jrc_cuda_delta.cu
* block loading delta calculation. should be much faster
* system('nvcc -ptx -m 64 -arch sm_35 jrc_cuda_rho.cu')
* iA is multiple of CHUNKSIZE (16)
* J. James Jun, Vidrio Technologies, LLC., 2017 Jun 11
*/
#include <cuda_runtime.h>
#include <math.h>
#define ABS(my_val) ((my_val) < 0) ? (-1*(my_val)) : (my_val)
#define MIN(A,B) ((A)<(B)) ? (A) : (B)
#define MAX(A,B) ((A)>(B)) ? (A) : (B)
#define NTHREADS 128
#define MAXDIM 45 // number of Channels
#define CHUNKSIZE 16 // previously defined as CHUNK
#define SINGLE_INF (3.402E+38)
/** Main entry point.
* Works out where the current thread should read/write to global memory
* and calls doIterations to do the actual work.
* Step through one B at a time
* 7/13/17: fDc_spk option added, which uses spike-specific distance cut-off (dc)
*/
__global__ void jrc_cuda_delta(float *delta, unsigned int *nneigh, const float *site_features, const int *spike_order, const int *rho_order, const int *site_constants, const float dist_cut2) {
int i1 = (blockIdx.x + blockIdx.y * gridDim.x) * CHUNKSIZE; // base index of i1
int thread_x = threadIdx.x;
int i1_thread_x = i1 + thread_x;
int n_spikes_primary = site_constants[0];
int n_spikes_all = site_constants[1];
int n_features = site_constants[2];
int time_dist_cut = site_constants[3];
int fDc_spk = site_constants[4];
__shared__ int spike_order_chunk[CHUNKSIZE];
__shared__ int rho_order_chunk[CHUNKSIZE];
__shared__ float features_primary[MAXDIM][CHUNKSIZE];
__shared__ float mrDelta1_[NTHREADS][CHUNKSIZE];
__shared__ unsigned int miNneigh1_[NTHREADS][CHUNKSIZE];
__shared__ float vrDc1_[CHUNKSIZE]; // use if fDc_spk == 1
// cache shared memory, 1/2
if (thread_x < n_features) { // use thread_x as iC
for (int i_c = 0; i_c < CHUNKSIZE; ++i_c) {
int i1_c = i_c + i1;
if (i1_c < n_spikes_primary) {
features_primary[thread_x][i_c] = site_features[thread_x + i1_c * n_features];
} else {
features_primary[thread_x][i_c] = 0.0f;
}
}
}
// cache shared memory, 2/2
if (thread_x < CHUNKSIZE && i1_thread_x < n_spikes_primary) {
spike_order_chunk[thread_x] = spike_order[i1_thread_x];
rho_order_chunk[thread_x] = rho_order[i1_thread_x];
}
float mindist_chunk[CHUNKSIZE];
unsigned int nneigh_chunk[CHUNKSIZE];
for (int i_c = 0; i_c < CHUNKSIZE; ++i_c) {
mindist_chunk[i_c] = SINGLE_INF;
nneigh_chunk[i_c] = i1 + i_c; // self
}
// calculate spike-specific distance cut-off vrDc1_ only if fDc_spk == 1
if (thread_x < CHUNKSIZE && fDc_spk == 1) {
vrDc1_[thread_x] = 0.0f; // init
for (int iC = 0; iC < n_features; ++iC) {
float temp_ = features_primary[iC][thread_x];
vrDc1_[thread_x] += (temp_ * temp_);
}
vrDc1_[thread_x] *= dist_cut2;
}
__syncthreads();
// fill in the shared memory A
for (int i12_tx = thread_x; i12_tx < n_spikes_all; i12_tx += blockDim.x) {
// compute time difference
char nearby_in_time[CHUNKSIZE];
int i_spike_order = spike_order[i12_tx];
int i_rho_order = rho_order[i12_tx];
for (int i_c = 0; i_c < CHUNKSIZE; ++i_c) {
char rho_is_larger = (i_rho_order < rho_order_chunk[i_c]); // is rho larger?
int time_dist = ABS(spike_order_chunk[i_c] - i_spike_order); // is the spike nearby in time?
nearby_in_time[i_c] = (time_dist <= time_dist_cut) && rho_is_larger;
}
// compute distance
float feature_dists2_chunk[CHUNKSIZE]; // square of pairwise feature distances for chunk
for (int i_c = 0; i_c < CHUNKSIZE; ++i_c) {
feature_dists2_chunk[i_c] = 0.0f;
}
for (int iC = 0; iC < n_features; ++iC) {
float fet12_tx = site_features[iC + i12_tx * n_features];
for (int i_c = 0; i_c < CHUNKSIZE; ++i_c) {
float temp = fet12_tx - features_primary[iC][i_c]; // z_i = x_i - y_i
feature_dists2_chunk[i_c] += temp * temp; // dist += z_i^2
}
}
// Compare the index and distance
for (int i_c = 0; i_c < CHUNKSIZE; ++i_c) {
if ((nearby_in_time[i_c] == 1) && (feature_dists2_chunk[i_c] < mindist_chunk[i_c])) {
mindist_chunk[i_c] = feature_dists2_chunk[i_c];
nneigh_chunk[i_c] = i12_tx;
}
}
} // for
// collect result from each thread
for (int i_c = 0; i_c < CHUNKSIZE; ++i_c) {
mrDelta1_[thread_x][i_c] = mindist_chunk[i_c];
miNneigh1_[thread_x][i_c] = nneigh_chunk[i_c];
}
__syncthreads();
// final count
if (thread_x < CHUNKSIZE) {
float minDist1 = SINGLE_INF;
unsigned int minIdx1 = i1_thread_x;
for (int tx1 = 0; tx1 < blockDim.x; ++tx1) {
if (mrDelta1_[tx1][thread_x] < minDist1) {
minDist1 = mrDelta1_[tx1][thread_x];
minIdx1 = miNneigh1_[tx1][thread_x];
}
}
if (i1_thread_x < n_spikes_primary) {
if (fDc_spk == 0) {
delta[i1_thread_x] = sqrtf(ABS(minDist1) / dist_cut2);
} else {
delta[i1_thread_x] = sqrtf(ABS(minDist1) / vrDc1_[thread_x]);
}
nneigh[i1_thread_x] = minIdx1 + 1; // Matlab index output
}
}
} // func |
22,345 | /*************************************************************************
> File Name: 05_0304.cu
> Author: dong xu
> Mail: gwmxyd@163.com
> Created Time: 2016年03月30日 星期三 13时37分15秒
************************************************************************/
#include <stdio.h>
#include <cuda_runtime.h>
cudaError_t addWithCuda(int *c, const int *a);
__global__ void addKernel(int *c, const int *a)
{
int i = blockIdx.x;
*c = *a + i;
printf("thread %d:a=%d,c=%d\n",i,*a,*c);
}
int main()
{
int a = 1;
int c = 0;
cudaError_t cudaStatus;
int num = 0;
cudaDeviceProp prop;
cudaStatus = cudaGetDeviceCount(&num);
for(int i = 0;i<num;i++)
{
cudaGetDeviceProperties(&prop,i);
}
cudaStatus = addWithCuda(&c, &a);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "addWithCuda failed!");
return 1;
}
printf("a=%d,c=%d\n",a,c);
cudaStatus = cudaThreadExit();
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaThreadExit failed!");
return 1;
}
return 0;
}
cudaError_t addWithCuda(int *c, const int *a)
{
int *dev_a = 0;
int *dev_c = 0;
cudaError_t cudaStatus;
cudaStatus = cudaSetDevice(0);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaSetDevice failed! Do you have a CUDA-capable GPU installed?");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_c, sizeof(int));
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_a, sizeof(int));
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMemcpy(dev_a, a,sizeof(int), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
cudaStatus = cudaMemcpy(dev_c, c,sizeof(int), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
printf("addKernel<<<7,1>>>(%d,%d)\n",*a,*c);
addKernel<<<7,1>>>(dev_c,dev_a);
// cudaStatus = cudaThreadSynchronize();
cudaStatus = cudaMemcpy(c, dev_c,sizeof(int), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
printf("The anwser:%d\n",*c);
printf("addKernel<<<1,7>>>(%d,%d)\n",*a,*c);
addKernel<<<1,7>>>(dev_c,dev_a);
cudaStatus = cudaThreadSynchronize();
cudaStatus = cudaMemcpy(c, dev_c,sizeof(int), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess)
{
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
printf("The anwser:%d\n",*c);
Error:
cudaFree(dev_c);
cudaFree(dev_a);
return cudaStatus;
}
|
22,346 | #include "includes.h"
__global__ void scale( float *a, int size, int c) {
int index=c,k=0;//size=b
for(k=index+1;k<size;k++) {
a[size*index + k] = (float) a[size*index + k] / a[size*index + index];
}
} |
22,347 | //seqBig.cu
#include <iostream>
using namespace std;
#include <thrust/reduce.h>
#include <thrust/sequence.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
int main()
{
const int N=1000000;
// task 1: create the array
thrust::device_vector<int> a(N);
// task 2: fill the array
thrust::sequence(a.begin(), a.end(), 0);
// task 3: calculate the sum of the array
unsigned long long sumA= thrust::reduce(a.begin(),a.end(),
(unsigned long long) 0, thrust::plus<unsigned long long>() );
// task 4: calculate the sum of 0 .. N-1
unsigned long long sumCheck=0;
for(int i=0; i < N; i++) sumCheck += i;
cerr << "host " << sumCheck << endl;
cerr << "device " << sumA << endl;
// task 5: check the results agree
if(sumA == sumCheck) cout << "Test Succeeded!" << endl;
else { cerr << "Test FAILED!" << endl; return(1);}
return(0);
}
|
22,348 | #pragma once
#include <cuda.h>
#include <cuda_runtime_api.h>
__global__ void kernelApply3x3MatrixOnImage(unsigned int* input, unsigned int* output, unsigned int* mat, unsigned int width, unsigned int height)
{
unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y * blockDim.y + threadIdx.y;
__shared__ unsigned int s_mat[9];
if (x >= width || y >= height)
return;
if (threadIdx.x == 0 && threadIdx.y == 0){
s_mat[0] = mat[0];
s_mat[1] = mat[1];
s_mat[2] = mat[2];
s_mat[3] = mat[3];
s_mat[4] = mat[4];
s_mat[5] = mat[5];
s_mat[6] = mat[6];
s_mat[7] = mat[7];
s_mat[8] = mat[8];
}
unsigned int pos = x + y*width;
unsigned int val = input[pos];
__syncthreads();
//numbers dictate the center of the matrix in coords relative to midpoint. each thread writes "his value" multiplied with factor in the realtive centerpoint of the matrix
if (y > 0)
{
//-1 -1
if (x > 0)
atomicAdd(&output[pos - width - 1], val * s_mat[8]);
//0 -1
atomicAdd(&output[pos - width], val * s_mat[7]);
//1 -1
if (x < width - 1)
atomicAdd(&output[pos - width + 1], val * s_mat[6]);
}
//-1 0
if (x > 0)
atomicAdd(&output[pos - 1], val * s_mat[5]);
//0 0
atomicAdd(&output[pos], val * s_mat[4]);
//1 0
if (x < width - 1)
atomicAdd(&output[pos + 1], val * s_mat[3]);
if (y < height - 1)
{
//-1 1
if (x > 0)
atomicAdd(&output[pos + width - 1], val * s_mat[2]);
//0 1
atomicAdd(&output[pos + width], val * s_mat[1]);
//1 1
if (x < width - 1)
atomicAdd(&output[pos + width + 1], val * s_mat[0]);
}
} |
22,349 | #include<cuda_runtime.h>
#include "kernel.cu"
#include<iostream>
#include<chrono>
#include<vector>
#include<string.h>
#include "helper.cu"
using namespace std;
using namespace std::chrono;
void index(float* input, int inputSize, int inputDim, // scale of input
float* projections, int tableNums, int hashSize, // scale of projection planes
float* bits, // bits for hash
float* hash_values){
/*
initialize gpu memory
*/
// Initialize the inputs on device
int input_bytes = inputSize * inputDim * sizeof(float);
float* gpu_input;
cudaMalloc(&gpu_input, input_bytes);
cudaMemcpy(gpu_input, input, input_bytes, cudaMemcpyHostToDevice);
// Initialize projection planes on device
int projection_plane_bytes = tableNums * inputDim * hashSize * sizeof(float);
float* gpu_projections;
cudaMalloc(&gpu_projections, projection_plane_bytes);
cudaMemcpy(gpu_projections, projections, projection_plane_bytes, cudaMemcpyHostToDevice);
// Initialize bits on device
float* gpu_bits;
int bits_bytes = hashSize * sizeof(float);
cudaMalloc(&gpu_bits, bits_bytes);
cudaMemcpy(gpu_bits, bits, bits_bytes, cudaMemcpyHostToDevice);
// initialize result
int results_bytes = tableNums * inputSize * hashSize * sizeof(float);
float* results = (float*) malloc(results_bytes);
float* gpu_results;
cudaMalloc(&gpu_results, results_bytes);
// initialize hash values
int hash_values_bytes = tableNums * inputSize * sizeof(float);
float* gpu_hash_values;
cudaMalloc(&gpu_hash_values, hash_values_bytes);
cudaMemcpy(gpu_hash_values, hash_values, hash_values_bytes, cudaMemcpyHostToDevice);
/*
Start indexing
*/
dim3 blockSize(2, 512);
dim3 gridSize((hashSize * tableNums + blockSize.x - 1) / blockSize.x, (inputSize + blockSize.y - 1) / blockSize.y);
hash_<<<gridSize, blockSize>>>(gpu_input, inputSize, inputDim, gpu_projections, inputDim, hashSize, gpu_results, inputSize, hashSize * tableNums, gpu_bits);
cudaDeviceSynchronize();
cudaError_t cudaStatus = cudaGetLastError();
cout << "Hashing : " << cudaGetErrorString(cudaStatus) << endl;
dim3 blockSizeSum(1, 1024);
dim3 gridSizeSum(tableNums, (inputSize + blockSizeSum.y - 1) / blockSizeSum.y);
vec_sum<<<gridSizeSum, blockSizeSum>>>(gpu_results, gpu_hash_values, inputSize, tableNums, hashSize);
cudaDeviceSynchronize();
cudaStatus = cudaGetLastError();
cout << "Sum bits: " << cudaGetErrorString(cudaStatus) << endl;
cudaMemcpy(hash_values, gpu_hash_values, hash_values_bytes, cudaMemcpyDeviceToHost);
free(results);
cudaFree(gpu_input);
cudaFree(gpu_projections);
cudaFree(gpu_bits);
cudaFree(gpu_results);
cudaFree(gpu_hash_values);
}
// void indexing(float* input, int inputSize, int inputDim, // scale of input
// float* projections, int tableNums, int hashSize, // scale of projection planes
// float* bits, // bits for hash
// float* hash_values) {
// index(input, inputSize, inputDim, projections, tableNums, hashSize, bits, hash_values);
// }
|
22,350 | /*
sparse_matrix.cu:
Cuda implementation Sparse Matrix Multiplication by Vector
compile & run:
nvcc sparse_matrix.cu -o sparse_matrix.sh -lm && ./sparse_matrix.sh 32768 256 256 1
input:
NNZ: None Zero Values
ROWS: The number of Rows (max 1024)
COLS: The number of Columns (max 1024)
DEBUG: 1 to debug, 0 to no-debug
output:
Time in MS
Throughput in GFLOPS
author: Ivan Reyes-Amezcua
date: June, 2020
*/
#include <stdio.h>
#include <math.h>
#include <time.h>
#include <iostream>
#include <fstream>
#include <stdlib.h>
using namespace std;
__global__ void spmv(int num_rows, int num_cols, int *row_ptrs,
int *col_index, double *values, double *x, double *y
) {
extern __shared__ double s_sum[]; // sum of the values per row per block
int tid = threadIdx.x; // Local: Thread ID
int g_tid = threadIdx.x + row_ptrs[blockIdx.x]; // Global: Thread ID + offset in row
int NNZ_in_row = row_ptrs[blockIdx.x + 1] - row_ptrs[blockIdx.x]; // Non-zero values in current row-block
s_sum[tid] = 0.0;
__syncthreads();
// TODO: check col_index vector, possible memory issue
if (tid < NNZ_in_row)
s_sum[tid] = values[g_tid] * x[col_index[g_tid]]; // Map: value[n] * X[index[n]]
__syncthreads();
// Inclusive Scan
double temp;
for (int j = 1; j < blockDim.x; j *= 2 ){
if ( (tid - j) >= 0)
temp = s_sum[tid - j];
__syncthreads();
if ( (tid - j) >= 0)
s_sum[tid] += temp;
__syncthreads();
}
// Save the result of Row-Block on global memory
if(tid == blockDim.x - 1)
y[blockIdx.x] = s_sum[tid];
}
int main(int argc, char *argv[]) {
// Get and validate arguments
if(argc != 5){
printf("Usage %s NNZ ROWS COLS DEBUG\n",argv[0]);
exit(0);
}
int NNZ = atoi ( argv[1] ); // Non Zero Values
int num_rows = atoi ( argv[2] ); // rows
int num_cols = atoi ( argv[3] ); // columns
int debug = atoi ( argv[4] ); // 1 for debug, 0 for NO-debug
double values[NNZ]; // CSR format
int col_index[NNZ]; // CSR format
int row_ptrs[num_rows + 1]; // CSR format
double x[num_cols]; // the vector to multiply
double y[num_rows]; // the output
double true_y[num_rows]; // the true Y results of operation
// Declare GPU memory pointers
double *d_values;
double *d_x;
double *d_y;
int *d_col_index;
int *d_row_ptrs;
// Allocate GPU memory
int r1 = cudaMalloc((void **) &d_values, NNZ*sizeof( double ));
int r2 = cudaMalloc((void **) &d_x, num_cols*sizeof( double ));
int r3 = cudaMalloc((void **) &d_y, num_rows*sizeof( double ));
int r4 = cudaMalloc((void **) &d_col_index, NNZ*sizeof( int ));
int r5 = cudaMalloc((void **) &d_row_ptrs, (num_rows + 1)*sizeof( int ));
if( r1 || r2 || r3 || r4 || r5 ) {
printf( "Error allocating memory in GPU\n" );
exit( 0 );
}
// Read the Values and Index:
std::ifstream values_file("./data/values.txt");
std::ifstream col_ind_file("./data/col_ind.txt");
for (int i = 0; i < NNZ; i++) {
values_file >> values[i];
double aux;
col_ind_file >> aux;
col_index[i] = (int) aux;
}
// Read the row_ptr and the True Ys:
std::ifstream row_ptr_file("./data/row_ptr.txt");
std::ifstream true_y_file("./data/y.txt");
for (int i = 0; i < (num_rows + 1); i++) {
double aux, aux2;
row_ptr_file >> aux;
true_y_file >> aux2;
row_ptrs[i] = (int) aux;
true_y[i] = (int) aux2;
}
// Read the X values:
std::ifstream x_file("./data/x.txt");
for (int i = 0; i < num_cols; i++)
x_file >> x[i];
// Transfer the arrays to the GPU:
cudaMemcpy(d_values, values, NNZ*sizeof( double ), cudaMemcpyHostToDevice);
cudaMemcpy(d_x, x, num_cols*sizeof( double ), cudaMemcpyHostToDevice);
cudaMemcpy(d_y, y, num_rows*sizeof( double ), cudaMemcpyHostToDevice);
cudaMemcpy(d_col_index, col_index, NNZ*sizeof( int ), cudaMemcpyHostToDevice);
cudaMemcpy(d_row_ptrs, row_ptrs, (num_rows + 1)*sizeof( int ), cudaMemcpyHostToDevice);
// Start Time:
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
// Call to kernel:
double size_sharedmem = num_cols*sizeof(double); // Size of shared memory
spmv<<<num_rows, num_cols, size_sharedmem>>>(num_rows, num_cols, d_row_ptrs, d_col_index, d_values, d_x, d_y);
cudaDeviceSynchronize();
// Stop Time:
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
// Transfer the values to the CPU:
cudaMemcpy(y, d_y, num_rows * sizeof(double), cudaMemcpyDeviceToHost);
// Get the error:
int errors = 0; // count of errors
float e = 500.0; // tolerance to error
for (int i = 0; i < num_rows; i++) {
if (abs(true_y[i] - y[i]) > e) {
errors++;
if(debug == 1)
printf("Error in Y%d, True: %f, Calc: %f\n", i, true_y[i], y[i]);
} else if ( i < 10) {
printf("Y%d, True: %f, Calc: %f\n", i, true_y[i], y[i]);
}
}
float error_rate = ((double)errors/(double)num_rows) * 100.0;
float density = ((float)NNZ/((float)num_cols*(float)num_rows))*100.0;
printf("\nM. Density: %0.2f%%, #Ys: %d, Errors: %d, Error Rate: %0.2f%%\n", density, num_rows, errors, error_rate);
// Free Memory
cudaFree( d_values );
cudaFree( d_x );
cudaFree( d_y );
cudaFree( d_col_index );
cudaFree( d_row_ptrs );
// Calculate Throughput:
float bw;
bw = (float )num_rows*(float )num_cols*log2((float) num_cols);
bw /= milliseconds * 1000000.0;
printf( "\nSpmV GPU execution time: %7.3f ms, Throughput: %6.2f GFLOPS\n\n", milliseconds, bw );
// Store Runtime
FILE *pFile = fopen("GPU_results.txt","a");
fprintf(pFile, "%d, %0.2f, %0.2f, %d, %d, %7.3f, %6.2f\n", NNZ, density, error_rate, num_cols, num_rows, milliseconds, bw);
fclose(pFile);
return 0;
} |
22,351 | #include <stdlib.h>
#include <stdio.h>
#include <curand.h>
#include <time.h>
#include <iostream>
#include <string>
#include <fstream>
using namespace std;
#define CURAND_CALL(x) do { if((x)!=CURAND_STATUS_SUCCESS) { \
printf("Error at %s:%d\n",__FILE__,__LINE__);\
return EXIT_FAILURE;}} while(0)
int main(int argc, char* argv[]) {
int *h_data,*d_data,L,tam,print;
size_t size;
curandRngType_t type = CURAND_RNG_PSEUDO_DEFAULT;
curandGenerator_t gen;
L=10;
if(argc > 1)
L = atoi(argv[1]);
if(argc > 2)
print = atoi(argv[2]);
if(argc > 3)
if(strcmp(argv[3],"CURAND_RNG_PSEUDO_DEFAULT")==0)
type = CURAND_RNG_PSEUDO_DEFAULT;
else if(strcmp(argv[3],"CURAND_RNG_PSEUDO_MRG32K3A")==0)
type = CURAND_RNG_PSEUDO_MRG32K3A;
else if(strcmp(argv[3],"CURAND_RNG_PSEUDO_MT19937")==0)
type = CURAND_RNG_PSEUDO_MT19937;
else if(strcmp(argv[3],"CURAND_RNG_PSEUDO_XORWOW")==0)
type = CURAND_RNG_PSEUDO_XORWOW;
else if(strcmp(argv[3],"CURAND_RNG_PSEUDO_MTGP32")==0)
type = CURAND_RNG_PSEUDO_MTGP32;
else if(strcmp(argv[3],"CURAND_RNG_PSEUDO_PHILOX4_32_10")==0)
type = CURAND_RNG_PSEUDO_PHILOX4_32_10;
else if(strcmp(argv[3],"CURAND_RNG_QUASI_DEFAULT")==0)
type = CURAND_RNG_QUASI_DEFAULT;
else if(strcmp(argv[3],"CURAND_RNG_QUASI_SCRAMBLED_SOBOL32")==0)
type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL32;
else if(strcmp(argv[3],"CURAND_RNG_QUASI_SCRAMBLED_SOBOL64")==0)
type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL64;
else if(strcmp(argv[3],"CURAND_RNG_QUASI_SOBOL32")==0)
type = CURAND_RNG_QUASI_SOBOL32;
else if(strcmp(argv[3],"CURAND_RNG_QUASI_SOBOL64")==0)
type = CURAND_RNG_QUASI_SOBOL64;
tam = L*L;
size = tam*sizeof(int);
// Allocate memory for the vectors on host memory.
h_data = (int*) malloc(size);
for (int i = 0; i < tam; i++)
h_data[i] = 0;
cudaMalloc((void **)&d_data, size);
if( curandCreateGenerator(&gen,type) != CURAND_STATUS_SUCCESS)
{
printf("Error at %s:%d\n",__FILE__,__LINE__);
return EXIT_FAILURE;
}
curandSetPseudoRandomGeneratorSeed(gen,0);
curandGenerate(gen,(unsigned int *)d_data, size);
cudaMemcpy(h_data, d_data, size, cudaMemcpyDeviceToHost);
ofstream out("data.txt");
if(print)
printf("\n\n");
for (int i = 0; i < tam; i++)
{
if(print)
if(i%L==0)
printf("\n");
out << h_data[i] << " ";
if(print)
printf(" %u",h_data[i]);
}
if(print)
printf("\n\n");
out.close();
curandDestroyGenerator(gen);
/* Free host memory */
free(h_data);
cudaFree(d_data);
return 0;
} /* main */
|
22,352 | #include <stdio.h>
#include <random>
#include <time.h>
#define ARRAY_SIZE 100000000
#define THREADS 256
#define RANDOM_MAX 100000
__global__ void saxpy_kernal(float *x, float *y, const float a, int length) {
const int id = threadIdx.x + blockIdx.x * blockDim.x;
if (id >= length) return;
y[id] = x[id] * a + y[id];
}
// Calculates SAXPY on CPU
void saxpy_on_cpu(float* x, float* y, const float a, int length) {
clock_t start = clock();
for (int i = 0; i < length; i++) {
y[i] = x[i] * a + y[i];
}
double time = (double)(clock() - start)/CLOCKS_PER_SEC;
printf("Computing SAXPY on the CPU Done in %f seconds\n", time);
}
// Checks if a1 and a2 have the same elements
bool isSame(float* a1, float* a2, int length) {
float margin = 0.0001;
for (int i = 0; i < length; i++) {
//printf("a1 %f a2 %f \n", a1[i], a2[i]);
if (fabs(a1[i] - a2[i]) > margin) {
return false;
}
}
return true;
}
int main() {
// Malloc x and y
float* x = (float*) malloc(ARRAY_SIZE * sizeof(float));
float* y = (float*) malloc(ARRAY_SIZE * sizeof(float));
const float a = 2;
//Store the result from gpu here
float* parallel_results = (float*)malloc(ARRAY_SIZE * sizeof(float));
//Fill random values in x and y
srand((unsigned int)time(NULL));
for (int i = 0; i < ARRAY_SIZE; i++) {
x[i] = ((float)rand() / (float)RAND_MAX) * RANDOM_MAX;
y[i] = ((float)rand() / (float)RAND_MAX) * RANDOM_MAX;
}
//Specify the amout of blocks and threads
//To ensure number of blocks is rounded up
dim3 numberOfBlocks((ARRAY_SIZE + THREADS -1) / THREADS);
dim3 numberOfThreads(THREADS);
float* x_parallel = 0;
float* y_parallel = 0;
//Start timer
clock_t start = clock();
//Allocate gpu memory
if (cudaMalloc(&x_parallel, sizeof(float) * ARRAY_SIZE) != cudaSuccess) {
printf("Error in cudamalloc 1 \n");
exit(-1);
}
if (cudaMalloc(&y_parallel, sizeof(float) * ARRAY_SIZE) != cudaSuccess) {
printf("Error in cudamalloc 2 \n");
exit(-1);
}
//Transfer to gpu memory
cudaMemcpy(x_parallel, x, sizeof(float) * ARRAY_SIZE, cudaMemcpyHostToDevice);
cudaMemcpy(y_parallel, y, sizeof(float) * ARRAY_SIZE, cudaMemcpyHostToDevice);
//Start kernel and calculate SAXPY
saxpy_kernal <<<numberOfBlocks, numberOfThreads >>> (x_parallel, y_parallel, a, ARRAY_SIZE);
cudaDeviceSynchronize();
cudaMemcpy(parallel_results, y_parallel, sizeof(float) * ARRAY_SIZE, cudaMemcpyDeviceToHost);
//Stop timer
double time = (double)(clock() - start) / CLOCKS_PER_SEC;
printf("Computing SAXPY on the GPU Done in %f seconds\n",time);
//Calculate SAXPY on cpu and store result in y
saxpy_on_cpu(x, y, a, ARRAY_SIZE);
//Check if the results from the gpu and cpu are the same
bool results = isSame(y, parallel_results, ARRAY_SIZE);
if (results) {
printf("Comparing the output for each implementation, Correct!\n");
}
else {
printf("Comparing the output for each implementation, Wrong \n");
}
//Free memory
cudaFree(x_parallel);
cudaFree(y_parallel);
free(x);
free(y);
free(parallel_results);
} |
22,353 | #include <stdio.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define LOG_DEBUG if(0)
#define LOG_INPUT if(0)
#define LOG_OUTPUT if(0)
__global__ void sumTriangle(float* M, float* V, int N);
__global__ void sumTriangleWithAtomics(float* M, float* V, int N);
__global__ void sumTriangle(float* M, float* V, int N){
int j=threadIdx.x;
float sum=0.0;
for (int i=0;i<j;i++)
sum+=M[i*N+j];
V[j]=sum;
__syncthreads();
if(j == N-1)
{ sum = 0.0;
for(int i=0;i<N;i++)
sum =sum + V[i];
V[N] = sum;
}
}
__global__ void sumTriangleWithAtomics(float* M, float* V, int N){
int j=threadIdx.x;
float sum=0.0;
__shared__ float totalSum;
if(j==0)
totalSum=0.0;
__syncthreads();
for (int i=0;i<j;i++)
sum+=M[i*N+j];
V[j]=sum;
atomicAdd(&totalSum, sum);
__syncthreads();
if(j == N-1)
{
V[N]=totalSum;
}
}
void print_matrix(float *A,int m,int n)
{
for(int i =0;i<m;i++)
{
for(int j=0;j<n;j++)
printf("%.2f ",A[i*n+j]);
printf("\n");
}
}
void init_matrix(float *A,int m,int n)
{
for(int i=0;i<m;i++)
{
for(int j=0;j<n;j++)
A[i*n+j]=j;
}
}
int main(void)
{
cudaError_t err = cudaSuccess;
int t=1;
int option;
LOG_INPUT printf("%d\n",t);
while(t--)
{
int mat_row;
scanf("%d %d",&mat_row,&option);
int mat_dim = mat_row;
int num_elems = mat_dim*mat_dim;
size_t size_M = num_elems*sizeof(float);
size_t size_V = (1+mat_dim)*sizeof(float);
float *h_input1 = (float *)malloc(size_M);
float *h_output1 = (float *)malloc(size_V);
if (h_input1 == NULL || h_output1 == NULL)
{
fprintf(stderr, "Failed to allocate host vectors!\n");
exit(EXIT_FAILURE);
}
init_matrix(h_input1,mat_dim,mat_dim);
LOG_INPUT print_matrix(h_input1,mat_dim,mat_dim);
float *d_input1 = NULL;
err = cudaMalloc((void **)&d_input1, size_M);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device vector d_input1 (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
float *d_output1 = NULL;
err = cudaMalloc((void **)&d_output1, size_V);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device vector d_input1 (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
LOG_DEBUG printf("Copy input data from the host memory to the CUDA device\n");
err = cudaMemcpy(d_input1, h_input1, size_M, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy vector h_input1 from host to device (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
int grid_dim = 1, block_dim = mat_dim;
cudaEvent_t seq_start, seq_end;
cudaEventCreate(&seq_start);
cudaEventCreate(&seq_end);
if(option==0)
{
cudaEventRecord(seq_start,0);
sumTriangle<<<grid_dim, block_dim>>>(d_input1, d_output1, mat_dim);
cudaEventRecord(seq_end,0);
cudaEventSynchronize(seq_end);
}
else
{
cudaEventRecord(seq_start,0);
sumTriangleWithAtomics<<<grid_dim, block_dim>>>(d_input1, d_output1, mat_dim);
cudaEventRecord(seq_end,0);
cudaEventSynchronize(seq_end);
}
err = cudaGetLastError();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to launch process_kernel1 kernel (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
LOG_DEBUG printf("Copy output data from the CUDA device to the host memory\n");
err = cudaMemcpy(h_output1, d_output1, size_V, cudaMemcpyDeviceToHost);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy vector d_input1 from device to host (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
LOG_OUTPUT print_matrix(h_output1,1,mat_dim+1);
err = cudaFree(d_input1);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device vector d_input1 (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
free(h_input1);
err = cudaFree(d_output1);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device vector d_input1 (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
free(h_output1);
float event_recorded_time=0.0;
cudaEventElapsedTime(&event_recorded_time, seq_start, seq_end);
printf("Execution Time: %f\n",event_recorded_time);
err = cudaDeviceReset();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to deinitialize the device! error=%s\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
LOG_DEBUG printf("Done\n");
}
return 0;
}
|
22,354 | #include "includes.h"
#define SIZ 20
#define num_inp 4
using namespace std;
typedef struct edge {
int first, second;
} edges;
__global__ void bfs(const edge *edges, int *vertices, int current_depth) {
int a = blockDim.x * blockIdx.x + threadIdx.x;
int vfirst = edges[a].first;
int dfirst = vertices[vfirst];
int vsecond = edges[a].second;
int dsecond = vertices[vsecond];
if ((dfirst == current_depth) && (dsecond == -1)) {
vertices[vsecond] = dfirst + 1;
}
if ((dfirst == -1) && (dsecond == current_depth)) {
vertices[vfirst] = dsecond + 1;
}
} |
22,355 | #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <cuda.h>
#include <cuda_profiler_api.h>
#include <curand.h>
#include <curand_kernel.h>
#define MAX 9
int ROWS = 9;
int COLUMNS = 9;
char* FILENAME;
void printGridToFile(int* board);
void startSeq(char* name);
int main ( int argc, char *argv[] ) {
// Read in file
if (argc < 2) {
printf("Sorry, we need a command line argument with the sudoku puzzle to solve");
exit(0);
}
else {
char* name;
name = strtok(argv[1], ".");
FILENAME = name;
startSeq(FILENAME);
}
}
void printGrid(int* board) {
int copyGrid[ROWS][COLUMNS];
memcpy(copyGrid, board, sizeof(int) * ROWS * COLUMNS);
printf("\n");
for (int row = 0; row < ROWS; row++) {
for (int column = 0; column < COLUMNS; column++) {
printf("%d ", copyGrid[row][column]);
}
printf("\n");
}
printf("\n");
}
void extractNumbers(char* fileName, int* grid) {
FILE *input;
input = fopen(fileName, "r");
char inp;
for (int row = 0; row < ROWS; row++) {
for (int column = 0; column < COLUMNS; column++) {
fscanf(input," %c", &inp);
int number = inp - '0';
grid[row * COLUMNS + column] = number;
}
}
fclose(input);
}
__device__ void d_swap (int *a, int *b) {
int temp = *a;
*a = *b;
*b = temp;
}
__device__ void d_randomize(int nineArray[], curandState_t state) {
int tryValid = curand(&state) % MAX;
for (int i = 8; i > 0; i--) {
int j = curand(&state) % (i+1);
d_swap(&nineArray[i], &nineArray[j]);
}
}
__device__ int d_numberPlacementValid(int numberToCheck, int checkingRow, int checkingColumn,
int board[MAX][MAX]) {
// Check if number to check exists in Column
int boardValue = 0;
for (int row = 0; row < MAX; row++) {
boardValue = board[row][checkingColumn];
if (boardValue == numberToCheck) {
return 0;
}
}
// Check if number to check exists in Row
for (int column = 0; column < MAX; column++) {
boardValue = board[checkingRow][column];
if (boardValue == numberToCheck) {
return 0;
}
}
// Check if exists in 3 x 3 grid
int rowGrid = checkingRow / 3;
int columnGrid = checkingColumn / 3;
for (int rowAdd = 0; rowAdd < 3; rowAdd++) {
for (int colAdd = 0; colAdd < 3; colAdd++) {
int rowValue = (rowGrid * 3) + rowAdd;
int colValue = (columnGrid * 3) + colAdd;
boardValue = board[rowValue][colValue];
if (boardValue == numberToCheck) {
return 0;
}
}
}
return 1;
}
__global__ void replaceZeros(int* d_sudoku, int* d_sudoku_solution, int timeCalled) {
__shared__ int shared_sudoku[9][9];
int thread_x = threadIdx.x;
int thread_y = threadIdx.y;
int blockId = blockIdx.x + blockIdx.y *gridDim.x;
int threadId = blockId * (blockDim.x * blockDim.y) +
(threadIdx.y * blockDim.x) + threadIdx.x;
shared_sudoku[thread_x][thread_y] = d_sudoku[thread_x+ 9*thread_y];
// Synch threads to synch shared data
__syncthreads();
curandState_t state;
curand_init(threadId, gridDim.y/2, timeCalled, &state);
// Create thread individual sudoku board
int local_sudoku[9][9];
for (int row = 0; row < 9; row++) {
for (int col = 0; col < 9; col++) {
local_sudoku[row][col] = shared_sudoku[row][col];
}
}
// For each element, try to random a value that is valid
for (int row = 0; row < 9; row++) {
for (int col = 0; col < 9; col++) {
if (local_sudoku[row][col] == 0) {
int insertNum = 0;
int nineArray[] = {1, 2, 3, 4, 5, 6, 7, 8, 9};
d_randomize(nineArray, state);
for (int i = 0; i < 9; i++) {
if (d_numberPlacementValid(nineArray[i], row, col, local_sudoku)) {
insertNum = nineArray[i];
break;
}
}
if (insertNum == 0) {
return;
}
else {
local_sudoku[row][col] = insertNum;
}
}
}
}
// Only get here with solved sudoku puzzle
// Placing values in solution
for (int row = 0; row < 9; row++) {
for (int col = 0; col < 9; col++) {
d_sudoku_solution[row * 9 + col] = local_sudoku[row][col];
}
}
}
void printGridToFile(int* board) {
char* extension = ".sol";
int stringSize = strlen(FILENAME) + 3;
char outputFileName[stringSize];
strcpy(outputFileName, FILENAME);
strcpy(outputFileName, extension);
FILE *ofp;
ofp = fopen(outputFileName, "w");
int copyGrid[ROWS][COLUMNS];
memcpy(copyGrid, board, sizeof(int) * ROWS * COLUMNS);
for (int row = 0; row < ROWS; row++) {
for (int column = 0; column < COLUMNS; column++) {
fprintf(ofp, "%d ", copyGrid[row][column]);
}
fprintf(ofp, "\n");
}
fclose(ofp);
}
void startSeq(char* name) {
int originalGrid[ROWS][COLUMNS];
FILENAME = name;
extractNumbers(name, &originalGrid[0][0]);
int sudokuSize = sizeof(int) * 81;
int *d_sudoku;
int *sudoku;
int *d_sudoku_solution;
int *sudoku_solution;
cudaHostAlloc((void**)&sudoku, sudokuSize, cudaHostAllocDefault);
cudaHostAlloc((void**)&sudoku_solution, sudokuSize, cudaHostAllocDefault);
for (int row = 0; row < ROWS; row++) {
for (int col = 0; col < COLUMNS; col++) {
sudoku[row * 9 + col] = originalGrid[row][col];
sudoku_solution[row * 9 + col] = 0;
}
}
dim3 dimGrid(12, 15);
dim3 dimBlock(9, 9);
cudaMalloc((void**)&d_sudoku, sudokuSize);
cudaMalloc((void**)&d_sudoku_solution, sudokuSize);
cudaMemcpy(d_sudoku, sudoku, sudokuSize, cudaMemcpyHostToDevice);
replaceZeros<<<dimGrid,dimBlock>>>(d_sudoku, d_sudoku_solution, 0);
cudaMemcpy(sudoku_solution, d_sudoku_solution, sudokuSize, cudaMemcpyDeviceToHost);
cudaFree(d_sudoku);
cudaFree(d_sudoku_solution);
printGridToFile(sudoku_solution);
}
|
22,356 | #include <cstdio>
#include <cstdlib>
#include <iostream>
#include <ctime>
using namespace std;
#define blockSize 32
#define DIM 1000
__device__ __managed__ float A[DIM][DIM], B[DIM][DIM], C[DIM][DIM];
void init() {
srand48(5L);
for (int i=0; i<DIM; i++)
for (int j=0; j<DIM; j++) {
A[i][j] = drand48();
B[i][j] = drand48();
}
}
__global__ void MatMulKernel(int mtxDim, int grid){
double CValue = 0;
int Row = blockIdx.y*blockSize + threadIdx.y;
int Col = blockIdx.x*blockSize + threadIdx.x;
const int &sRow = threadIdx.y;
const int &sCol = threadIdx.x;
const int zOff = blockIdx.z * blockSize;
__shared__ float As[blockSize][blockSize];
__shared__ float Bs[blockSize][blockSize];
if(Row>=mtxDim || Col>=mtxDim) return;
if(zOff+sCol < mtxDim)
As[sRow][sCol]=A[Row][sCol+zOff];
else{
As[sRow][sCol]=0;
}
if(zOff+sRow < mtxDim)
Bs[sRow][sCol]=B[sRow+zOff][Col];
else{
Bs[sRow][sCol]=0;
}
__syncthreads();
for(int j=0;j<blockSize;j++){
CValue+=As[sRow][j]*Bs[j][sCol];
}
atomicAdd(&(C[Row][Col]),CValue);
}
int main(int argc, char* argv[]){
struct timespec start, finish;
double elapsed;
cout<<"generating random numbers"<<endl;
init();
cout<<"done"<<endl;
int gridSize=(DIM+blockSize-1)/blockSize;
dim3 dimBlock(blockSize,blockSize);
dim3 dimGrid(gridSize, gridSize, gridSize);
clock_gettime(CLOCK_MONOTONIC, &start);
MatMulKernel<<<dimGrid, dimBlock>>>(DIM, gridSize);
cudaDeviceSynchronize();
clock_gettime(CLOCK_MONOTONIC, &finish);
cout<<" C[3][3]: "<<C[3][3]<<" C[100][200]: "<<C[100][200]<<endl;
elapsed = (finish.tv_sec - start.tv_sec);
elapsed += (finish.tv_nsec - start.tv_nsec) / 1000000000.0;
cout<<"Time elapsed: "<<elapsed<<" seconds"<<endl;
cudaDeviceReset();
return 0;
}
|
22,357 | #include <stdio.h>
#include <cuda_runtime.h>
#include <math.h>
#include <time.h>
static double a = 1.0E-10;
void FillMatrix(double *matrixA, double *matrixB, int size);
void OpenFile(int *N);
void SaveFile(struct tm *start, struct tm *end, double error, double elapsed, int N);
__global__ void Multiply(double* A, double* B, double* C, int N)
{
double result; // Acumula la suma del renglon por la columna
int index; // Indice del vector
int ix; // Indica el renglon
int iy; // Toma valores solo entre 0 a N-1
int k; // Iterador
index = blockIdx.x * blockDim.x + threadIdx.x;
if(index < N * N)
{
ix = index / N;
iy = index % N;
result = 0.0;
for(k = 0; k < N; k++)
result += A[k + N * ix] * B[k * N + iy ];
C[iy + N * ix] = result;
}
}
__global__ void AddMatrix(double* C, int N, double* result)
{
long i;
long j;
for(i = 0; i < N; i++)
for(j = 0; j < N; j++)
*result += C[i + N * j];
}
int main(int argc, char *argv[])
{
//Variables
int N; // Tamaño de la matriz cuadrada.
size_t size; // Tamaño total en memoria.
size_t sizeDouble; // Tamaño en memoria del tipo doble.
double* h_matrixA; // Matriz A en el equipo.
double* h_matrixB; // Matriz B en el equipo.
double* h_matrixC; // Matriz C (resultado) en el equipo.
double* d_matrixA; // Matriz A en la memoria de la GPU.
double* d_matrixB; // Matriz B en la memoria de la GPU.
double* d_matrixC; // Matriz C (resultado) en la memoria de la GPU.
double* h_result; // Sumatoria de los valores de la multiplicacion de matrices en el equipo.
double* d_result; // Sumatoria de los valores de la multiplicacion de matrices en la GPU.
int Tam; // Numero de datos que se manejan
int threads; // Hilos por bloque
int blocks; // Numero de bloques necesario para procesar los datos
double estimation; // Estimacion del calculo
double error; // Error encontrado
double elapsed; // Tiempo que tomo ejecutarse el programa
time_t t; // Variable de tiempo
struct tm *start; // Hora de inicio
struct tm *end; // Hora de termino
clock_t start_clock; // Tiempo de inicio
clock_t stop_clock; // Tiempo de termino
// Inicia la variable de tiempo
t = time(NULL);
// Establece la hora de inicio
start = localtime(&t);
start_clock = clock();
// Asigna la dimension de la matriz
OpenFile(&N);
// Establece el tamaño total de la matriz en memoria
size = N * sizeof(double) * N;
// Establecec el tamaño del tipo de dato double
sizeDouble = sizeof(double);
// Asigna el numero de hilos y calcula el numero de bloques
Tam = N * N;
cudaDeviceGetAttribute(&threads, cudaDevAttrMaxThreadsPerBlock, 0);
blocks = Tam / threads;
if(Tam % threads > 0) //Si sobran datos, aumenta los bloques en 1
blocks++;
//En la memoria del equipo
h_matrixA = (double*)malloc(size);
h_matrixB = (double*)malloc(size);
h_matrixC = (double*)malloc(size);
h_result = (double*)malloc(sizeDouble);
//En la memoria de la GPU
cudaMalloc(&d_matrixA, size);
cudaMalloc(&d_matrixB, size);
cudaMalloc(&d_matrixC, size);
cudaMalloc(&d_result, sizeDouble);
// Llena las matrices h_matrixA y h_matrixB
FillMatrix(h_matrixA, h_matrixB, N);
// Copia los arreglos de memoria del CPU a memoria de la GPU
cudaMemcpy(d_matrixA, h_matrixA, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_matrixB, h_matrixB, size, cudaMemcpyHostToDevice);
// Mandar llamar la multiplicacion de matrices.
Multiply<<<blocks, threads >>>(d_matrixA, d_matrixB, d_matrixC, N);
// Inicializa la variable d_result con 0.0 y lo copia a la memoria de la GPU
*h_result = 0.0;
cudaMemcpy(d_result, h_result, sizeDouble, cudaMemcpyHostToDevice);
// Suma los valores de la multiplicacion de matrices
AddMatrix<<<1, 1>>>(d_matrixC, N, d_result);
//Copia el resultado de la suma de los elementos de la matriz en la memoria
cudaMemcpy(h_result, d_result, sizeDouble, cudaMemcpyDeviceToHost);
// Calculo estimado con la formula a^2*N^3.
estimation = pow(N, 3) * pow(a, 2);
// Calcula el % de error.
error = fabs(*h_result - estimation) / estimation * 100.0;
// Libera espacio del equipo
cudaFree(d_matrixA);
cudaFree(d_matrixB);
cudaFree(d_matrixC);
cudaFree(d_result);
// Libera espacio de la tarjeta de video
free(h_matrixA);
free(h_matrixB);
free(h_matrixC);
free(h_result);
// Establece la hora en que termino de calcular
end = localtime(&t);
stop_clock = clock();
// Calcula el tiempo que tomo ejecutar el programa
elapsed = (double)(stop_clock - start_clock) / CLOCKS_PER_SEC;
// Guarda el resultado en un archivo
SaveFile(start, end, error, elapsed, N);
return 0;
}
// Llena las dos matrices con el valor constante
void FillMatrix(
double *matrixA, // Primera matriz
double *matrixB, // Segunda matriz
int N // Dimension de la matriz
)
{
int i; // Indice el renglon
int j; // Indice de la columna
for (i = 0; i < N; i++)
for (j = 0; j < N; j++)
{
matrixA[(i * N) + j] = a;
matrixB[(i * N) + j] = a;
}
}
// Abre un archivo con la dimension de la matriz
void OpenFile(
int *N // Dimension de la matriz
)
{
FILE *file;
file = fopen("parameters.dat", "r");
if (file == NULL)
printf("No se puede abrir el archivo.\n");
else
{
fscanf(file, "%d", N);
fclose(file);
}
}
// Crea un archivo de salida con la hora de inicio, de termino y el tiempo que tomo correr el programa
// Asi como el porcentaje de error
void SaveFile(
struct tm *start, // Hora de inicio
struct tm *end, // Hora de termino
double error, // Porcentaje de error
double elapsed, // Tiempo que paso
int N // Dimension de la matriz
)
{
FILE *file;
char file_name[64];
char output[50];
sprintf(file_name, "cuda-%d-%d-%d-%d-%d.txt", N, end->tm_mday, end->tm_hour, end->tm_min, end->tm_sec);
file = fopen(file_name, "w+");
strftime(output, sizeof(output), "%c", start);
fprintf(file, "Hora de inicio\n%s\n", output);
strftime(output, sizeof(output), "%c", end);
fprintf(file, "Hora de termino\n%s\n", output);
fprintf(file, "Tiempo de ejecucion\n%.15lf\n", elapsed);
fprintf(file, "Error\n%.15le\n", error);
fclose(file);
}
|
22,358 | #include <stdio.h>
#include <iostream>
#include <curand.h>
#include <curand_kernel.h>
#define N 16
const int blocksize = 4;
__global__
void init_random(curandState_t *state)
{
int idx = threadIdx.x + blockDim.x * blockIdx.x;
curand_init(0,idx,0,&state[idx]);
}
__global__
void generate_random(curandState_t *state, double *b)
{
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx < N){
b[idx] = curand_normal_double(&state[idx]);
}
}
int main()
{
cudaError_t ierrAsync;
cudaError_t ierrSync;
double *b;
curandState_t *state;
// allocate memory in GPU for random number creation state objects.
cudaMallocManaged(&state, N * sizeof(curandState_t));
cudaMallocManaged(&b, N * sizeof(double));
dim3 dimBlock( blocksize, 1 );
dim3 dimGrid( N/blocksize+1, 1 );
init_random<<<dimGrid, dimBlock>>>(state);
ierrSync = cudaGetLastError();
ierrAsync = cudaDeviceSynchronize();
if (ierrSync != cudaSuccess) { printf("Sync error: %s\n", cudaGetErrorString(ierrSync)); }
if (ierrAsync != cudaSuccess) { printf("Async error: %s\n", cudaGetErrorString(ierrAsync)); }
generate_random<<<dimGrid, dimBlock>>>(state,b);
ierrSync = cudaGetLastError();
ierrAsync = cudaDeviceSynchronize();
if (ierrSync != cudaSuccess) { printf("Sync error: %s\n", cudaGetErrorString(ierrSync)); }
if (ierrAsync != cudaSuccess) { printf("Async error: %s\n", cudaGetErrorString(ierrAsync)); }
for (int i=0; i<N; i++){
std::cout << b[i] << std::endl;
}
cudaFree( state );
cudaFree( b );
return EXIT_SUCCESS;
}
|
22,359 | // Author: Sudnya Padalikar
// Date: 01/23/2014
// Brief: simple matrix multiplication kernel in cuda
#include <stdio.h>
#include <cassert>
#include <iostream>
// Kernel that executes on the CUDA device
__global__ void matrixMultiply(float * A, float * B, float * C,
int numARows, int numAColumns,
int numBRows, int numBColumns,
int numCRows, int numCColumns) {
int rowIdx = blockIdx.y * blockDim.y + threadIdx.y;
int colIdx = blockIdx.x * blockDim.x + threadIdx.x;
//bounds check
if (rowIdx < numCRows && colIdx < numCColumns) {
float temp = 0.0;
for (int i = 0; i < numBRows; ++i) {
temp += A[(rowIdx*numAColumns)+i]*B[colIdx+(i*numBColumns)];
}
C[(rowIdx*numCColumns)+colIdx] = temp;
}
}
void printMatrix(float* M, int rows, int columns) {
for (int i = 0; i < rows; ++i) {
std::cout << "[ ";
for (int j = 0; j < columns; ++j) {
std::cout << " " << M[(i*columns) + j];
}
std::cout << " ]\n";
}
std::cout << "\n";
}
int main()
{
float *a_h;
float *a_d; // Pointer to host & device arrays
float *b_h;
float *b_d;
float *c_h;
float *c_d;
int numARows = 3; // number of rows in the matrix A
int numAColumns = 4; // number of columns in the matrix A
int numBRows = 4; // number of rows in the matrix B
int numBColumns = 2; // number of columns in the matrix B
int numCRows = 3; // number of rows in the matrix C (you have to set this)
int numCColumns = 2; // number of columns in the matrix C (you have to set this)
size_t a_bytes = numARows * numAColumns * sizeof(float);
size_t b_bytes = numBRows * numBColumns * sizeof(float);
size_t c_bytes = numCRows * numCColumns * sizeof(float);
assert(numAColumns == numBRows);
assert(numARows == numCRows);
assert(numBColumns == numCColumns);
a_h = (float *)malloc(a_bytes); // Allocate array on host
cudaMalloc((void **) &a_d, a_bytes); // Allocate array on device
b_h = (float *)malloc(b_bytes); // Allocate array on host
cudaMalloc((void **) &b_d, b_bytes); // Allocate array on device
// initialize A, B
for (int i = 0; i < numARows; ++i) {
for (int j = 0; j < numAColumns; ++j) {
a_h[(i*numAColumns) + j] = (i*numAColumns) + j;
}
}
printMatrix(a_h, numARows, numAColumns);
for (int i = 0; i < numBRows; ++i) {
for (int j = 0; j < numBColumns; ++j) {
b_h[(i*numBColumns) +j] = (i*numBColumns) + j;
}
}
printMatrix(b_h, numBRows, numBColumns);
c_h = (float *)malloc(c_bytes); // Allocate array on host
cudaMalloc((void **) &c_d, c_bytes); // Allocate array on device
cudaMemcpy(a_d, a_h, a_bytes, cudaMemcpyHostToDevice);
cudaMemcpy(b_d, b_h, b_bytes, cudaMemcpyHostToDevice);
// Do calculation on device:
dim3 block_size = dim3(16, 16, 1);
dim3 num_blocks = dim3((numCColumns + 16 - 1)/16, (numCRows + 16 - 1)/ 16);
matrixMultiply <<< num_blocks, block_size >>> (a_d, b_d, c_d, numARows, numAColumns, numBRows, numBColumns, numCRows, numCColumns);
// Retrieve result from device and store it in host array
cudaMemcpy(c_h, c_d, c_bytes, cudaMemcpyDeviceToHost);
printMatrix(c_h, numCRows, numCColumns);
// Cleanup
free(a_h);
cudaFree(a_d);
free(b_h);
cudaFree(b_d);
free(c_h);
cudaFree(c_d);
}
|
22,360 | //
// $ nvcc add.cu -o add_cuda
// $ ./add_cuda
// Max error: 0.000000
//
#include <cuda_runtime.h>
#include <iostream>
#include <math.h>
using namespace std;
// Kernel function to add the elements of two arrays
__global__
void add(int n, float *x, float *y, float *z, int batch_size)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
i *= batch_size;
if(i<n) {
int e = min(n, i + batch_size);
while ( i < e) {
z[i] = x[i] + y[i];
++i;
}
}
}
int main(void) {
int N = 1<<30;
float *dx;
float *dy;
float *dz;
// Allocate Unified Memory – accessible from CPU or GPU --- probably SLOW
cudaMallocManaged(&dx, N*sizeof(float));
cudaMallocManaged(&dy, N*sizeof(float));
cudaMallocManaged(&dz, N*sizeof(float));
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
dx[i] = i;
dy[i] = -i;
}
int block_size = 4096;
int threadsPerBlock = 4096;
int blocksPerGrid =(N/block_size + threadsPerBlock - 1) / threadsPerBlock;
cout << "CUDA kernel launch with " << blocksPerGrid << " blocks of " << threadsPerBlock << " threads\n";
add<<<blocksPerGrid,threadsPerBlock>>>(N, dx, dy, dz, block_size);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
// Check for errors (all values should be 0.0f)
float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(dz[i]-0.0f));
std::cout << "Max error: " << maxError << std::endl;
cudaFree(dx);
cudaFree(dy);
cudaFree(dz);
return 0;
}
|
22,361 | #include <stdio.h>
__global__ void helloFromGPU(void) { // The qualifier __global__ tells the compiler
// that the function will be called from the CPU and executed on the GPU.
printf("Hello World from GPU from thread %d\n", threadIdx.x);
}
int main(void) {
printf("Hello World from CPU!\n");
// Triple angle brackets mark a call from the host thread to the code on the device side. A kernel is
// executed by an array of threads and all threads run the same code. The parameters within the triple
// angle brackets are the execution configuration, which specifies how many threads will execute the
// kernel. In this example, you will run 10 GPU threads.
helloFromGPU <<<1, 10>>>();
cudaDeviceReset(); // The function cudaDeviceReset() will explicitly destroy and
// clean up all resources associated with the current device in the current process.
return 0;
}
|
22,362 | #include "includes.h"
__global__ void kCopy(float* srcStart, float* destStart, unsigned int copyWidth, unsigned int jumpWidth, unsigned int numElements) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
if(idx < numElements)
destStart[(idx / copyWidth) * jumpWidth + idx % copyWidth] = srcStart[(idx / copyWidth) * jumpWidth + idx % copyWidth];
} |
22,363 | /***************************************************************************//**
* \file calculateForce.cu
* \author Christopher Minar (minarc@oregonstate.edu)
* \brief
*/
#include "calculateForce.h"
namespace kernels
{
__global__//kernel should be of size totalPoints
void force_pressure(double *force_pressure, double *body_intercept_p,
double *body_intercept_p_x, double *body_intercept_p_y,
double *bx, double *by, double *xv, double *yu, int *ghostTagsP,
int *i_start, int *j_start, int width, int height, int totalPoints, int nx, int ny, double midX, double midY)
{
//initialise
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= totalPoints)
return;
int ii = i_start[0],
jj = j_start[0],
ip;
int I0,
If,
J0,
Jf,
count = 0,
thetaID,
minID,
maxID;
//find 16 surrounding nodes
double theta,
thetaNode,
min=-10,
max=10;
while (xv[ii] < bx[idx])
ii++;
I0 = ii-2;
If = ii+1;
while (yu[jj] < by[idx])
jj++;
J0=jj-2;
Jf=jj+1;
thetaNode = asin((by[idx]-midY)/sqrt(pow(bx[idx]-midX,2)+pow(by[idx]-midY,2)));
if (bx[idx] < midX)//this janky if statement forces theta to be continuous
{
thetaNode = M_PI-thetaNode;
}
if (thetaNode > M_PI*5/4 || thetaNode < -M_PI/4)
{
if(bx[idx]>midX)
thetaNode += 2*M_PI;
}
//sweep over nodes calculating theta
//find theta above and below node
for (int i = I0; i<=If; i++)
{
for(int j=J0;j<=Jf;j++)
{
ip = j*nx+i;
if (ghostTagsP[ip]>0)
{
theta = asin((body_intercept_p_y[ip]-midY)/sqrt(pow(body_intercept_p_x[ip]-midX,2)+pow(body_intercept_p_y[ip]-midY,2)));
if (body_intercept_p_x[ip]<midX)
{
theta = M_PI-theta;
}
if (thetaNode > M_PI*5/4 || thetaNode < -M_PI/4)
{
if(body_intercept_p_x[ip]>midX)
theta += 2*M_PI;
}
thetaID = ip;
if (theta > thetaNode && theta < max)
{
max = theta;
maxID = thetaID;
}
if (theta<thetaNode && theta > min)
{
min = theta;
minID = thetaID;
}
}
count ++;
}
}
//interp for node
force_pressure[idx] = body_intercept_p[minID] + (body_intercept_p[maxID] - body_intercept_p[minID]) * (thetaNode-min) / (max-min);
}
__global__
void force_velocity_x(double *force_dudx, double *uB, double *u,
double *bx, double *by, double *xu, double *yu,
int *i_start, int *j_start, int width, int height, int totalPoints, int nx, int ny, double midX, double midY, double dx)
{
//initialise
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= totalPoints)
return;
int ii = i_start[0],
jj = j_start[0];
double y3, q3, q4,
x1, x2, y1, q1, q2;
//find extended image point
double rise = by[idx]-midY,
run = bx[idx]-midX,
radius = sqrt(rise*rise+run*run);
double dn = dx*sqrt(2.0),//distance from body to calc normal at, needs to be at least sqrt(2)*dx to place it a full node away from the body
ipx = bx[idx] + dn/radius*run,
ipy = by[idx] + dn/radius*rise;
//find points bounding extended image point
while (xu[ii] < ipx)
ii++;
x1 = xu[ii-1]; x2 = xu[ii];
while (yu[jj] < ipy)
jj++;
y3 = yu[jj];
y1 = yu[jj-1];
q3 = u[(jj)*(nx-1)+(ii-1)]; q4 = u[(jj)*(nx-1)+(ii)];
q1 = u[(jj-1)*(nx-1)+(ii-1)]; q2 = u[(jj-1)*(nx-1)+(ii)];
//interp for u at extended image point //flag grid must be uniform
//http://www.ajdesigner.com/phpinterpolation/bilinear_interpolation_equation.php
double topleft = (x2-ipx)*(y1-ipy)/(x2-x1)/(y1-y3)*q3,
topright = (ipx-x1)*(y1-ipy)/(x2-x1)/(y1-y3)*q4,
botleft = (x2-ipx)*(ipy-y3)/(x2-x1)/(y1-y3)*q1,
botright = (ipx-x1)*(ipy-y3)/(x2-x1)/(y1-y3)*q2,
ipu = botleft + botright + topleft + topright;
//calc normal derivative
force_dudx[idx] = (ipu-uB[0])/dn;
}
__global__
void force_velocity_y(double *force_dvdx, double *vB, double *u,
double *bx, double *by, double *xv, double *yv,
int *i_start, int *j_start, int width, int height, int totalPoints, int nx, int ny, double midX, double midY, double dx)
{
//initialise
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= totalPoints)
return;
int ii = i_start[0],
jj = j_start[0];
double y3, q3, q4,
x1, x2, y1, q1, q2;
//find extended image point
double rise = by[idx]-midY,
run = bx[idx]-midX,
radius = sqrt(rise*rise+run*run);
double dn = dx*sqrt(2.0),//distance from body to calc normal at, needs to be at least sqrt(2)*dx to place it a full node away from the body
ipx = bx[idx] + dn/radius*run,
ipy = by[idx] + dn/radius*rise;
//find points bounding extended image point
while (xv[ii] < ipx)
ii++;
x1 = xv[ii-1]; x2 = xv[ii];
while (yv[jj] < ipy)
jj++;
y3 = yv[jj];
y1 = yv[jj-1];
q3 = u[(jj)*(nx-1)+(ii-1) + ny*(nx-1)]; q4 = u[(jj)*(nx-1)+(ii) + ny*(nx-1)];
q1 = u[(jj-1)*(nx-1)+(ii-1) + ny*(nx-1)]; q2 = u[(jj-1)*(nx-1)+(ii) + ny*(nx-1)];
//interp for u at extended image point //flag grid must be uniform
//http://www.ajdesigner.com/phpinterpolation/bilinear_interpolation_equation.php
double topleft = (x2-ipx)*(y1-ipy)/(x2-x1)/(y1-y3)*q3,
topright = (ipx-x1)*(y1-ipy)/(x2-x1)/(y1-y3)*q4,
botleft = (x2-ipx)*(ipy-y3)/(x2-x1)/(y1-y3)*q1,
botright = (ipx-x1)*(ipy-y3)/(x2-x1)/(y1-y3)*q2,
ipu = botleft + botright + topleft + topright;
//calc normal derivative
force_dvdx[idx] = (ipu-vB[0])/dn;
}
__global__
void force(double *force_x, double *force_y, double *pressure, double *dudn, double *dvdn,
double *bx, double *by,
int totalPoints, double midX, double midY, double nu)
{
//initialise
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx >= totalPoints)
return;
//get area
double area = sqrt(pow(bx[0]-bx[1],2) + pow(by[0]-by[1],2));
//get normal vector
double h = sqrt(pow(by[idx]-midY,2) + pow(bx[idx]-midX,2)),
n1 = (bx[idx]-midX)/h,
n2 = (by[idx]-midY)/h;
//calc tang stress
double mu = nu,
tau_x = mu*((1-n1*n1)*dudn[idx]+(-n1*n2)*dvdn[idx]),
tau_y = mu*((-n1*n2)*dudn[idx]+(1-n2*n2)*dvdn[idx]);
//integrate
force_x[idx] = area * tau_x - area * n1 * pressure[idx];
force_y[idx] = area * tau_y - area * n2 * pressure[idx];
}
}
|
22,364 | #include <stdio.h>
#define imin(a,b) (a<b?a:b)
const int threadsPerBlock = 256;
__global__ void gpu_partial_dot_product( double *a, double *b, double *c, int N) {
__shared__ double cache[threadsPerBlock];
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int cacheIndex = threadIdx.x;
double temp = 0;
while (tid < N) {
temp += a[tid] * b[tid];
tid += blockDim.x * gridDim.x;
}
cache[cacheIndex] = temp;
__syncthreads();
int i = blockDim.x/2;
while (i != 0) {
if (cacheIndex < i)
cache[cacheIndex] += cache[cacheIndex + i];
__syncthreads();
i /= 2;
}
if (cacheIndex == 0)
c[blockIdx.x] = cache[0];
}
double gpu_full_dot_product(const double *a, const double *b, int N) {
const int blocksPerGrid = imin( 256, (N+threadsPerBlock-1) / threadsPerBlock );
double *partial_sum;
double *dev_a, *dev_b, *dev_partial_sum;
// allocate memory on the cpu side
partial_sum = (double*)malloc( blocksPerGrid*sizeof(double) );
// allocate the memory on the GPU
cudaMalloc((void**)&dev_a, N*sizeof(double));
cudaMalloc((void**)&dev_b, N*sizeof(double));
cudaMalloc((void**)&dev_partial_sum, blocksPerGrid*sizeof(double));
// copy the arrays 'a' and 'b' to the GPU
cudaMemcpy( dev_a, a, N*sizeof(double), cudaMemcpyHostToDevice);
cudaMemcpy( dev_b, b, N*sizeof(double), cudaMemcpyHostToDevice);
gpu_partial_dot_product<<<blocksPerGrid,threadsPerBlock>>>(dev_a, dev_b,
dev_partial_sum, N);
cudaMemcpy(partial_sum, dev_partial_sum,
blocksPerGrid*sizeof(double),
cudaMemcpyDeviceToHost);
double sum = 0;
for (int i=0; i<blocksPerGrid; i++) {
sum += partial_sum[i];
}
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_partial_sum);
// free memory on the cpu side
free(partial_sum);
return sum;
}
|
22,365 | #include "includes.h"
__global__ void vecadd( int * v0, int * v1, std::size_t size )
{
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
if( tid < size )
{
v0[ tid ] += v1[ tid ];
}
} |
22,366 | extern "C"
__global__ void bhsm_forward_backward(
const float *x,
const float *w,
const int *ts,
const int *paths,
const float *codes,
const int *begins,
const int n_in,
const int max_len,
const int n_ex,
float *ls,
float *gx,
float *gW
) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n_ex * max_len) {
int idx = i / max_len;
int offset = i - idx * max_len;
int t = ts[idx];
int begin = begins[t];
int length = begins[t+1] - begin;
if (offset < length) {
int p = begin + offset;
int node = paths[p];
float wx = 0;
int w_start = n_in * node;
int x_start = n_in * idx;
for (int j = 0; j < n_in; ++j) {
// int w_i = w_start + j;
// int x_i = x_start + j;
// wx += (w[w_i] * x[x_i]);
wx +=(w[w_start + j] * x[x_start + j]);
}
wx *= codes[p];
float g = -codes[p] / (1.0f + exp(wx));
ls[i] = log(1 + exp(-wx));
for (int j = 0; j < n_in; ++j) {
int w_i = w_start + j;
int x_i = x_start + j;
// gx[x_i] += g * w[w_i];
// gW[w_i] += g * x[x_i];
atomicAdd(gx + x_i, g * w[w_i]);
atomicAdd(gW + w_i, g * x[x_i]);
}
}
}
}
|
22,367 | /*
* Copyright 1993-2010 NVIDIA Corporation. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual property and
* proprietary rights in and to this software and related documentation.
* Any use, reproduction, disclosure, or distribution of this software
* and related documentation without an express license agreement from
* NVIDIA Corporation is strictly prohibited.
*
* Please refer to the applicable NVIDIA end user license agreement (EULA)
* associated with this source code for terms and conditions that govern
* your use of this NVIDIA software.
*
*/
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#define N 10
__global__ void add( int *a, int *b, int *c ) {
int tid = blockIdx.x; // this thread handles the data at its thread id
if (tid < N)
c[tid] = a[tid] + b[tid];
}
int main( void ) {
// allocate the memory on the CPU
thrust::host_vector<int> h_vec_a(N);
thrust::host_vector<int> h_vec_b(N);
thrust::host_vector<int> h_vec_c(N);
// fill the arrays 'a' and 'b' on the CPU
for (int i=0; i<N; i++) {
h_vec_a[i] = -i;
h_vec_b[i] = i * i;
}
// copy the arrays 'a' and 'b' to the GPU
thrust::device_vector<int> d_vec_a = h_vec_a;
thrust::device_vector<int> d_vec_b = h_vec_b;
thrust::device_vector<int> d_vec_c(N);
int *t_a = thrust::raw_pointer_cast(&d_vec_a[0]);
int *t_b = thrust::raw_pointer_cast(&d_vec_b[0]);
int *t_c = thrust::raw_pointer_cast(&d_vec_c[0]);
//invoke add kernal with correct parameters
add<<<N,1>>>(t_a, t_b, t_c); //because we use blockIdx.x, we should use 10 blocks, 1 thread each beacuse the operation is not parallelizable, and we need to hit all 10 indexesi
thrust::copy(d_vec_c.begin(), d_vec_c.end(), h_vec_c.begin());
// display the results
for (int i=0; i<N; i++) {
printf( "%d + %d = %d\n", h_vec_a[i], h_vec_b[i], h_vec_c[i] );
}
return 0;
}
|
22,368 | #include "includes.h"
__global__ void LSTMDeltaKernelBPTT( float* deltas, float* cellStates, float* previousCellStates, float* cellStateErrors, float* nextCellStateErrors, float* outputGateDeltas, float* forgetGateDeltas, float* nextForgetGateDeltas, float* inputGateDeltas, float* nextInputGateDeltas, float* cellInputDeltas, float* cellInputActivations, float* cellStateActivations, float* outputGateActivations, float* nextForgetGateActivations, float* inputGateActivations, float* cellInputActivationDerivatives, float* cellStateActivationDerivatives, float* outputGateActivationDerivatives, float* forgetGateActivationDerivatives, float* inputGateActivationDerivatives, float* cellInputWeights, float* outputGateWeights, float* forgetGateWeights, float* inputGateWeights, int inputCount, int cellCount, int cellsPerBlock )
{
int memoryBlockId = blockDim.x * blockIdx.y * gridDim.x //rows preceeding current row in grid
+ blockDim.x * blockIdx.x //blocks preceeding current block
+ threadIdx.x;
if (memoryBlockId < cellCount / cellsPerBlock)
{
outputGateDeltas[memoryBlockId] = 0;
for (int cellId = memoryBlockId * cellsPerBlock; cellId < (memoryBlockId + 1) * cellsPerBlock; cellId++)
{
outputGateDeltas[memoryBlockId] += cellStateActivations[cellId] * deltas[cellId];
}
outputGateDeltas[memoryBlockId] *= outputGateActivationDerivatives[memoryBlockId];
for (int cellId = memoryBlockId * cellsPerBlock; cellId < (memoryBlockId + 1) * cellsPerBlock; cellId++)
{
int relativeCellId = cellId - (memoryBlockId * cellsPerBlock);
int peepHoleWeightId = (memoryBlockId * (inputCount + cellCount + cellsPerBlock + 1)) + inputCount + cellCount + relativeCellId;
cellStateErrors[cellId] = deltas[cellId] * outputGateActivations[memoryBlockId] * cellStateActivationDerivatives[cellId] +
nextCellStateErrors[cellId] * nextForgetGateActivations[memoryBlockId] +
nextInputGateDeltas[memoryBlockId] * inputGateWeights[peepHoleWeightId] +
nextForgetGateDeltas[memoryBlockId] * forgetGateWeights[peepHoleWeightId] +
outputGateDeltas[memoryBlockId] * outputGateWeights[peepHoleWeightId];
cellInputDeltas[cellId] = inputGateActivations[memoryBlockId] * cellInputActivationDerivatives[cellId] * cellStateErrors[cellId];
}
inputGateDeltas[memoryBlockId] = 0;
forgetGateDeltas[memoryBlockId] = 0;
for (int cellId = memoryBlockId * cellsPerBlock; cellId < (memoryBlockId + 1) * cellsPerBlock; cellId++)
{
inputGateDeltas[memoryBlockId] += cellStateErrors[cellId] * cellInputActivations[cellId];
forgetGateDeltas[memoryBlockId] += cellStateErrors[cellId] * previousCellStates[cellId];
}
inputGateDeltas[memoryBlockId] *= inputGateActivationDerivatives[memoryBlockId];
forgetGateDeltas[memoryBlockId] *= forgetGateActivationDerivatives[memoryBlockId];
}
} |
22,369 | #ifndef _LIST_CU_CUDA
#define _LIST_CU_CUDA
template <typename T>
struct Node {
T data;
Node *next;
};
template <typename T> class List {
private:
Node<T> *head;
int count = 0;
public:
__device__ __host__ List() {
head = NULL;
}
__device__ __host__ bool isEmpty()
{
return (count == 0);
}
__device__ __host__ void push(T val) {
Node<T> *n = new Node<T>();
n->data = val;
n->next = head;
head = n;
count++;
}
__device__ __host__ T pop() {
if (isEmpty())
{
return NULL;
}
else
{
if (head) {
T p = head->data;
head = head->next;
count--;
return p;
}
}
}
__device__ __host__ bool search(T val) {
Node<T> *temp = head;
while (temp->next) {
if (temp->data == val) return true;
else temp = temp->next;
}
delete temp;
return false;
}
__device__ __host__ int size()
{
return count;
}
__device__ __host__ T operator[](int index) {
int itr = 0;
Node<T> *temp = head;
while (itr != index && temp->next)
{
temp = temp->next;
itr++;
}
return temp->data;
}
__device__ __host__ T get_index(int index)
{
int itr = 0;
Node<T> *temp = head;
while (itr != index && temp->next)
{
temp = temp->next;
itr++;
}
return temp->data;
}
/*__device__ __host__ T operator[](int & index) {
int itr = 0;
Node<T> *temp = head;
while (itr != index && temp->next)
{
temp = temp->next;
itr++;
}
return temp->data;
}*/
};
#endif //_LIST_CU_CUDA |
22,370 |
// Babak Poursartip
// 02/6/2021
// CUDA
//topic: gather
// y = f(a_m, b_n, ... )
// black scholes (option price)
#define _USE_MATH_DEFINES
#include "cstdio"
#include "curand.h"
#include <math.h>
// ====================================
__device__ __host__ __inline__ float N(float x)
{
return 0.5+0.5*erf(x* M_SQRT1_2);
}
// ====================================
__device__ __host__ void price(float k, float s, float t, float r, float v, float *c, float *p)
{
float srt = v * sqrtf(t);
float d1 = (logf(s/k)+(r+0.5*v*v)*t) / srt;
float d2 = d1 - srt;
float kert = k * expf(-r*t);
*c = N(d1)*s - N(d2)*kert;
*p = kert - s + *c;
}
// ====================================
__global__ void price(float *k, float *s, float *t, float *r, float *v, float *c, float *p )
{
int idx = threadIdx.x;
price(k[idx], s[idx], t[idx], r[idx], v[idx], &c[idx], &p[idx]);
}
// ====================================
int main(){
printf(" starts ... \n");
const int count = 512; //no of elements
const int size = count * sizeof(float);
float *args[5];
curandGenerator_t gen;
curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_MTGP32);
for (int i = 0; i < 5; ++i)
{
cudaMalloc(&args[i], size);
curandGenerateUniform(gen, args[i], count);
}
float *dc, *dp;
cudaMalloc(&dc, size);
cudaMalloc(&dp, size);
price<<<1, count>>>(args[0], args[1], args[2], args[3], args[4], dc, dp);
printf(" done. \n");
return 0;
}
|
22,371 | #include <stdio.h>
#include <assert.h>
#include <cuda.h>
// taken from Dr.Dobbs
// http://www.ddj.com/cpp/207200659
// the next line was changed from
// void cudaCompute(void)
// to
// extern "C" void cudaCompute(void)
extern "C" void cudaCompute(void)
{
float *a_h, *b_h; // pointers to host memory
float *a_d, *b_d; // pointers to device memory
int N = 5;
int i;
// allocate arrays on host
a_h = (float *)malloc(sizeof(float)*N);
b_h = (float *)malloc(sizeof(float)*N);
// allocate arrays on device
cudaMalloc((void **) &a_d, sizeof(float)*N);
cudaMalloc((void **) &b_d, sizeof(float)*N);
// initialize host data
printf("initialize host data\n");
for (i=0; i<N; i++) {
a_h[i] = 10.f+i; // a = 10 to 14
b_h[i] = 0.f; // b = 0
printf(" a_h[%d] = %f\t b_h[%d] = %f\n", i, a_h[i], i, b_h[i]);
}
// send data from host to device: a_h to a_d
// target, source, size, direction
cudaMemcpy(a_d, a_h, sizeof(float)*N, cudaMemcpyHostToDevice);
// copy data within device: a_d to b_d
cudaMemcpy(b_d, a_d, sizeof(float)*N, cudaMemcpyDeviceToDevice);
// retrieve data from device: b_d to b_h
cudaMemcpy(b_h, b_d, sizeof(float)*N, cudaMemcpyDeviceToHost);
// check result
printf("assert received data\n");
for (i=0; i<N; i++) {
assert(a_h[i] == b_h[i]);
// if correct a_h = 10 to 14
// if correct b_h = 10 to 14
printf(" a_h[%d] = %f\t b_h[%d] = %f\n", i, a_h[i], i, b_h[i]);
}
// cleanup
free(a_h); free(b_h);
cudaFree(a_d); cudaFree(b_d);
} |
22,372 | extern "C"{
void
matmult_kmn(int m, int n, int k, double *A, double *B, double *C) {
int i1, i2,i3;
for(i1 = 0; i1< m; i1++){
for(i2 = 0; i2 < n; i2++){
C[i1*n+i2]=0;
}
}
for(i3=0; i3<k; i3++){
for(i1=0; i1<m; i1++){
for(i2 = 0; i2 < n; i2++){
C[i1*n+i2]+=A[i1*k+i3]*B[i3*n+i2];
}
}
}
}
}
|
22,373 | // nvidia_properties.cu
//
// Fred J. Frigo
// 07-Oct-2021
//
//
// See CUDA by Example, J Sanders & E Kandrot, p 33
//
// To compile: nvcc nvidia_properties.cu -o nvidia_props
//
#include <stdio.h>
#include <unistd.h>
#include <err.h>
#include <stdint.h>
// you must first call the cudaGetDeviceProperties() function, then pass
// the devProp structure returned to this function:
int getSPcores(cudaDeviceProp devProp)
{
int cores = 0;
int mp = devProp.multiProcessorCount;
switch (devProp.major){
case 2: // Fermi
if (devProp.minor == 1) cores = mp * 48;
else cores = mp * 32;
break;
case 3: // Kepler
cores = mp * 192;
break;
case 5: // Maxwell
cores = mp * 128;
break;
case 6: // Pascal
if ((devProp.minor == 1) || (devProp.minor == 2)) cores = mp * 128;
else if (devProp.minor == 0) cores = mp * 64;
else printf("Unknown device type\n");
break;
case 7: // Volta and Turing
if ((devProp.minor == 0) || (devProp.minor == 5)) cores = mp * 64;
else printf("Unknown device type\n");
break;
case 8: // Ampere
if (devProp.minor == 0) cores = mp * 64;
else if (devProp.minor == 6) cores = mp * 128;
else printf("Unknown device type\n");
break;
default:
printf("Unknown device type\n");
break;
}
return cores;
}
int main( void )
{
cudaDeviceProp prop;
int count;
cudaGetDeviceCount( &count );
for (int i=0; i< count; i++) {
cudaGetDeviceProperties( &prop, i );
printf( " --- General Information for device %d ---\n", i );
printf( "Name: %s\n", prop.name );
printf( "Total cores = %d\n", getSPcores(prop));
printf( "Compute capability: %d.%d\n", prop.major, prop.minor );
printf( "Clock rate: %d\n", prop.clockRate );
printf( "Device copy overlap: " );
if (prop.deviceOverlap)
printf( "Enabled\n" );
else
printf( "Disabled\n" );
printf( "Kernel execition timeout : " );
if (prop.kernelExecTimeoutEnabled)
printf( "Enabled\n" );
else
printf( "Disabled\n" );
printf( " --- Memory Information for device %d ---\n", i );
printf( "Total global mem: %ld\n", prop.totalGlobalMem );
printf( "Total constant Mem: %ld\n", prop.totalConstMem );
printf( "Max mem pitch: %ld\n", prop.memPitch );
printf( "Texture Alignment: %ld\n", prop.textureAlignment );
printf( " --- MP Information for device %d ---\n", i );
printf( "Multiprocessor count: %d\n", prop.multiProcessorCount );
printf( "Shared mem per mp: %ld\n", prop.sharedMemPerBlock );
printf( "Registers per mp: %d\n", prop.regsPerBlock );
printf( "Threads in warp: %d\n", prop.warpSize );
printf( "Max threads per block: %d\n", prop.maxThreadsPerBlock );
printf( "Max thread dimensions: (%d, %d, %d)\n",
prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2] );
printf( "Max grid dimensions: (%d, %d, %d)\n",
prop.maxGridSize[0], prop.maxGridSize[1],prop.maxGridSize[2] );
printf( "\n" );
}
return(0);
}
|
22,374 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <assert.h>
#include <cuda.h>
#include <cuda_runtime.h>
#define MAX_ERR 1e-6
//__global__ void vector_add(float *out, float *a, float *b, int n) {
// int stride = 1;
// int tid = blockIdx.x * blockDim.x + threadIdx.x;
// 0 * 256 + 1 = 1 | BLOCK0 |
// 0 * 256 + 2 = 2
// 1 * 256 + 1 = 257 | BLOCK1 |
// 1 * 256 + 2 = 258
// out[tid] = a[tid] + b[tid];
//}
int calculate_no_threads(int array_size){
if(array_size/256 < 1){
return 1;
} else {
return array_size/256;
}
}
void print_results(float *ARRAY, int array_size){
printf("[");
for(int i = 0; i < array_size; i++){
printf("{");
for(int j = 0; j < array_size; j++){
printf("%1.1f,",ARRAY[(i * array_size) +j]);
}
printf("}\n");
}
printf("]");
printf("\n");
}
__global__ void vector_dot_product(float *CUDA_A, float *CUDA_B, float *CUDA_C,float *CUDA_T,int array_size,int no_threads) {
int tid = threadIdx.x;
int bid = blockIdx.x;
long long int increment = 431441;
int row_no = array_size;
int col_no = array_size;
double *mul = (double *)malloc(sizeof(double) * increment);
double *sum = (double *)malloc(sizeof(double) * increment);
//Make multiplications
for (int p = 0; p < array_size; p++){
for(int i = 0; i < array_size; i++){
for (int j = 0; j < array_size; j++){
mul[((i*array_size)+j) + p*array_size*array_size] = CUDA_A[p * col_no + j] * CUDA_B[ j * row_no + i];
}
}
}
float res=0.0;
//sum all multiplications a1.a2+b1.b3
for(int r=0;r<array_size;r++){
for(int j=0;j<array_size;j++){
for(int k=0;k<array_size;k++){
res += mul[k+(j*array_size)+(r*(array_size*array_size))];
}
sum[j+(r*array_size)] = res;
res = 0;
}
}
for(int j = 0;j<(array_size*array_size);j++){
//CUDA_C[(i*array_size) + j] = mul[(i*row_no)+j];
//place all the results back to the array
CUDA_T[j] = sum[j];
}
__syncthreads();
}
int main(){
int array_size = 83;
float *C, *A, *B, *T;
float *CUDA_A, *CUDA_B, *CUDA_C, *CUDA_T;
A = (float *)malloc(array_size * array_size * sizeof(float));
B = (float *)malloc(array_size * array_size * sizeof(float));
T = (float *)malloc((array_size*array_size*array_size) * sizeof(float));
float a = 4.0;
for(int i = 0; i<(array_size * array_size); i++){
A[i] = ((float)rand()/(float)(RAND_MAX)) * a;
B[i] = ((float)rand()/(float)(RAND_MAX)) * a;
}
//Fill remaining bytes in array with 1s
//for(int i = 0; i<(array_size*array_size*array_size);i++){
// T[i] = 1;
// }
C = (float *)malloc(array_size * array_size * sizeof(float) );
// Allocate device memory
cudaMalloc((void**)&CUDA_A, sizeof(float) * array_size * array_size);
cudaMalloc((void**)&CUDA_B, sizeof(float) * array_size * array_size);
cudaMalloc((void**)&CUDA_C, sizeof(float) * array_size * array_size);
cudaMalloc((void**)&CUDA_T, sizeof(float) * (array_size*array_size*array_size));
// Transfer data from host to device memory
cudaMemcpy(CUDA_A, A, sizeof(float) * array_size * array_size, cudaMemcpyHostToDevice);
cudaMemcpy(CUDA_B, B, sizeof(float) * array_size * array_size, cudaMemcpyHostToDevice);
cudaMemcpy(CUDA_T, T, sizeof(float) * array_size * array_size, cudaMemcpyHostToDevice);
printf("calculate_no_threads %d\n",calculate_no_threads(array_size));
vector_dot_product<<<1,calculate_no_threads(array_size)>>>(CUDA_A, CUDA_B, CUDA_C, CUDA_T,array_size,calculate_no_threads(array_size));
cudaMemcpy(C, CUDA_C, sizeof(float) * array_size * array_size, cudaMemcpyDeviceToHost);
cudaMemcpy(T, CUDA_T, sizeof(float) * (array_size*array_size*array_size), cudaMemcpyDeviceToHost);
puts("DOT_PRODUCT");
print_results(A,array_size);
print_results(B,array_size);
puts("MATRIX MULTI");
print_results(T,array_size);
// Deallocate device memory
cudaFree(CUDA_A);
cudaFree(CUDA_B);
cudaFree(CUDA_C);
free(C);
// Deallocate host memory
}
|
22,375 | #include <cuda.h>
#include <cuda_runtime.h>
#include <stdlib.h>
#include <stdio.h>
__global__ void vector_add(int *out, int *a, int *b, int n){
int index = threadIdx.x;
int stride = blockDim.x;
for (int i = index; i < n; i+=stride) {
out[i] = a[i] + b[i];
}
}
int main() {
int n = 10;
int *a, *b, *out, *dev_a, *dev_b, *dev_out;
a = (int*) malloc(n * sizeof(int));
b = (int*) malloc(n * sizeof(int));
out = (int*) malloc(n * sizeof(int));
for (int i = 0; i < n; i++) {
a[i] = 1;
b[i] = 2;
}
// Allocate device memory
cudaMalloc((void**)&dev_a, sizeof(float) * n);
cudaMalloc((void**)&dev_b, sizeof(float) * n);
cudaMalloc((void**)&dev_out, sizeof(float) * n);
// Transfer data from host to device memory
cudaMemcpy(dev_a, a, sizeof(float) * n, cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, sizeof(float) * n, cudaMemcpyHostToDevice);
// Executing kernel
vector_add<<<1,256>>>(dev_out, dev_a, dev_b, n);
// Transfer data back to host memory
cudaMemcpy(out, dev_out, sizeof(float) * n, cudaMemcpyDeviceToHost);
for (int i = 0; i < n; i++) {
printf("%d\n", out[i]);
}
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_out);
delete[] a;
delete[] out;
delete[] b;
return EXIT_SUCCESS;
}
|
22,376 | #include <stdio.h>
#include <cuda_runtime.h>
#define DATA_TYPE int
#define ARRAY_SIZE 32
__global__ void copy_kernel(DATA_TYPE *d_a, DATA_TYPE *d_b) {
int i;
i = threadIdx.x;
d_b[i] = d_a[i];
}
int main(int argc, char* argv[]) {
cudaError_t err = cudaSuccess;
DATA_TYPE* h_a = NULL;
DATA_TYPE* h_b = NULL;
DATA_TYPE* d_a = NULL;
DATA_TYPE* d_b = NULL;
size_t size;
int i;
// Calculate array memory in bytes
size = sizeof(DATA_TYPE) * ARRAY_SIZE;
// Assign memory for h_a, h_b on host side
h_a = (DATA_TYPE*)malloc(size);
if (h_a == NULL) {
printf("Failed to malloc h_a!\n");
return -1;
}
h_b = (DATA_TYPE*)malloc(size);
if (h_b == NULL) {
printf("Failed to malloc h_b!\n");
free(h_a);
return -1;
}
// Assign memory for d_a, d_b on device side
err = cudaMalloc((void**)&d_a, size);
if (err != cudaSuccess) {
printf("Failed to cudaMalloc d_a!\n");
free(h_a);
free(h_b);
return -1;
}
err = cudaMalloc((void**)&d_b, size);
if (err != cudaSuccess) {
printf("Failed to cudaMalloc d_b!\n");
free(h_a);
free(h_b);
cudaFree(d_a);
return -1;
}
// Init h_a values
for (i = 0; i < ARRAY_SIZE; i++) {
h_a[i] = i;
}
// Copy h_a to d_a
err = cudaMemcpy(d_a, h_a, size, cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
printf("Failed to cudaMemcpy from h_a to d_a!\n");
free(h_a);
free(h_b);
cudaFree(d_a);
cudaFree(d_b);
return -1;
}
// Call the kernel
copy_kernel<<<1, ARRAY_SIZE>>>(d_a, d_b);
err = cudaGetLastError();
if (err != cudaSuccess) {
printf("Failed to invoke kernel!\n");
free(h_a);
free(h_b);
cudaFree(d_a);
cudaFree(d_b);
return -1;
}
// Release memory
free(h_a);
free(h_b);
cudaFree(d_a);
cudaFree(d_b);
return 0;
}
|
22,377 | // Joshua Donnoe, Kyle Evens, and Dominik Haeflinger
#include <stdlib.h>
#include <stdio.h>
#include <cuda.h>
#define NUM_THREADS 256
|
22,378 | #include <cuda_runtime.h>
#include <stdio.h>
__global__ void checkIndex(void)
{
printf("threadIdx:(%d,%d,%d) blockIdx:(%d,%d,%d) blockDim:(%d,%d,%d)\
gridDim(%d,%d,%d)\n",threadIdx.x,threadIdx.y,threadIdx.z,
blockIdx.x,blockIdx.y,blockIdx.z,blockDim.x,blockDim.y,blockDim.z,
gridDim.x,gridDim.y,gridDim.z);
}
int main(int argc,char **argv)
{
int nElem=6;
dim3 block(3);
dim3 grid((nElem+block.x-1)/block.x);
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);
checkIndex<<<grid,block>>>();
cudaDeviceReset();
return 0;
}
|
22,379 | #include <stdio.h>
#include <time.h>
#include <stdlib.h>
#include <cuda.h>
#define n 3
#define m 3
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
void fillMatrix(double *w){
double count = 0;
for(int i=0; i<n; i++){
for(int j=0; j<n; j++){
w[i*n+j] = count;
count++;
}
}
}
void print(double *w){
for(int i=0; i<n; i++){
for(int j=0; j<n; j++){
printf("%.4lf ", w[i*n+j]);
}
printf("\n");
}
}
__global__ void product_shared(double* d_x, double* d_y, double* d_z) {
double CValue = 0;
int Row = blockIdx.y*m + threadIdx.y;
int Col = blockIdx.x*m + threadIdx.x;
__shared__ double As[m][m];
__shared__ double Bs[m][m];
for (int k = 0; k < (m + n - 1)/m; k++) {
if (k*m + threadIdx.x < n && Row < n) As[threadIdx.y][threadIdx.x] = d_x[Row*n + k*m + threadIdx.x];
else As[threadIdx.y][threadIdx.x] = 0.0;
if (k*m + threadIdx.y < n && Col < n) Bs[threadIdx.y][threadIdx.x] = d_y[(k*m + threadIdx.y)*n + Col];
else Bs[threadIdx.y][threadIdx.x] = 0.0;
__syncthreads();
for (int i = 0; i < m; ++i) CValue += As[threadIdx.y][i] * Bs[i][threadIdx.x];
__syncthreads();
}
if (Row < n && Col < n) d_z[((blockIdx.y * blockDim.y + threadIdx.y)*n)+(blockIdx.x*blockDim.x)+threadIdx.x]=CValue;
}
int main(int argc, char const *argv[])
{
int size = n*n*sizeof(double);
double *x = (double*)malloc(size);
double *y = (double*)malloc(size);
double *z = (double*)malloc(size);
fillMatrix(x);
fillMatrix(y);
clock_t begin, end;
double time_spent;
begin = clock();
double *d_x;
double *d_y;
double *d_z;
cudaMalloc((void**)&d_x, size);
cudaMalloc((void**)&d_y, size);
cudaMalloc((void**)&d_z, size);
cudaMemcpy(d_x, x, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_y, y, size, cudaMemcpyHostToDevice);
int threads = m;
dim3 dimBlock(threads,threads);
dim3 dimGrid((n+dimBlock.x-1)/dimBlock.x, (n+dimBlock.y-1)/dimBlock.y);
product_shared<<<dimGrid,dimBlock>>>(d_x, d_y, d_z);
cudaMemcpy(z,d_z,size,cudaMemcpyDeviceToHost);
print(x);
printf("\n");
print(y);
printf("\n");
print(z);
end = clock();
time_spent = (double)(end - begin) / CLOCKS_PER_SEC;
printf("%lf\n", time_spent);
return 0;
} |
22,380 | #include <iostream>
#include <fstream>
#include <chrono>
__global__
void saxpy(int n, float a, float *x, float *y) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < n)
y[i] = a * x[i] + y[i];
}
using namespace std;
using namespace chrono;
#define CU_CHK(ERRORCODE) \
{cudaError_t error = ERRORCODE; \
if (error != 0) \
{ cerr << cudaGetErrorName(error) << ": " << cudaGetErrorString(error) << \
" at " << __FILE__ << ":" << __LINE__ << "\n";}}
int main(int argc, char **argv) {
int N = 1 * (1 << 20);
float *x, *y, *res, *d_x, *d_y;
x = (float *) malloc(N * sizeof(float));
y = (float *) malloc(N * sizeof(float));
res = (float *) malloc(N * sizeof(float));
CU_CHK(cudaMalloc(&d_x, N * sizeof(float)));
CU_CHK(cudaMalloc(&d_y, N * sizeof(float)));
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f + float(i);
}
cout << "CTAs: " << (N + 511) / 512 << "\n";
auto t0 = high_resolution_clock::now();
CU_CHK(cudaMemcpy(d_x, x, N * sizeof(float), cudaMemcpyHostToDevice));
CU_CHK(cudaMemcpy(d_y, y, N * sizeof(float), cudaMemcpyHostToDevice));
auto t1 = high_resolution_clock::now();
saxpy <<< (N + 511) / 512, 512 >>> (N, 2.0f, d_x, d_y);
CU_CHK(cudaGetLastError());
CU_CHK(cudaDeviceSynchronize());
auto t2 = high_resolution_clock::now();
CU_CHK(cudaMemcpy(res, d_y, N * sizeof(float), cudaMemcpyDeviceToHost));
auto t3 = high_resolution_clock::now();
if (cudaGetLastError() != 0)
return -1;
for (int i = 0; i < N; i++) {
float y_host = 2.0f * x[i] + y[i];
float diff = y_host - res[i];
if (diff > 1.0f)
cout << "Error at y[" << i << "]: " << y_host << " vs. " << res[i] << "\n";
}
cout << "Duaration memcpy to device: " << duration_cast<microseconds>(t1 - t0).count() << endl;
cout << "Duaration kernel: " << duration_cast<microseconds>(t2 - t1).count() << endl;
cout << "Duaration memcpy to host: " << duration_cast<microseconds>(t3 - t1).count() << endl;
// write to file
if (argc > 1) {
ofstream file(argv[1], std::ios::binary);
file.write((char *) y, N * sizeof(float));
}
return 0;
}
|
22,381 | #include <cstdio>
__global__ void init7(int* arr,size_t pitch,int n){
int idx=blockDim.x*blockIdx.x+threadIdx.x;
int idy=blockDim.y*blockIdx.y+threadIdx.y;
if(idx <n && idy<n){
int* row=(int*)( (char*)arr +idy*pitch);
row[idx]=7;
}
}
__host__ void printMtrx(int* const mtrx,const int n){
for(int i=0;i<n;i++){
for(int j=0;j<n;j++)
printf("%d ",mtrx[i*n+j]);
printf("\n");
}
printf("\n");
}
__host__ int main(int argc,char* argv[]){
const int n = 5;
size_t pitch;
size_t size=n*sizeof(n);
int* devPtr=NULL;
cudaMallocPitch((void**)&devPtr,&pitch,size,n);
int* mt1=(int*)malloc(n*size);
memset(mt1,0,n*size);
dim3 grid(n,n,1);
init7<<<grid,1>>>(devPtr,pitch,n);
cudaMemcpy2D(mt1,size,devPtr,pitch,size,n,cudaMemcpyDeviceToHost);
printMtrx(mt1,n);
cudaFree(devPtr);
free(mt1);
return 0;
}
|
22,382 | #include <stdio.h>
#include <string.h>
#include <stdlib.h>
#define BLOCK_SIZE 16
__global__
void MultiMatrices(float *d_ma, float *d_mb, float *d_mp, int Width, int m, int n){
int Row = blockIdx.y*blockDim.y+threadIdx.y;
int Col = blockIdx.x*blockDim.x+threadIdx.x;
if ((Row < m) && (Col < n)) {
float Pvalue = 0;
for (int k = 0; k < Width; k++) {
Pvalue += d_ma[Row*Width+k]*d_mb[k*n+Col];
}
d_mp[Row*n+Col] = Pvalue;
}
}
void MM_cpu(float *h_ma, float *h_mb, float *h_mp, int Width, int m, int n) {
float Pvalue = 0;
for(int Row = 0; Row < m; Row++) {
for(int Col = 0; Col < n; Col++) {
for (int k = 0; k < Width; k++) {
Pvalue += h_ma[Row*Width+k]*h_mb[k*n+Col];
}
h_mp[Row*n+Col] = Pvalue;
Pvalue = 0;
}
}
}
void llenarMatriz(float *matrix, int x, int y, float v){
for (int i = 0; i < x; i++) {
for (int j = 0; j < y; j++) {
matrix[(i*y)+j] = v;
}
}
}
void imprimirMatriz(float *matrix, int x, int y){
for (int i = 0; i < x; i++) {
for (int j = 0; j < y; j++) {
printf("%f ", matrix[(i*y)+j]);
}
printf("\n");
}
}
int main(int argc, char *argv[]){
//Programa <archivo>
if ( argc != 2 ) {
//Salir del programa
printf("Fallo al ingresar el argumento\n");
}
else
{
FILE *fp;
float floatBuffer;
int ma, ka, kb, nb;
int m, k, n;
float *h_a, *h_b, *h_c;
float *d_a, *d_b, *d_c;
fp = fopen (argv[1], "r");
if (fp == NULL) {
perror ("Error al abrir el archivo");
}
else
{
fscanf(fp, "%d", &ma);
fscanf(fp, "%d", &ka);
cudaMallocHost((void **) &h_a, sizeof(float)*ma*ka);
for (int i = 0; i < ma; i++) {
for (int j = 0; j < ka; j++) {
fscanf(fp, "%f", &floatBuffer);
h_a[(i*ka) + j] = floatBuffer;
}
}
fscanf(fp, "%d", &kb);
fscanf(fp, "%d", &nb);
cudaMallocHost((void **) &h_b, sizeof(float)*kb*nb);
for (int i = 0; i < kb; i++) {
for (int j = 0; j < nb; j++) {
fscanf(fp, "%f", &floatBuffer);
h_b[(i*nb) + j] = floatBuffer;
}
}
fclose(fp);
}
if (ka != kb) {
printf("la matriz no cumple con la condicion de multiplicatividad");
return 0;
}
m = ma;
k = ka;
n = nb;
cudaMallocHost((void **) &h_c, sizeof(float)*m*n);
llenarMatriz(h_c, m, n, 0);
imprimirMatriz(h_a, m, k);
imprimirMatriz(h_b, k, n);
imprimirMatriz(h_c, m, n);
//MM_cpu(h_a, h_b, h_c, k, m, n);
//no hay que olvidarse de declarar espacio en el device
cudaMalloc((void **) &d_a, sizeof(float)*m*k);
cudaMalloc((void **) &d_b, sizeof(float)*k*n);
cudaMalloc((void **) &d_c, sizeof(float)*m*n);
cudaMemcpy(d_a, h_a, sizeof(float)*m*k, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, h_b, sizeof(float)*k*n, cudaMemcpyHostToDevice);
cudaMemcpy(d_c, h_c, sizeof(float)*m*n, cudaMemcpyHostToDevice);
unsigned int grid_rows = (m + BLOCK_SIZE - 1) / BLOCK_SIZE;
unsigned int grid_cols = (n + BLOCK_SIZE - 1) / BLOCK_SIZE;
dim3 dimGrid(grid_cols, grid_rows);
dim3 dimBlock(BLOCK_SIZE, BLOCK_SIZE);
cudaError_t MMErr;
cudaError_t asyncErr;
MultiMatrices<<<dimGrid, dimBlock>>>(d_a, d_b, d_c, k, m, n);
cudaMemcpy(h_c, d_c, sizeof(float)*m*n, cudaMemcpyDeviceToHost);
MMErr = cudaGetLastError();
if(MMErr != cudaSuccess) printf("Error: %s\n", cudaGetErrorString(MMErr));
asyncErr = cudaDeviceSynchronize();
if(asyncErr != cudaSuccess) printf("Error: %s\n", cudaGetErrorString(asyncErr));
imprimirMatriz(h_c, m, n);
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
cudaFreeHost(h_a);
cudaFreeHost(h_b);
cudaFreeHost(h_c);
}
return 0;
}
|
22,383 | __global__ void ZerosToOnes(float* V, int M, int N, float eps) {
/*
Turn zeros into ones (for use when something goes in the denomenator)
:param V: An MxN input matrix
:param M, N: Dimensions
:param eps: The value below which to consider things zero
*/
int i = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
int idx;
if (i < M && j < N) {
idx = i*N + j;
if (V[idx] < eps) {
V[idx] = 1;
}
}
}
__global__ void TileWDenom(float* WDenomIn, float* WDenomOut, int T, int M, int K) {
/*
(Since broadcasting doesn't work as advertised in skcuda, I had to revert to this)
:param WDenomIn: A T x 1 x K array
:param WDenomOut: A T x M x K array, where each [:, i, :] holds WDenomIn
:param T, M, K: Dimensions
*/
int t = blockIdx.x*blockDim.x + threadIdx.x;
int k = blockIdx.y*blockDim.y + threadIdx.y;
int MK = M*K;
float x;
int i;
if (t < T && k < K) {
x = WDenomIn[t*K + k];
for (i = 0; i < M; i++) {
WDenomOut[MK*t + K*i + k] = x;
}
}
}
__global__ void TileHDenom(float* HDenomIn, float* HDenomOut, int F, int K, int N) {
/*
(Since broadcasting doesn't work as advertised in skcuda, I had to revert to this)
:param HDenomIn: A F x K x 1 array
:param HDenomOut: A F x K x N array, where each [:, :, j] holds HDenomIn
:param F, K, N: Dimensions
*/
int f = blockIdx.x*blockDim.x + threadIdx.x;
int k = blockIdx.y*blockDim.y + threadIdx.y;
int KN = K*N;
float x;
int j;
if (f < F && k < K) {
x = HDenomIn[f*K + k];
for (j = 0; j < N; j++) {
HDenomOut[KN*f + N*k + j] = x;
}
}
}
//TODO: Bitonic sort doesn't work yet, and it's slow in global memory
__global__ void bitonicSortNonneg(float* X, float* XPow2, int M, int N, int NPow2) {
/*
Do a bitonic sort so that every row of a matrix X is in sorted
order
:param X: Pointer to matrix
:param XPow2: Pointer to matrix holding power of 2 zeropadded X
:param M: Size of each column of X
:param N: Size of each row of X
:param NPow2: Size of each row of X rounded up to nearest power of 2
*/
extern __shared__ float x[];
int i = blockIdx.x*blockDim.x + threadIdx.x;
int j = blockIdx.y*blockDim.y + threadIdx.y;
int j1, j2;
float x1, x2;
float min, max;
int size = 2;
int stride;
int diffPow2 = (NPow2 - N);
if (i >= M || j >= NPow2) {
return;
}
//Step 2: Perform bitonic sort
while (size < NPow2 << 1) {
stride = size >> 1;
while (stride > 0) {
j1 = stride*2*(j/stride) + j%stride;
j2 = j1 + stride;
x1 = XPow2[i*NPow2 + j1];
x2 = XPow2[i*NPow2 + j2];
if (x1 < x2) {
min = x1;
max = x2;
}
else {
min = x2;
max = x1;
}
if (j/(size/2)%2 > 0) {
XPow2[i*NPow2 + j1] = min;
XPow2[i*NPow2 + j2] = max;
}
else {
XPow2[i*NPow2 + j1] = max;
XPow2[i*NPow2 + j2] = min;
}
stride = stride >> 1;
__syncthreads();
}
size = size << 1;
}
//Step 3: Copy Result Back, noting that the first (NPow2-N)
//values in each row are dummy values
j = j*2;
if (j >= diffPow2) {
X[i*N + j - diffPow2] = XPow2[i*N + j];
}
j++;
if (j >= diffPow2) {
X[i*N + j - diffPow2] = XPow2[i*N + j];
}
} |
22,384 | #include<stdio.h>
#include<iostream>
#include<cuda.h>
using namespace std;
//Catch Cuda errors
void catchCudaError(cudaError_t error){
if(error!=cudaSuccess) {
printf("\n====== Cuda Error Code %i ======\n %s\n",error,cudaGetErrorString(error));
exit(-1);
}
}
//=====================================================================
#define DIM 32
#define ROW1 40
#define COMMON_WIDTH 30
#define COL2 4000
//Kernel function
__global__ void multiply(float a[][COMMON_WIDTH], float b[][COL2], float c[][COL2]){
//Skip till required block + the required thread index in the block
uint x = blockDim.x * blockIdx.x + threadIdx.x;
uint y = blockDim.y * blockIdx.y + threadIdx.y;
float cell = 0;
if(x < ROW1 && y < COL2){
for(uint i = 0; i < COMMON_WIDTH; ++i)
cell += a[x][i]*b[i][y];
c[x][y] = cell;//c has ROW1 x COL2 dim.
}
}
int main(){
float a[ROW1][COMMON_WIDTH], b[COMMON_WIDTH][COL2], c[ROW1][COL2]; //Host 2-d arrays
float (*d_a)[COMMON_WIDTH], (*d_b)[COL2], (*d_c)[COL2]; //Device 2-d arrays
clock_t start, end;
cudaEvent_t d_start, d_end;
catchCudaError(cudaEventCreate(&d_start));
catchCudaError(cudaEventCreate(&d_end));
size_t sizeA = ROW1 * COMMON_WIDTH * sizeof(float);
size_t sizeB = COMMON_WIDTH * COL2 * sizeof(float);
size_t sizeC = ROW1 * COL2 * sizeof(float);
//Allocate device memory(double ptr as assigning value to a pointer as defined in CUDA API)
catchCudaError(cudaMalloc((void **)&d_a, sizeA));
catchCudaError(cudaMalloc((void **)&d_b, sizeB));
catchCudaError(cudaMalloc((void **)&d_c, sizeC));
//Initial values of a,b random
for(uint i=0; i < ROW1; ++i)
for(uint j=0; j < COMMON_WIDTH; ++j)
a[i][j] = i+j;
for(uint i=0; i < COMMON_WIDTH; ++i)
for(uint j=0; j < COL2; ++j)
b[i][j] = i-j;
//Copy to Device
catchCudaError(cudaMemcpy(d_a, a, sizeA, cudaMemcpyHostToDevice));
catchCudaError(cudaMemcpy(d_b, b, sizeB, cudaMemcpyHostToDevice));
catchCudaError(cudaEventRecord(d_start));
dim3 dimGrid(DIM, DIM);
dim3 dimBlock(ceil(1.0*ROW1/DIM), ceil(1.0*COL2/DIM)) ;
//Max 1024 threads in each block(max 65,535)
multiply <<< dimGrid, dimBlock >>>(d_a, d_b, d_c);
catchCudaError(cudaEventRecord(d_end));
//Copy to Host
catchCudaError(cudaMemcpy(c, d_c, sizeC, cudaMemcpyDeviceToHost));
//Wait for all threads to finish
//catchCudaError(cudaDeviceSynchronize());
//Waits till event is recorded
catchCudaError(cudaEventSynchronize(d_end));
float cell;
start = clock();
for(uint i=0; i<ROW1; ++i)
for(uint j=0; j<COL2; ++j){
cell = 0;
for(uint k=0; k<COMMON_WIDTH; ++k)
cell += a[i][k]*b[k][j];
}
end = clock();
float time_taken = 1000.0* (end - start)/CLOCKS_PER_SEC;
float d_time_taken;
cudaEventElapsedTime(&d_time_taken, d_start, d_end);
printf("Host time = %f ms\nDevice Time = %f ms\n", time_taken, d_time_taken);
//Free device memory
catchCudaError(cudaFree(d_a));
catchCudaError(cudaFree(d_b));
catchCudaError(cudaFree(d_c));
}
/*
Output
Correct matrix multiplication
Host time = 11.943000 ms
Device Time = 0.252992 ms
*/
|
22,385 | #include <iostream>
/*
Ryan McDonald
CSUF Spring 2021
CPSC 479 - Dr. Bein
*/
__global__
void setVal(int n, int* x) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride)
x[i]=0;
}
__global__
void addVal(int n, int* x){
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride)
x[i] +=i;
}
int main(int argc, char* argv[])
{
//Set size of array
int N = 1024;
int* x;
// Allocate Unified Memory – accessible from CPU or GPU
cudaMallocManaged(&x, N * sizeof(int));
//Run Kernel on 1024 elements on the GPU
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
setVal<<<numBlocks, blockSize>>>(N, x);
addVal<<<numBlocks, blockSize>>>(N, x);
//Waits for GPU to finish before accessing data on the host
cudaDeviceSynchronize();
//Optional code to show value at each index.
//Used to confirm setVal and addVal function worked.
/*
for (int i=0; i<N; i++)
{
std::cout << "Index x[" << i << "] has value: "<< x[i] << std::endl;
}
*/
//Free memory
cudaFree(x);
return 0;
}
|
22,386 | /*
multiplication table using CUDA
code downloaded from http://blog.daum.net/heoly/7
$ nvcc -o multiplication multiplication.cu
*/
#include <stdio.h>
#include <malloc.h>
#include <cuda_runtime.h>
#define BLOCK_SIZE 5 // upto 5+1 dan
#define THREAD_SIZE 9
// Device code
__global__ void test(int *result)
{
int tidx, bidx;
tidx = threadIdx.x; //x-coordinate of thread
bidx = blockIdx.x; //x-coordinate of block
result[THREAD_SIZE * bidx + tidx] = (bidx + 2) * (tidx + 1);
}
// Host code
int main()
{
int *host_Result; //Save result data of host
int *device_Result; //Save result data of device
int i=0, j=0;
//Allocate host memory
host_Result = (int *)malloc( BLOCK_SIZE * THREAD_SIZE * sizeof(int) );
//Allocate device memory
cudaMalloc( (void**) &device_Result, sizeof(int) * BLOCK_SIZE * THREAD_SIZE);
//Function name <<BLOCK_SIZE, THREAD_SIZE>>> parameters
test <<<BLOCK_SIZE, THREAD_SIZE>>>(device_Result); //Execute Device code
//Copy device result to host result
cudaMemcpy( host_Result, device_Result, sizeof(int) * BLOCK_SIZE * THREAD_SIZE, cudaMemcpyDeviceToHost );
//Print result
for(j=0; j<BLOCK_SIZE; j++)
{
printf("%3d step\n", (j + 2));
for(i=0; i<THREAD_SIZE; i++)
{
printf("%3d X %3d = %3d\n", j+2, i+1, host_Result[j * THREAD_SIZE + i]);
}
printf("\n");
}
free(host_Result); //Free host memory
cudaFree(device_Result); //Free device memory
return 1;
}
|
22,387 | #include <iostream>
int main(void){
cudaDeviceProp prop;
int count;
cudaGetDeviceCount(&count);
printf("Number of devices: %d\n", count);
printf("\n");
for (int i=0; i<count; i++){
cudaGetDeviceProperties(&prop, i);
printf("Information for device %d\n", i);
printf("=========================\n");
printf("Name: %s\n", prop.name);
printf("Compute capability: %d.%d\n", prop.major, prop.minor);
printf("Clock rate: %d\n", prop.clockRate);
printf("Total global memory: %u\n", prop.totalGlobalMem);
printf("Total constant memory: %u\n", prop.totalConstMem);
printf("Multiprocessor count: %d\n", prop.multiProcessorCount);
printf("Number of 32-bit registers per block: %d\n", prop.regsPerBlock);
printf("Max shared memory per block: %d\n", prop.sharedMemPerBlock);
printf("Max threads per block: %d\n", prop.maxThreadsPerBlock);
if(prop.integrated){ printf("Integrated GPU...\n"); }
else{ printf("Discrete GPU...\n"); }
printf("\n\n");
}
return 0;
}
|
22,388 |
#include <iostream>
#include <fstream>
#include <stdio.h>
#include <stdlib.h>
#include <vector>
#include <math.h>
using namespace std;
// CUDA KERNEL FUNCTIONS
__global__ void Hello(int *neighbours)
{
//int globalidx = threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
int globalidx = blockIdx.x * blockDim.x + threadIdx.x;
// printf("blockIdx.x %d\n",blockIdx.x);
printf("Hello from blockx = %d\t tx = %d\t ty=%d\t tz=%d\t gi=%d\n",blockIdx.x, threadIdx.x, threadIdx.y, threadIdx.z, globalidx);
for (int i = 0; i < 3; i++)
{
neighbours[i] = i;
}
for (int i = 0; i < 3; i++)
{
printf("neighbours[%d] = %d\n",i,neighbours[i]);
}
}
__global__ void Bluring(int array_2D_size, int *array_1D_cuda, int *blured_1D_cuda, int *neighbours)
{
int globalidx = blockIdx.x * blockDim.x + threadIdx.x;
// printf("Hello from blockx = %d\t tx = %d\t ty=%d\t tz=%d\t gi=%d\n",blockIdx.x, threadIdx.x, threadIdx.y, threadIdx.z, globalidx);
int N = array_2D_size;
if(globalidx<N*N)
{
if ((globalidx >= 0 && globalidx <= N-1) | (globalidx % N == 0) | (globalidx >= N*N-N) | (globalidx % N >= N -1 && globalidx % N <= N*N -1))
{
blured_1D_cuda[globalidx]=array_1D_cuda[globalidx];
} else
{
int top, bottom, left, right;
top = -N + globalidx; // [i-1][j]
bottom = N + globalidx; // [i+1][j]
left = -1 + globalidx; // [i][j-1]
right = 1 + globalidx; // [i][j+1]
int cross1, cross2,cross3,cross4;
cross1 = globalidx - N - 1; // [i-1][j-1]
cross2 = globalidx - N + 1; // [i-1][j+1]
cross3 = globalidx + N - 1; // [i+1][j-1]
cross4 = globalidx + N + 1; // [i+1][j+1]
/*
if (globalidx == 7)
{
printf("top = %d\t bottom = %d\t left = %d\t right = %d\n", top, bottom, left, right);
printf("cross1 = %d\t cross2 = %d\t cross3 = %d\t cross4 = %d\n", cross1, cross2, cross3, cross4);
}
*/
neighbours[0] = array_1D_cuda[top];
neighbours[1] = array_1D_cuda[bottom];
neighbours[2] = array_1D_cuda[left];
neighbours[3] = array_1D_cuda[right];
neighbours[4] = array_1D_cuda[cross1];
neighbours[5] = array_1D_cuda[cross2];
neighbours[6] = array_1D_cuda[cross3];
neighbours[7] = array_1D_cuda[cross4];
neighbours[8] = array_1D_cuda[globalidx];
/*
if (globalidx == 7)
{
for (int i = 0; i < 9; i++)
{
printf("neighbours[%d] = %d\n",i,neighbours[i]);
}
}
*/
// Сортировка массива пузырьком
int size = 9;
for (int i = 0; i < size - 1; i++)
{
for (int j = (size - 1); j > i; j--) // для всех элементов после i-ого
{
if (neighbours[j - 1] > neighbours[j]) // если текущий элемент меньше предыдущего
{
int temp = neighbours[j - 1]; // меняем их местами
neighbours[j - 1] = neighbours[j];
neighbours[j] = temp;
}
}
}
/*
if (globalidx == 7)
{
printf("After bubble sort\n");
for (int i = 0; i < 9; i++)
{
printf("neighbours[%d] = %d\n",i,neighbours[i]);
}
}
*/
blured_1D_cuda[globalidx] = neighbours[4];
}
}
}
// CPU FUNCTIONS
int ** malloc_matrix(int N)
{
int ** matrix = (int **)malloc(N * sizeof(int *));
for (int i = 0; i < N; ++i)
{
matrix[i] = (int *)malloc(N * sizeof(int));
}
return matrix;
}
float ** malloc_matrix_float(int N)
{
float ** matrix = (float **)malloc(N * sizeof(float *));
for (int i = 0; i < N; ++i)
{
matrix[i] = (float *)malloc(N * sizeof(float));
}
return matrix;
}
void print_matrix(int * matrix, int N)
{
for (int j = 0; j < N; j++)
{
cout << matrix[j] << " ";
}
cout << endl;
}
void print_matrix(int ** matrix, int N)
{
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
cout << matrix[i][j] << " ";
}
cout << endl;
}
cout << endl;
}
void print_matrix(float ** matrix, int N)
{
for (int i = 0; i < N; i++)
{
for (int j = 0; j < N; j++)
{
cout << matrix[i][j] << " ";
}
cout << endl;
}
cout << endl;
}
void print_matrix(float * matrix, int N)
{
for (int j = 0; j < N; j++)
{
cout << matrix[j] << " ";
}
cout << endl;
}
void convert_1d_to_2d (int array_size, int * array_1D)
{
int ** array_2D;
int array_2D_size = sqrt(array_size);
array_2D = malloc_matrix(array_2D_size);
for (int i = 0; i < array_2D_size; i++)
{
for (int j = 0; j < array_2D_size; j++)
{
array_2D[i][j] = array_1D[i*array_2D_size + j];
}
}
print_matrix(array_2D,array_2D_size);
}
void convert_1d_to_2d_float (int array_size, float * array_1D)
{
float ** array_2D;
int array_2D_size = sqrt(array_size);
array_2D = malloc_matrix_float(array_2D_size);
for (int i = 0; i < array_2D_size; i++)
{
for (int j = 0; j < array_2D_size; j++)
{
array_2D[i][j] = array_1D[i*array_2D_size + j];
}
}
print_matrix(array_2D,array_2D_size);
}
/* Function to sort an array using insertion sort*/
void insertionSort(int arr[], int n)
{
int i, key, j;
for (i = 1; i < n; i++)
{
key = arr[i];
j = i - 1;
/* Move elements of arr[0..i-1], that are
greater than key, to one position ahead
of their current position */
while (j >= 0 && arr[j] > key)
{
arr[j + 1] = arr[j];
j = j - 1;
}
arr[j + 1] = key;
}
}
// MAIN FUNCTION
int main(int argc, char *argv[]){
// IMAGE TO ARRAY
ifstream file("image_array.txt");
vector<int> image;
if(file.is_open())
{
while (!file.eof())
{
int temp;
file >> temp;
image.push_back(temp);
}
}
file.close();
image.pop_back();
// ARRAY CREATION
int array_1D_size = image.size();
int *array_1D = (int*)malloc(sizeof(int)*array_1D_size);
for (int i = 0; i < array_1D_size; i++)
{
array_1D[i] = image[i];
}
// convert_1d_to_2d(array_1D_size, array_1D);
// ---------- CUDA ZONE -------------
// printf("This is done by CPU before CUDA threads\n");
// ARRAY TO DEVICE
int *array_1D_cuda;
cudaMalloc(&array_1D_cuda, sizeof(int)*array_1D_size);
cudaMemcpy(array_1D_cuda, array_1D, array_1D_size * sizeof(int), cudaMemcpyHostToDevice);
// NEIGHBOURS ARRAY CREATION TO DEVICE
int *neighbours;
cudaMalloc(&neighbours, sizeof(int)*9);
cudaDeviceSynchronize();
// BLURED ARRAY ON HOST
int blured_1D_size = array_1D_size;
int *blured_1D = (int*)malloc(sizeof(int)*blured_1D_size);
// BLURED ARRAY ON DEVICE
int *blured_1D_cuda;
cudaMalloc(&blured_1D_cuda, sizeof(int)*blured_1D_size);
cudaMemcpy(blured_1D_cuda, blured_1D, blured_1D_size * sizeof(int), cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
// BLURING PROCESS
int blocksDim = 10;
int threadsDim = array_1D_size /blocksDim;
// printf("array_1D_size = %d\n",array_1D_size );
int array_2D_size = sqrt(array_1D_size);
Bluring<<<blocksDim,threadsDim>>>(array_2D_size,array_1D_cuda, blured_1D_cuda, neighbours);
cudaMemcpy(blured_1D, blured_1D_cuda, blured_1D_size*sizeof(int), cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
convert_1d_to_2d(blured_1D_size, blured_1D);
// printf("End of program");
return 0;
} |
22,389 | #include <cstdio>
__global__ void VecAddKernel(float* dA, float* dB, float* dC)
{
int i = threadIdx.x;
dC[i] = dA[i] + dB[i];
}
void VecAdd( const float* pA, const float* pB, float *pC, int vectorSize)
{
size_t vectorMemSize = vectorSize * sizeof(float);
// Allocate
float* dA;
cudaMalloc( &dA, vectorMemSize );
float* dB;
cudaMalloc( &dB, vectorMemSize );
float* dC;
cudaMalloc( &dC, vectorMemSize );
//vectors in device memory
// copy vectors from host memory to device memory
cudaMemcpy( dA, pA, vectorMemSize, cudaMemcpyHostToDevice);
cudaMemcpy( dB, pB, vectorMemSize, cudaMemcpyHostToDevice);
dim3 numBlocks(1,1,1);
dim3 numThreadsPerBlock(vectorSize,1,1);
// Kernel invocation
VecAddKernel<<<numBlocks, numThreadsPerBlock>>>(dA, dB, dC);
// copy the resulting vector from device memory to host memory
cudaMemcpy( pC, dC, vectorMemSize, cudaMemcpyDeviceToHost);
// free device memory
cudaFree( dA );
cudaFree( dB );
cudaFree( dC );
}
void VecFill( float * pVector, int vectorSize, float firstValue, float increment )
{
for( int i = 0 ; i < vectorSize; ++i)
{
pVector[i] = firstValue+(increment*i);
}
}
void VecPrint( const float * pVector, int vectorSize, const char * pLabel="noname" )
{
printf("%s : \n", pLabel);
for( int i = 0 ; i < vectorSize; ++i)
{
printf("[%d] : %f\n", i, pVector[i]);
}
}
int main(int argc, char* argv[])
{
int vectorSize = 10;
size_t vectorMemSize = vectorSize * sizeof(float);
float *pA = (float *)malloc(vectorMemSize);
float *pB = (float *)malloc(vectorMemSize);
float *pC = (float *)malloc(vectorMemSize);
VecFill(pA, vectorSize, 0.0f, 1.0f);
VecPrint(pA, vectorSize, "A");
VecFill(pB, vectorSize, 10.0f, -0.5f);
VecPrint(pB, vectorSize, "B");
VecAdd(pA, pB, pC, vectorSize);
VecPrint(pC, vectorSize, "A+B");
free(pA);
free(pB);
free(pC);
return 0;
}
|
22,390 | //nvcc -o gol gol.cu
#define NEPOCHS 1000
#define DIMENSIONX 100
#define DIMENSIONY 100
#include <stdio.h>
#include <cstdlib>
#include <iostream>
#include <fstream>
__global__
void Evolve(bool *u, int n, int dx, int dy)
{
int n_next = n + 1;
bool active_pre, active_post;
__shared__ bool shared_u[DIMENSIONX*DIMENSIONY];
//__shared__ bool shared_u_next[DIMENSIONX*DIMENSIONY];
//copy state in shared memory
for (int i=threadIdx.x; i<DIMENSIONX*DIMENSIONY; i+=blockDim.x) {
shared_u[i] = u[n*dx*dy+i];
}
__syncthreads();
for (int entry_index=threadIdx.x+blockIdx.x*blockDim.x; entry_index<DIMENSIONX*DIMENSIONY; entry_index+=blockDim.x*gridDim.x) {
int i = entry_index / dy;
int j = entry_index % dy;
//get number of neighbors
size_t NActiveNeighbors = 0;
int nmin_x = max(0, i - 1);
int nmax_x = min(i + 1, dx - 1);
int nmin_y = max(0, j - 1);
int nmax_y = min(j + 1, dy - 1);
for (int ii = nmin_x; ii <= nmax_x; ii++)
for (int jj = nmin_y; jj <= nmax_y; jj++)
{
if ((i == ii) && (j == jj))
continue;
if (shared_u[ii*dy+jj]) NActiveNeighbors += 1;
}
active_pre = shared_u[i*dy+j];
active_post = false;
if (active_pre && (NActiveNeighbors == 2))
active_post = true;
else if (active_pre && (NActiveNeighbors == 3))
active_post = true;
else if ((!active_pre) && (NActiveNeighbors == 3))
active_post = true;
u[n_next*dx*dy+i*dy+j] = active_post;
}
/*
__syncthreads();
for (int entry_index=threadIdx.x+blockIdx.x*blockDim.x; entry_index<DIMENSIONX*DIMENSIONY; entry_index+=blockDim.x*gridDim.x) {
int i = entry_index / dy;
int j = entry_index % dy;
u[n_next*dx*dy+i*dy+j] = shared_u_next[i*dy+j];
}
__syncthreads();
*/
return;
}
bool *allocate_universe(int n, int dx, int dy)
{
int Nelements = n*dx*dy;
bool *universe = new bool[Nelements];
for (int i = 0; i < Nelements; i++)
{
universe[i] = false;
}
return universe;
}
bool glider_gun_field(int i, int j) {
if (((i%40)==1)&&((j%30)==5)) return true;
if (((i%40)==1)&&((j%30)==6)) return true;
if (((i%40)==2)&&((j%30)==5)) return true;
if (((i%40)==2)&&((j%30)==6)) return true;
if (((i%40)==11)&&((j%30)==5)) return true;
if (((i%40)==11)&&((j%30)==6)) return true;
if (((i%40)==11)&&((j%30)==7)) return true;
if (((i%40)==12)&&((j%30)==4)) return true;
if (((i%40)==12)&&((j%30)==8)) return true;
if (((i%40)==13)&&((j%30)==3)) return true;
if (((i%40)==13)&&((j%30)==9)) return true;
if (((i%40)==14)&&((j%30)==3)) return true;
if (((i%40)==14)&&((j%30)==9)) return true;
if (((i%40)==15)&&((j%30)==6)) return true;
if (((i%40)==16)&&((j%30)==4)) return true;
if (((i%40)==16)&&((j%30)==8)) return true;
if (((i%40)==17)&&((j%30)==5)) return true;
if (((i%40)==17)&&((j%30)==6)) return true;
if (((i%40)==17)&&((j%30)==7)) return true;
if (((i%40)==18)&&((j%30)==6)) return true;
if (((i%40)==21)&&((j%30)==3)) return true;
if (((i%40)==21)&&((j%30)==4)) return true;
if (((i%40)==21)&&((j%30)==5)) return true;
if (((i%40)==22)&&((j%30)==3)) return true;
if (((i%40)==22)&&((j%30)==4)) return true;
if (((i%40)==22)&&((j%30)==5)) return true;
if (((i%40)==23)&&((j%30)==2)) return true;
if (((i%40)==23)&&((j%30)==6)) return true;
if (((i%40)==25)&&((j%30)==1)) return true;
if (((i%40)==25)&&((j%30)==2)) return true;
if (((i%40)==25)&&((j%30)==6)) return true;
if (((i%40)==25)&&((j%30)==7)) return true;
if (((i%40)==35)&&((j%30)==3)) return true;
if (((i%40)==35)&&((j%30)==4)) return true;
if (((i%40)==36)&&((j%30)==3)) return true;
if (((i%40)==36)&&((j%30)==4)) return true;
return false;
}
void set_initial_conditions(bool *u, int dx, int dy, float p)
{
for (int i = 0; i < dx; i++)
for (int j = 0; j < dy; j++)
{
//float _value = (rand() % 10000)/10000.;//((i*173+j*51) % 100) / 100.;
//if (_value < p)
if (glider_gun_field(i, j))
{
u[i*dy+j] = true;
}
}
rand();
}
int main(void)
{
long int NENTRIES = NEPOCHS*DIMENSIONX*DIMENSIONY;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
std::cout << "Defining the universe and creating the initial conditions" << std::endl;
bool *universe = allocate_universe(NEPOCHS, DIMENSIONX, DIMENSIONY);
set_initial_conditions(universe, DIMENSIONX, DIMENSIONY, 0.25);
bool *cuda_universe;
cudaMalloc((void**)&cuda_universe, NENTRIES*sizeof(bool));
cudaMemcpy(cuda_universe, universe, NENTRIES*sizeof(bool), cudaMemcpyHostToDevice);
cudaEventRecord(start);
for (int n=0; n<NEPOCHS-1; n++) {
Evolve<<<32, 64>>>(cuda_universe, n, DIMENSIONX, DIMENSIONY);
cudaDeviceSynchronize();
}
cudaEventRecord(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("Total execution time (ms): %f\n ", milliseconds);
cudaMemcpy(universe, cuda_universe, NENTRIES*sizeof(bool), cudaMemcpyDeviceToHost);
//write everything to file
std::ofstream outfile;
outfile.open ("evolution.txt");
outfile << NEPOCHS << " , " << DIMENSIONX << " , " << DIMENSIONY << "\n";
int nepoch, nx, ny;
for (int i=0; i<NENTRIES; i++) {
if (universe[i]) {
nepoch = i/(DIMENSIONX*DIMENSIONY);
nx = (i % (DIMENSIONX*DIMENSIONY)) / DIMENSIONY;
ny = i % DIMENSIONY;
outfile << nepoch << " , " << nx << " , " << ny << "\n";
}
}
outfile.close();
//clean up
cudaFree(cuda_universe);
free(universe);
}
|
22,391 | #include<stdio.h>
#include<stdlib.h>
#include<sys/time.h>
#define CUDA_ERROR_EXIT(str) do{\
cudaError err = cudaGetLastError();\
if( err != cudaSuccess){\
printf("Cuda Error: '%s' for %s\n", cudaGetErrorString(err), str);\
exit(-1);\
}\
}while(0);
#define TDIFF(start, end) ((end.tv_sec - start.tv_sec) * 1000000UL + (end.tv_usec - start.tv_usec))
__global__ void calculate(int *mem, int num, int offs)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if(i-1 >= (num/(2*offs)))
return;
if((2*offs*i + offs) >= num)
return;
mem[2*offs*i] = mem[2*offs*i]^mem[2*offs*i + offs];
}
int main(int argc, char **argv)
{
struct timeval start, end, t_start, t_end;
int i,num;
int *ptr;
int *gpu_mem;
int blocks;
unsigned int seed;
if(argc == 3){
num = atoi(argv[1]);
seed = atoi(argv[2]);
}
srand(seed);
ptr = (int *)malloc(num*sizeof(int));
for(i=0; i<num; ++i){
ptr[i] = random();
}
gettimeofday(&t_start, NULL);
cudaMalloc(&gpu_mem, num * sizeof(int));
CUDA_ERROR_EXIT("cudaMalloc");
cudaMemcpy(gpu_mem, ptr, num * sizeof(int) , cudaMemcpyHostToDevice);
CUDA_ERROR_EXIT("cudaMemcpy");
gettimeofday(&start, NULL);
blocks = num /1024;
if(num % 1024)
++blocks;
for(int x=1;x<num;x*=2)
{
calculate<<<blocks, 1024>>>(gpu_mem, num, x);
}
CUDA_ERROR_EXIT("kernel invocation");
gettimeofday(&end, NULL);
cudaMemcpy(ptr, gpu_mem, num * sizeof(int) , cudaMemcpyDeviceToHost);
CUDA_ERROR_EXIT("memcpy");
gettimeofday(&t_end, NULL);
printf("Total time = %ld microsecs Processsing =%ld microsecs\n", TDIFF(t_start, t_end), TDIFF(start, end));
cudaFree(gpu_mem);
printf("result=%d\n",ptr[0]);
free(ptr);
}
|
22,392 | #include "includes.h"
__global__ void cudaSPadding_kernel( unsigned int nbOutputs, unsigned int outputWidth, unsigned int outputHeight, unsigned int nbChannels, unsigned int batchSize, unsigned int inputWidth, unsigned int inputHeight, int leftPad, int rightPad, int topPad, int botPad, const float* input, float* outputs)
{
const unsigned int inputOffset
= (blockIdx.z * blockDim.z + threadIdx.z) * nbChannels*inputWidth*inputHeight;
const unsigned int outputOffset
= (blockIdx.z * blockDim.z + threadIdx.z) * nbOutputs*outputWidth*outputHeight;
// nbCh = nbChannels for propagate
// = nbOutputs for back-propagate
const unsigned int nbCh = min(nbChannels, nbOutputs);
for (unsigned int ch = blockIdx.x; ch < nbCh; ch += gridDim.x)
{
for (unsigned int oy = threadIdx.y; oy < outputHeight; oy += blockDim.y)
{
for (unsigned int ox = threadIdx.x; ox < outputWidth; ox += blockDim.x)
{
float outputValue = 0.0;
int ix = (int) ox - leftPad;
int iy = (int) oy - topPad;
if( ix >= 0 && ix < (int) inputWidth
&& iy >= 0 && iy < (int) inputHeight )
{
outputValue = input[ix +
iy*inputWidth
+ ch*inputWidth*inputHeight
+ inputOffset];
}
outputs[ ox + oy*outputWidth
+ ch*outputWidth*outputHeight + outputOffset] = outputValue;
}
}
}
} |
22,393 | //pass
//--blockDim=1024 --gridDim=1 --no-inline
#include <cuda.h>
#include <stdio.h>
#define N 2 //1024
__global__ void definitions (int* A, unsigned int* B, unsigned long long int* C)
{
atomicCAS(A,2,11);
atomicCAS(B,5,1);
atomicCAS(C,7,3);
}
|
22,394 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
//int main()
//{
// int iDev = 0;
// cudaDeviceProp iProp;
//
// cudaGetDeviceProperties(&iProp, iDev);
// printf("Max threads per SM : %d \n",
// iProp.maxThreadsPerMultiProcessor);
//
// return 0;
//} |
22,395 | #include "merge_func.cuh"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
__global__ void MergeRank(float * d_input, float * d_output)
{
int indexA = blockIdx.x * blockDim.x + threadIdx.x;
int indexB = indexA + 2048;
float temp1 = d_input[indexA];
float temp2 = d_input[indexB];
int indexAB = 2048;
while (d_input[indexAB] < temp1) {
indexAB++;
}
int indexBA = 0;
while (d_input[indexBA] < temp2) {
indexBA++;
}
__syncthreads();
d_output[indexA + indexAB + 1] = temp1;
d_output[indexB + indexBA + 1] = temp2;
}
void orderBitonicArray(float* d_in, int size, int part_size, float* d_out, bool log)
{
/**
* \brief Order output array of the bitonic sort function
* \param d_in - a partially sorted array, global memory, gpu
* \param size - the size of the input array
* \param part_size - the size of a sorted subarray
* \param d_out - a pointer to the output array, global memory, gpu, where
* function execution result will be stored
* \param log - show information about performance during each step
* \return
* void
*/
int iter_number = static_cast<int>(log2(size / part_size));
int init_num_threads = size / (2 * part_size);
int init_num_blocks = ((init_num_threads - 1) / 1024) + 1;
if (log)
{
std::cout << "--------------------------start log--------------------------------" << std::endl;
std::cout << "Number of steps\t" << iter_number << std::endl;
}
float* t_d_in = d_in;
for (int i = 0; i < iter_number; i++)
{
if (log)
{
std::cout << "-------------------------------------------------------------------" << std::endl;
std::cout << "Merging step #" << i << std::endl;
std::cout << "Number of blocks\t" << init_num_threads << std::endl;
std::cout << "Number of threads\t" << init_num_threads << std::endl;
}
mergingKernel << <init_num_blocks, init_num_threads >> >(t_d_in, part_size, d_out);
part_size *= 2;
init_num_threads = init_num_threads / 2;
init_num_blocks = ((init_num_threads - 1) / 1024) + 1;
cudaFree(t_d_in);
cudaMalloc((void **)&t_d_in, size * sizeof(int));
cudaMemcpy(t_d_in, d_out, size * sizeof(int), cudaMemcpyDeviceToDevice);
if (log)
{
float *out = new float[size];
cudaMemcpy(out, d_out, size * sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i < size; i++) {
std::cout << out[i] << "\t";
}
std::cout << std::endl;
std::cout << std::endl;
delete[] out;
if (i == iter_number - 1)
{
std::cout << "----------------------------end log--------------------------------" << std::endl;
}
}
}
}
__global__ void mergingKernel(float* in_array, int part_size, float* out_array)
{
/**
* \brief kernel function for merging of the arrays of the partially sorted array
* \param in_array - the input array
* \param part_size - the size of a sorted subarray
* \param out_array - a pointer to the output array,
* where result of merging will be stored
* \return
* void
*/
int index = blockDim.x * blockIdx.x + threadIdx.x;
float* arr_left = in_array + 2 * part_size * index;
float* arr_right = arr_left + part_size;
int out_shift = 2 * part_size * index;
mergeArraysAsc(arr_left, arr_right, part_size, part_size, out_array, out_shift);
__syncthreads();
}
__device__ void mergeArraysAsc(float* arr_left, float* arr_right, int length_left, int length_right, float* out, int out_shift)
{
/**
* \brief Helper function for the mergingKernel function, merges subarrays
* \param arr_left - the first sorted array
* \param arr_right - the second sorted array
* \param length_left - size of the first array
* \param length_right - size of the second array
* \param out - a pointer to the output array, where result will be stored
* \param out_shift - shift, from which to start writing in output array.
* \return
* void
*/
int totalLength = length_left + length_right;
//running indices
int i = 0;
int j = 0;
int index = out_shift;
while (i < length_left && j < length_right)
{
if (arr_left[i] <= arr_right[j])
{
out[index] = arr_left[i];
i++;
index++;
}
else {
out[index] = arr_right[j];
j++;
index++;
}
}
//only one of these two loops will run
while (i < length_left)
{
out[index] = arr_left[i];
index++;
i++;
}
while (j < length_right)
{
out[index] = arr_right[j];
index++;
j++;
}
} |
22,396 | /*
timing_of.c
Calculates timing given struct rusage.
$Id: timing_of.c,v 1.3 2002/07/12 20:48:49 eschrich Exp $
Steven Eschrich
Copyright (C) 2002 University of South Florida
This program is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation; either version 2 of the License, or (at
your option) any later version.
This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
General Public License for more details.
You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
#include <stdlib.h>
#include <time.h>
#include <sys/times.h>
#include <sys/resource.h>
/* Get time, in seconds */
double *timing_of(struct rusage start, struct rusage stop)
{
long totaluMicroseconds, totalsMicroseconds;
static double totalTime[2];
totaluMicroseconds = stop.ru_utime.tv_usec - start.ru_utime.tv_usec;
totalsMicroseconds = stop.ru_stime.tv_usec - start.ru_stime.tv_usec;
/* If we need to borrow, do so */
if ( totaluMicroseconds < 0 ) {
stop.ru_utime.tv_sec--;
totaluMicroseconds=1000000L + stop.ru_utime.tv_usec - start.ru_utime.tv_usec;
}
if ( totalsMicroseconds < 0 ) {
stop.ru_stime.tv_sec--;
totalsMicroseconds=1000000L + stop.ru_stime.tv_usec - start.ru_stime.tv_usec;
}
totalTime[0] = (stop.ru_utime.tv_sec - start.ru_utime.tv_sec) + 0.000001 * totaluMicroseconds;
totalTime[1] = (stop.ru_stime.tv_sec - start.ru_stime.tv_sec) + 0.000001 * totalsMicroseconds;
return totalTime;
}
|
22,397 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <string>
#include <vector>
#include <iostream>
#include <fstream>
#include <sstream>
#include <random>
#include <cmath>
#include <ctime>
#include <chrono>
#define DEVICE 0
#define MAX_THREADS 6'000
#define RAND_CONST_1 61746235
#define RAND_CONST_2 18234565
#define RAND_CONST_3 14256765
#define SAMPLE_NUM 60'000
#define BATCH_SIZE 20
#define TOTAL_NEURONS 110
#define SIGMOID(x) ( 1/ (1+exp(-x)) )
#define D_SIGMOID(x) ( (exp(-x)) / (pow((1 + (exp(-x))), 2)) )
#pragma warning(push)
#pragma warning(disable : 6386)
#pragma warning(disable : 6385)
class timer {
private:
std::chrono::system_clock::time_point measure_start;
std::chrono::system_clock::time_point measure_end;
public:
timer() {}
void start()
{
measure_start = std::chrono::system_clock::now();
}
void check(std::string message)
{
measure_end = std::chrono::system_clock::now();
int elapsed = static_cast<int>(std::chrono::duration_cast<std::chrono::milliseconds>(measure_end - measure_start).count());
std::cout << message << elapsed << std::endl;
}
};
__host__ void check_cuda_errors(cudaError_t operation_status, std::string error_report, void* ptr = nullptr)
{
if (operation_status != cudaSuccess)
{
std::cerr << error_report << std::endl;
if (ptr != nullptr) cudaFree(ptr);
exit(-1);
}
}
__host__ double random_number(double mean, double sigma)
{
std::random_device rd;
std::mt19937 mersenne_twister(rd());
std::normal_distribution<double> nd(mean, sigma);
return nd(mersenne_twister);
}
__host__ void init_weights(double* weights, int* layers, int layer_count)
{
size_t arr_position = 0;
for (size_t layer = 0; layer < layer_count; ++layer)
{
for (size_t w = 0; w < layers[2 * layer] * layers[2 * layer + 1]; ++w)
{
weights[arr_position + w] = random_number(0, 1 / sqrt(layers[2 * layer + 1]));
}
arr_position += layers[2 * layer] * layers[2 * layer + 1];
}
}
__host__ void init_biases(double* vector, size_t size)
{
for (size_t i = 0; i < size; ++i)
{
vector[i] = random_number(0, 1.0);
}
}
__host__ void check_device_properties()
{
cudaDeviceProp device_properties;
cudaGetDeviceProperties(&device_properties, DEVICE);
std::cout << " Device #" << DEVICE << ": [" << device_properties.name << "] props: " << std::endl;
std::cout << " Multiprocessors: " << device_properties.multiProcessorCount << std::endl;
std::cout << " Max threads per multiprocessor: " << device_properties.maxThreadsPerMultiProcessor << std::endl;
std::cout << " Max threads per block: " << device_properties.maxThreadsPerBlock << std::endl;
std::cout << " Max blocks per multiprocessor: " << device_properties.maxBlocksPerMultiProcessor << std::endl;
std::cout << " Shared memory per block: " << device_properties.sharedMemPerBlock << std::endl;
std::cout << " Max grid dimensions: [" << device_properties.maxGridSize[0] << " " << device_properties.maxGridSize[1] << " ";
std::cout << device_properties.maxGridSize[2] << "]" << std::endl;
std::cout << " Max block dimensions: [" << device_properties.maxThreadsDim[0] << " " << device_properties.maxThreadsDim[1] << " ";
std::cout << device_properties.maxThreadsDim[2] << "]" << std::endl;
std::cout << " Max threads total: " << device_properties.multiProcessorCount * device_properties.maxThreadsPerMultiProcessor << std::endl;
std::cout << " Const memory total: " << device_properties.totalConstMem << std::endl;
std::cout << " Global memory total: " << device_properties.totalGlobalMem << std::endl;
std::cout << " Registers per block available: " << device_properties.regsPerBlock << std::endl;
std::cout << " Registers per multiprocessor available: " << device_properties.regsPerMultiprocessor << std::endl;
std::cout << " * * * * " << std::endl;
if (BATCH_SIZE * TOTAL_NEURONS * 3 * sizeof(float) >= device_properties.sharedMemPerBlock)
{
std::cout << " Warning: not enough shared memory per block for such large net. Exiting" << std::endl;
exit(-1);
}
}
__host__ void read_data(double* input, double* output, size_t sample_count, std::string digits, std::string labels)
{
std::ifstream mnist_digits;
std::ifstream mnist_labels;
mnist_digits.open(digits, std::ios::binary);
mnist_labels.open(labels, std::ios::in);
if (!mnist_digits.is_open() || !mnist_labels.is_open())
{
std::cout << "Error: at least one of files cannot be open." << std::endl;
exit(-1);
}
mnist_digits.seekg(16);
uint8_t byte;
int label;
for (size_t sample = 0; sample < sample_count; ++sample)
{
// Reading image
for (size_t pix = 0; pix < 28 * 28; ++pix)
{
mnist_digits.read(reinterpret_cast<char*>(&byte), sizeof(char));
//input[sample * 28 * 28 + pix] = static_cast<double>(byte) / 255;
if (byte != 0)
{
input[sample * 28 * 28 + pix] = 1;
}
else
{
input[sample * 28 * 28 + pix] = 0;
}
}
// Reading label
mnist_labels >> label;
for (size_t digit = 0; digit < 10; ++digit)
{
output[sample * 10 + digit] = 0.0;
}
output[sample * 10 + label] = 1.0;
}
mnist_digits.close();
mnist_labels.close();
}
__host__ void print_sample(double* input, int sample_number)
{
for (int y = 0; y < 28; ++y)
{
for (int x = 0; x < 28; ++x)
{
double byte = input[sample_number * 28 * 28 + y * 28 + x];
if (byte == 0) std::cout << ". ";
if (byte > 0) std::cout << "@ ";
}
std::cout << std::endl;
}
}
__host__ void checkpoint(int num)
{
cudaError_t last_error = cudaGetLastError();
if (last_error == cudaSuccess)
{
std::cout << " Checkpoint [" << num << "]: No errors " << std::endl;
}
else
{
std::cout << " Checkpoint [" << num << "]: Error <" << last_error << ">: " << cudaGetErrorString(last_error) << std::endl;
}
}
__host__ void save_weights(double* weights, double* biases, int* layers, int layer_count, std::string network_name)
{
for (size_t layer = 0; layer < layer_count; ++layer)
{
std::ofstream outfile;
std::stringstream filename;
filename << network_name << "_" << layer << ".txt";
outfile.open(filename.str(), std::ios::out);
if (!outfile.is_open()) std::cout << "Cannot create file." << std::endl, exit(-1);
outfile << layers[2 * layer] << " " << layers[2 * layer + 1] << std::endl;
int weights_pos = 0, bias_pos = 0;
for (int i = 0; i < layer; ++i) weights_pos += layers[2 * i] * layers[2 * i + 1], bias_pos += layers[2 * i];
for (size_t row = 0; row < layers[2 * layer]; ++row)
{
for (size_t col = 0; col < layers[2 * layer + 1]; ++col)
{
outfile << weights[weights_pos + row * layers[2 * layer + 1] + col] << " ";
}
outfile << biases[bias_pos + row] << std::endl;
}
outfile.close();
}
}
#define SAMPLE_ID(a, b, seed) (((RAND_CONST_1 * a + RAND_CONST_2 + seed)*(RAND_CONST_3 + b)) % SAMPLE_NUM)
__global__ void learn_kernel(double* train_inp, double* train_out, double* weights, double* biases, int* layers, int* shifts, int layer_num, double learning_rate, int seed)
{
__shared__ float a_buffer[BATCH_SIZE * TOTAL_NEURONS];
__shared__ float z_buffer[BATCH_SIZE * TOTAL_NEURONS];
__shared__ float d_buffer[BATCH_SIZE * TOTAL_NEURONS];
int l, i, j, layer_shift = 0, weights_shift = 0, layer_shift_ = 0;
float tmp_val;
// 1. Feeding input forward through net to gain weighted sums and activations
// A. First layer getting perception from input
for (j = 0; j < layers[0]; ++j)
{
tmp_val = 0.0;
for (i = 0; i < layers[1]; ++i) tmp_val += weights[layers[1]*j+i]*train_inp[784* SAMPLE_ID(threadIdx.x, blockIdx.x, seed) +i];
z_buffer[TOTAL_NEURONS * threadIdx.x + j] = tmp_val + biases[j];
a_buffer[TOTAL_NEURONS * threadIdx.x + j] = SIGMOID(z_buffer[TOTAL_NEURONS * threadIdx.x + j]);
}
// B. Other layers:
for (l = 1; l < layer_num; ++l)
{
layer_shift = shifts[l];
for (i = 0; i < l; ++i) weights_shift += layers[2 * i] * layers[2 * i + 1];
//for (i = 0; i < l - 1; ++i) layer_shift_ += layers[2 * i];
layer_shift_ = layer_shift - layers[2 * (i-1)];
for (j = 0; j < layers[2 * l]; ++j)
{
tmp_val = 0.0;
for (i = 0; i < layers[2 * l + 1]; ++i) tmp_val += weights[weights_shift+layers[2*l+1]*j+i]*a_buffer[threadIdx.x*TOTAL_NEURONS+layer_shift_+i];
z_buffer[TOTAL_NEURONS * threadIdx.x + layer_shift + j] = tmp_val + biases[layer_shift + j];
a_buffer[TOTAL_NEURONS * threadIdx.x + layer_shift + j] = SIGMOID(z_buffer[TOTAL_NEURONS * threadIdx.x + layer_shift + j]);
}
}
layer_shift = 0; layer_shift_ = 0; weights_shift = 0;
// 2. Calculating Lastlayer delta:
layer_shift = shifts[layer_num - 1]; /// ~~!!
for (j = 0; j < 10; ++j)
{
d_buffer[TOTAL_NEURONS*threadIdx.x+layer_shift+j] = (a_buffer[TOTAL_NEURONS*threadIdx.x+layer_shift+j]-train_out[10* SAMPLE_ID(threadIdx.x, blockIdx.x, seed) +j])*D_SIGMOID(z_buffer[TOTAL_NEURONS*threadIdx.x+layer_shift+j]);
}
layer_shift = 0; layer_shift_ = 0; weights_shift = 0;
// 3. Backpropagating error through layers:
for (l = layer_num - 2; l >= 0; --l)
{
for (i = 0; i < l + 1; ++i) weights_shift += layers[2 * i] * layers[2 * i + 1];
layer_shift_ = shifts[l + 1];
layer_shift = layer_shift_ - layers[2 * (i - 1)];
for (j = 0; j < layers[2 * l]; ++j)
{
tmp_val = 0.0;
for (i = 0; i < layers[2*(l+1)]; ++i) tmp_val += d_buffer[TOTAL_NEURONS*threadIdx.x+layer_shift_+i]*weights[weights_shift+layers[2*(l+1)+1]*i+j];
d_buffer[TOTAL_NEURONS * threadIdx.x + layer_shift + j] = tmp_val * D_SIGMOID(z_buffer[TOTAL_NEURONS*threadIdx.x + layer_shift + j]);
}
}
layer_shift = 0; layer_shift_ = 0; weights_shift = 0;
// 4. Updating weights and biases
// A'. Biases
for (j = 0; j < layers[0]; ++j)
{
biases[j] -= (learning_rate / BATCH_SIZE) * d_buffer[TOTAL_NEURONS * threadIdx.x + j];
}
// B'. Weights
for (i = 0; i < layers[0]; ++i)
{
for (j = 0; j < layers[1]; ++j)
{
weights[i * layers[1] + j] -= (learning_rate / BATCH_SIZE) * d_buffer[TOTAL_NEURONS * threadIdx.x + i] * train_inp[28 * 28 * SAMPLE_ID(threadIdx.x, blockIdx.x, seed) + j];
}
}
for (l = 1; l < layer_num; ++l)
{
for (i = 0; i < l; ++i) weights_shift += layers[2 * i] * layers[2 * i + 1];
layer_shift = shifts[l];
layer_shift_ = layer_shift - layers[2 * (i-1)];
// A. Biases
for (j = 0; j < layers[2 * l]; ++j)
{
biases[layer_shift + j] -= (learning_rate / BATCH_SIZE) * d_buffer[TOTAL_NEURONS * threadIdx.x + layer_shift + j];
}
// B. Weights
for (i = 0; i < layers[2 * l]; ++i)
{
for (j = 0; j < layers[2 * l + 1]; ++j)
{
weights[weights_shift+i*layers[2*l+1]+j] -= (learning_rate/BATCH_SIZE)*d_buffer[TOTAL_NEURONS*threadIdx.x+layer_shift+i]*a_buffer[TOTAL_NEURONS*threadIdx.x+layer_shift_+j];
}
}
}
}
__host__ int main(int argc, char* argv[])
{
timer T;
srand(time(nullptr));
check_cuda_errors(cudaSetDevice(DEVICE), "Error: cannot set device.");
check_device_properties();
size_t sample_count = SAMPLE_NUM;
size_t digit_shape = 28 * 28;
double learning_rate = 7.0;
int batch_size = BATCH_SIZE;
std::string path = "C:\\Users\\mihai\\Desktop\\progy\\C & C++\\Digits_Identifier\\Data";
std::string digits = "\\train-images.idx3-ubyte";
std::string labels = "\\train_labels.txt";
double* train_input = new double[(sample_count*digit_shape)];
double* train_output = new double[(sample_count * 10)];
double* layers_weights;
double* layers_biases;
int* layers;
int* host_shifts;
size_t layer_count = 2;
layers = new int[layer_count * 2];
host_shifts = new int[layer_count];
size_t total_weights = 0;
size_t total_neurons = 0;
// ==============================================================
layers[0] = 100; layers[1] = 784; layers[2] = 10; layers[3] = 100;
host_shifts[0] = 0; host_shifts[1] = 100;
// ==============================================================
for (size_t layer = 0; layer < layer_count; ++layer)
{
total_weights += layers[2 * layer] * layers[2 * layer + 1];
total_neurons += layers[2 * layer];
}
// Setting grid parameters:
dim3 grid_dim(sample_count / batch_size, 1, 1);
dim3 block_dim(batch_size, 1, 1);
//std::cout << " Block number: " << sample_count / BATCH_SIZE << std::endl;
// Initializing weights and biases
layers_weights = new double[total_weights];
layers_biases = new double[total_neurons];
init_weights(layers_weights, layers, layer_count);
init_biases(layers_biases, total_neurons);
save_weights(layers_weights, layers_biases, layers, layer_count, "Cuda_Learned_Net_Start");
// Reading train data
read_data(train_input, train_output, sample_count, path + digits, path + labels);
std::cout << " Data scanned." << std::endl;
// Allocating GPU device memory
double* device_train_inp;
double* device_train_out;
double* device_weights;
double* device_biases;
int* device_layers;
int* device_shifts;
check_cuda_errors(cudaMalloc(&device_shifts, layer_count * sizeof(int)), "MemAlloc failed.");
check_cuda_errors(cudaMalloc(&device_layers, 2 * layer_count * sizeof(int)), "MemAlloc failed.");
check_cuda_errors(cudaMalloc(&device_train_inp, sample_count * digit_shape * sizeof(double)), "MemAlloc failed. ");
check_cuda_errors(cudaMalloc(&device_train_out, sample_count * 10 * sizeof(double)), "MemAlloc failed. ");
check_cuda_errors(cudaMalloc(&device_weights, total_weights * sizeof(double)), "MemAlloc failed. ");
check_cuda_errors(cudaMalloc(&device_biases, total_neurons * sizeof(double)), "MemAlloc failed. ");
checkpoint(1); /// ~~~ error_point
// Transfering data to GPU device
cudaMemcpyKind HTD = cudaMemcpyHostToDevice;
cudaMemcpyKind DTH = cudaMemcpyDeviceToHost;
T.start();
check_cuda_errors(cudaMemcpy(device_shifts, host_shifts, layer_count * sizeof(int), HTD), "MemCopy failed.", device_shifts);
check_cuda_errors(cudaMemcpy(device_layers, layers, 2 * layer_count * sizeof(int), HTD), "MemCopy failed.", device_layers);
check_cuda_errors(cudaMemcpy(device_train_inp, train_input, sample_count * digit_shape * sizeof(double), HTD), "MemCopy failed. ", device_train_inp);
check_cuda_errors(cudaMemcpy(device_train_out, train_output, sample_count * 10 * sizeof(double), HTD), "MemCopy failed. ", device_train_out);
check_cuda_errors(cudaMemcpy(device_weights, layers_weights, total_weights*sizeof(double), HTD), "MemCopy failed. ", device_weights);
check_cuda_errors(cudaMemcpy(device_biases, layers_biases, total_neurons * sizeof(double), HTD), "MemCopy failed. ", device_biases);
T.check(" Memcopy-1 taken: ");
checkpoint(2); /// ~~~ error_point
// Initializing random states and invoking kernel
T.start();
/// ==========================================================================================================================
learn_kernel <<<grid_dim, block_dim>>> (device_train_inp, device_train_out, device_weights, device_biases, device_layers, device_shifts, layer_count, learning_rate, 0);
cudaThreadSynchronize();
/// ==========================================================================================================================
T.check(" Kernel taken: ");
checkpoint(3); /// ~~~ error_point
// Transfering data from GPU device
T.start();
check_cuda_errors(cudaMemcpy(layers_weights, device_weights, total_weights * sizeof(double), DTH), "MemCopy failed. ", device_weights);
check_cuda_errors(cudaMemcpy(layers_biases, device_biases, total_neurons * sizeof(double), DTH), "MemCopy failed. ", device_biases);
T.check(" Memcopy-2 taken: ");
checkpoint(4); /// ~~~ error_point
// Releasing device memory
check_cuda_errors(cudaFree(device_shifts), "MemFree failed.");
check_cuda_errors(cudaFree(device_layers), "MemFree failed.");
check_cuda_errors(cudaFree(device_train_inp), "MemFree failed. ");
check_cuda_errors(cudaFree(device_train_out), "MemFree failed. ");
check_cuda_errors(cudaFree(device_weights), "MemFree failed. ");
check_cuda_errors(cudaFree(device_biases), "MemFree failed. ");
save_weights(layers_weights, layers_biases, layers, layer_count, "C:\\Users\\mihai\\Desktop\\progy\\C & C++\\Network\\Network\\Cuda_Learned_Net");
checkpoint(5); /// ~~~ error_point
// Releasing host memory
delete[] layers_weights;
delete[] layers_biases;
delete[] train_input;
delete[] layers;
delete[] train_output;
delete[] host_shifts;
return 0;
}
#pragma warning(pop)
|
22,398 | #include <stdio.h>
#include <cuda.h>
/* Matrices are stored in row-major order: */
/* M(row, col) = (M.width*row +col); */
typedef struct{
/* suppose we use only square matrices */
int width;
float *elements;
} Matrix;
/* Thread block size */
#define TILE_WIDTH 2
/***********************/
/* TODO, write KERNEL */
/***********************/
__global__ void MatMul(const float* Md, const float* Nd, float* Pd, const int Width)
{
__shared__ float Mds[TILE_WIDTH][TILE_WIDTH];
__shared__ float Nds[TILE_WIDTH][TILE_WIDTH];
int bx = blockIdx.x; int by = blockIdx.y;
int tx = threadIdx.x; int ty = threadIdx.y;
// Identify the row and column of the Pd element to work on
int Row = by * TILE_WIDTH + ty;
int Col = bx * TILE_WIDTH + tx;
float Pvalue = 0;
//Loop over the Md and Nd tiles required to compute the Pd element
for (int m = 0; m < Width/TILE_WIDTH; m++) {
// Coolaborative loading of Md and Nd tiles into shared memory
Mds[ty][tx] = Md[Row*Width + (m*TILE_WIDTH + tx)];
Nds[ty][tx] = Nd[Col + (m*TILE_WIDTH + ty)*Width];
__syncthreads();
for (int k = 0; k < TILE_WIDTH; k++) Pvalue += Mds[ty][k] * Nds[k][tx];
__syncthreads();
}
Pd[Row*Width+Col] = Pvalue;
}
/**/
void test(const Matrix C);
/**/
int main(int argc, char* argv[])
{
int i;
/* init matrices */
Matrix h_A, h_B, h_C;
cudaEvent_t start;
cudaEvent_t stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
/*******************/
/** READING INPUT **/
/*******************/
int size = 0; //dimension of matrices
scanf("%d", &size);
int full_size = sizeof(float)*size*size;
h_A.width = size;
h_B.width = size;
h_C.width = size;
/* Allocate host memory */
h_A.elements = (float*)malloc(full_size);
h_B.elements = (float*)malloc(full_size);
h_C.elements = (float*)malloc(full_size);
/**/
for(i=0;i<size*size;++i){ scanf("%f", &h_A.elements[i]);}
for(i=0;i<size*size;++i){ scanf("%f", &h_B.elements[i]);}
/********************/
/** FINISHED INPUT **/
/********************/
/*************************/
/* allocate device */
/* memory for A,B,C */
/*************************/
Matrix d_A, d_B, d_C;
d_A.width = size;
d_B.width = size;
d_C.width = size;
cudaMalloc(&d_A.elements, full_size);
cudaMalloc(&d_B.elements, full_size);
cudaMalloc(&d_C.elements, full_size);
cudaEventRecord(start,0);
/***********************************/
/* copy vectors A&B to device */
/***********************************/
cudaMemcpy(d_A.elements, h_A.elements, full_size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B.elements, h_B.elements, full_size, cudaMemcpyHostToDevice);
/*********************/
/* call kernel */
/*********************/
dim3 dimBlock(TILE_WIDTH, TILE_WIDTH);
dim3 dimGrid(h_B.width/dimBlock.x, h_A.width/dimBlock.y);
MatMul<<<dimGrid,dimBlock>>>(d_A.elements, d_B.elements, d_C.elements,d_A.width);
/**************************/
/* copy result back */
/**************************/
cudaMemcpy(h_C.elements, d_C.elements, full_size, cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime, start, stop);
fprintf(stderr,"Elapsed time = %f (s)\n",elapsedTime);
cudaEventDestroy(start);
cudaEventDestroy(stop);
/*******************************************/
/** Testing output, don't change anything! */
/*******************************************/
test(h_C);
/* free device memory */
cudaFree(d_A.elements);
cudaFree(d_B.elements);
cudaFree(d_C.elements);
/* free host memory */
free(h_A.elements);
free(h_B.elements);
free(h_C.elements);
return 0;
}
//function to test the input, don't change anything!
void test(const Matrix C)
{
int i,j;
//int size = C.width*C.width;
for(i=0;i<C.width;++i)
{
for(j=0;j<C.width;++j) printf("%4.1f ", C.elements[i*C.width+j]);
printf("\n");
}
}
|
22,399 |
#include <stdio.h>
#include <stdlib.h>
// Kernel to add two integers
__global__ void add(int *a, int *b, int *c){
*c = *a + *b;
}
// Main program
int main(void){
int *a,*b,*c; // Host copies
int *a_dev,*b_dev,*c_dev; // Device copies
int size = sizeof(int);
// Allocate host memory
a = (int *) malloc (size);
b = (int *) malloc (size);
c = (int *) malloc (size);
// Allocate device memory
cudaMalloc( (void**)&a_dev, size);
cudaMalloc( (void**)&b_dev, size);
cudaMalloc( (void**)&c_dev, size);
// Initialize
*a = 1;
*b = 2;
// Copy inputs to device
cudaMemcpy( a_dev, a, size, cudaMemcpyHostToDevice );
cudaMemcpy( b_dev, b, size, cudaMemcpyHostToDevice );
// Launch kernel on device
add <<<1,1>>> (a_dev,b_dev,c_dev);
// Copy device result back to host
cudaMemcpy( c, c_dev, size, cudaMemcpyDeviceToHost );
// Print result
printf("%d\n",*c);
// Free device memory
cudaFree(a_dev);
cudaFree(b_dev);
cudaFree(c_dev);
// Free host memory
free(a);
free(b);
free(c);
return 0;
}
|
22,400 | #include <cstdlib>
#include <ctime>
#include <random>
#include <iostream>
#include <cmath>
#include <stdio.h>
#include <iomanip>
#include <chrono>
#include <unistd.h>
#include <map>
using namespace std::chrono;
using namespace std;
#define N_THREADS_PER_BLOCK 1024
__device__ double atomicAddDouble(double* address, double val)
{
unsigned long long int* address_as_ull =
(unsigned long long int*)address;
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = atomicCAS(address_as_ull, assumed, __double_as_longlong(val + __longlong_as_double(assumed)));
} while (assumed != old);
return __longlong_as_double(old);
}
class MatrixCUDA{
public:
int *dim;
float *matrix;
int *dev_dim;
float *dev_matrix;
MatrixCUDA(int *n){
matrix = (float* )malloc(sizeof(float)*(*n)*(*n));
cudaMalloc( (void**)&dev_matrix, sizeof(float)*(*n)*(*n));
for(int i=0; i< (*n)*(*n); i++){
matrix[i] = 0.0;
}
cudaMemcpy( dev_matrix, matrix, sizeof(float)*(*n)*(*n), cudaMemcpyHostToDevice );
dim = (int* )malloc(sizeof(int));
*dim = *n;
cudaMalloc((void**)&dev_dim, sizeof(int) );
cudaMemcpy( dev_dim, dim, sizeof(int), cudaMemcpyHostToDevice);
}
void random_init(float *lower_limit, float *higher_limit);
void print_matrix();
void clear_mem();
};
void MatrixCUDA::clear_mem(){
/*for(int i=0 ; i<(*dim)*(*dim); i++) {
cudaFree(dev_matrix[i]);
}*/
free(dim);
cudaFree(dev_dim);
free(matrix);
cudaFree(dev_matrix);
return;
}
void MatrixCUDA::random_init(float *lower_limit, float *higher_limit){
//float **random_numbers;
//random_numbers = (float** )malloc(sizeof(float*)*(*dim));
for(int i=0; i< (*dim)*(*dim); i++){
//random_numbers[i] = (float* )malloc(sizeof(float)*(*dim));
//for(int j=0; j< *dim; j++){
//float random_number;
//random_number = (float* )malloc(sizeof(float));
//random_number =
matrix[i] = *lower_limit + static_cast <float> (rand()) /(static_cast <float> (RAND_MAX/( *higher_limit - *lower_limit)));
//free(random_number);
//}
}
cudaMemcpy( dev_matrix, matrix, sizeof(float)*(*dim)*(*dim), cudaMemcpyHostToDevice );
return;
}
void MatrixCUDA::print_matrix(){
cudaMemcpy( matrix, dev_matrix, sizeof(float)*(*dim)*(*dim), cudaMemcpyDeviceToHost );
for(int i=0; i<*dim; i++){
for(int j=0; j<*dim; j++){
cout << matrix[i*(*dim)+j]<< " ";
}
cout << endl;
}
cout << "Dimensions is " << *dim << " X " << *dim << endl;
return;
}
__global__ void mult(float *a_matrix, float *b_matrix, float *c_matrix, int *dim){
int n = *dim;
__shared__ float temp_sum[N_THREADS_PER_BLOCK];
int z = int((n+N_THREADS_PER_BLOCK-1)/N_THREADS_PER_BLOCK);
int dim1 = int((blockIdx.x)/(z*n));
int dim2 = int((blockIdx.x)/z)%n;
int dim3 = (blockIdx.x)%z;
int index = dim3*(N_THREADS_PER_BLOCK) + threadIdx.x;
if( index > (*dim)){
temp_sum[threadIdx.x] = 0;
}
else{
temp_sum[threadIdx.x] = a_matrix[dim1*(*dim) + index]* b_matrix[(index)*(*dim) + dim2];
// (*counta)++;
}
__syncthreads();
if( 0 == threadIdx.x){
float block_sum = 0;
for (int i = 0; i < (N_THREADS_PER_BLOCK) ; i++){
block_sum += temp_sum[i];
// (*countb)++;
}
float new_sum = block_sum;
atomicAdd(&(c_matrix[dim1*(*dim)+dim2]), new_sum);
}
return;
}
int main(){
// 500 - 51
// 1000 - 51
// 2000 - 86
// 4000 - 223
// 8000 - 753
// 10000 - 1165
// 12000 - 1669
// 14000 - 2264
// 16000 - 1991
// 18000 - 2493
// 20000 - 1547
// 22000 - 1867
// 26000 - 2598
std::map<int, int> load;
load[50] = 1000;
load[75] = 2000;
load[200] = 4000;
load[750] = 8000;
load[1100] = 10000;
load[1600] = 12000;
load[1850] = 22000;
load[2000] = 16000;
load[2200] = 14000;
load[2500] = 18000;
load[2600] = 26000;
int load_wanted;// = 8000;
cout << "Enter load in MB and press enter" << endl;
cout << "Select from the following (50, 75, 200, 750, 1100, 1600, 1850, 2000, 2200, 2500 or 2600)" << endl;
srand (static_cast <unsigned> (5));
cin >> load_wanted;
cout << "Loaded" << endl;
cout << "Press Ctrl C if you love your GPU" << endl;
int matrix_dim = load[load_wanted];
while(true){
MatrixCUDA a(&matrix_dim);
MatrixCUDA b(&matrix_dim);
MatrixCUDA c(&matrix_dim);
float lower_limit;
float upper_limit;
lower_limit = -2;
upper_limit = 2;
a.random_init(&lower_limit, &upper_limit);
b.random_init(&lower_limit, &upper_limit);
int dimension3 = ceil((matrix_dim*1.0)/float(N_THREADS_PER_BLOCK));
int n_blocks = (matrix_dim*matrix_dim*dimension3);
float dur;
cudaEvent_t start, stop;
cudaEventCreate(&start) ;
cudaEventCreate(&stop) ;
cudaEventRecord(start, 0) ;
mult <<< n_blocks, N_THREADS_PER_BLOCK, N_THREADS_PER_BLOCK>>>(a.dev_matrix, b.dev_matrix, c.dev_matrix, a.dev_dim);
cudaEventRecord(stop, 0) ;
cudaEventSynchronize(stop) ;
cudaEventElapsedTime(&dur, start, stop);
a.clear_mem();
b.clear_mem();
c.clear_mem();
}
//}
return 0;
}
// srand (static_cast <unsigned> (5));
// int matrix_size[4] = {16, 128, 1024,1024*16,};
// //cout << "Matrix dimension" << " N_THREADS_PER_BLOCK" << " time(microseconds)"<< endl;
// //for (int i =0 ; i<4; i++){
// int matrix_dim = 1024;// matrix_size[i];
// MatrixCUDA a(&matrix_dim);
// MatrixCUDA b(&matrix_dim);
// MatrixCUDA c(&matrix_dim);
// float lower_limit;
// float upper_limit;
// lower_limit = -1000.0;
// upper_limit = 1000.0;
// a.random_init(&lower_limit, &upper_limit);
// //a.print_matrix();
// b.random_init(&lower_limit, &upper_limit);
// c.random_init(&lower_limit, &upper_limit);
// //c.print_matrix();
// //int threads[9] = {4,8,16,32,64,128,256,512,1024};
// //for(int j = 0; j<9; j++){
// int average = 0;
// const int N_THREADS_PER_BLOCK = 1024;//threads[j];///threads[j];
// // if (N_THREADS_PER_BLOCK > matrix_dim){
// // break;
// // }
// //for (int k = 0; k<10; k++){
// //cudaEvent_t start, stop;
// //cudaEventCreate(&start);
// //cudaEventCreate(&stop);
// //cudaEventRecord(start);
// //cout<<"here"<<endl;
// auto start = high_resolution_clock::now();
// int n_blocks = ceil((matrix_dim*matrix_dim*matrix_dim*1.0)/float(N_THREADS_PER_BLOCK));
// cout<<n_blocks<<" "<<N_THREADS_PER_BLOCK << endl;
// mult <<< n_blocks, N_THREADS_PER_BLOCK, N_THREADS_PER_BLOCK>>>(a.dev_matrix, b.dev_matrix, c.dev_matrix, a.dev_dim, N_THREADS_PER_BLOCK);
// //cout<<"2"<<endl;
// //cudaEventRecord(stop);
// //cout << "end"<<endl;
// //cudaEventSynchronize(stop);
// //float milliseconds = 0;
// //cudaEventElapsedTime(&milliseconds, start, stop);
// auto stop = high_resolution_clock::now();
// auto duration = duration_cast<nanoseconds>(stop - start);
// //cout << "Matrix dimension is " << matrix_dim << " X "<< matrix_dim << " and the N_THREADS_PER_BLOCK are " << N_THREADS_PER_BLOCK << "and the time take is " << duration.count() << " microseconds" << endl;//<< milliseconds << endl;//
// // if(k >1){
// // average += duration.count();
// // }
// // }
// average =average/8;
// cout << matrix_dim << " X "<< matrix_dim << " \t \t " << N_THREADS_PER_BLOCK << " \t \t " << average << endl;
// //}
// // a.print_matrix();
// // b.print_matrix();
// //c.print_matrix();
// a.clear_mem();
// b.clear_mem();dim
// c.clear_mem();
// //}
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