serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
10,601 | //xfail:BOOGIE_ERROR
//--blockDim=2 --gridDim=1 --no-inline
//Write by thread .+kernel\.cu:8:21:
#include <cuda.h>
#include <curand.h>
#include <curand_kernel.h>
#include <stdio.h>
#include <assert.h>
#define N 4
__global__ void curand_test(curandState *state, float *A) { // test: replace curandState for curandStateXORWOW_t
A[threadIdx.x] = curand_uniform(state);
}
|
10,602 | /*
=================================================
Adithya H K Upadhya
MS CS, Virginia Tech
adithyau@vt.edu
4/25/2018
=================================================
*/
#include "gpu_device_code.cuh"
__global__ void parallelGaussianNLM(float *__restrict noisy, float *__restrict kernel, float *__restrict denoisedImage,
const int f, const int t, const float h,
const int sizeX, const int sizeY, const int sizeZ) {
const int x = f + threadIdx.x + blockIdx.x * blockDim.x,
y = f + threadIdx.y + blockIdx.y * blockDim.y,
z = f + threadIdx.z + blockIdx.z * blockDim.z,
denoiseIndex = (z - f) * sizeY * sizeX + (x - f) * sizeY + (y - f);
if (x >= (sizeX + f) || y >= (sizeY + f) || z >= (sizeZ + f)) return;
denoisedImage[denoiseIndex] = 0;
float normalize = 0, maximum = 0;
// Iterating over the Search Window of the voxel.
// Boundary checks applied here through MAX & MIN macros.
for (int zSearch = MAX(z - t, f); zSearch <= MIN(z + t, sizeZ + f - 1); zSearch++)
for (int ySearch = MAX(y - t, f); ySearch <= MIN(y + t, sizeY + f - 1); ySearch++)
for (int xSearch = MAX(x - t, f); xSearch <= MIN(x + t, sizeX + f - 1); xSearch++) {
if (xSearch == x && ySearch == y && zSearch == z) continue;
float d = 0;
// Iterating over the local window of a voxel
for (int lz = -f; lz <= f; lz++)
for (int ly = -f; ly <= f; ly++)
for (int lx = -f; lx <= f; lx++) {
const int searchIndex = (zSearch + lz) * (sizeY + 2 * f) * (sizeX + 2 * f) +
(xSearch + lx) * (sizeY + 2 * f) + (ySearch + ly),
targetIndex = (z + lz) * (sizeY + 2 * f) * (sizeX + 2 * f) +
(x + lx) * (sizeY + 2 * f) + (y + ly),
kernelIndex = (lz + f) * (2 * f + 1) * (2 * f + 1) +
(lx + f) * (2 * f + 1) + (ly + f);
d += kernel[kernelIndex] * (noisy[searchIndex] - noisy[targetIndex]) *
(noisy[searchIndex] - noisy[targetIndex]);
}
d = expf(-d / (h*h));
normalize += d;
maximum = MAX(maximum, d);
denoisedImage[denoiseIndex] += d * noisy[zSearch * (sizeY + 2 * f) * (sizeX + 2 * f) +
xSearch * (sizeY + 2 * f) + ySearch];
}
denoisedImage[denoiseIndex] += maximum * noisy[z * (sizeY + 2 * f) * (sizeX + 2 * f) + x * (sizeY + 2 * f) + y];
denoisedImage[denoiseIndex] /= (normalize + maximum);
}
__global__ void parallelLkNormNLM(float *__restrict noisy, float *__restrict denoisedImage,
const int f, const int t,
const int sizeX, const int sizeY, const int sizeZ, const int weight) {
const int x = f + threadIdx.x + blockIdx.x * blockDim.x,
y = f + threadIdx.y + blockIdx.y * blockDim.y,
z = f + threadIdx.z + blockIdx.z * blockDim.z,
denoiseIndex = (z - f) * sizeY * sizeX + (x - f) * sizeY + (y - f);
if (x >= (sizeX + f) || y >= (sizeY + f) || z >= (sizeZ + f)) return;
denoisedImage[denoiseIndex] = 0;
float X = 0, Y = 0, minimum = 3.4e38;
int count = -1;
// Iterating over the Search Window of the voxel.
// Boundary checks applied here through MAX & MIN macros.
for (int zSearch = MAX(z - t, f); zSearch <= MIN(z + t, sizeZ + f - 1); zSearch++)
for (int ySearch = MAX(y - t, f); ySearch <= MIN(y + t, sizeY + f - 1); ySearch++)
for (int xSearch = MAX(x - t, f); xSearch <= MIN(x + t, sizeX + f - 1); xSearch++) {
++count;
if (xSearch == x && ySearch == y && zSearch == z) continue;
float d = 0;
// Iterating over the local window of a voxel
for (int lz = -f; lz <= f; lz++)
for (int ly = -f; ly <= f; ly++)
for (int lx = -f; lx <= f; lx++) {
const int searchIndex = (zSearch + lz) * (sizeY + 2 * f) * (sizeX + 2 * f) +
(xSearch + lx) * (sizeY + 2 * f) + (ySearch + ly),
targetIndex = (z + lz) * (sizeY + 2 * f) * (sizeX + 2 * f) +
(x + lx) * (sizeY + 2 * f) + (y + ly);
d += powf(fabs(noisy[searchIndex] - noisy[targetIndex]), weight);
}
minimum = MIN(minimum, d);
X += d;
Y += noisy[zSearch * (sizeY + 2 * f) * (sizeX + 2 * f) +
xSearch * (sizeY + 2 * f) + ySearch];
denoisedImage[denoiseIndex] += d * noisy[zSearch * (sizeY + 2 * f) * (sizeX + 2 * f) +
xSearch * (sizeY + 2 * f) + ySearch];
}
X += minimum;
Y += noisy[z * (sizeY + 2 * f) * (sizeX + 2 * f) + x * (sizeY + 2 * f) + y];
denoisedImage[denoiseIndex] += minimum * noisy[z * (sizeY + 2 * f) * (sizeX + 2 * f) + x * (sizeY + 2 * f) + y];
denoisedImage[denoiseIndex] = (X * Y - denoisedImage[denoiseIndex]) / (count * X);
}
void launchParallelNLM(float *noisyData, float *kernel, float *denoisedImage, int f, int t, float h,
int sizeX, int sizeY, int sizeZ, WeightingFunction weight) {
float *d_noisyData, *d_kernel, *d_denoisedImage;
dim3 blocks((sizeX + 7) / 8, (sizeY + 7) / 8, (sizeZ + 3) / 4), threads(8, 8, 4);
cudaEvent_t start, end;
cudaEventCreate(&start);
cudaEventCreate(&end);
cudaMalloc((void**)&d_noisyData, (sizeX + 2 * f) * (sizeY + 2 * f) * (sizeZ + 2 * f) * sizeof(float));
cudaMalloc((void**)&d_kernel, (2 * f + 1) * (2 * f + 1) * (2 * f + 1) * sizeof(float));
cudaMalloc((void**)&d_denoisedImage, sizeX * sizeY * sizeZ * sizeof(float));
cudaMemcpy((void*)d_noisyData, (void*)noisyData, (sizeX + 2 * f) * (sizeY + 2 * f) * (sizeZ + 2 * f) * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy((void*)d_kernel, (void*)kernel, (2 * f + 1) * (2 * f + 1) * (2 * f + 1) * sizeof(float), cudaMemcpyHostToDevice);
switch (weight)
{
case GAUSSIAN_WEIGHTING:
cudaEventRecord(start);
parallelGaussianNLM << <blocks, threads >> > (d_noisyData, d_kernel, d_denoisedImage, f, t, h, sizeX, sizeY, sizeZ);
cudaEventRecord(end);
break;
case L1_NORM:
case L2_NORM:
case L3_NORM:
case L4_NORM:
cudaEventRecord(start);
parallelLkNormNLM << <blocks, threads >> > (d_noisyData, d_denoisedImage, f, t, sizeX, sizeY, sizeZ, weight);
cudaEventRecord(end);
break;
}
cudaMemcpy(denoisedImage, d_denoisedImage, sizeX * sizeY * sizeZ * sizeof(float), cudaMemcpyDeviceToHost);
cudaEventSynchronize(end);
cudaEventElapsedTime(&h, start, end);
printf("\nParallel execution time L%d-NORM = %f seconds", weight, h / 1000);
cudaCheckError();
cudaFree(d_noisyData);
cudaFree(d_kernel);
cudaFree(d_denoisedImage);
cudaEventDestroy(start);
cudaEventDestroy(end);
}
|
10,603 | //
// Created by root on 2020/11/26.
//
#include "cuda_runtime.h"
#include "stdio.h"
#define W 50
#define H 50
#define TX 32
#define TY 32
__device__ char clip(int n) {
return n < 0 ? 0 : n > 255 ? 255 : n;
}
__global__ void distanceKernel(uchar4 *d_out, int w, int h, float2 pos) {
int x = blockDim.x * blockIdx.x + threadIdx.x;
int y = blockDim.y * blockIdx.y + threadIdx.y;
int idx = x + y * w;
if (x >= w || y >= h) {
return;
}
int d = sqrtf((x - pos.x) * (x - pos.x) + (y - pos.y) * (y - pos.y));
char intensity = clip(255 - d);
d_out[idx].x = intensity;
d_out[idx].y = intensity;
d_out[idx].z = 0;
d_out[idx].w = 255;
}
int main() {
int size = W * H;
int nBytes = size * sizeof(uchar4);
uchar4 *out = (uchar4 *) malloc(nBytes);
uchar4 *d_out;
cudaMalloc(&d_out, nBytes);
float2 pos = {0.0f, 0.0f};
dim3 block(TX, TY);
dim3 grid((W + TX - 1) / TX, (H + TY - 1) / TY);
distanceKernel<<<grid, block>>>(d_out, W, H, pos);
cudaMemcpy(out, d_out, nBytes, cudaMemcpyDeviceToHost);
for (int i = 0; i < H; i++) {
for (int j = 0; j < W; j++) {
printf("(%d, %d, %d, %d)\t",
out[i * W + j].x, out[i * W + j].y, out[i * W + j].z, out[i * W + j].w);
}
printf("\n");
}
free(out);
cudaFree(d_out);
return 0;
} |
10,604 | #include "includes.h"
__global__ void Matrix_getRow_FloatId_naive(const float * A, int Acount, int Acols, float * out0, int out0count, int out0cols, float row_id) {
int id = blockDim.x*blockIdx.y*gridDim.x + blockDim.x*blockIdx.x + threadIdx.x;
if (id < Acols) {
out0[id] = A[id + (int)row_id*Acols];
}
} |
10,605 |
#include <thrust/device_vector.h>
#include <thrust/reduce.h>
#include <thrust/iterator/transform_iterator.h>
#include <iostream>
int main()
{
// initialize vector
thrust::device_vector<int> vec(3);
vec[0] = 10; vec[1] = 20; vec[2] = 30;
// create iterator (type omitted)
auto first = thrust::make_transform_iterator(vec.begin(), thrust::negate<int>());
auto last = thrust::make_transform_iterator(vec.end(), thrust::negate<int>());
std::cout << first[0] << std::endl; // returns -10
std::cout << first[1] << std::endl; // returns -20
std::cout << first[2] << std::endl; // returns -30
// sum of [first, last)
int result = thrust::reduce(first, last); // returns -60 (i.e. -10 + -20 + -30)
std::cout << "Result = " << result << std::endl;
return 0;
}
|
10,606 | #include "includes.h"
__global__ void dwt_average(float *d_ip_v, float *d_ip_ir, int app_len) {
const int X = blockIdx.x * blockDim.x + threadIdx.x;
if (X < app_len)
{
d_ip_v[X] = (d_ip_v[X] + d_ip_ir[X]) / 2;
}
} |
10,607 | #include <cuda_runtime.h>
#include <cufft.h>
#include <stdlib.h>
#include <stdio.h>
int imin(int a, int b)
{
return (a < b ? a : b);
}
/* Number of samples per spectrum */
#define NX 1024
/* Number of frequencies per spectrum */
#define NF (NX/2+1)
/* Total number of samples in a time bin */
#define NTOT 20000000
/* Number of samples to average per channel per time bin
* remaining samples are skipped. */
#define N ((NTOT / NX) * NX)
/* Number of FFTs to perform in one batch */
#define BATCH (N / NX)
/* Number of samples after FFT */
#define NS (NF*BATCH)
const int threadsPerBlock = 32;
const int blocksPerGrid = imin(32, (NF + threadsPerBlock-1) / threadsPerBlock);
__global__ void correlate(float *c, float *s, cufftComplex *a, cufftComplex *b)
{
float temp_c = 0;
float temp_s = 0;
cufftComplex ccorr;
int tid = threadIdx.x + blockIdx.x * blockDim.x;
/* We launch more threads then needed, so some do nothing */
if (tid < NF) {
while (tid < NS) {
/* Normalize FFT */
a[tid].x /= NX;
a[tid].y /= NX;
b[tid].x /= NX;
b[tid].y /= NX;
/* Correlate */
ccorr = cuCmulf(a[tid], cuConjf(b[tid]));
/* Sum channel over time */
temp_c += cuCrealf(ccorr);
temp_s += cuCimagf(ccorr);
/* Go to next time step, NF frequencies away */
tid += NF;
}
c[threadIdx.x + blockIdx.x * blockDim.x] = temp_c;
s[threadIdx.x + blockIdx.x * blockDim.x] = temp_s;
}
}
int main(int argc, char* argv[])
{
int i, j;
FILE *fp, *fo;
char *buffer;
float *c, *s, *dev_c, *dev_s;
float *a, *b, *dev_a, *dev_b;
cufftComplex *cdev_a, *cdev_b;
cudaError_t err;
cufftHandle plan;
/* Allocate memory on host */
buffer = (char*) malloc(2*NTOT*sizeof(char));
a = (float*) malloc(N*sizeof(float));
b = (float*) malloc(N*sizeof(float));
c = (float*) malloc(NF*sizeof(float));
s = (float*) malloc(NF*sizeof(float));
/* Allocate memory on device */
err = cudaMalloc(&dev_c, NF*sizeof(float));
if (err != cudaSuccess) {
fprintf(stderr, "Error %s\n", cudaGetErrorString(err));
return 1;
}
err = cudaMalloc(&dev_s, NF*sizeof(float));
if (err != cudaSuccess) {
fprintf(stderr, "Error %s\n", cudaGetErrorString(err));
return 1;
}
err = cudaMalloc(&dev_a, N*sizeof(float));
if (err != cudaSuccess) {
fprintf(stderr, "Error %s\n", cudaGetErrorString(err));
return 1;
}
err = cudaMalloc(&dev_b, N*sizeof(float));
if (err != cudaSuccess) {
fprintf(stderr, "Error %s\n", cudaGetErrorString(err));
return 1;
}
err = cudaMalloc(&cdev_a, NF*BATCH*sizeof(cufftComplex));
if (err != cudaSuccess) {
fprintf(stderr, "Error %s\n", cudaGetErrorString(err));
return 1;
}
err = cudaMalloc(&cdev_b, NF*BATCH*sizeof(cufftComplex));
if (err != cudaSuccess) {
fprintf(stderr, "Error %s\n", cudaGetErrorString(err));
return 1;
}
/* Create FFT plan */
if (cufftPlan1d(&plan, NX, CUFFT_R2C, BATCH) != CUFFT_SUCCESS) {
fprintf(stderr, "CUFFT error: Plan creation failed");
return 1;
}
/* Open input file */
fp = fopen(argv[1], "rb");
if (fp == NULL) {
fprintf(stderr, "Error: could not open input file!\n");
return 1;
}
/* Open output file */
fo = fopen(argv[2], "w");
if (fo == NULL) {
fprintf(stderr, "Error: could not open output file!\n");
return 1;
}
i = 0;
while (fread(buffer, sizeof(char), 2*NTOT, fp) == 2*NTOT*sizeof(char)) {
printf("%d\n", i);
/* Copy data to device */
for (j=0; j<N; j++) {
a[j] = (float) buffer[2*j];
b[j] = (float) buffer[2*j+1];
}
err = cudaMemcpy(dev_a, a, N*sizeof(float), cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
printf("Error %s\n", cudaGetErrorString(err));
}
err = cudaMemcpy(dev_b, b, N*sizeof(float), cudaMemcpyHostToDevice);
if (err != cudaSuccess) {
printf("Error %s\n", cudaGetErrorString(err));
}
/* Perform FFT on device */
if (cufftExecR2C(plan, dev_a, cdev_a) != CUFFT_SUCCESS){
fprintf(stderr, "CUFFT error: ExecC2C Forward failed");
return 1;
}
if (cudaThreadSynchronize() != cudaSuccess){
fprintf(stderr, "Cuda error: Failed to synchronize\n");
return 1;
}
/* Perform FFT on device */
if (cufftExecR2C(plan, dev_b, cdev_b) != CUFFT_SUCCESS){
fprintf(stderr, "CUFFT error: ExecC2C Forward failed");
return 1;
}
if (cudaThreadSynchronize() != cudaSuccess){
fprintf(stderr, "Cuda error: Failed to synchronize\n");
return 1;
}
correlate<<<blocksPerGrid,threadsPerBlock>>>(dev_c, dev_s, cdev_a, cdev_b);
err = cudaMemcpy(c, dev_c, NF*sizeof(float), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
printf("Error %s\n", cudaGetErrorString(err));
}
err = cudaMemcpy(s, dev_s, NF*sizeof(float), cudaMemcpyDeviceToHost);
if (err != cudaSuccess) {
printf("Error %s\n", cudaGetErrorString(err));
}
/* From sum to average on the CPU */
for (j=0; j<NF; j++) {
c[j] /= BATCH;
s[j] /= BATCH;
fprintf(fo, "%d %.3f\t%.3f\n", j, c[j], s[j]);
}
i++;
}
/* Cleanup */
free(a);
free(b);
free(buffer);
cufftDestroy(plan);
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(cdev_a);
cudaFree(cdev_b);
/* Close file */
fclose(fp);
fclose(fo);
return 0;
}
|
10,608 | #include <cuda_runtime.h>
#include <stdio.h>
#include <time.h>
#define AxCheckError(err) CheckError(err,__FUNCTION__, __LINE__)
#define AxCheckErrorMsg(err, msg) CheckErrorMsg(err, msg, __FUNCTION__, __LINE__)
// Gera dados de teste
void GenerateTestData(int const N, float* const a, float* const b, float* const c, float* const ref);
// Compara dados
void CompareData(int const N, float const* const a, float const* const b);
// Checa erros
void CheckError(cudaError_t const err, char const* const fun, const int line);
void CheckErrorMsg(cudaError_t const err, char const* const msg, char const* const fun, int const line);
// kernel
__global__ void SumArrays(float* const a, float* const b, float* const c, int const N)
{
int i = blockIdx.x * blockDim.x + threadIdx.x; // global index das threads - Total de threads
if (i < N)
c[i] = a[i] + b[i];
}
int main()
{
// Vari?veis do host
float *aH, *bH, *cH, *refH;
// Vari?veis do device
float *aD, *bD, *cD; // matrizes na GPU
// CUDA error
cudaError_t e = cudaSuccess;
// Dimens?es
dim3 gridSize;
dim3 blockSize;
// Constantes
int const N = 2053;
int const N_BYTES = N * sizeof(float);
int const BLOCK_SIZE = 512;
// Aloca??o de mem?ria no host
aH = (float*)malloc(N_BYTES);
bH = (float*)malloc(N_BYTES);
cH = (float*)malloc(N_BYTES);
refH = (float*)malloc(N_BYTES);
// Gera dados de teste
GenerateTestData(N, aH, bH, cH, refH);
// Aloca mem?ria na GPU
e = cudaMalloc((void**)&aD, N_BYTES);
AxCheckError(e);
e = cudaMalloc((void**)&bD, N_BYTES);
AxCheckError(e);
e = cudaMalloc((void**)&cD, N_BYTES);
AxCheckError(e);
// Copia os valores das vari?veis na mem?ria do host para a mem?ria do device
e = cudaMemcpy(aD, aH, N_BYTES, cudaMemcpyHostToDevice);
AxCheckError(e);
e = cudaMemcpy(bD, bH, N_BYTES, cudaMemcpyHostToDevice);
AxCheckError(e);
// Define as dimens?es
blockSize.x = BLOCK_SIZE; blockSize.y = 1; blockSize.z = 1;
gridSize.x = ((N + BLOCK_SIZE - 1) / BLOCK_SIZE); gridSize.y = 1; gridSize.z = 1;
// Executa o kernel - Soma os arrays
SumArrays<<<gridSize, blockSize>>>(aD, bD, cD, N);
// Obt?m erros de execu??o do kernel
cudaDeviceSynchronize();
e = cudaGetLastError();
AxCheckError(e);
// Copia o resultado da mem?ria do device para a mem?ria do host
e = cudaMemcpy(cH, cD, N_BYTES, cudaMemcpyDeviceToHost);
AxCheckError(e);
// Compara os dados
CompareData(N, cH, refH);
// Libera a mem?ria
cudaFree(aD); cudaFree(bD); cudaFree(cD);
free(aH); free(bH); free(cH); free(refH);
AxCheckError(cudaDeviceReset());
return 0;
}
// Fun??o para gerar os dados - CPU
void GenerateTestData(int const N, float* const a, float* const b, float* const c, float* const ref)
{
int i;
srand((unsigned)time(NULL));
for(i = 0; i < N; i++)
{
a[i] = (float)rand();
b[i] = (float)rand();
c[i] = 0.0f;
ref[i] = a[i] + b[i];
}
}
// Fun??o para comparar os dados
void CompareData(int const N, float const* const a, float const* const b)
{
int i;
int different = 0;
for(i = 0; i < N; i++)
{
different = (a[i] != b[i]);
if(different)
break;
}
if(different)
{
printf("Arrays diferentes.\n");
}
else
{
printf("Arrays match.\n");
}
}
// Fun??es para checar e imprimir o c?digo de erro
void CheckError(cudaError_t const err, char const* const fun, const int line)
{
if (err)
{
printf("CUDA Error Code[%d]: %s %s():%d\n",err,cudaGetErrorString(err),fun,line);
exit(1);
}
}
void CheckErrorMsg(cudaError_t const err, char const* const msg, char const* const fun, int const line)
{
if (err)
{
printf("CUDA Error Code[%d]: %s %s() %d\n%s\n",err,cudaGetErrorString(err),fun,line,msg);
exit(1);
}
}
|
10,609 | #include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <time.h>
struct ElementWiseProductBasic : public thrust::binary_function<float2,float2,float2>
{
__host__ __device__
float2 operator()(const float2& v1, const float2& v2) const
{
float2 res;
res.x = v1.x * v2.x - v1.y * v2.y;
res.y = v1.x * v2.y + v1.y * v2.x;
return res;
}
};
/**
* See: http://www.embedded.com/design/embedded/4007256/Digital-Signal-Processing-Tricks--Fast-multiplication-of-complex-numbers%5D
*/
struct ElementWiseProductModified : public thrust::binary_function<float2,float2,float2>
{
__host__ __device__
float2 operator()(const float2& v1, const float2& v2) const
{
float2 res;
float a, b, c, d, k;
a = v1.x;
b = v1.y;
c = v2.x;
d = v2.y;
k = a * (c + d);
d = d * (a + b);
c = c * (b - a);
res.x = k -d;
res.y = k + c;
return res;
}
};
int get_random_int(int min, int max)
{
return min + (rand() % (int)(max - min + 1));
}
thrust::host_vector<float2> init_vector(const size_t N)
{
thrust::host_vector<float2> temp(N);
for(size_t i = 0; i < N; i++)
{
temp[i].x = get_random_int(0, 10);
temp[i].y = get_random_int(0, 10);
}
return temp;
}
int main(void)
{
const size_t N = 100000;
const bool compute_basic_product = true;
const bool compute_modified_product = true;
srand(time(NULL));
thrust::host_vector<float2> h_A = init_vector(N);
thrust::host_vector<float2> h_B = init_vector(N);
thrust::device_vector<float2> d_A = h_A;
thrust::device_vector<float2> d_B = h_B;
thrust::host_vector<float2> h_result(N);
thrust::host_vector<float2> h_result_modified(N);
if (compute_basic_product)
{
thrust::device_vector<float2> d_result(N);
thrust::transform(d_A.begin(), d_A.end(),
d_B.begin(), d_result.begin(),
ElementWiseProductBasic());
h_result = d_result;
}
if (compute_modified_product)
{
thrust::device_vector<float2> d_result_modified(N);
thrust::transform(d_A.begin(), d_A.end(),
d_B.begin(), d_result_modified.begin(),
ElementWiseProductModified());
h_result_modified = d_result_modified;
}
std::cout << std::fixed;
for (size_t i = 0; i < 4; i++)
{
float2 a = h_A[i];
float2 b = h_B[i];
std::cout << "(" << a.x << "," << a.y << ")";
std::cout << " * ";
std::cout << "(" << b.x << "," << b.y << ")";
if (compute_basic_product)
{
float2 prod = h_result[i];
std::cout << " = ";
std::cout << "(" << prod.x << "," << prod.y << ")";
}
if (compute_modified_product)
{
float2 prod_modified = h_result_modified[i];
std::cout << " = ";
std::cout << "(" << prod_modified.x << "," << prod_modified.y << ")";
}
std::cout << std::endl;
}
return 0;
}
|
10,610 | #include "includes.h"
__global__ void gpu_init(int *mapad, int max, int size){
/*Identificaciones necesarios*/
int IDX_Thread = threadIdx.x; /*Identificacion del hilo en la dimension*/
int IDY_Thread = threadIdx.y; /*Identificacion del hilo en la dimension y*/
int IDX_block = blockIdx.x; /*Identificacion del bloque en la dimension x*/
int IDY_block = blockIdx.y; /*Identificacion del bloque en la dimension y */
int shapeGrid_X = gridDim.x; /*Numeros del bloques en la dimension */
int threads_per_block = blockDim.x * blockDim.y; /* Numero de hilos por bloque (1 dimension) */
/*Formula para calcular la posicion*/ //Posicion del vector dependiendo del hilo y del bloque
int position = threads_per_block * ((IDY_block * shapeGrid_X)+IDX_block)+((IDY_Thread*blockDim.x)+IDX_Thread);
//inicializamos
if(position<size) mapad[position] = max;
} |
10,611 | #include <stdio.h>
#include <stdlib.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
__global__ void register_usage_test(int * results, int size)
{
int gid = blockDim.x * blockIdx.x + threadIdx.x;
int x1 = 3465;
int x2 = 1768;
int x3 = 453;
int x7 = 3465;
int x5 = 1768;
int x6 = 453;
int x4 = x1 + x2 + x3 + x7 + x5 + x6;
if (gid < size)
{
results[gid] = x4;
}
}
//int main()
//{
// int size = 1 << 22;
// int byte_size = sizeof(int)*size;
//
// int * h_ref = (int*) malloc(byte_size);
// int * d_results;
// cudaMalloc((void**)&d_results, byte_size);
// cudaMemset(d_results, 0, byte_size);
//
// dim3 blocks(128);
// dim3 grid((size+blocks.x-1)/blocks.x);
//
// printf("launching the kernel \n");
// register_usage_test << <grid,blocks >> > (d_results, size);
// cudaDeviceSynchronize();
//
// cudaMemcpy(h_ref, d_results, byte_size, cudaMemcpyDeviceToHost);
// printf("Results have arrived \n");
//
// int sum = 0;
//
// for (int i = 0; i < size; i++)
// {
// sum += h_ref[i];
// }
//
// printf("final sum : %d \n",sum);
//
// return 0;
//}
|
10,612 | #include <stdio.h>
#include <cuda_runtime_api.h>
#include <time.h>
/****************************************************************************
* This is a program of password cracking of two alphabets and four digits using cuda.
* AV72, FT27, IR75, SC55 these passwords were provided and adding some digits to make four digits.
*
To Compile:
nvcc -o CudaCrack CudaCrack.cu
To run:
./CudaCrack
*****************************************************************************/
__device__ char password_text[4][7]={"AV7212","ES2112","GT5912","RB9612"};
__device__ void displayResult(char *password)
{
printf("Password found is: %s\n",password);
}
__device__ void is_a_match(char *perform) {
char *a = perform;
char *b = perform;
char *c = perform;
char *d = perform;
char *ps_1 = password_text[0];
char *ps_2 = password_text[1];
char *ps_3 = password_text[2];
char *ps_4 = password_text[3];
while(*a == *ps_1) {
if(*a == '\0')
{
displayResult(perform);
break;
}
a++;
ps_1++;
}
while(*b == *ps_2) {
if(*b == '\0')
{
displayResult(perform);
break;
}
b++;
ps_2++;
}
while(*c == *ps_3) {
if(*c == '\0')
{
displayResult(perform);
break;
}
c++;
ps_3++;
}
while(*d == *ps_4) {
if(*d == '\0')
{
displayResult(perform);
break;
}
d++;
ps_4++;
}
return;
}
__global__ void kernel() {
char w,x,y,z;
char password[7];
password[6] = '\0';
int i = blockIdx.x+65;
int j = threadIdx.x+65;
char first_val = i;
char second_val = j;
password[0] = first_val;
password[1] = second_val;
for(w='0'; w<='9'; w++){
for(x='0'; x<='9'; x++){
for(y='0'; y<='9'; y++){
for(z='0'; z<='9'; z++){
password[2] = w;
password[3] = x;
password[4] = y;
password[5] = z;
is_a_match(password);
}
}
}
}
}
int time_difference(struct timespec *start,
struct timespec *finish,
long long int *difference) {
long long int ds = finish->tv_sec - start->tv_sec;
long long int dn = finish->tv_nsec - start->tv_nsec;
if(dn < 0 ) {
ds--;
dn += 1000000000;
}
*difference = ds * 1000000000 + dn;
return !(*difference > 0);
}
int main() {
struct timespec start, finish;
long long int time_elapsed;
clock_gettime(CLOCK_MONOTONIC, &start);
dim3 block_Dim(26,1,1), thread_Dim(26,1,1);
kernel <<<block_Dim,thread_Dim>>>();
cudaThreadSynchronize();
clock_gettime(CLOCK_MONOTONIC, &finish);
time_difference(&start, &finish, &time_elapsed);
printf("Time elapsed was %lldns or %0.9lfs\n", time_elapsed, (time_elapsed/1.0e9));
return 0;
}
|
10,613 | #include <cstdio>
__global__ void init5(int* arr, int n){
int idx=blockDim.x*blockIdx.x+threadIdx.x;
if(idx<n*n)
arr[idx]=5;
}
__global__ void init7(int* arr,int n){
int idx=blockDim.x*blockIdx.x+threadIdx.x;
int idy=blockDim.y*blockIdx.y+threadIdx.y;
if(idx<n && idy<n)
arr[idy*n+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 bytes=sizeof(int)*n*n;
int* cuMtrx=NULL;
cudaMalloc((void**)&cuMtrx,bytes);
int* mt1=(int*)malloc(bytes);
memset(mt1,0,bytes);
cudaMemcpy(cuMtrx,mt1,bytes,cudaMemcpyHostToDevice);
init5<<<n*n,1>>>(cuMtrx,n);
cudaMemcpy(mt1,cuMtrx,bytes,cudaMemcpyDeviceToHost);
printMtrx(mt1,n);
dim3 grid(n,n,1);
init7<<<grid,1>>>(cuMtrx,n);
cudaMemcpy(mt1,cuMtrx,bytes,cudaMemcpyDeviceToHost);
printMtrx(mt1,n);
cudaFree(cuMtrx);
return 0;
}
|
10,614 | #include "includes.h"
__global__ void ComputeAverageThetaVelocitiesKernel(double *Vtheta, double *VMed, int nsec, int nrad)
{
int i = threadIdx.x + blockDim.x*blockIdx.x;
double moy = 0.0;
if (i<nrad){
for (int j = 0; j < nsec; j++)
moy += Vtheta[i*nsec + j];
VMed[i] = moy/(double)nsec;
}
} |
10,615 | __device__ float backwardTanh (float forward, float chain)
{
return chain * (1.0 - powf(forward, 2.0));
}
extern "C"
__global__ void backwardTanhKernel (int length, float *forward, float *chain, float *destination)
{
int index = blockDim.x * blockIdx.x + threadIdx.x;
if(index < length) {
destination[index] = backwardTanh(forward[index], chain[index]);
}
} |
10,616 | #include <stdio.h>
#include <cuda.h>
#include <time.h>
#include <string.h>
#include <assert.h>
#include <map>
#include <iostream>
#include <cassert>
#include <sstream>
#include <vector>
#include <algorithm> // std::transform
#define maxWordSize 1024
#define max_number_of_threads 512
using namespace std;
typedef struct gpuStringArray {
unsigned int * pos;
unsigned int * length; // could be a smaller type if strings are short
char * data; // 32 bit data type will improve memory throughput, could be 8 bit
unsigned int size;
} gpuStringArray;
__device__ int cmp4(const char & c1, const char & c2)
{
return c1==c2;
}
__device__ int strncmp4(const char * s1, const char * s2, const unsigned int nwords)
{
for(unsigned int i=0; i<nwords; i++) {
int result = cmp4(s1[i], s2[i]);
if (result == 0) return result;
}
return 1;
}
__global__ void tkernel(struct gpuStringArray *a, gpuStringArray *b, int * result, int *threadsNumber)
{
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int wordsCount=b->size;
int iterationsPerThread=wordsCount/(*threadsNumber)+1;
for(int i=0; i<iterationsPerThread; i++)
{
if(idx>wordsCount) return;
for(int j=0; j< a->size; j++)
{
char * s1 = a->data + a->pos[j];
char * s2 = b->data + b->pos[idx];
unsigned int slen = max(a->length[j], b->length[idx]);
if(a->length[j]!= b->length[idx]) result[idx]=0;
else result[idx]=strncmp4(s1, s2, slen);
if(result[idx]==1) break;
}
idx+=*threadsNumber;
}
}
void makeStringWithoutBadSigns(char *sign)
{
int length=strlen(sign);
if(sign[length-1]>0 && sign[length-1]>32 && sign[length-1]<65)
{
sign[length-1]=0;
makeStringWithoutBadSigns(sign);
}
}
dim3 getBlockSettings(int threads)
{
dim3 block_size;
if(threads>max_number_of_threads)
block_size.x = max_number_of_threads;
else
block_size.x = threads;
block_size.y = 1;
return block_size;
}
dim3 getGridSettings(int threadsNumber)
{
int gridNumbers = threadsNumber / max_number_of_threads;
if (gridNumbers * max_number_of_threads < threadsNumber) ++gridNumbers;
dim3 grid_size;
grid_size.x=gridNumbers;
grid_size.y=1;
return grid_size;
}
void read_words (FILE *f, map<string, int> &m) {
char x[maxWordSize];
vector < string > dane;
while (fscanf(f, " %1023s", x) == 1) {
//cout << x << endl;
makeStringWithoutBadSigns(x);
m[x]++;
string s = std::string(x);
dane.push_back( s );
}
}
gpuStringArray* allocateGpuStringArray(vector<unsigned int> positions, vector<unsigned int> lenghts, string str)
{
unsigned int *cudaPos;
char *cudaData;
unsigned int *cudaLength;
unsigned int* inputPositions = &positions[0];
size_t positionsSize=sizeof(unsigned int)*(positions.size()+1);
cudaMalloc((void **) &cudaPos, positionsSize);
cudaMemcpy(cudaPos, inputPositions, positionsSize, cudaMemcpyHostToDevice);
char dataArray[str.size()+1];//as 1 char space for null is also required
strcpy(dataArray, str.c_str());
size_t dataSize=sizeof(char)*(str.size()+1);
cudaMalloc((void **) &cudaData, dataSize);
cudaMemcpy(cudaData, dataArray, dataSize, cudaMemcpyHostToDevice);
unsigned int* inputLengths= &lenghts[0];
size_t lengthSize=sizeof(unsigned int)*(lenghts.size()+1);
cudaMalloc((void **) &cudaLength, lengthSize);
cudaMemcpy(cudaLength, inputLengths, lengthSize, cudaMemcpyHostToDevice);
gpuStringArray *stringArray;
stringArray=(gpuStringArray*) malloc(sizeof(gpuStringArray));
stringArray->pos=cudaPos;
stringArray->length=cudaLength;
stringArray->data=cudaData;
stringArray->size=lenghts.size();
gpuStringArray *cudaStringArray;
size_t cudaArray=sizeof(gpuStringArray);
cudaMalloc((void **) &cudaStringArray, cudaArray);
cudaMemcpy(cudaStringArray, stringArray, cudaArray, cudaMemcpyHostToDevice); // Skopiowanie danych do GPU
return cudaStringArray;
}
template <typename T1, typename T2>
struct less_second {
typedef pair<T1, T2> type;
bool operator ()(type const& a, type const& b) const {
return a.second < b.second;
}
};
vector<pair<string, int> > sortMapByKeys(map<string, int> m)
{
//copy to vector to sort by values
vector<pair<string, int> > mapcopy(m.begin(), m.end());
sort(mapcopy.begin(), mapcopy.end(), less_second<string, int>());
return mapcopy;
}
int main(int argc, char** argv)
{
char* fileInput;
char* fileInput2;
int threadsNumber;
bool isDataShouldShow=false;
fileInput = argv[2];
fileInput2 = argv[3];
threadsNumber = atoi(argv[5]);
isDataShouldShow = atoi(argv[7]);
//time
float time;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0); //start time
map<string, int> m;
FILE *inputFile;
inputFile = fopen(fileInput, "r");
read_words(inputFile, m);
//1
stringstream ss;
vector<unsigned int> positions;
vector<unsigned int> lenghts;
unsigned int previousPos=0;
for (std::map<string,int>::iterator it=m.begin(); it!=m.end(); ++it)
{
ss << it->first;
positions.push_back(previousPos);
lenghts.push_back(it->first.length());
previousPos+=it->first.length();
}
string str = ss.str();
gpuStringArray *inputCuda=allocateGpuStringArray(positions, lenghts, str);
//2
map<string, int> m2;
FILE *outputFile;
outputFile = fopen(fileInput2, "r");
read_words(outputFile, m2);
stringstream ss2;
vector<unsigned int> positions2;
vector<unsigned int> lenghts2;
unsigned int previousPos2=0;
for (std::map<string,int>::iterator it=m2.begin(); it!=m2.end(); ++it)
{
//std::cout << it->first << " => " << it->second << '\n';
ss2 << it->first;
positions2.push_back(previousPos2);
lenghts2.push_back(it->first.length());
previousPos2+=it->first.length();
}
string str2 = ss2.str();
gpuStringArray *tempCuda=allocateGpuStringArray(positions2, lenghts2, str2);
size_t resultSize=sizeof(int)*m2.size();
int *result=(int*) malloc(resultSize);
int *cudaResult;
cudaMalloc((void **) &cudaResult, resultSize);
size_t sizeThreadsNumber=sizeof(int);
int *cudaThreadsNumber;
cudaMalloc((void **) &cudaThreadsNumber, sizeThreadsNumber);
cudaMemcpy(cudaThreadsNumber, &threadsNumber, sizeThreadsNumber, cudaMemcpyHostToDevice);
dim3 block_size=getBlockSettings(threadsNumber);
dim3 grid_size=getGridSettings(threadsNumber);
tkernel<<< grid_size, block_size>>> (inputCuda, tempCuda, cudaResult, cudaThreadsNumber);
cudaMemcpy(result, cudaResult, resultSize, cudaMemcpyDeviceToHost);
int finalResult=0;
for(int i=0; i<m2.size() ;i++)
{
finalResult+=result[i];
//cout << i << " " << result[i] << endl;
}
if(isDataShouldShow)
{
vector<pair<string, int> > mapcopy=sortMapByKeys(m);
cout << "\n 10 most used words in input of neural network" << endl;
for(int i = mapcopy.size()-10; i < mapcopy.size(); i++)
{
cout << mapcopy[i].first << " -> " << mapcopy[i].second << endl;
}
cout << "\n 10 most used words in output of neural network" << endl;
vector<pair<string, int> > mapcopy2=sortMapByKeys(m2);
for(int i = mapcopy2.size()-10; i < mapcopy2.size(); i++)
{
cout << mapcopy2[i].first << " -> " << mapcopy2[i].second << endl;
}
}
cout << "final result " << (((float)finalResult/m2.size())*100) << " %" << endl;
cudaEventRecord(stop, 0); // timer stop
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time, start, stop);
printf ("Time for the kernel: %f ms\n", time);
return 0;
} |
10,617 | #include "includes.h"
__global__ void Inc1(float *Ad, float *Bd) {
int tx = threadIdx.x + blockIdx.x * blockDim.x;
if (tx < 1) {
for (int i = 0; i < ITER; ++i) {
Ad[tx] = Ad[tx] + 1.0f;
for (int j = 0; j < 256; ++j) {
Bd[tx] = Ad[tx];
}
}
}
} |
10,618 | #include "includes.h"
__global__ void ExponentialFunctionKernel(float exponent, float* input, float* output, int size)
{
int id = blockDim.x * blockIdx.y * gridDim.x
+ blockDim.x * blockIdx.x
+ threadIdx.x;
if (id < size)
{
output[id] = pow(input[id], exponent);
}
} |
10,619 | //pass
//--blockDim=64 --gridDim=1 --equality-abstraction --no-inline
#include "cuda.h"
#include <stdio.h>
#include <assert.h>
#define N 2
__global__ void foo(int* p) {
__shared__ int A[10];
A[0] = 1;
p[0] = A[0];
}
|
10,620 | /****************************************************************************************************
* Tyler Griffith *
* December 1st, 2018 *
* Project 8: Sorting Algorithms on GPU *
* CSC-4310-01 PROF: R. Shore *
* Desc: Use the GPU to merge sort arrays and compare CPU and GPU timings *
* To Compile: *
* nvcc sort.cu -o sort *
* To Run: *
* ./sort <array file> *
*****************************************************************************************************/
#include <iostream>
#include <cstdio>
#include <fstream> //for I/O
#include <time.h> //for timing
using namespace std;
//function declarations
int* readArray(char* fileName);
int getSize(char* fileName);
double cuda_merge_sort(int* array, int length, dim3 dimThread, dim3 dimBlock);
void merge(int* array, int left, int mid, int right);
void mergeSort(int* array, int left, int right);
//gpu function declarations
__device__ void cudaMerge(int* a, int* b, int beg, int mid, int end);
__global__ void cudaMergeSort(int* a, int* b, int width, int length, int pieces, dim3* thread, dim3* block);
__device__ unsigned int getThreadIdx(dim3* thread, dim3* block);
//global find min function
#define min(a, b) (a < b ? a : b)
//driver
int main(int argc, char* argv[]){
//variable declaration
int size;
dim3 dimThread, dimBlock; //threads per block and blocks per grid;
//set initial values for testing
dimThread.x = 32;
dimThread.y = 1;
dimThread.z = 1;
dimBlock.x = 8;
dimBlock.y = 1;
dimBlock.z = 1;
//make sure correct syntax is used
if(argc != 2){
cout << "Error! You do not have an element to your command!" << endl;
cout << "To sort an array please use the following syntax:" << endl;
cout << "./sort <array file>" << endl;
return -1;
}
//read in size of the array
size = getSize(argv[1]);
int* gpuArray = new int[size];
//read in array
gpuArray = readArray(argv[1]);
//created different array so both could be sorted
int* cpuArray = new int[size];
cpuArray = gpuArray;
//GPU timing
double gpuDuration = cuda_merge_sort(gpuArray, size, dimThread, dimBlock);
//output sorted GPU array to separate file
ofstream gpuSortedArray("gpuSortedArray");
for(int i=0; i<size; i++){
gpuSortedArray << gpuArray[i] << " ";
}
gpuSortedArray.close();
//CPU timing
clock_t cpuStart = clock();
//call cpu merge sort function
mergeSort(cpuArray, 0, size-1);
//CPU timing
clock_t cpuEnd = clock();
double cpuDuration = (double)(cpuEnd-cpuStart)/CLOCKS_PER_SEC;
//print timing
cout << "gpu sort completed in " << gpuDuration*1000 << " milliseconds!" << endl;
cout << "cpu sort completed in " << cpuDuration*1000 << " milliseconds!" << endl;
return 0;
}
/****************************************************
* Function: getSize - fetch and return the size *
* of the array from I/O *
* precondition: filename is fetched from argv[1] *
* in the main *
* postcondition: the size of the array is *
* returned to main *
****************************************************/
int getSize(char* fileName){
//variable delcaration
int size;
//initialize and open file
ifstream inFile(fileName);
if(inFile.is_open()){
//read in size
inFile >> size;
}
//close file
inFile.close();
return size;
}
/****************************************************
* Function: readArray - fetch and return the array *
* from I/O *
* precondition: filename is fetched from argv[1] *
* in the main *
* postcondition: the array is returned to main *
****************************************************/
int* readArray(char* fileName){
//variable declaration
int size;
ifstream inFile(fileName);
//read in array size
inFile >> size;
//allocate memory
int *array = new int[size];
//read in array
for(int i=0; i<size; i++)
inFile >> array[i];
//close file and return array
inFile.close();
return array;
}
/****************************************************
* Function: mergeSort - merge sort the given array *
* on the CPU *
* precondition: - array is established in the main *
* through readArray() *
* - right is established through *
* getsize() *
* postcondition: the array is sorted *
****************************************************/
void mergeSort(int* array, int left, int right){
//left is left index right is right index
if(left < right){
//same as (left+right)/2, but avoids overflow for large nums
int mid = left+(right-left)/2;
//sort first and seconds halves
mergeSort(array, left, mid);
mergeSort(array, mid+1, right);
//merge the pieces together
merge(array, left, mid, right);
}
}
/****************************************************
* Function: merge - merges two subarrays *
* precondition: - array is established in the main *
* through readArray() *
* - right is established through *
* getsize() *
* - mid is established in mergeSort()*
* postcondition: the two subarrays are merged *
****************************************************/
void merge(int* array, int left, int mid, int right){
//variable declaration
int i,j,k;
int n1 = mid-left+1;
int n2 = right-mid;
//temp arrays
int L[n1], R[n2];
//copy data to temp arrays
for(i=0; i<n1; i++)
L[i] = array[left+i];
for(j=0; j<n2; j++)
R[j] = array[mid+1+j];
//merge temp arrays back
i=0;//index for first array
j=0;//index for second array
k=left;//index for merged array
while(i<n1 && j<n2){
if(L[i]<=R[j]){
array[k] = L[i];
i++;
}
else{
array[k] = R[j];
j++;
}
k++;
}
//copy remaining elements of L
while(i<n1){
array[k] = L[i];
i++;
k++;
}
//copy remaining elements of R
while(j<n2){
array[k] = R[j];
j++;
k++;
}
}
/***********************************************************
* Function: cuda_merge_sort - allocates memory on GPU for *
* array then copies the array *
* to the GPU and calls the *
* GPU merge sort function *
* precondition: - array is established in the main *
* through readArray() *
* - length is established in the main *
* through getSize() *
* - dimThread and dimBlock are established *
* in the main *
* postcondition: the array is sorted on the GPU and the *
* time taken to sort the array on the *
* GPU is returned back to main *
***********************************************************/
double cuda_merge_sort(int* array, int length, dim3 dimThread, dim3 dimBlock){
//variable declaration
int* dArray;
int* dSwap;
dim3* dThread;
dim3* dBlock;
//allocate memory for array on gpu
cudaMalloc((void**)&dArray, length*sizeof(int));
cudaMalloc((void**)&dSwap, length*sizeof(int));
//copy array to gpu
cudaMemcpy(dArray, array, length*sizeof(int), cudaMemcpyHostToDevice);
//allocate memory for the thread and block info on gpu
cudaMalloc((void**)&dThread, sizeof(dim3));
cudaMalloc((void**)&dBlock, sizeof(dim3));
//copy thread and block info to gpu
cudaMemcpy(dThread, &dimThread, sizeof(dim3), cudaMemcpyHostToDevice);
cudaMemcpy(dBlock, &dimBlock, sizeof(dim3), cudaMemcpyHostToDevice);
//for copying
int* x = dArray;
int* y = dSwap;
//get thread count
int threadCount = dimThread.x * dimThread.y * dimThread.z * dimBlock.x * dimBlock.y * dimBlock.z;
//timing
clock_t start = clock();
//cut the array into different pieces and give those pieces to each thread
for(int width = 2; width < (length << 1); width <<= 1){
//variable delcaration
int pieces = length / ((threadCount) * width) + 1;
//call the sort
cudaMergeSort<<<dimBlock, dimThread>>>(x, y, width, length, pieces, dThread, dBlock);
//can swap the input/output arrays instead of copying
x = x == dArray ? dSwap : dArray;
y = y == dArray ? dSwap : dArray;
}
//timing
clock_t end = clock();
double duration = (double)(end-start)/CLOCKS_PER_SEC;
//retrieve array from the GPU
cudaMemcpy(array, dArray, length*sizeof(int), cudaMemcpyDeviceToHost);
//free memory
cudaFree(dArray);
cudaFree(dSwap);
return duration;
}
/*************************************************************
* Global Function: cudaMergeSort - merge sorts the given *
* array on the GPU *
* precondition: - a is established in cuda_merge_sort() *
* - b is established in cuda_merge_sort() *
* - width is established in cuda_merge_sort() *
* - length is established in getSize() *
* - pieces is established in cuda_merge_sort()*
* - thread and block are both established *
* within the main *
* postcondition: the array is sorted on the GPU *
*************************************************************/
__global__ void cudaMergeSort(int* a, int* b, int width, int length, int pieces, dim3* thread, dim3* block){
//set the thread index
unsigned int idx = getThreadIdx(thread, block);
//initialize positioning
int beg = width * idx * pieces;
int mid, end;
//for each of the pieces
for(int piece=0; piece<pieces; piece++){
if (beg >= length)
break;
//initialize the middle of the array
mid = min(beg+(width>>1), length);
//initialize the end of the array
end = min(beg+width, length);
//merge
cudaMerge(a, b, beg, mid, end);
//update the beginning of the array
beg += width;
}
}
/*************************************************************
* Device Function: cudaMerge - merges two subarrays *
* precondition: - a is established in cuda_merge_sort() *
* - b is established in cuda_merge_sort() *
* - beg, mid and end are established within *
* the cudaMergeSort global function *
* postcondition: the two subarrays are merged together *
*************************************************************/
__device__ void cudaMerge(int* a, int* b, int beg, int mid, int end){
//initialize positioning
int i = beg;
int j = mid;
//loop through the chunk
for(int k=beg; k<end; k++){
//swap
if(i < mid && (j>=end || a[i]<a[j])){
b[k] = a[i];
i++;
} else {
b[k] = a[j];
j++;
}
}
}
/*************************************************************
* Device Function: getThreadIdx - fetches the current *
* thread index *
* precondition: - thread and block are established within *
* the main *
* postcondition: the current thread index is returned *
*************************************************************/
__device__ unsigned int getThreadIdx(dim3* thread, dim3* block) {
int x;
//calculates and returns the index of the current thread
return threadIdx.x +
threadIdx.y * (x = thread->x) +
threadIdx.z * (x *= thread->y) +
blockIdx.x * (x *= thread->z) +
blockIdx.y * (x *= block->z) +
blockIdx.z * (x *= block->y);
} |
10,621 | #include <iostream>
#include <cuda.h>
using namespace std;
__global__ void AddIntsCUDA(int *a, int *b)
{
a[0] += b[0];
}
int main()
{
int a=5, b=9;
int *d_a, *d_b;
cudaMalloc(&d_a, sizeof(int));
cudaMalloc(&d_b, sizeof(int));
cudaMemcpy(d_a, &a, sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_b, &b, sizeof(int), cudaMemcpyHostToDevice);
AddIntsCUDA<<<1,1>>>(d_a,d_b);
cudaMemcpy(&a, d_a, sizeof(int), cudaMemcpyDeviceToHost);
cout<<"The answer is"<<a<<endl;
cudaFree(d_a);
cudaFree(d_b);
return 0;
}
|
10,622 | #include <cstdio>
#define N 50
#define T 128
#define B 2
__global__ void div(int* in, int* out)
{
int tid = threadIdx.x + blockDim.x*blockIdx.x;
if(tid < N)
{
if(tid % 2 == 0)
out[tid] = in[tid] - 1;
else
out[tid] = in[tid] + 1;
}
}
int main()
{
int* in = (int*) malloc(N*sizeof(int));
for(int i = 0; i < N; i++)
in[i] = i;
int* din, *dout;
cudaMalloc((void**)&din, N*sizeof(int));
cudaMalloc((void**)&dout,N*sizeof(int));
cudaMemcpy(din, in, N*sizeof(int), cudaMemcpyHostToDevice);
div<<<B,T>>>(din,dout);
cudaMemcpy(in, dout, N*sizeof(int), cudaMemcpyDeviceToHost);
for(int i = 0; i < N; i++)
printf("%d ", in[i]);
printf("\n");
free(in); cudaFree(din); cudaFree(dout);
} |
10,623 | __global__ void per_row_kernel(int *in, int N) //1D grid, 2D block
{
int cno;
int id = threadIdx.x*blockDim.y + threadIdx.y + blockIdx.x*(blockDim.x * blockDim.y);
int rno = id;
if(rno<N)
{
for(cno=0;cno<N;++cno) //i is column number
{
if(rno>cno)
{
in[cno*N + rno] = in[rno*N + cno];
in[rno*N + cno] = 0;
}
}
}
}
__global__ void per_element_kernel(int *in, int N) //3D grid, 1D block
{
int id = blockIdx.z*blockDim.x*gridDim.x*gridDim.y + blockIdx.y*blockDim.x*gridDim.x + blockIdx.x*blockDim.x + threadIdx.x;
int cno,rno;
cno = id%N;
rno = id/N;
if(rno<N)
{
if(rno > cno)
{
in[cno*N + rno] = in[rno*N + cno];
in[rno*N + cno] = 0;
}
}
}
__global__ void per_element_kernel_2D(int *in, int N) //2D grid, 2D block
{
int id = blockIdx.y*blockDim.x*blockDim.y*gridDim.x + blockIdx.x*blockDim.x*blockDim.y + threadIdx.y*blockDim.x + threadIdx.x;
int cno,rno;
cno = id%N;
rno = id/N;
if(rno<N)
{
if(rno > cno)
{
in[cno*N + rno] = in[rno*N + cno];
in[rno*N + cno] = 0;
}
}
}
|
10,624 | #include <stdio.h>
#include <math.h>
#include <time.h>
#include <unistd.h>
#include <cuda_runtime_api.h>
typedef struct point_t {
double x;
double y;
} point_t;
int n_data = 1000;
__device__ int d_n_data = 1000;
point_t data[] = {
{77.00,124.15},{85.46,132.35},{75.96,131.75},{69.01,119.60},
{80.88,126.87},{79.22,128.71},{86.09,136.48},{69.32,100.38},
{89.87,141.00},{86.51,133.53},{62.98,102.93},{99.52,155.33},
{11.40,57.72},{10.84,35.73},{14.51,30.02},{37.76,62.82},
{98.89,163.52},{95.00,143.14},{89.23,142.64},{96.46,135.73},
{75.56,130.86},{ 6.44,46.63},{20.75,28.09},{28.52,64.59},
{ 3.83,11.08},{69.66,118.21},{47.49,75.63},{ 1.95,11.92},
{25.20,57.23},{20.13,62.76},{ 9.82,44.90},{70.47,110.59},
{76.09,133.51},{74.91,136.24},{48.56,90.14},{43.33,87.28},
{97.37,171.56},{63.14,99.55},{11.76,25.55},{71.69,119.20},
{52.34,98.05},{18.85,39.09},{13.53,58.23},{90.04,156.33},
{67.10,98.40},{25.10,54.30},{37.95,74.55},{82.60,139.51},
{26.86,59.53},{ 9.07,58.06},{46.20,83.88},{78.78,155.64},
{22.43,53.76},{14.49,52.95},{66.20,116.17},{58.35,109.06},
{31.60,70.11},{95.28,162.53},{69.87,121.46},{19.45,51.42},
{20.49,52.36},{33.39,81.69},{83.06,139.44},{24.80,51.42},
{82.98,126.48},{47.24,70.45},{39.13,73.20},{80.93,132.39},
{28.49,56.98},{63.37,100.61},{25.65,59.28},{77.83,136.18},
{10.89,34.23},{28.35,72.52},{45.42,85.08},{25.29,61.28},
{35.04,84.54},{33.54,51.32},{96.22,146.16},{36.83,63.13},
{59.29,101.08},{64.96,123.38},{17.41,46.40},{61.67,79.52},
{10.17,51.90},{15.62,47.91},{88.34,150.97},{41.24,68.58},
{88.66,158.67},{95.70,155.38},{37.74,87.48},{10.23,43.84},
{88.59,134.34},{81.95,126.65},{72.60,139.65},{59.85,95.81},
{94.72,154.29},{10.40,26.80},{23.92,61.28},{16.62,39.58},
{11.05,43.76},{52.61,96.88},{78.00,127.79},{80.62,126.14},
{55.32,76.03},{82.83,130.39},{23.78,74.00},{76.57,112.14},
{32.76,67.37},{26.05,62.92},{42.08,89.82},{65.94,112.86},
{54.95,93.33},{27.57,44.14},{44.88,82.49},{17.69,46.62},
{61.04,118.00},{76.77,119.21},{73.64,122.06},{43.11,65.85},
{24.35,59.05},{79.02,127.41},{81.83,139.41},{13.05,28.21},
{50.83,104.29},{96.04,145.10},{83.52,145.43},{64.88,109.66},
{51.35,105.12},{84.77,154.89},{ 3.31,13.35},{ 5.04,40.30},
{73.26,115.95},{43.98,73.13},{12.32,36.73},{77.89,136.05},
{26.78,57.95},{94.06,162.38},{35.61,70.37},{ 5.79,22.66},
{22.64,58.81},{80.70,133.69},{88.71,173.01},{94.47,150.32},
{33.56,93.30},{32.64,73.74},{96.94,181.35},{25.87,57.58},
{59.94,113.11},{ 3.43,24.40},{15.24,46.83},{37.12,74.14},
{87.94,135.15},{29.14,67.87},{63.59,117.34},{32.08,47.76},
{30.76,74.67},{72.49,118.65},{45.82,80.40},{28.27,54.70},
{26.06,36.60},{16.83,30.54},{67.90,126.63},{75.96,113.08},
{ 0.81,18.53},{51.80,94.42},{88.87,139.66},{54.10,88.79},
{ 0.64, 8.59},{ 2.41,27.91},{46.64,100.62},{17.31,42.56},
{48.36,88.15},{28.90,78.92},{60.80,99.95},{39.95,82.50},
{55.00,102.53},{36.09,79.07},{62.95,112.38},{31.36,77.81},
{11.56,35.49},{ 0.19,23.36},{12.11,34.15},{50.17,85.23},
{92.74,154.70},{32.11,71.47},{12.92,33.14},{67.81,110.12},
{31.96,53.52},{69.78,128.17},{10.30,42.16},{25.65,41.86},
{32.48,70.22},{89.41,140.21},{69.58,124.91},{68.80,107.82},
{29.92,54.32},{26.67,76.45},{85.89,140.10},{44.42,104.09},
{57.57,93.33},{37.34,78.14},{53.16,99.49},{21.39,42.91},
{84.26,131.84},{10.97,44.89},{ 2.46,38.17},{29.79,50.76},
{ 1.43,30.86},{77.76,126.91},{ 1.16,31.25},{60.21,91.02},
{38.05,65.07},{77.53,123.05},{54.77,105.84},{79.43,132.05},
{90.34,143.31},{14.54,28.84},{82.30,139.38},{93.34,144.07},
{98.87,169.79},{38.22,78.59},{66.57,130.68},{64.78,113.78},
{82.16,123.31},{69.69,119.73},{16.78,40.58},{60.69,89.44},
{11.99,34.84},{25.94,55.05},{62.98,123.02},{64.48,125.00},
{90.08,145.33},{39.15,82.46},{72.53,138.85},{91.56,149.85},
{58.57,93.09},{34.51,68.12},{81.11,140.78},{78.78,137.19},
{79.10,155.27},{20.18,64.68},{64.81,107.78},{23.98,31.78},
{ 8.17,45.13},{57.58,95.92},{18.87,46.19},{22.28,30.21},
{50.19,87.47},{14.67,26.14},{61.78,107.75},{60.75,112.30},
{ 0.22,25.68},{49.62,92.06},{88.36,146.39},{36.43,87.05},
{13.50,51.28},{85.39,138.35},{80.38,121.58},{87.33,144.62},
{77.31,137.20},{57.02,78.34},{28.92,74.13},{62.27,96.97},
{61.60,106.52},{92.31,132.40},{98.90,153.22},{56.26,97.22},
{24.78,56.25},{63.92,108.28},{ 5.60,30.31},{41.82,91.61},
{27.07,56.08},{94.90,151.00},{60.91,112.39},{43.33,77.02},
{27.98,68.92},{43.35,75.21},{28.77,72.13},{42.96,57.34},
{82.17,159.82},{73.79,112.69},{79.00,132.55},{67.15,125.81},
{22.40,41.87},{69.46,111.91},{98.52,145.35},{ 5.78,34.61},
{78.93,131.95},{53.87,105.11},{96.94,143.11},{13.83,27.72},
{89.91,130.31},{30.53,56.27},{12.01,22.04},{42.46,80.84},
{73.10,114.58},{32.16,67.12},{64.16,99.06},{56.84,124.28},
{76.46,125.42},{14.44,49.10},{11.31,38.71},{22.62,40.29},
{61.82,119.33},{96.45,155.17},{77.87,129.40},{89.76,141.48},
{20.23,38.74},{66.86,112.71},{43.87,100.36},{ 7.49,20.38},
{56.03,106.70},{57.14,109.60},{30.24,71.35},{ 0.33,36.21},
{82.05,142.47},{24.67,44.66},{40.40,80.29},{28.49,68.15},
{26.54,51.10},{86.72,147.40},{92.68,165.27},{ 1.22,26.92},
{84.19,141.66},{31.82,45.42},{25.19,51.79},{50.01,95.20},
{11.99,44.39},{ 7.15,18.03},{39.93,77.42},{53.61,98.02},
{45.29,79.64},{13.17,35.88},{82.72,129.61},{87.86,141.13},
{63.60,98.04},{35.03,74.68},{63.25,89.67},{10.03,21.02},
{71.47,128.96},{72.56,124.27},{75.25,135.63},{64.76,112.69},
{ 0.22,25.12},{93.74,149.56},{36.19,71.28},{78.80,142.35},
{57.65,129.85},{53.55,94.62},{45.45,94.46},{69.54,101.07},
{91.49,155.19},{63.05,102.95},{68.74,123.69},{ 6.86,41.70},
{79.41,116.85},{ 5.86,29.25},{91.95,161.65},{61.98,120.50},
{28.42,53.84},{13.63,38.33},{48.40,87.38},{26.94,50.47},
{51.88,98.42},{36.77,72.93},{81.06,116.87},{ 3.49,32.57},
{17.05,41.41},{61.38,107.48},{73.97,132.12},{61.60,114.05},
{23.76,52.57},{62.61,95.81},{51.35,93.98},{12.55,42.17},
{41.23,90.08},{84.25,149.87},{38.77,76.77},{91.70,152.36},
{18.79,36.94},{95.71,167.56},{41.92,82.80},{93.72,141.10},
{35.17,77.92},{67.68,137.29},{91.49,133.22},{67.84,127.33},
{90.29,152.12},{64.17,112.11},{65.96,120.66},{19.67,53.62},
{36.70,70.05},{58.20,111.46},{ 3.50,32.37},{75.94,127.16},
{93.88,133.16},{97.73,165.13},{22.92,65.73},{88.79,144.13},
{95.20,142.71},{70.14,110.85},{69.90,103.27},{13.61,37.61},
{95.06,156.51},{34.43,88.15},{37.57,74.54},{76.29,122.57},
{97.94,153.53},{77.62,135.61},{45.93,66.91},{86.27,135.23},
{42.32,80.93},{ 2.70,18.74},{54.02,90.65},{68.62,109.37},
{40.26,64.98},{55.96,96.39},{ 9.69,33.89},{ 5.02,33.77},
{70.44,123.05},{75.34,113.53},{20.30,16.95},{25.95,56.57},
{23.66,55.81},{68.56,110.15},{87.02,145.33},{72.90,113.29},
{26.88,50.51},{22.88,47.44},{62.58,107.72},{15.53,37.90},
{56.90,99.06},{59.82,99.51},{67.41,110.97},{ 1.43,13.31},
{44.87,76.20},{38.73,64.18},{60.03,111.94},{23.27,58.47},
{38.08,83.91},{67.55,116.67},{71.49,128.14},{64.75,118.79},
{49.95,102.59},{61.13,105.03},{38.06,67.16},{ 4.25,-4.27},
{30.25,56.85},{76.24,117.62},{65.60,107.81},{71.82,112.39},
{87.87,142.68},{79.17,126.05},{59.28,122.86},{24.86,49.83},
{40.12,94.86},{84.17,142.02},{72.24,120.40},{ 9.65,28.18},
{80.33,137.96},{ 1.40,16.07},{54.58,86.40},{37.55,87.13},
{ 7.36,14.02},{90.68,130.07},{ 5.14,44.04},{ 2.47,15.96},
{50.78,86.32},{ 1.68,16.30},{ 1.88,22.30},{29.51,63.89},
{88.32,147.48},{69.32,131.47},{55.54,106.40},{39.10,90.59},
{87.72,162.58},{11.05,43.11},{51.12,98.75},{65.82,113.02},
{96.71,148.61},{28.85,69.93},{16.58,39.84},{33.89,79.80},
{93.38,155.68},{15.21,26.68},{25.32,55.29},{ 6.91,41.98},
{50.09,98.63},{ 8.88,37.96},{87.59,147.51},{35.11,57.85},
{ 7.28,32.76},{15.85,27.68},{11.47,49.07},{27.17,76.32},
{70.75,125.91},{78.57,142.54},{93.52,128.90},{15.20,20.78},
{ 4.09,24.31},{46.85,86.10},{55.57,92.75},{32.50,82.22},
{54.86,119.50},{28.45,58.15},{30.85,74.73},{53.91,91.64},
{11.00,28.12},{ 9.91,41.76},{11.20,22.85},{36.56,62.03},
{72.27,109.14},{ 2.76,26.68},{41.23,72.59},{11.05,44.07},
{78.64,136.22},{ 3.93,25.46},{71.10,125.52},{74.66,132.51},
{70.03,114.48},{78.80,127.56},{52.09,97.69},{13.50,52.37},
{39.62,72.88},{60.86,105.51},{33.85,63.89},{ 4.35, 3.91},
{11.05,40.75},{10.17,14.95},{96.63,151.17},{62.76,97.55},
{ 3.01,24.52},{77.90,126.53},{76.26,122.92},{61.17,120.58},
{69.84,111.71},{51.98,80.17},{54.44,100.61},{49.37,83.42},
{ 9.75,14.76},{18.36,31.82},{99.18,158.29},{88.35,150.58},
{28.46,63.93},{96.34,172.37},{37.24,95.83},{23.21,39.61},
{27.54,48.96},{83.85,125.52},{75.37,135.83},{92.54,162.11},
{13.79,43.93},{71.22,124.91},{24.71,53.19},{ 9.88,23.42},
{66.38,107.40},{87.48,125.86},{95.33,153.59},{41.70,79.52},
{14.36,36.81},{ 4.83,32.41},{71.08,127.77},{54.87,87.34},
{21.40,49.87},{99.24,170.66},{83.29,136.88},{65.85,116.93},
{33.28,69.45},{57.15,104.19},{59.24,104.05},{59.21,100.87},
{51.23,110.16},{52.78,93.74},{18.38,58.12},{51.33,90.07},
{21.59,63.04},{49.61,93.77},{54.34,104.12},{14.17,45.91},
{92.12,151.69},{25.27,75.63},{21.14,57.92},{27.45,65.53},
{32.14,80.24},{76.35,128.49},{72.20,112.63},{78.45,133.04},
{73.31,96.68},{63.72,119.73},{66.15,109.48},{10.40,36.25},
{32.49,60.39},{97.94,156.21},{38.00,58.30},{66.74,122.81},
{ 6.80,53.92},{78.34,116.49},{59.68,100.63},{63.89,107.91},
{89.60,148.99},{83.50,133.50},{26.03,55.86},{55.19,88.15},
{76.40,138.12},{43.75,94.01},{93.76,158.02},{37.07,74.05},
{31.42,71.76},{ 8.63,35.30},{32.94,66.81},{24.45,51.15},
{29.64,54.82},{60.50,99.00},{91.82,146.99},{89.82,134.06},
{21.44,61.61},{72.04,100.09},{74.03,101.22},{ 0.24,20.56},
{58.22,88.94},{56.40,92.54},{15.22,49.45},{73.73,113.76},
{84.70,130.33},{13.03,38.78},{27.29,52.05},{41.55,76.99},
{45.38,86.78},{32.51,75.80},{97.99,156.06},{22.34,45.91},
{96.98,161.69},{68.83,103.89},{45.55,77.17},{66.56,109.25},
{75.43,121.82},{89.42,141.31},{89.28,143.20},{96.26,154.37},
{48.15,84.71},{32.64,77.01},{48.67,77.19},{68.40,127.38},
{70.63,124.64},{37.18,63.21},{87.28,130.62},{27.12,51.43},
{76.23,130.76},{ 5.56,26.39},{82.60,137.08},{24.73,60.51},
{45.02,97.66},{97.61,171.32},{25.99,62.94},{ 4.95, 8.08},
{ 9.70,18.56},{85.94,166.71},{75.35,113.14},{76.97,109.62},
{ 6.60,26.15},{ 4.55,43.39},{99.93,158.12},{78.47,141.95},
{67.90,134.36},{66.44,125.08},{99.26,163.53},{32.53,61.39},
{33.67,61.97},{ 4.17,23.64},{65.16,108.15},{32.77,52.10},
{78.09,150.40},{36.01,75.28},{ 0.81, 4.16},{88.76,145.03},
{20.47,50.03},{57.41,107.28},{65.21,120.40},{23.37,47.26},
{25.31,82.01},{47.12,96.17},{46.61,77.12},{65.49,111.83},
{82.91,153.85},{10.78,27.99},{92.00,147.11},{29.84,43.84},
{39.84,65.54},{83.62,139.12},{94.56,156.00},{67.82,113.99},
{59.11,100.60},{71.26,118.63},{70.37,117.26},{67.50,115.18},
{72.99,121.50},{47.65,85.41},{38.98,78.62},{13.22,46.67},
{93.47,143.62},{32.59,70.97},{69.72,125.63},{69.09,129.53},
{64.11,115.82},{33.78,69.17},{79.88,143.69},{87.92,148.83},
{94.13,160.66},{98.41,187.42},{23.38,54.92},{54.57,104.77},
{ 1.58,21.23},{39.29,84.65},{23.63,55.82},{ 8.41,24.55},
{47.97,78.95},{85.48,142.33},{36.16,66.95},{66.97,110.84},
{94.66,159.40},{73.80,131.33},{17.02,42.98},{28.11,59.40},
{58.20,108.71},{45.80,95.23},{73.20,127.45},{ 8.89,29.35},
{94.18,153.50},{47.86,103.00},{11.95,50.32},{81.24,132.60},
{ 1.70,21.22},{20.05,33.57},{70.37,110.06},{13.12,35.66},
{80.72,136.02},{52.52,91.99},{27.16,51.53},{20.86,50.31},
{37.31,71.62},{ 7.38,46.89},{85.87,130.07},{46.37,83.18},
{59.46,87.66},{61.20,119.66},{11.12,42.42},{48.45,92.93},
{68.82,133.65},{82.72,133.12},{89.46,143.11},{55.39,105.11},
{63.81,120.08},{17.87,66.67},{71.67,119.86},{72.68,129.01},
{39.83,63.76},{48.45,94.58},{20.90,26.60},{73.57,124.34},
{70.99,130.75},{47.82,94.15},{66.22,106.16},{71.83,114.80},
{86.68,146.08},{81.92,129.38},{ 9.91,20.32},{71.15,117.45},
{21.76,46.44},{ 6.35,38.20},{79.10,115.33},{45.53,91.12},
{75.69,116.74},{92.95,158.05},{15.07,33.21},{57.47,99.59},
{42.04,84.29},{65.51,120.58},{16.33,54.63},{66.90,103.70},
{18.73,44.68},{62.46,113.99},{91.98,159.39},{33.94,69.36},
{51.63,89.15},{18.48,52.65},{25.96,58.78},{18.80,54.29},
{68.70,105.86},{50.07,100.65},{29.56,61.88},{15.90,46.01},
{28.48,57.33},{51.92,83.74},{12.46,50.28},{36.76,83.38},
{31.71,62.90},{67.91,106.69},{ 0.94,15.60},{52.41,91.62},
{71.36,144.21},{34.00,85.15},{75.71,136.71},{43.26,78.75},
{29.67,54.08},{87.34,141.18},{98.61,153.16},{77.10,121.27},
{16.02,50.51},{42.97,77.13},{16.77,37.92},{35.95,59.52},
{84.29,129.61},{86.91,130.96},{21.97,41.62},{13.69,45.94},
{28.40,63.47},{62.90,105.14},{44.20,79.43},{73.01,116.43},
{33.80,97.11},{ 3.31,23.58},{10.78,33.49},{17.18,57.84},
{15.64,32.82},{55.54,102.36},{65.58,116.80},{93.38,152.59},
{33.75,58.62},{41.84,76.76},{ 5.50,16.95},{ 5.12,24.69},
{31.56,40.15},{ 5.46,38.26},{66.23,117.07},{99.18,154.68},
{60.56,100.25},{91.33,159.90},{23.54,53.12},{24.24,42.69},
{86.83,154.59},{37.59,83.95},{12.62,46.88},{25.13,55.77},
{86.93,159.89},{91.77,137.11},{34.92,58.21},{38.19,74.68},
{78.73,138.07},{26.11,55.46},{50.28,93.34},{ 7.48,35.37},
{95.25,166.74},{95.31,156.11},{81.78,123.88},{59.89,94.77},
{76.74,126.92},{73.21,128.29},{24.76,67.17},{26.89,58.45},
{50.73,82.47},{46.11,92.88},{65.75,94.86},{69.00,115.18},
{84.17,144.39},{80.68,152.37},{11.52,37.91},{28.07,66.32},
{71.65,123.87},{46.99,90.46},{36.49,71.73},{93.39,152.77},
{30.19,63.03},{ 3.33,40.09},{93.28,144.26},{ 3.74,29.82},
{26.90,68.16},{ 4.69,32.32},{42.80,78.31},{83.53,118.97},
{ 4.19, 9.57},{88.81,135.97},{23.99,61.35},{64.94,97.50},
{69.88,123.07},{82.28,137.11},{30.95,66.15},{12.04,60.33},
{24.00,65.66},{46.58,81.98},{ 5.52,24.59},{ 6.88,29.19},
{92.26,141.48},{ 7.06,26.28},{48.04,96.30},{43.26,73.81},
{61.23,109.35},{11.17,46.96},{81.51,139.92},{ 7.65,45.47},
{73.58,128.11},{64.96,125.03},{ 5.62,33.84},{41.05,74.11},
{44.23,88.98},{85.48,146.28},{83.46,134.43},{89.12,138.21},
{33.55,81.88},{73.97,120.73},{95.79,156.05},{58.05,96.43},
{ 3.67,15.45},{39.97,80.57},{ 8.31,23.51},{30.66,66.85},
{77.70,129.49},{44.34,96.76},{86.73,139.19},{ 9.66,23.60},
{11.05,40.61},{ 3.20,35.91},{47.42,95.63},{38.48,72.55},
{30.76,61.28},{74.13,136.71},{64.87,98.16},{75.18,130.55},
{72.62,121.31},{69.67,128.37},{60.78,99.35},{39.70,82.06},
{44.41,82.01},{38.25,58.83},{ 9.28,26.61},{79.05,132.85},
{73.00,124.54},{38.98,87.59},{70.53,104.93},{52.27,89.06},
{ 3.64,20.61},{37.05,68.66},{47.95,87.27},{72.30,135.30},
{40.03,91.63},{72.92,118.47},{91.44,160.05},{76.98,133.69},
{55.59,95.99},{12.48,34.88},{74.03,108.83},{36.68,75.31},
{31.54,55.96},{17.32,40.93},{16.98,40.24},{84.90,147.20},
{17.08,42.49},{48.66,93.15},{97.92,163.63},{64.20,127.62},
{98.51,151.10},{83.77,141.52},{19.13,23.21},{17.88,22.26},
{53.28,99.44},{95.94,149.23},{10.91,48.95},{76.57,115.11},
{ 7.16,33.21},{48.19,90.86},{19.53,46.70},{44.05,90.13},
{17.98,34.66},{26.63,71.10},{23.96,62.29},{31.25,58.85},
{44.43,93.77},{43.27,96.28},{86.56,153.15},{14.75,60.70},
{33.82,64.50},{45.13,88.49},{68.18,100.80},{68.61,97.94},
{70.53,118.60},{14.26,36.56},{23.73,56.96},{41.89,79.85},
{68.39,103.08},{52.07,103.07},{81.25,145.22},{87.04,151.34},
{34.46,82.36},{ 0.71,37.38},{91.37,148.55},{99.54,158.72},
{28.58,74.39},{95.80,168.80},{58.82,99.53},{74.06,127.98}
};
int time_difference(struct timespec *start, struct timespec *finish, long long int *difference)
{
long long int ds = finish->tv_sec - start->tv_sec;
long long int dn = finish->tv_nsec - start->tv_nsec;
if(dn < 0 )
{
ds--;
dn += 1000000000;
}
*difference = ds * 1000000000 + dn;
return !(*difference > 0);
}
double residual_error(double x, double y, double m, double c) {
double e = (m * x) + c - y;
return e * e;
}
double rms_error(double m, double c) {
int i;
double mean;
double error_sum = 0;
for(i=0; i<n_data; i++) {
error_sum += residual_error(data[i].x, data[i].y, m, c);
}
mean = error_sum / n_data;
return sqrt(mean);
}
__device__ double d_residual_error(double x, double y, double m, double c) {
double e = (m * x) + c - y;
return e * e;
}
__global__ void d_rms_error(double *m, double *c, double *error_sum_arr, point_t *d_data) {
int i = threadIdx.x + blockIdx.x * blockDim.x;
error_sum_arr[i] = d_residual_error(d_data[i].x, d_data[i].y, *m, *c);
}
int main() {
int i;
double bm = 1.3;
double bc = 10;
double be;
double dm[8];
double dc[8];
double e[8];
double step = 0.01;
double best_error = 999999999;
int best_error_i;
int minimum_found = 0;
double om[] = {0,1,1, 1, 0,-1,-1,-1};
double oc[] = {1,1,0,-1,-1,-1, 0, 1};
struct timespec start, finish;
long long int time_elapsed;
clock_gettime(CLOCK_MONOTONIC, &start);
cudaError_t error;
double *d_dm;
double *d_dc;
double *d_error_sum_arr;
point_t *d_data;
be = rms_error(bm, bc);
error = cudaMalloc(&d_dm, (sizeof(double) * 8));
if(error){
fprintf(stderr, "cudaMalloc on d_dm returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
error = cudaMalloc(&d_dc, (sizeof(double) * 8));
if(error){
fprintf(stderr, "cudaMalloc on d_dc returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
error = cudaMalloc(&d_error_sum_arr, (sizeof(double) * 1000));
if(error){
fprintf(stderr, "cudaMalloc on d_error_sum_arr returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
error = cudaMalloc(&d_data, sizeof(data));
if(error){
fprintf(stderr, "cudaMalloc on d_data returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
while(!minimum_found) {
for(i=0;i<8;i++) {
dm[i] = bm + (om[i] * step);
dc[i] = bc + (oc[i] * step);
}
error = cudaMemcpy(d_dm, dm, (sizeof(double) * 8), cudaMemcpyHostToDevice);
if(error){
fprintf(stderr, "cudaMemcpy to d_dm returned %d %s\n", error,
cudaGetErrorString(error));
}
error = cudaMemcpy(d_dc, dc, (sizeof(double) * 8), cudaMemcpyHostToDevice);
if(error){
fprintf(stderr, "cudaMemcpy to d_dc returned %d %s\n", error,
cudaGetErrorString(error));
}
error = cudaMemcpy(d_data, data, sizeof(data), cudaMemcpyHostToDevice);
if(error){
fprintf(stderr, "cudaMemcpy to d_data returned %d %s\n", error,
cudaGetErrorString(error));
}
for(i=0;i<8;i++) {
double h_error_sum_arr[1000];
double error_sum_total;
double error_sum_mean;
d_rms_error <<<100,10>>>(&d_dm[i], &d_dc[i], d_error_sum_arr, d_data);
cudaThreadSynchronize();
error = cudaMemcpy(&h_error_sum_arr, d_error_sum_arr, (sizeof(double) * 1000), cudaMemcpyDeviceToHost);
if(error){
fprintf(stderr, "cudaMemcpy to error_sum returned %d %s\n", error,
cudaGetErrorString(error));
}
for(int j=0; j<n_data; j++) {
//Add each error sum to the error sum total.
error_sum_total += h_error_sum_arr[j];
}
error_sum_mean = error_sum_total / n_data;
e[i] = sqrt(error_sum_mean);
if(e[i] < best_error) {
best_error = e[i];
best_error_i = i;
}
//Reset the error sum total.
error_sum_total = 0;
}
printf("best m,c is %lf,%lf with error %lf in direction %d\n",
dm[best_error_i], dc[best_error_i], best_error, best_error_i);
if(best_error < be) {
be = best_error;
bm = dm[best_error_i];
bc = dc[best_error_i];
} else {
minimum_found = 1;
}
}
//Free memory for d_dm
error = cudaFree(d_dm);
if(error){
fprintf(stderr, "cudaFree on d_dm returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
//Free memory for d_dc
error = cudaFree(d_dc);
if(error){
fprintf(stderr, "cudaFree on d_dc returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
error = cudaFree(d_data);
if(error){
fprintf(stderr, "cudaFree on d_data returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
error = cudaFree(d_error_sum_arr);
if(error){
fprintf(stderr, "cudaFree on d_error_sum_arr returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
printf("minimum m,c is %lf,%lf with error %lf\n", bm, bc, be);
clock_gettime(CLOCK_MONOTONIC, &finish);
time_difference(&start, &finish, &time_elapsed);
printf("Time elapsed was %lldns or %0.9lfs\n", time_elapsed,
(time_elapsed/1.0e9));
return 0;
}
|
10,625 | // Created By: Sandeep Katragadda
// https://github.com/ksandeep4u/CUDA-examples
#pragma once
#include "cuda.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "gpuProgram.cuh"
#include<iostream>
#include<string>
using namespace std;
// CUDA kernel
__global__ void add_kernel(int n, float* x, float* y) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
int stride = gridDim.x * blockDim.x;
for (int i = idx; i < n; i += stride)
y[i] = x[i] + y[i];
}
void gpuProgram(int n) {
cout << "GPU program!" << endl;
int v = 0;
cudaRuntimeGetVersion(&v);
string version = to_string(v / 1000) + "." + to_string(v % 1000);
cout << "CUDA run time version: " << version << endl;
cudaDriverGetVersion(&v);
version = to_string(v / 1000) + "." + to_string(v % 1000);
cout << "CUDA driver version: " << version << endl;
int N = n;
// initialization
float* x;
float* y;
cudaMallocManaged(&x, N * sizeof(float));
cudaMallocManaged(&y, N * sizeof(float));
for (int i = 0; i < N; i++) {
x[i] = 1;
y[i] = 2;
}
// call to kernel
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
add_kernel << <numBlocks, blockSize >> > (N, x, y);
cudaDeviceSynchronize();
// display result
//for (int i = 0; i < N; i++)
// cout << y[i] << endl;
//check for error
float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(y[i] - 3.0f));
cout << "Max error = " << maxError << endl;
cudaFree(x);
cudaFree(y);
}
|
10,626 | #include <stdlib.h>
#include <stdio.h>
#include <cuda_runtime.h>
#include <curand_kernel.h>
struct struct_linked_list_node;
typedef struct struct_linked_list_node {
int data;
struct struct_linked_list_node *next;
} linked_list_node;
typedef struct {
linked_list_node *head, *tail;
} linked_list;
__device__ bool _ll_is_marked(linked_list_node *n) {
return ((unsigned long long int) n & 1);
}
__device__ linked_list_node *_ll_marked(linked_list_node *n) {
return (linked_list_node *) ((unsigned long long int) n | 1);
}
__device__ linked_list_node *_ll_unmarked(linked_list_node *n) {
return (linked_list_node *) ((unsigned long long int) n & (~1));
}
__device__ linked_list_node *ll_create_node(int data) {
linked_list_node *lln = (linked_list_node *) malloc(sizeof(linked_list_node));
lln->data = data;
lln->next = NULL;
return lln;
}
__device__ linked_list_node *ll_first(linked_list *ll) {
return ll->head->next;
}
__device__ void ll_reset(linked_list *ll) {
ll->head = ll_create_node(INT_MIN);
ll->tail = ll_create_node(INT_MAX);
ll->head->next = ll->tail;
}
__device__ linked_list *ll_create() {
linked_list *ll = (linked_list *) malloc(sizeof(linked_list));
ll_reset(ll);
return ll;
}
__device__ void ll_print(linked_list *ll) {
linked_list_node *n = ll->head;
while (n) {
printf("%d @ ", n->data);
printf("%x\n", n);
n = _ll_unmarked(n->next);
}
}
__device__ void _ll_free_nodes(linked_list_node *from, linked_list_node *to) {
//*
from = _ll_unmarked(from);
while (from && from != to) {
linked_list_node *next = from->next;
free(from);
from = _ll_unmarked(next);
}
//*/
}
__device__ linked_list_node *ll_search(linked_list *ll,
int data,
linked_list_node **left_node
) {
linked_list_node *left_node_next, *right_node;
while (true) {
linked_list_node *t = ll->head;
linked_list_node *t_next = ll->head->next;
do {
if (!_ll_is_marked(t_next)) {
*left_node = t;
left_node_next = t_next;
}
t = _ll_unmarked(t_next);
if (t == ll->tail)
break;
t_next = t->next;
} while (_ll_is_marked(t_next) || (t->data < data));
right_node = t;
if (left_node_next == right_node)
if ((right_node != ll->tail) && _ll_is_marked(right_node->next))
continue;
else
return right_node;
unsigned long long int old = (unsigned long long int) left_node_next;
if (old == atomicCAS((unsigned long long int *) &((*left_node)->next),
old,
(unsigned long long int ) right_node
)
) {
_ll_free_nodes((linked_list_node *) old, right_node);
if ((right_node != ll->tail) && _ll_is_marked(right_node->next))
continue;
else
return right_node;
}
}
}
__device__ bool ll_insert(linked_list *ll, int data) {
linked_list_node *new_node = ll_create_node(data);
linked_list_node *right_node, *left_node;
while (true) {
right_node = ll_search(ll, data, &left_node);
if ((right_node != ll->tail) && (right_node->data == data))
return false;
new_node->next = right_node;
unsigned long long int old = (unsigned long long int) right_node;
if (old == atomicCAS((unsigned long long int *) &(left_node->next),
old,
(unsigned long long int ) new_node
)
)
return true;
}
}
__device__ bool ll_remove(linked_list *ll, int data) {
linked_list_node * right_node, *right_node_next, *left_node;
//printf("ll_remove(%d)\n", data);
while (true) {
right_node = ll_search(ll, data, &left_node);
//printf("ll_remove: found %d\n", right_node->data);
if ((right_node == ll->tail) || (right_node->data != data))
return false;
right_node_next = right_node->next;
if (!_ll_is_marked(right_node_next)) {
unsigned long long int old = (unsigned long long int) right_node_next;
if (old == atomicCAS((unsigned long long int *) &(right_node->next),
old,
(unsigned long long int ) _ll_marked(right_node_next)
)
)
break;
}
}
unsigned long long int old = (unsigned long long int) right_node;
if (old == atomicCAS((unsigned long long int *) &(left_node->next),
old,
(unsigned long long int ) right_node_next
)
)
_ll_free_nodes((linked_list_node *) old, right_node_next);
else
right_node = ll_search(ll, right_node->data, &left_node);
return true;
}
// call ONCE!
__device__ void ll_free(linked_list *ll) {
// todo
if (ll) {
linked_list_node *lln = ll->head;
while (lln) {
linked_list_node *next = lln->next;
free(lln);
lln = next;
}
free(ll);
ll = NULL;
}
}
__device__ void ll_free_safe(linked_list *ll) {
if (ll) {
linked_list_node *lln = _ll_unmarked(ll->head);
while (lln) {
linked_list_node *next = _ll_unmarked(lln->next);
free(lln);
lln = next;
}
free(ll);
ll = NULL;
}
}
__device__ void ll_to_array(linked_list *ll, int *arr, unsigned int l) {
linked_list_node *node = ll->head;
if (node) // skip head
node = node->next;
unsigned int i = 0;
while (node && i < l) {
node = node->next;
arr[i++] = node->data;
}
// fill remaining cells with 0, if any
while (i < l)
arr[i++] = 13373; // todo
}
__device__ void ll_to_array_safe(linked_list *ll, int *arr, unsigned int l) {
linked_list_node *node = ll->head;
linked_list_node *unmarked = _ll_unmarked(node);
if (node) { // skip head
node = node->next;
unmarked = _ll_unmarked(node);
}
unsigned int i = 0;
while (unmarked && i < l) {
if (!_ll_is_marked(unmarked->next))
arr[i++] = unmarked->data;
node = unmarked->next;
unmarked = _ll_unmarked(node);
}
while (i < l)
arr[i++] = 13373; // todo
}
// grid
typedef struct {
float lower_x, lower_y, cell_width, cell_height;
unsigned int cols, rows;
linked_list **cells;
} grid;
/*
__device__ grid *grid_create() {
grid *g = (grid *) malloc(sizeof(grid));
return g;
}
*/
grid *grid_host_create(float lower_x,
float lower_y,
float cell_width,
float cell_height,
unsigned int cols,
unsigned int rows
) {
grid *d_grid, h_grid;
cudaMalloc((void **) &d_grid, sizeof(grid));
h_grid.lower_x = lower_x;
h_grid.lower_y = lower_y;
h_grid.cell_width = cell_width;
h_grid.cell_height = cell_height;
h_grid.cols = cols;
h_grid.rows = rows;
linked_list **cells;
cudaMalloc((void **) &cells, rows * cols * sizeof(linked_list *));
h_grid.cells = cells;
printf("h_grid.cells %x\n", cells);
printf("h_grid.cell_height %f\n", h_grid.cell_height);
printf("h_grid.cell_width %f\n", h_grid.cell_width);
cudaMemcpy(d_grid,
&h_grid,
sizeof(grid),
cudaMemcpyHostToDevice
);
return d_grid;
}
__device__ void grid_alloc_cells(grid *g) {
unsigned int num_cells = g->rows * g->cols;
printf("num_cells %d\n", num_cells);
printf("grid.cells %x\n", g->cells);
for (unsigned int i = 0; i < num_cells; i++) {
g->cells[i] = ll_create();
//printf("created cell ll at %x\n", g->cells[i]);
}
}
__device__ linked_list *_grid_cell(grid *g, float2 pos) {
//printf("x %f\n", pos.x);
//printf("y %f\n", pos.y);
//printf("cell_width %f\n", g->cell_width);
//printf("cell_height %f\n", g->cell_height);
unsigned int col = floor((pos.x - g->lower_x) / g->cell_width);
unsigned int row = floor((pos.y - g->lower_y) / g->cell_height);
//printf("col %d\n", col);
//printf("row %d\n", row);
unsigned int i = row * (g->cols) + col;
if (col < g->cols && row < g->rows) {
//printf("_grid_cell: %x\n", g->cells[i]);
return g->cells[i];
} else {
printf("_grid_cell: NULL\n");
return NULL;
}
}
__device__ linked_list *grid_add_node(grid *g, float2 pos, int n) {
linked_list *cell = _grid_cell(g, pos);
/*
printf("not calling ll_insert\n");
return cell;
//*/
/*
printf("cell at %x\n", cell);
return cell;
*/
if (cell) {
//printf("calling ll_insert NOT\n");
ll_insert(cell, n);
}
return cell;
}
__device__ bool grid_remove_node(grid *g, float2 pos, int n) {
linked_list *cell = _grid_cell(g, pos);
if (cell)
return ll_remove(cell, n);
else
return false;
}
__device__ linked_list **grid_neighbours(grid *g, float2 pos) {
unsigned int col = floor((pos.x - g->lower_x) / g->cell_width);
unsigned int row = floor((pos.y - g->lower_y) / g->cell_height);
if (col >= g->cols || row >= g->rows) {
printf("this cell does not exist: col %d, row %d\n", col, row);
printf("no such cell, pos: %f, %f\n", pos.x, pos.y);
return NULL;
}
linked_list **neighbours = (linked_list **) malloc(9 * sizeof(linked_list *));
unsigned int i = 0;
neighbours[i++] = g->cells[row * g->cols + col];
unsigned int left_most = (col > 0) ? col - 1 : 0;
unsigned int right_most = (col + 1 < g->cols) ? col + 1 : col;
if (row > 0) {
for (unsigned int c = left_most; c <= right_most; c++) {
neighbours[i++] = g->cells[(row - 1) * g->cols + c];
}
}
if (left_most < col)
neighbours[i++] = g->cells[row * g->cols + left_most];
if (right_most > col)
neighbours[i++] = g->cells[row * g->cols + right_most];
if (row + 1 < g->rows) {
for (unsigned int c = left_most; c <= right_most; c++) {
neighbours[i++] = g->cells[(row + 1) * g->cols + c];
}
}
while (i < 9)
neighbours[i++] = NULL;
return neighbours;
}
typedef struct {
float2 pos;
float2 waypoint;
float vel;
int pause;
float so;
} random_waypoint_node;
typedef struct {
float2 pos;
float direction;
float vel;
int pause;
float so;
} random_direction_node;
typedef struct {
float x_lower;
float x_upper;
float y_lower;
float y_upper;
} world_aabb;
typedef struct {
world_aabb world;
float max_velocity;
float min_so, max_so;
unsigned int pause;
} random_waypoint_config;
__device__ float random_float_in_range(curandState *rand_state,
int i,
float lower,
float upper
) {
return curand_uniform(&rand_state[i]) * (upper - lower) + lower;
}
__device__ unsigned int random_int_in_range(curandState *rand_state,
int i,
unsigned int lower,
unsigned int upper
) {
return curand(&rand_state[i]) % (upper - lower) + lower;
}
__global__ void grid_init(grid *grid,
unsigned int num_nodes
) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i == 0) {
printf("grid_alloc_cells\n");
grid_alloc_cells(grid);
}
}
__global__ void random_waypoint_init(curandState *rand_state,
random_waypoint_config* config,
random_waypoint_node* nodes,
grid *grid,
unsigned int num_nodes
) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < num_nodes) {
curand_init(clock64(), i, 0, &rand_state[i]);
nodes[i].pos = make_float2(random_float_in_range(rand_state,
i,
config->world.x_lower,
config->world.x_upper
),
random_float_in_range(rand_state,
i,
config->world.y_lower,
config->world.y_upper
)
);
nodes[i].waypoint = nodes[i].pos;
nodes[i].vel = 0.0;
nodes[i].pause = 1;
nodes[i].so = random_float_in_range(rand_state,
i,
config->min_so,
config->max_so
);
grid_add_node(grid, nodes[i].pos, i);
printf("%d pos %f, %f\n", i, nodes[i].pos.x, nodes[i].pos.y);
}
}
__device__ bool random_waypoint_is_safe(random_waypoint_node *nodes,
grid *grid,
int i,
float2 pos
) {
linked_list **neighbours = grid_neighbours(grid, pos);
unsigned int count = 0;
for (unsigned int j = 0; j < 9; j++)
if (neighbours[j]) {
count++;
linked_list_node *n = ll_first(neighbours[j]);
while (n != neighbours[j]->tail) {
if (n->data != i) {
float dx = nodes[n->data].pos.x - pos.x;
float dy = nodes[n->data].pos.y - pos.y;
float dsq = dx * dx + dy * dy;
float safe_d = nodes[i].so + nodes[n->data].vel + nodes[n->data].so;
//printf("dsq %f, safe_d**2 %f\n", dsq, safe_d * safe_d);
if (dsq < (safe_d * safe_d)) {
/*
printf("neighbour at %f, %f is too close to %f, %f\n",
nodes[n->data].pos.x,
nodes[n->data].pos.y,
nodes[i].pos.x,
nodes[i].pos.y
);
*/
free(neighbours);
return false;
}
}
n = n->next;
}
}
//printf("%d neighbour regions, all safe\n", count);
free(neighbours);
return true;
}
__device__ bool random_direction_is_safe(random_direction_node *nodes,
grid *grid,
int i,
float2 pos
) {
linked_list **neighbours = grid_neighbours(grid, pos);
unsigned int count = 0;
for (unsigned int j = 0; j < 9; j++) {
//printf("neighbours %x\n", neighbours[j]);
if (neighbours[j]) {
count++;
linked_list_node *n = ll_first(neighbours[j]);
while (n && n != neighbours[j]->tail) {
//printf("neighbour %x\n", n);
while (_ll_is_marked(n->next)) {
//printf("skipping %x\n", n);
n = _ll_unmarked(n->next);
}
if (n) {
if (n->data != i) {
float dx = nodes[n->data].pos.x - pos.x;
float dy = nodes[n->data].pos.y - pos.y;
float dsq = dx * dx + dy * dy;
float safe_d = nodes[i].so + nodes[n->data].vel + nodes[n->data].so;
//printf("dsq %f, safe_d**2 %f\n", dsq, safe_d * safe_d);
if (dsq < (safe_d * safe_d)) {
/*
printf("neighbour at %f, %f is too close to %f, %f\n",
nodes[n->data].pos.x,
nodes[n->data].pos.y,
nodes[i].pos.x,
nodes[i].pos.y
);
*/
free(neighbours);
return false;
}
}
n = _ll_unmarked(n->next);
}
}
}
}
//printf("%d neighbour regions, all safe\n", count);
free(neighbours);
return true;
}
__global__ void random_waypoint_step(curandState *rand_state,
random_waypoint_config* config,
random_waypoint_node* nodes,
grid *grid,
unsigned int num_nodes
) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < num_nodes) {
//*
if (nodes[i].pause > 0) {
if (--nodes[i].pause <= 0) {
nodes[i].waypoint =
make_float2(random_float_in_range(rand_state,
i,
config->world.x_lower,
config->world.x_upper
),
random_float_in_range(rand_state,
i,
config->world.y_lower,
config->world.y_upper
)
);
nodes[i].vel = curand_uniform(&rand_state[i]) * config->max_velocity;
}
} else {
float2 to_waypoint = make_float2(nodes[i].waypoint.x - nodes[i].pos.x,
nodes[i].waypoint.y - nodes[i].pos.y
);
float d = sqrt(to_waypoint.x * to_waypoint.x
+ to_waypoint.y * to_waypoint.y
);
if (d <= nodes[i].vel) {
nodes[i].pos = nodes[i].waypoint;
nodes[i].pause = config->pause;
} else {
to_waypoint.x *= nodes[i].vel / d;
to_waypoint.y *= nodes[i].vel / d;
float2 candidate = make_float2(nodes[i].pos.x + to_waypoint.x,
nodes[i].pos.y + to_waypoint.y
);
if (random_waypoint_is_safe(nodes, grid, i, candidate)) {
//printf("moving %d\n", i);
if (grid_remove_node(grid, nodes[i].pos, i))
grid_add_node(grid, candidate, i);
nodes[i].pos = candidate;
} else {
printf("can't move %d\n", i);
}
}
}
//*/
}
}
// random direction
__global__ void random_direction_init(curandState *rand_state,
random_waypoint_config* config,
random_direction_node* nodes,
grid *grid,
unsigned int num_nodes
) {
int i = blockDim.x * blockIdx.x + threadIdx.x;
//*
if (i < num_nodes) {
curand_init(clock64(), i, 0, &rand_state[i]);
nodes[i].pos = make_float2(random_float_in_range(rand_state,
i,
config->world.x_lower,
config->world.x_upper
),
random_float_in_range(rand_state,
i,
config->world.y_lower,
config->world.y_upper
)
);
nodes[i].direction = 0.0;
nodes[i].vel = 0.0;
nodes[i].pause = 1;
nodes[i].so = random_float_in_range(rand_state,
i,
config->min_so,
config->max_so
);
linked_list *cell = grid_add_node(grid, nodes[i].pos, i);
}
//*/
}
__device__ float2 cut(float2 p1, float2 p2, float2 p3, float2 p4) {
return make_float2((
(p4.x - p3.x) * (p2.x * p1.y - p1.x * p2.y)
- (p2.x - p1.x) * (p4.x * p3.y - p3.x * p4.y)
)
/
(
(p4.y - p3.y) * (p2.x - p1.x)
- (p2.y - p1.y) * (p4.x - p3.x)
),
(
(p1.y - p2.y) * (p4.x * p3.y - p3.x * p4.y)
- (p3.y - p4.y) * (p2.x * p1.y - p1.x * p2.y)
)
/
(
(p4.y - p3.y) * (p2.x - p1.x)
- (p2.y - p1.y) * (p4.x - p3.x)
)
);
}
__device__ float distance_squared(float2 p1, float2 p2) {
float dx = p1.x - p2.x;
float dy = p1.y - p2.y;
return dx * dx + dy * dy;
}
__global__ void random_direction_step(curandState *rand_state,
random_waypoint_config* config,
random_direction_node* nodes,
grid *grid,
unsigned int num_nodes
) {
printf("%d * %d + %d\n", blockDim.x, blockIdx.x, threadIdx.x);
int i = blockDim.x * blockIdx.x + threadIdx.x;
printf("%d\n", i);
float2 top_left = make_float2(config->world.x_lower, config->world.y_lower);
float2 bottom_left = make_float2(config->world.x_lower, config->world.y_upper);
float2 top_right = make_float2(config->world.x_upper, config->world.y_lower);
float2 bottom_right = make_float2(config->world.x_upper, config->world.y_upper);
//printf("tl %f, %f\n", top_left.x, top_left.y);
//printf("bl %f, %f\n", bottom_left.x, bottom_left.y);
//printf("tr %f, %f\n", top_right.x, top_right.y);
//printf("br %f, %f\n", bottom_right.x, bottom_right.y);
if (i < num_nodes) {
printf("node %d\n", i);
if (nodes[i].pause > 0) {
if (--nodes[i].pause <= 0) {
printf("%d: choose new direction\n", i);
nodes[i].direction =
random_float_in_range(rand_state,
i,
0,
2 * M_PI
);
nodes[i].vel = curand_uniform(&rand_state[i]) * config->max_velocity;
}
} else {
float2 forward = make_float2(cos(nodes[i].direction),
sin(nodes[i].direction)
);
float2 candidate = make_float2(nodes[i].pos.x + forward.x,
nodes[i].pos.y + forward.y
);
printf("candidate for %d: %f, %f\n", i, candidate.x, candidate.y);
// out of bounds?
bool oob_left = candidate.x < config->world.x_lower;
bool oob_right = candidate.x > config->world.x_upper;
bool oob_up = candidate.y < config->world.y_lower;
bool oob_down = candidate.y > config->world.y_upper;
//printf("%d: %f, %f | oob? l%d r%d u%d d%d\n",
// i,
// candidate.x,
// candidate.y,
// oob_left,
// oob_right,
// oob_up,
// oob_down
// );
if (oob_left) {
if (oob_up) {
float2 c1 = cut(nodes[i].pos,
candidate,
top_left,
bottom_left
);
float2 c2 = cut(nodes[i].pos,
candidate,
top_left,
top_right
);
float d1 = distance_squared(candidate, c1);
float d2 = distance_squared(candidate, c2);
if (d1 < d2)
candidate = c1;
else
candidate = c2;
}
else if (oob_down) {
float2 c1 = cut(nodes[i].pos,
candidate,
top_left,
bottom_left
);
float2 c2 = cut(nodes[i].pos,
candidate,
bottom_left,
bottom_right
);
float d1 = distance_squared(candidate, c1);
float d2 = distance_squared(candidate, c2);
if (d1 < d2)
candidate = c1;
else
candidate = c2;
}
else {
candidate = cut(nodes[i].pos,
candidate,
top_left,
bottom_left
);
}
} else if (oob_right) {
if (oob_up) {
float2 c1 = cut(nodes[i].pos,
candidate,
top_right,
bottom_right
);
float2 c2 = cut(nodes[i].pos,
candidate,
top_left,
top_right
);
float d1 = distance_squared(candidate, c1);
float d2 = distance_squared(candidate, c2);
if (d1 < d2)
candidate = c1;
else
candidate = c2;
}
else if (oob_down) {
float2 c1 = cut(nodes[i].pos,
candidate,
top_right,
bottom_right
);
float2 c2 = cut(nodes[i].pos,
candidate,
bottom_left,
bottom_right
);
float d1 = distance_squared(candidate, c1);
float d2 = distance_squared(candidate, c2);
if (d1 < d2)
candidate = c1;
else
candidate = c2;
}
else {
candidate = cut(nodes[i].pos,
candidate,
top_right,
bottom_right
);
}
} else if (oob_up) {
candidate = cut(nodes[i].pos,
candidate,
top_left,
top_right
);
} else if (oob_down) {
candidate = cut(nodes[i].pos,
candidate,
bottom_left,
bottom_right
);
}
__syncthreads();
if (random_direction_is_safe(nodes, grid, i, candidate)) {
//printf("moving %d\n", i);
if (grid_remove_node(grid, nodes[i].pos, i))
grid_add_node(grid, candidate, i);
nodes[i].pos = candidate;
if (oob_left || oob_right || oob_up || oob_down) {
nodes[i].pause = config->pause;
printf("clamped to %f, %f\n", candidate.x, candidate.y);
}
} else {
printf("can't move %d\n", i);
}
}
}
}
int main(int argc, char **argv) {
cudaError_t err;
unsigned int num_threads = 32; // 1024;
unsigned int num_blocks = 1;
unsigned int num_nodes = num_threads * num_blocks;
unsigned int num_frames = 10;
random_waypoint_node *h_nodes;
//random_direction_node *h_nodes;
random_waypoint_config h_config;
h_config.world.x_lower = -10.0;
h_config.world.x_upper = 10.0;
h_config.world.y_lower = -10.0;
h_config.world.y_upper = 10.0;
h_config.max_velocity = 0.5;
h_config.pause = 2;
h_config.min_so = 0.01;
h_config.max_so = 0.1;
float cell_size = h_config.max_so * 2 + h_config.max_velocity;
unsigned int grid_cols =
ceil((h_config.world.x_upper - h_config.world.x_lower) / cell_size);
unsigned int grid_rows =
ceil((h_config.world.y_upper - h_config.world.y_lower) / cell_size);
printf("%d nodes\n", num_nodes);
//printf("grid: %d x %d\n", grid_cols, grid_rows);
random_waypoint_node *d_nodes;
//random_direction_node *d_nodes;
random_waypoint_config *d_config;
grid *d_grid;
curandState *d_rand_state;
h_nodes = (random_waypoint_node *)
malloc(num_nodes * sizeof(random_waypoint_node));
/*
h_nodes = (random_direction_node *)
malloc(num_nodes * sizeof(random_direction_node));
*/
cudaMalloc((void **) &d_nodes, num_nodes * sizeof(random_waypoint_node));
//cudaMalloc((void **) &d_nodes, num_nodes * sizeof(random_direction_node));
cudaMalloc((void **) &d_config, sizeof(random_waypoint_config));
cudaMalloc((void **) &d_rand_state, num_threads * sizeof(curandState));
cudaMemcpy(d_config,
&h_config,
sizeof(random_waypoint_config),
cudaMemcpyHostToDevice
);
d_grid = grid_host_create(h_config.world.x_lower,
h_config.world.y_lower,
cell_size,
cell_size,
grid_cols,
grid_rows
);
printf("d_grid %x\n", d_grid);
printf("init\n");
printf("grid_init\n");
grid_init<<<num_blocks, num_threads>>>(d_grid,
num_nodes
);
err = cudaGetLastError();
if (err != cudaSuccess)
printf("ERR: %s\n", cudaGetErrorString(err));
printf("random_waypoint_init\n");
random_waypoint_init<<<num_blocks, num_threads>>>(d_rand_state,
//random_direction_init<<<num_blocks, num_threads>>>(d_rand_state,
d_config,
d_nodes,
d_grid,
num_nodes
);
err = cudaGetLastError();
if (err != cudaSuccess)
printf("ERR: %s\n", cudaGetErrorString(err));
printf("/init\n");
for (unsigned int i = 0; i < num_frames; i++) {
printf("\nframe %u\n", i);
//*
random_waypoint_step<<<num_blocks, num_threads>>>(d_rand_state,
//random_direction_step<<<num_blocks, num_threads>>>(d_rand_state,
d_config,
d_nodes,
d_grid,
num_nodes
);
err = cudaGetLastError();
if (err != cudaSuccess)
printf("ERR: %s\n", cudaGetErrorString(err));
//*/
printf("memcpy %x (device) to %x (host)\n", d_nodes, h_nodes);
cudaMemcpy(h_nodes,
d_nodes,
num_nodes * sizeof(random_waypoint_node),
//num_nodes * sizeof(random_direction_node),
cudaMemcpyDeviceToHost
);
err = cudaGetLastError();
if (err != cudaSuccess)
printf("ERR: %s\n", cudaGetErrorString(err));
for (unsigned int i = 0; i < num_nodes; i++) {
printf("%u: %f,%f (v = %f) -> %f, %f | p = %d\n",
i,
h_nodes[i].pos.x,
h_nodes[i].pos.y,
h_nodes[i].vel,
//h_nodes[i].direction,
h_nodes[i].waypoint.x,
h_nodes[i].waypoint.y,
h_nodes[i].pause
);
}
}
cudaFree(d_rand_state);
cudaFree(d_config);
cudaFree(d_nodes);
free(h_nodes);
exit(EXIT_SUCCESS);
}
|
10,627 | #include <vector>
#include <algorithm>
#include<iostream>
struct Result {
int y0;
int x0;
int y1;
int x1;
float outer[3];
float inner[3];
};
static inline void check(cudaError_t err, const char* context) {
if (err != cudaSuccess) {
std::cerr << "CUDA error: " << context << ": "
<< cudaGetErrorString(err) << std::endl;
std::exit(EXIT_FAILURE);
}
}
static inline int divup(int a, int b) {
return (a + b - 1)/b;
}
#define CHECK(x) check(x, #x)
__global__ void scan(int ny, int nx, float* sum_data, float* errors, int* coordinates){
//same as Is4, fix window size and scan different position
int width = threadIdx.x + blockIdx.x * blockDim.x + 1;
int height = threadIdx.y + blockIdx.y * blockDim.y + 1;
if(width>nx || height>ny) return;
int new_nx=nx+1;
float err_=0;
int x0=0, y0=0, x1=0, y1=0;
float sum_all=sum_data[nx+ny*new_nx];
int all=nx*ny;
int in=width*height; //the number of pixels inside window
int out=all-in;
float reverse_in=1.0/in;
float reverse_out=1.0/out;
float reverse_sum=reverse_in+reverse_out;
float temp=reverse_out*sum_all;
for(int offset_y=0; offset_y<=(ny-height);offset_y++){
for(int offset_x=0; offset_x<=(nx-width);offset_x++){
int anchor_y1=offset_y+height;
int anchor_x1=offset_x+width;
int y1x=new_nx*anchor_y1;
int yx=new_nx*offset_y;
float sum_in=sum_data[anchor_x1+y1x]-sum_data[offset_x+y1x]
-sum_data[anchor_x1+ yx]+sum_data[offset_x+ yx];
float cost=temp*sum_all+sum_in*(float(-2.0)*temp+sum_in*reverse_sum);
if(cost>err_){
err_=cost;
x0=offset_x;
y0=offset_y;
x1=anchor_x1;
y1=anchor_y1;
}
}
}
//same as tuple in Is4
int w=width-1;
int h=height-1;
int i=w+nx*h;
errors[i]=err_;
coordinates[4*i+0]=y0;
coordinates[4*i+1]=x0;
coordinates[4*i+2]=y1;
coordinates[4*i+3]=x1;
}
/*
This is the function you need to implement. Quick reference:
- x coordinates: 0 <= x < nx
- y coordinates: 0 <= y < ny
- color components: 0 <= c < 3
- input: data[c + 3 * x + 3 * nx * y]
*/
Result segment(int ny, int nx, const float *data) {
Result result{0, 0, 0, 0, {0, 0, 0}, {0, 0, 0}};
const int new_nx=nx+1;
std::vector<float> sum_data((ny+1)*new_nx);//padding 1 column at the left and 1 row at the top
std::vector<float> err(ny*nx);
std::vector<int> coord(4*ny*nx);
for(int x=0;x<=nx;x++){
//y=0
sum_data[x]=0;
}
for(int y=0;y<=ny;y++){
//x=0
sum_data[new_nx*y]=0;
}
for(int y1=1; y1<=ny; y1++){
float temp=0;
for(int x1=1;x1<=nx;x1++){
int x=x1-1;
int y=y1-1;
temp+=data[x*3+3*nx*y]; //c1=c2=c3, so c1 is enough.
sum_data[x1+new_nx*y1]=sum_data[x1+new_nx*y]+temp;
}
}
float sum_all=sum_data[nx+new_nx*ny];
int all=nx*ny; //the number of pixels
float* cuda_sum_data=NULL;
float* errors=NULL;
int* coordinates=NULL; //same as tuple in is4, pointer to tuple seems hard to pass to gpu.
CHECK(cudaMalloc((void**)&cuda_sum_data, (ny+1)*new_nx*sizeof(float)));
CHECK(cudaMalloc((void**)&errors, all*sizeof(float)));
CHECK(cudaMalloc((void**)&coordinates, 4*all*sizeof(int)));
CHECK(cudaMemcpy(cuda_sum_data, sum_data.data(), (ny+1)*new_nx*sizeof(float), cudaMemcpyHostToDevice));
dim3 dimBlock(32,32);
dim3 dimGrid(divup(nx, 32), divup(ny, 32));
scan<<<dimGrid, dimBlock>>>(ny,nx,cuda_sum_data,errors,coordinates);
CHECK(cudaGetLastError());
CHECK(cudaMemcpy(err.data(), errors, all*sizeof(float), cudaMemcpyDeviceToHost));
CHECK(cudaMemcpy(coord.data(), coordinates, 4*all*sizeof(int), cudaMemcpyDeviceToHost));
auto maximum=std::max_element(err.begin(), err.end());
int i=std::distance(err.begin(), maximum);
result.y0=coord[4*i+0];
result.x0=coord[4*i+1];
result.y1=coord[4*i+2];
result.x1=coord[4*i+3];
float inner=sum_data[result.x1+new_nx*result.y1]-sum_data[result.x0+new_nx*result.y1]
-sum_data[result.x1+new_nx*result.y0]+sum_data[result.x0+new_nx*result.y0];
float outer=sum_all-inner;
int in=(result.y1-result.y0)*(result.x1-result.x0);
int out=all-in;
for(int i=0; i<3; i++){
result.inner[i]=inner/in;
result.outer[i]=outer/out;
}
CHECK(cudaFree(cuda_sum_data));
CHECK(cudaFree(errors));
CHECK(cudaFree(coordinates));
return result;
}
|
10,628 | #include "includes.h"
__global__ void aypb_i32 (int a, int* y, int b, int len) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < len) {
y[idx] = a * y[idx] + b;
}
} |
10,629 | __global__ void test_arrays() {
int a[5];
a[4] = 42;
a[5] = 42;
a[2 + 3] = 42;
a['a'] = 42;
a[0] = a[5] + a[6];
int y = -1;
a[y] = 42;
int z;
a[z] = 42;
int p;
p = 1;
a[p] = 42;
int b[5][6];
b[4][5] = 42;
b[5][5] = 42;
b[4][6] = 42;
} |
10,630 | /*
* Kazi Ahmed
* Problem 3 - HW9
* ME759 Fall 2015
*/
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <thrust/device_ptr.h>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/count.h>
#include <thrust/sort.h>
int main( int argc, char** argv)
{
//day 0 0 1 2 5 5 6 6 7 8 9 9 9 10 11
thrust::host_vector<int> hday(15,0);
//site 2 3 0 1 1 2 0 1 2 1 3 4 0 1 2
thrust::host_vector<int> hsit(15,0);
//msrmnt 9 5 6 3 3 8 2 6 5 10 9 11 8 4 1
thrust::host_vector<int> hmsr(15,0);
hday[ 0]= 0; hday[ 1]= 0; hday[ 2]= 1;
hday[ 3]= 2; hday[ 4]= 5; hday[ 5]= 5;
hday[ 6]= 6; hday[ 7]= 6; hday[ 8]= 7;
hday[ 9]= 8; hday[10]= 9; hday[11]= 9;
hday[12]= 9; hday[13]=10; hday[14]=11;
hsit[ 0]= 2; hsit[ 1]= 3; hsit[ 2]= 0;
hsit[ 3]= 1; hsit[ 4]= 1; hsit[ 5]= 2;
hsit[ 6]= 0; hsit[ 7]= 1; hsit[ 8]= 2;
hsit[ 9]= 1; hsit[10]= 3; hsit[11]= 4;
hsit[12]= 0; hsit[13]= 1; hsit[14]= 2;
hmsr[ 0]= 9; hmsr[ 1]= 5; hmsr[ 2]= 6;
hmsr[ 3]= 3; hmsr[ 4]= 3; hmsr[ 5]= 8;
hmsr[ 6]= 2; hmsr[ 7]= 6; hmsr[ 8]= 5;
hmsr[ 9]=10; hmsr[10]= 9; hmsr[11]=11;
hmsr[12]= 8; hmsr[13]= 4; hmsr[14]= 1;
//Now make the device vectors
thrust::device_vector<int> dday(15,0);
thrust::device_vector<int> dsit(15,0);
thrust::device_vector<int> dmsr(15,0);
thrust::copy(hday.begin(),hday.end(),dday.begin());
thrust::copy(hsit.begin(),hsit.end(),dsit.begin());
thrust::copy(hmsr.begin(),hmsr.end(),dmsr.begin());
using namespace thrust::placeholders;
printf("See comments for explanation of logic \n");
/* Number of days for which rainfall exceeded 5 at any one location
*
* According to the prof, this should be # of days at a given location
* that rainfall exceeded 5, then that number summed over all locations.
* Therefore the answer should be 8.
* We asked this in class and so I made the code only to calculate based
* on that interpretation.
*
* Alternatively if days should not be counted twice, answer is 6.
* This would require additional code, didn't think about it.
*/
int n5;
n5 = thrust::count_if(dmsr.begin(), dmsr.end(), _1>5);
printf("Sum over locations of days > 5 rain: %d \n",n5);
/* Total rainfall at each site
*
* Expected answer 16, 26, 23, 14, 11
*
* Sort by site, then reduce by key. Use copied versions of the data since
* this doesn't zip and so won't keep the dates in tact as well.
*/
thrust::device_vector<int> sit(15,0);
thrust::device_vector<int> msr(15,0);
thrust::copy(dsit.begin(),dsit.end(),sit.begin());
thrust::copy(dmsr.begin(),dmsr.end(),msr.begin());
thrust::sort_by_key(sit.begin(), sit.end(), msr.begin());
thrust::reduce_by_key(sit.begin(),sit.end(),msr.begin(),dsit.begin(),dmsr.begin());
thrust::copy(dsit.begin(),dsit.end(),hsit.begin());
thrust::copy(dmsr.begin(),dmsr.end(),hmsr.begin());
for (int i=0; i<5; i++)
{
printf("Site %d rainfall is %d \n", hsit[i], hmsr[i]);
}
return EXIT_SUCCESS;
} |
10,631 | #include<iostream>
#include<math.h>
using namespace std;
#define imgH 100
#define imgW 100
#define h 1
#define patchW 3
void printImg(float img[imgH][imgW])
{
for(int i=0; i<imgH; i++)
{
for(int j=0; j<imgW; j++)
{
cout<<img[i][j]<<" ";
}
cout<<endl;
}
return;
}
__global__ void NLM(float* img, float* imgTemp, float* C)
{
int tx = threadIdx.x;
int ty = threadIdx.y;
int bx = blockIdx.x;
int by = blockIdx.y;
int j = by*blockDim.y + ty;
int i = bx*blockDim.x + tx;
for(int k=i; k<imgH - patchW + 1; k++)
for(int l=0; l<imgW - patchW + 1; l++)
{
if(l != j)
{
float v = 0;
for(int p=k; p<k+patchW; p++)
for(int q=l; q<l+patchW; q++)
{
v += (img[(i+p-k)*imgW + j+q-l] - img[p*imgW + q]);
v = v*v;
}
float w = exp(-v/(h*h));
atomicAdd(imgTemp + i*imgW + j, w * img[k*imgW + l]);
atomicAdd(C + i*imgW + j, w);
atomicAdd(imgTemp + k*imgW + l, w * img[i*imgW + j]);
atomicAdd(C + k*imgW + l, w);
}
}
}
int main()
{
float img[imgH][imgW] = {0}, C[imgH][imgW] = {0};
float imgTemp[imgH][imgW] = {0};
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
for(int i=0; i<imgH; i++)
for(int j=0; j<imgW; j++)
{
img[i][j] = 1.2;
}
float* deviceImg;
float* deviceImgTemp;
float* deviceC;
//Memory Allocation
cudaMalloc((void**)&deviceImg, imgH * imgW * sizeof(float));
cudaMalloc((void**)&deviceImgTemp, imgH * imgW * sizeof(float));
cudaMalloc((void**)&deviceC, imgH * imgW * sizeof(float));
cudaMemset(deviceImgTemp, 0, imgH * imgW * sizeof(float));
cudaMemset(deviceC, 0, imgH * imgW * sizeof(float));
//Memory Copy
cudaMemcpy(deviceImg, img, imgH * imgW * sizeof(float), cudaMemcpyHostToDevice);
dim3 block(1, 1, 1);
dim3 grid(imgH - patchW + 1, imgW - patchW + 1, 1);
cudaEventRecord(start);
//Invoke Kernel
NLM<<<grid, block>>>(deviceImg, deviceImgTemp, deviceC);
cudaEventRecord(stop);
//Memory
cudaMemcpy(imgTemp, deviceImgTemp, imgH * imgW * sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(C, deviceC, imgH * imgW * sizeof(float), cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
for(int i=0; i<imgH - patchW + 1; i++)
for(int j=0; j<imgW - patchW + 1; j++)
{
img[i][j] = imgTemp[i][j]/C[i][j];
}
cout<<"Time elapsed for H = "<<imgH<<" and W = "<<imgW<<" is: "<<(float)milliseconds<<" milliseconds";
// printImg(img);
cudaFree(deviceImg);
cudaFree(deviceImgTemp);
cudaFree(deviceC);
return 0;
} |
10,632 | #include<stdio.h>
// any 2 <= mod <= 2^31 should work
__host__ __device__ unsigned mod_sum(unsigned a, unsigned b, unsigned mod) {
unsigned c = a+b;
return c >= mod ? c-mod : c;
}
// each block solves a case
// block size must be power of 2 and <= 2^(n-1)
// thread size must be n - 1
__global__ void my_hamilton(int n, int *adj, int *ret, unsigned int mod) {
__shared__ unsigned qc[31];
__shared__ unsigned a_n_1[31]; // adj[n-1][i]
int tid = threadIdx.x;
int lv = 31 - __clz(gridDim.x);
unsigned s = blockIdx.x; // case as a bit field
unsigned total = 0;
// prefetch
unsigned a_i = 0;
for (int i = 0; i < n; i++) {
a_i = a_i | adj[tid*n + i]<<i;
}
a_n_1[tid] = adj[(n-1)*n + tid];
// test each case in this block
for (unsigned stride = 0; stride < 1U<<(n-1-lv); stride++, s += 1U<<lv) {
// active means this thread is selected
unsigned active = s>>tid & 1;
// first transition
qc[tid] = active * (a_i>>(n-1) & 1);
unsigned row = active * a_i;
__syncthreads();
// calculate each transition, uses GPU SIMD feature
for (int t = 1; t < n-1; t++) {
unsigned sum = 0;
for (int i = 0; i < n-1; i++) {
sum = mod_sum(sum, qc[i] * (row>>i & 1), mod);
}
__syncthreads();
qc[tid] = sum;
}
// last transition
unsigned count = 0;
for (int i = 0; i < n-1; i++) {
count = mod_sum(count, qc[i] * a_n_1[i], mod);
}
// adjust sign for inclusion-exclusion principle
int sign = (n - __popc(s)) & 1;
unsigned count_with_sign = sign ? count : (count ? mod-count : 0);
total = mod_sum(total, count_with_sign, mod);
}
if (tid == 0) {
// output total for this block
ret[blockIdx.x] = total;
}
}
// thread size must be >= 64 and power of 2
__global__ void sum_all(int n, int *data, int *sum, unsigned mod) {
__shared__ int tmp_sum[1024];
int blockSize = blockDim.x;
int stride = gridDim.x * blockSize;
int id = threadIdx.x;
int i = id + blockSize * blockIdx.x;
// sum part of data
tmp_sum[id] = 0;
while (i < n) {
tmp_sum[id] = mod_sum(tmp_sum[id], data[i], mod);
i += stride;
}
__syncthreads();
// merge threads
if (blockSize >= 1024) {
if (id < 512) tmp_sum[id] = mod_sum(tmp_sum[id], tmp_sum[id + 512], mod);
__syncthreads();
}
if (blockSize >= 512) {
if (id < 256) tmp_sum[id] = mod_sum(tmp_sum[id], tmp_sum[id + 256], mod);
__syncthreads();
}
if (blockSize >= 256) {
if (id < 128) tmp_sum[id] = mod_sum(tmp_sum[id], tmp_sum[id + 128], mod);
__syncthreads();
}
if (blockSize >= 128) {
if (id < 64) tmp_sum[id] = mod_sum(tmp_sum[id], tmp_sum[id + 64], mod);
__syncthreads();
}
if (id < 32) {
// now, only 1 warp is active
volatile int *tmp = tmp_sum;
tmp[id] = mod_sum(tmp[id], tmp[id + 32], mod);
tmp[id] = mod_sum(tmp[id], tmp[id + 16], mod);
tmp[id] = mod_sum(tmp[id], tmp[id + 8], mod);
tmp[id] = mod_sum(tmp[id], tmp[id + 4], mod);
tmp[id] = mod_sum(tmp[id], tmp[id + 2], mod);
tmp[id] = mod_sum(tmp[id], tmp[id + 1], mod);
}
// write back to global memory
if (id == 0) {
sum[blockIdx.x] = tmp_sum[0];
}
}
int n, a[32*32], sum[1<<7];
int main(int argc, char *argv[]) {
if (scanf("%d", &n) != 1) return 1;
if (n < 3 || n > 32) return 1;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
int aij = 1; scanf("%d", &aij);
if (i == j) a[i*n+j] = 0;
else a[i*n+j] = aij;
}
}
int block_size = 1;
const int MAX_BLOCK_LV = 16;
if (n <= MAX_BLOCK_LV) block_size = 1<<(n-1);
else block_size = 1<<MAX_BLOCK_LV;
int sum_size = 128;
int *gpu_a, *gpu_ans, *gpu_sum;
cudaMalloc(&gpu_a, sizeof a);
cudaMalloc(&gpu_ans, sizeof(int) * block_size); // only resides in GPU!
cudaMalloc(&gpu_sum, sizeof(int) * sum_size);
cudaMemcpy(gpu_a, a, sizeof a, cudaMemcpyHostToDevice);
for (int i = 1; i < argc; i++) {
unsigned mod = 0;
sscanf(argv[i], "%u", &mod);
my_hamilton<<<block_size, n-1>>>(n, gpu_a, gpu_ans, mod);
sum_all<<<sum_size, 256>>>(block_size, gpu_ans, gpu_sum, mod);
cudaDeviceSynchronize();
cudaMemcpy(sum, gpu_sum, sizeof(int) * sum_size, cudaMemcpyDeviceToHost);
unsigned ans = 0;
for (int j = 0; j < sum_size; j++) ans = mod_sum(ans, sum[j], mod);
printf("%u\n", ans);
}
} |
10,633 | #include <cstdio>
#include <stdio.h>
//kernel program for the device (GPU): compiled by NVcc
__global__ void addKernel(int*c, const int *a, const int *b){
int i = threadIdx.x;
c[i] = a[i] + b[i];
}
#define SIZE 1000000
int main(void){//create
int *a = new int[SIZE];
int *b = new int[SIZE];
int *c = new int[SIZE];
for(int postion = 0 ; postion < SIZE; postion++){
a[postion] = 2;
b[postion] = 2;
}
//device-side data
int *dev_a = 0;
int *dev_b = 0;
int *dev_c = 0;
//allocate device memory
cudaMalloc((void**)&dev_a, SIZE * sizeof(int));
cudaMalloc((void**)&dev_b, SIZE * sizeof(int));
cudaMalloc((void**)&dev_c, SIZE * sizeof(int));
//copy from host to device
cudaMemcpy(dev_a, a, SIZE * sizeof(int), cudaMemcpyHostToDevice);// dev_a = a;
cudaMemcpy(dev_b, b, SIZE * sizeof(int), cudaMemcpyHostToDevice);// dev_b = b;
cudaMemcpy(dev_c, b, SIZE * sizeof(int), cudaMemcpyHostToDevice);// dev_b = b;
//launch a Kernel on the GPU with one thread for each element.
addKernel<<<1,SIZE>>>(dev_c, dev_a, dev_b);
//copy from device to host
cudaMemcpy(c, dev_c, SIZE * sizeof(int), cudaMemcpyDeviceToHost);// c = dev_c;
//free device memory
cudaFree(dev_c);
cudaFree(dev_a);
cudaFree(dev_b);
//print the result
printf("{%d,%d,%d,%d,%d} + {%d,%d,%d,%d,%d}"
"={%d,%d,%d,%d,%d}\n",
a[0],a[1],a[2],a[3],a[4],
b[0],b[1],b[2],b[3],b[4],
c[0],c[1],c[2],c[3],c[4]);
return 0;
}
|
10,634 | #include <stdio.h>
#include <stdlib.h>
#include <iostream>
#include <cuda_runtime.h>
// same kernel with different name
__global__ void Kernel_00(float*x, int len)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if(tid < len) {
float sum = x[tid];
int iter = 0;
while(iter++ < len) {
sum += 1;
}
x[tid] = sum;
}
}
__global__ void Kernel_01(float*x, int len)
{
int tid = threadIdx.x + blockIdx.x * blockDim.x;
if(tid < len) {
float sum = x[tid];
int iter = 0;
while(iter++ < len) {
sum += 1;
}
x[tid] = sum;
}
}
int main(int argc, char **argv)
{
const int streamsNum = 2;
int N=1<<10; // 1K
if (argc == 2) {
N = atoi(argv[1]);
}
if (argc > 2) {
fprintf(stderr, "Too many arguments! ./stream_sync N .\n");
exit(1);
}
std::cout << "Running " << N << " (floats) as the input data size." << std::endl;
std::cout << "Launching " << streamsNum << " cuda streams." << std::endl;
// device property
cudaDeviceProp device_prop;
int devID = 0; // use 1st device as default
cudaGetDeviceProperties(&device_prop, devID);
// query for priority
int priority_l;
int priority_h;
cudaDeviceGetStreamPriorityRange(&priority_l, &priority_h);
printf("Stream priority range: LOW: %d to HIGH: %d on %s\n", priority_l,priority_h, device_prop.name);
// host
float *h_a = NULL;
float *h_b = NULL;
cudaMallocHost((void**)&h_a, sizeof(float) * N);
cudaMallocHost((void**)&h_b, sizeof(float) * N);
// init
for(int i=0; i<N; i++) {
h_a[i] = 0;
h_b[i] = 0;
}
// device
float*d_a = NULL;
float*d_b = NULL;
cudaMalloc((void**)&d_a, sizeof(float) * N);
cudaMalloc((void**)&d_b, sizeof(float) * N);
// streams
cudaStream_t streams[streamsNum];
cudaEvent_t events[streamsNum]; // events for streams
for(int i=0; i<streamsNum; i++) {
cudaStreamCreate(&streams[i]);
cudaEventCreate(&events[i]);
}
// configure priority for streams
cudaStreamCreateWithPriority(&streams[0], cudaStreamNonBlocking, priority_l); // stream 0 with low priority
cudaStreamCreateWithPriority(&streams[1], cudaStreamNonBlocking, priority_h); // stream 1 with high priority
//cudaStreamCreateWithPriority(&streams[0], cudaStreamNonBlocking, priority_h); // stream 0 with low priority
//cudaStreamCreateWithPriority(&streams[1], cudaStreamNonBlocking, priority_l); // stream 1 with high priority
// h2d
cudaMemcpyAsync(d_a, h_a, sizeof(float)*N, cudaMemcpyHostToDevice, streams[0]);
cudaMemcpyAsync(d_b, h_b, sizeof(float)*N, cudaMemcpyHostToDevice, streams[1]);
// kernel
dim3 block = dim3(128,1,1);
dim3 grid = dim3((N + block.x - 1) / block.x,1,1);
// low priority kernel
Kernel_00 <<< grid, block, 0, streams[0] >>> (d_a, N); // a + x
cudaEventRecord(events[0], streams[0]);
// high priority kernel
Kernel_01 <<< grid, block, 0, streams[1] >>> (d_b, N); // b + x
cudaEventRecord(events[1], streams[1]);
cudaEventSynchronize(events[0]);
cudaEventSynchronize(events[1]);
// d2h
cudaMemcpyAsync(h_a, d_a, sizeof(float)*N, cudaMemcpyDeviceToHost, streams[0]);
cudaMemcpyAsync(h_b, d_b, sizeof(float)*N, cudaMemcpyDeviceToHost, streams[1]);
cudaDeviceSynchronize(); // NOTE: this is needed to make sure prev dev opt is done!
// check results
int error_a = 0;
for(int i=0; i<N; i++) {
if(h_a[i] != N) {
printf("h_a[%d] = %f\n",i, h_a[i]);
error_a += 1;
}
}
if(error_a == 0) {
printf("Pass test on h_a!\n");
}
int error_b = 0;
for(int i=0; i<N; i++) {
if(h_b[i] != N) {
printf("h_b[%d] = %f\n",i, h_b[i]);
error_b += 1;
}
}
if(error_b == 0) {
printf("Pass test on h_b!\n");
}
// free
for(int i=0; i<streamsNum; i++) {
cudaStreamDestroy(streams[i]);
cudaEventDestroy(events[i]);
}
cudaFree(d_a);
cudaFree(d_b);
cudaFreeHost(h_a);
cudaFreeHost(h_b);
return 0;
}
|
10,635 | #include "includes.h"
__global__ void TgvUpdateDualVariablesTGVKernel(float* u_, float2 *v_, float alpha0, float alpha1, float sigma, float eta_p, float eta_q, float* a, float* b, float*c, float4* grad_v, float2* p, float4* q, int width, int height, int stride)
{
int iy = blockIdx.y * blockDim.y + threadIdx.y; // current row
int ix = blockIdx.x * blockDim.x + threadIdx.x; // current column
float desiredRadius = (float)width / 2.20f;
float halfWidth = (float)width / 2.0f;
float halfHeight = (float)height / 2.0f;
float radius = sqrtf((iy - halfHeight) * (iy - halfHeight) + (ix - halfWidth) * (ix - halfWidth));
if ((iy < height) && (ix < width))
{
int pos = ix + iy * stride;
if (radius >= desiredRadius)
{
p[pos] = make_float2(0.0f, 0.0f);
q[pos] = make_float4(0.0f, 0.0f, 0.0f, 0.0f);
}
else {
int right = (ix + 1) + iy * stride;
int down = ix + (iy + 1) * stride;
int left = (ix - 1) + iy * stride;
int up = ix + (iy - 1) * stride;
//u_x = dxp(u_) - v_(:, : , 1);
float u_x, u_y;
if ((ix + 1) < width) u_x = u_[right] - u_[pos] - v_[pos].x;
else u_x = u_[pos] - u_[left] - v_[pos].x;
//u_y = dyp(u_) - v_(:, : , 2);
if ((iy + 1) < height) u_y = u_[down] - u_[pos] - v_[pos].y;
else u_y = u_[pos] - u_[up] - v_[pos].y;
//du_tensor_x = a.*u_x + c.*u_y;
float du_tensor_x = a[pos] * u_x + c[pos] * u_y;
//du_tensor_y = c.*u_x + b.*u_y;
float du_tensor_y = c[pos] * u_x + b[pos] * u_y;
//p(:, : , 1) = p(:, : , 1) + alpha1*sigma / eta_p.*du_tensor_x;
p[pos].x = p[pos].x + (alpha1*sigma / eta_p) * du_tensor_x;
//p(:, : , 2) = p(:, : , 2) + alpha1*sigma / eta_p.*du_tensor_y;
p[pos].y = p[pos].y + (alpha1*sigma / eta_p) * du_tensor_y;
//projection
//reprojection = max(1.0, sqrt(p(:, : , 1). ^ 2 + p(:, : , 2). ^ 2));
float reprojection = sqrtf(p[pos].x * p[pos].x + p[pos].y * p[pos].y);
if (reprojection < 1.0f) {
reprojection = 1.0f;
}
//p(:, : , 1) = p(:, : , 1). / reprojection;
p[pos].x = p[pos].x / reprojection;
//p(:, : , 2) = p(:, : , 2). / reprojection;
p[pos].y = p[pos].y / reprojection;
//grad_v(:, : , 1) = dxp(v_(:, : , 1));
if ((ix + 1) < width) grad_v[pos].x = v_[right].x - v_[pos].x;
else grad_v[pos].x = v_[pos].x - v_[left].x;
//grad_v(:, : , 2) = dyp(v_(:, : , 2));
if ((iy + 1) < height) grad_v[pos].y = v_[down].y - v_[pos].y;
else grad_v[pos].y = v_[pos].y - v_[up].y;
//grad_v(:, : , 3) = dyp(v_(:, : , 1));
if ((iy + 1) < height) grad_v[pos].z = v_[down].x - v_[pos].x;
else grad_v[pos].z = v_[pos].x - v_[up].x;
//grad_v(:, : , 4) = dxp(v_(:, : , 2));
if ((ix + 1) < width) grad_v[pos].w = v_[right].y - v_[pos].y;
else grad_v[pos].w = v_[pos].y - v_[left].y;
//q = q + alpha0*sigma / eta_q.*grad_v;
float ase = alpha0 * sigma / eta_q;
float4 qpos;
qpos.x = q[pos].x + ase * grad_v[pos].x;
qpos.y = q[pos].y + ase * grad_v[pos].y;
qpos.z = q[pos].z + ase * grad_v[pos].z;
qpos.w = q[pos].w + ase * grad_v[pos].w;
//reproject = max(1.0, sqrt(q(:, : , 1). ^ 2 + q(:, : , 2). ^ 2 + q(:, : , 3). ^ 2 + q(:, : , 4). ^ 2));
float reproject = sqrtf(qpos.x * qpos.x + qpos.y * qpos.y + qpos.z * qpos.z + qpos.w * qpos.w);
if (reproject < 1.0f) {
reproject = 1.0f;
}
//q(:, : , 1) = q(:, : , 1). / reproject;
q[pos].x = qpos.x / reproject;
//q(:, : , 2) = q(:, : , 2). / reproject;
q[pos].y = qpos.y / reproject;
//q(:, : , 3) = q(:, : , 3). / reproject;
q[pos].z = qpos.z / reproject;
//q(:, : , 4) = q(:, : , 4). / reproject;
q[pos].w = qpos.w / reproject;
}
}
} |
10,636 | /******************************************************************************/
/* CUDA Sample Program (Vector add) monotone-RK 2014.08.21 */
/******************************************************************************/
#include <stdio.h>
__global__
void vecadd(float *a, float *b, float *c) {
c[threadIdx.x] = a[threadIdx.x] + b[threadIdx.x];
}
int main(int argc, char *argv[]) {
const int num = 16;
float *a;
float *b;
float *c;
cudaMallocHost(&a, num*sizeof(float));
cudaMallocHost(&b, num*sizeof(float));
cudaMallocHost(&c, num*sizeof(float));
for (int i=0; i<num; i++) {
a[i] = 1.0;
b[i] = 2.0;
c[i] = 0.0;
}
dim3 grid_size = dim3(1, 1, 1); // determine the number of blocks
dim3 block_size = dim3(num, 1, 1); // determine the number of threads
vecadd<<<grid_size, block_size>>>(a, b, c);
cudaThreadSynchronize();
for (int i=0; i<num; ++i) printf("c[%2d]: %f\n", i, c[i]);
cudaFreeHost(a);
cudaFreeHost(b);
cudaFreeHost(c);
return 0;
} |
10,637 | #include <stdio.h>
#include <stdlib.h>
#define RADIUS 8
#define BLOCK_SIZE 512
__global__ void stencil_shared(double *in, double *out, int vector_size) {
// RADIUS is # of elements on either side of the element
__shared__ double temp[BLOCK_SIZE + (2 * RADIUS)];
// Global index used to access the global memory array
int gindex = threadIdx.x + blockIdx.x * blockDim.x;
// Local index used to access the shared memory array. Notice its initialized value
int lindex = threadIdx.x + RADIUS;
// This if statement is added to avoid illegal memory accesses
if (gindex < vector_size){
// Read input elements into shared memory
temp[lindex] = in[gindex];
// At both end of a block, the sliding window moves beyond the block boundary (into halo cells).
// E.g, for thread id = 512, we will read in[505] and in[1030] into temp.
if (threadIdx.x < RADIUS) {
temp[lindex - RADIUS] = ((gindex - RADIUS) > 0) ? in[gindex - RADIUS] : 0.0;
temp[lindex + BLOCK_SIZE] = ((gindex + BLOCK_SIZE) < vector_size) ? in[gindex + BLOCK_SIZE] : 0.0;
}
} else {
temp[lindex] = 0.0;
}
__syncthreads();
if (gindex < vector_size){
// Apply the stencil
double result = 0.0;
for (int offset = -RADIUS ; offset <= RADIUS ; ++offset)
result += temp[lindex + offset];
// Store the result in the output array in global memory
out[gindex] = result;
}
}
int main( int argc, char* argv[] ) {
// Parse Input arguments
// Check the number of arguments (we only receive command + vector size)
if (argc != 2) {
// Tell the user how to run the program
printf ("Usage: %s vector_size\n", argv[0]);
// "Usage messages" are a conventional way of telling the user
// how to run a program if they enter the command incorrectly.
return 1;
}
// Set GPU Variables based on input arguments
int vector_size = atoi(argv[1]);
int grid_size = ((vector_size-1)/BLOCK_SIZE) + 1;
// Set device that we will use for our cuda code
// It will be 0, 1, 2 or 3
cudaSetDevice(0);
// Time Variables
cudaEvent_t start, stop;
float time;
cudaEventCreate (&start);
cudaEventCreate (&stop);
// set mem shared size
//cudaDeviceSetSharedMemConfig(cudaSharedMemBankSizeFourByte);
//cudaDeviceSetSharedMemConfig(cudaSharedMemBankSizeEightByte);
// CPU Struct
double *in_cpu = new double [vector_size];
double *out_cpu = new double [vector_size];
double *out_gpu_on_cpu = new double [vector_size];
// fill the arrays 'a' and 'b' on the CPU
printf("Initializing input arrays.\n");
for (int i = 0; i < vector_size; i++) {
in_cpu[i] = (rand()%100)*cos(i);
out_cpu[i] = 0.0;
out_gpu_on_cpu[i] = 0.0;
}
// allocate the memory on the GPU
double *in_gpu;
double *out_gpu;
cudaMalloc (&in_gpu, vector_size*sizeof(double));
cudaMalloc (&out_gpu, vector_size*sizeof(double));
// copy the input to the GPU
cudaMemcpy (in_gpu, in_cpu, vector_size*sizeof(double), cudaMemcpyHostToDevice);
//
// CPU Calculation
//////////////////
printf("Running sequential job.\n");
cudaEventRecord(start,0);
// Calculate C in the CPU
for (int i = 0; i < vector_size; ++i) {
for (int offset = -RADIUS ; offset <= RADIUS ; ++offset)
out_cpu[i] += (i + offset >= 0 && i + offset < vector_size) ? in_cpu[i + offset] : 0.0;
}
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time, start, stop);
printf("\tSequential Job Time: %.2f ms\n", time);
//
// GPU Calculation
////////////////////////
printf("Running parallel job.\n");
cudaEventRecord(start,0);
// call the kernel
stencil_shared<<<grid_size, BLOCK_SIZE>>>(in_gpu, out_gpu, vector_size);
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&time, start, stop);
printf("\tParallel Job Time: %.2f ms\n", time);
// copy the array 'c' back from the GPU to the CPU
cudaMemcpy (out_gpu_on_cpu, out_gpu, vector_size*sizeof(double), cudaMemcpyDeviceToHost);
// compare the results
int error = 0;
for (int i = 0; i < vector_size; i++) {
if (out_cpu[i] != out_gpu_on_cpu[i]){
error = 1;
printf( "Mistake at element %d\n", i);
int start = (i-RADIUS<0)?0:i-RADIUS;
int end = (i+RADIUS>vector_size)?vector_size:i+RADIUS;
for (int offset = start ; offset <= end ; offset++)
printf( "index = %d \tin = %.5lf \tout GPU = %.5lf \tCPU %.5lf\n", offset,
in_cpu[offset],
out_gpu_on_cpu[offset],
out_cpu[offset] );
}
if (error) break;
}
if (error == 0){
printf ("Correct result. No errors were found.\n");
}
// free CPU data
free (in_cpu);
free (out_cpu);
free (out_gpu_on_cpu);
// free the memory allocated on the GPU
cudaFree (in_gpu);
cudaFree (out_gpu);
return 0;
}
|
10,638 | /*
* Copyright 1993-2007 NVIDIA Corporation. All rights reserved.
*
* NOTICE TO USER:
*
* This source code is subject to NVIDIA ownership rights under U.S. and
* international Copyright laws. Users and possessors of this source code
* are hereby granted a nonexclusive, royalty-free license to use this code
* in individual and commercial software.
*
* NVIDIA MAKES NO REPRESENTATION ABOUT THE SUITABILITY OF THIS SOURCE
* CODE FOR ANY PURPOSE. IT IS PROVIDED "AS IS" WITHOUT EXPRESS OR
* IMPLIED WARRANTY OF ANY KIND. NVIDIA DISCLAIMS ALL WARRANTIES WITH
* REGARD TO THIS SOURCE CODE, INCLUDING ALL IMPLIED WARRANTIES OF
* MERCHANTABILITY, NONINFRINGEMENT, AND FITNESS FOR A PARTICULAR PURPOSE.
* IN NO EVENT SHALL NVIDIA BE LIABLE FOR ANY SPECIAL, INDIRECT, INCIDENTAL,
* OR CONSEQUENTIAL DAMAGES, OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
* OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE
* OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE
* OR PERFORMANCE OF THIS SOURCE CODE.
*
* U.S. Government End Users. This source code is a "commercial item" as
* that term is defined at 48 C.F.R. 2.101 (OCT 1995), consisting of
* "commercial computer software" and "commercial computer software
* documentation" as such terms are used in 48 C.F.R. 12.212 (SEPT 1995)
* and is provided to the U.S. Government only as a commercial end item.
* Consistent with 48 C.F.R.12.212 and 48 C.F.R. 227.7202-1 through
* 227.7202-4 (JUNE 1995), all U.S. Government End Users acquire the
* source code with only those rights set forth herein.
*
* Any use of this source code in individual and commercial software must
* include, in the user documentation and internal comments to the code,
* the above Disclaimer and U.S. Government End Users Notice.
*/
//24-bit multiplication is faster on G80,
//but we must be sure to multiply integers
//only within [-8M, 8M - 1] range
#define IMUL(a, b) __mul24(a, b)
////////////////////////////////////////////////////////////////////////////////
// Kernel configuration
////////////////////////////////////////////////////////////////////////////////
#define KERNEL_RADIUS 8
#define KERNEL_W (2 * KERNEL_RADIUS + 1)
__device__ __constant__ float d_Kernel[KERNEL_W];
// Assuming ROW_TILE_W, KERNEL_RADIUS_ALIGNED and dataW
// are multiples of coalescing granularity size,
// all global memory operations are coalesced in convolutionRowGPU()
#define ROW_TILE_W 128
#define KERNEL_RADIUS_ALIGNED 16
// Assuming COLUMN_TILE_W and dataW are multiples
// of coalescing granularity size, all global memory operations
// are coalesced in convolutionColumnGPU()
#define COLUMN_TILE_W 16
#define COLUMN_TILE_H 48
////////////////////////////////////////////////////////////////////////////////
// Loop unrolling templates, needed for best performance
////////////////////////////////////////////////////////////////////////////////
template<long long int i> __device__ float convolutionRow(float *data){
return
data[KERNEL_RADIUS - i] * d_Kernel[i]
+ convolutionRow<i - 1>(data);
}
template<> __device__ float convolutionRow<-1>(float *data){
return 0;
}
template<long long int i> __device__ float convolutionColumn(float *data){
return
data[(KERNEL_RADIUS - i) * COLUMN_TILE_W] * d_Kernel[i]
+ convolutionColumn<i - 1>(data);
}
template<> __device__ float convolutionColumn<-1>(float *data){
return 0;
}
////////////////////////////////////////////////////////////////////////////////
// Row convolution filter
////////////////////////////////////////////////////////////////////////////////
__global__ void convolutionRowGPU(
float *d_Result,
float *d_Data,
int dataW,
int dataH
){
//Data cache
__shared__ float data[KERNEL_RADIUS + ROW_TILE_W + KERNEL_RADIUS];
//Current tile and apron limits, relative to row start
const int tileStart = IMUL(blockIdx.x, ROW_TILE_W);
const int tileEnd = tileStart + ROW_TILE_W - 1;
const int apronStart = tileStart - KERNEL_RADIUS;
const int apronEnd = tileEnd + KERNEL_RADIUS;
//Clamp tile and apron limits by image borders
const int tileEndClamped = min(tileEnd, dataW - 1);
const int apronStartClamped = max(apronStart, 0);
const int apronEndClamped = min(apronEnd, dataW - 1);
//Row start index in d_Data[]
const int rowStart = IMUL(blockIdx.y, dataW);
//Aligned apron start. Assuming dataW and ROW_TILE_W are multiples
//of half-warp size, rowStart + apronStartAligned is also a
//multiple of half-warp size, thus having proper alignment
//for coalesced d_Data[] read.
const int apronStartAligned = tileStart - KERNEL_RADIUS_ALIGNED;
const int loadPos = apronStartAligned + threadIdx.x;
//Set the entire data cache contents
//Load global memory values, if indices are within the image borders,
//or initialize with zeroes otherwise
if(loadPos >= apronStart){
const int smemPos = loadPos - apronStart;
data[smemPos] =
((loadPos >= apronStartClamped) && (loadPos <= apronEndClamped)) ?
d_Data[rowStart + loadPos] : 0;
}
//Ensure the completness of the loading stage
//because results, emitted by each thread depend on the data,
//loaded by another threads
__syncthreads();
const int writePos = tileStart + threadIdx.x;
//Assuming dataW and ROW_TILE_W are multiples of half-warp size,
//rowStart + tileStart is also a multiple of half-warp size,
//thus having proper alignment for coalesced d_Result[] write.
if(writePos <= tileEndClamped){
const int smemPos = writePos - apronStart;
float sum = 0;
#ifdef UNROLL_INNER
sum = convolutionRow<2 * KERNEL_RADIUS>(data + smemPos);
#else
for(int k = -KERNEL_RADIUS; k <= KERNEL_RADIUS; k++)
sum += data[smemPos + k] * d_Kernel[KERNEL_RADIUS - k];
#endif
d_Result[rowStart + writePos] = sum;
}
}
////////////////////////////////////////////////////////////////////////////////
// Column convolution filter
////////////////////////////////////////////////////////////////////////////////
__global__ void convolutionColumnGPU(
float *d_Result,
float *d_Data,
int dataW,
int dataH,
int smemStride,
int gmemStride
){
//Data cache
__shared__ float data[COLUMN_TILE_W * (KERNEL_RADIUS + COLUMN_TILE_H + KERNEL_RADIUS)];
//Current tile and apron limits, in rows
const int tileStart = IMUL(blockIdx.y, COLUMN_TILE_H);
const int tileEnd = tileStart + COLUMN_TILE_H - 1;
const int apronStart = tileStart - KERNEL_RADIUS;
const int apronEnd = tileEnd + KERNEL_RADIUS;
//Clamp tile and apron limits by image borders
const int tileEndClamped = min(tileEnd, dataH - 1);
const int apronStartClamped = max(apronStart, 0);
const int apronEndClamped = min(apronEnd, dataH - 1);
//Current column index
const int columnStart = IMUL(blockIdx.x, COLUMN_TILE_W) + threadIdx.x;
//Shared and global memory indices for current column
int smemPos = IMUL(threadIdx.y, COLUMN_TILE_W) + threadIdx.x;
int gmemPos = IMUL(apronStart + threadIdx.y, dataW) + columnStart;
//Cycle through the entire data cache
//Load global memory values, if indices are within the image borders,
//or initialize with zero otherwise
for(int y = apronStart + threadIdx.y; y <= apronEnd; y += blockDim.y){
data[smemPos] =
((y >= apronStartClamped) && (y <= apronEndClamped)) ?
d_Data[gmemPos] : 0;
smemPos += smemStride;
gmemPos += gmemStride;
}
//Ensure the completness of the loading stage
//because results, emitted by each thread depend on the data,
//loaded by another threads
__syncthreads();
//Shared and global memory indices for current column
smemPos = IMUL(threadIdx.y + KERNEL_RADIUS, COLUMN_TILE_W) + threadIdx.x;
gmemPos = IMUL(tileStart + threadIdx.y , dataW) + columnStart;
//Cycle through the tile body, clamped by image borders
//Calculate and output the results
for(int y = tileStart + threadIdx.y; y <= tileEndClamped; y += blockDim.y){
float sum = 0;
#ifdef UNROLL_INNER
sum = convolutionColumn<2 * KERNEL_RADIUS>(data + smemPos);
#else
for(int k = -KERNEL_RADIUS; k <= KERNEL_RADIUS; k++)
sum +=
data[smemPos + IMUL(k, COLUMN_TILE_W)] *
d_Kernel[KERNEL_RADIUS - k];
#endif
d_Result[gmemPos] = sum;
smemPos += smemStride;
gmemPos += gmemStride;
}
}
|
10,639 | /*
A program that shows the effect of increasing the thread count
while keeping the input size and block count constant.
Performance is improved as the number of threads increase.
*/
/*******************************
1 - Install nvidia-cuda-toolkit
2 - Compile this program using:
nvcc add.cu -o add_cuda.out
*******************************/
#include <iostream>
#include <math.h>
#include <ctime>
#include <cstdio>
//CUDA kernel to add elements of the matrix
// __global__ converts a function into a CUDA kernel
__global__
void add(int n, float *x, float *y)
{
// index of the current thread within the block
int index = blockIdx.x * blockDim.x + threadIdx.x;
// number of threads in a block
int stride = blockDim.x * gridDim.x;
// run each addition on a separate thread
for (int i = index; i < n; i+=stride)
y[i] = x[i] + y[i];
}
int main(void)
{
for(int t = 32; t <= 1024; t+=32)
{
int N = 1<<24; // 1M elements
// Memory allocation in CUDA is done with cudaMallocManaged( , )
float *x; float *y;
cudaMallocManaged( &x, N*sizeof(float) );
cudaMallocManaged( &y, N*sizeof(float) );
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f;
}
std::clock_t start = clock();
// Launch the 'add' kernel, which invokes it in the GPU
// Run kernel on 1M elements on the CPU
add<<<1,t>>>(N, x, y);
// Wait for the GPU to synchronize before accessign through host(CPU)
cudaDeviceSynchronize();
std::clock_t stop = clock();
int duration = 1000 * (stop - start) / (double)CLOCKS_PER_SEC;
//std::cout << "Running time using " << t << " threads = " << duration << "\n";
std::cout << duration << "\n";
// Check for errors (all values should be 3.0f)
/*float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(y[i]-3.0f));
std::cout << "Max error: " << maxError << std::endl;
*/
// Deallocating memory using cudaFree()
cudaFree(x);
cudaFree(y);
}
return 0;
}
|
10,640 | // // This is a good place to start looking to
// // grasp teh basics.
// #include <stdio.h>
// #include <stdlib.h>
// #include <ctime>
// #include <ratio>
// #include <chrono>
// #include "main_cuda.cuh"
// using namespace std::chrono;
// static const int BLOCK_SIZE = 256;
// // Device Portion of Quick Sort
// __global__ void vecSquare(int *a, int *c, int n)
// {
// int id = blockIdx.x * blockDim.x + threadIdx.x;
// if (id < n)
// c[id] = a[id] * a[id];
// }
// // Device Portion of Quick Sort
// duration<double> square_vector_gpu(int size)
// {
// int *ha, *hc, *da, *dc;
// ha = (int *)malloc(sizeof(int) * size);
// hc = (int *)malloc(sizeof(int) * size);
// for (int i = 0; i < size; i++)
// {
// ha[i] = rand();
// hc[i] = 0;
// }
// high_resolution_clock::time_point start = high_resolution_clock::now();
// gpuErrchk(cudaMalloc((void **)&da, sizeof(int) * size));
// gpuErrchk(cudaGetLastError());
// gpuErrchk(cudaMalloc((void **)&dc, sizeof(int) * size));
// gpuErrchk(cudaGetLastError());
// gpuErrchk(cudaMemcpy(da, ha, sizeof(int) * size, cudaMemcpyHostToDevice));
// gpuErrchk(cudaGetLastError());
// int grid = ceil(size * 1.0 / BLOCK_SIZE);
// vecSquare<<<grid, BLOCK_SIZE>>>(da, dc, size);
// cudaDeviceSynchronize();
// gpuErrchk(cudaGetLastError());
// cudaMemcpy(hc, dc, sizeof(int) * size, cudaMemcpyDeviceToHost);
// cudaFree(da);
// cudaFree(dc);
// cudaDeviceReset();
// high_resolution_clock::time_point end = high_resolution_clock::now();
// // Testing that sort is working, keep commented out on large values of N (say N > 1000)
// // for (int i = 0; i < size; i++) {
// // printf("\t %d\n", hc[i]);
// // }
// free(ha);
// free(hc);
// return time_calc(start, end);
// }
|
10,641 | #include "includes.h"
__global__ void roipool_fp_cuda_(int nProposal, int C, float *feats, int *proposals_offset, float *output_feats, int *output_maxidx){
for(int pp_id = blockIdx.x; pp_id < nProposal; pp_id += gridDim.x){
int start = proposals_offset[pp_id];
int end = proposals_offset[pp_id + 1];
for(int plane = threadIdx.x; plane < C; plane += blockDim.x){
int argmax_idx = -1;
float max_val = -1e50;
for(int i = start; i < end; i++){
if(feats[i * C + plane] > max_val){
argmax_idx = i;
max_val = feats[i * C + plane];
}
}
output_maxidx[pp_id * C + plane] = argmax_idx;
output_feats[pp_id * C + plane] = max_val;
}
}
} |
10,642 | #include <cuda.h>
#include <cuda_runtime.h>
#include <cuda_fp16.h>
#include <vector_types.h>
#include <stdio.h>
#include <random>
using copy1_t = int;
using copy2_t = int2;
using copy4_t = int4;
__global__
void copy(float* buffer) {
extern __shared__ float shmem[];
const int lane_id = threadIdx.x % 32;
#pragma unroll
for (int i = 0; i < 15; ++i) {
*((copy4_t *)(shmem + i * 128) + lane_id) = *((copy4_t *)(buffer + i * 128) + lane_id);
}
}
void initRandMatrix(float* mem, int rows, int cols) {
static std::random_device rd;
static std::mt19937 gen(rd());
static std::uniform_int_distribution<int> dis(0, 1);
for (int i = 0; i < rows; ++i) {
for (int j = 0; j < cols; ++j) {
mem[j + cols * i] = dis(gen);
}
}
return;
}
int main(void) {
dim3 dimBlock(32, 1);
dim3 dimGrid(1, 1);
int shmem_bytes = 65536;
float* h = new float[128 * 16];
float* d;
cudaError_t err = cudaMalloc((void **) &d, 128 * 16 * sizeof(float));
initRandMatrix(h, 16, 128);
cudaMemcpy(d, h, 128 * 16 * sizeof(float), cudaMemcpyHostToDevice);
cudaFuncSetAttribute(copy, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem_bytes);
copy<<<dimGrid, dimBlock, shmem_bytes>>>(d);
delete [] h;
cudaFree(d);
return 0;
}
/*
printf(" Total amount of shared memory per block: %zu bytes\n",
deviceProp.sharedMemPerBlock);
*/
|
10,643 | #include <iostream>
#include <stdlib.h>
#include <stdio.h>
using namespace std;
typedef int (*compfn)(const void*, const void*);
struct Point
{
int x;
int y;
};
Point* h_data;
int compare(struct Point * elem1, struct Point * elem2)
{
if ( elem1->x < elem2->x)
return -1;
else if (elem1->x > elem2->x)
return 1;
else if (elem1->x == elem2->x)
{
if ( elem1->y < elem2->y)
return -1;
else if (elem1->y > elem2->y)
return 1;
}
else
return 0;
}
int main()
{
FILE* input;
input = fopen("circle_65536.txt", "r");
//get number of points
int numPoints;
fscanf(input, "%d", &numPoints);
fscanf(input, "%d", &numPoints);
//printf("%d\n", numPoints);
//system("PAUSE");
h_data = (Point*)malloc(sizeof(Point) * numPoints);
//initialize input
for (int i = 0; i < numPoints; i++){
fscanf(input, "%d %d", &h_data[i].x, &h_data[i].y);
}
qsort((void *) h_data, 65536, sizeof(Point), (compfn)compare);
for (int i = 0; i < numPoints; i++)
{
cout << h_data[i].x << " " << h_data[i].y << endl;
}
return 0;
} |
10,644 | #include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <cuda.h>
#pragma comment(lib, "cudart.lib")
void ReadInputData(char *fileName, int *matrix)
{
FILE *fp;
char temp[10];
fp = fopen(fileName, "r");
for (int i=0; i<9; i++)
{
fscanf(fp,"%s",temp);
for (int j=0; j<9; j++)
{
matrix[i*9+j] = temp[j]-'0';
}
}
fclose(fp);
}
void OutputResultData(char *fileName, int *d_matrix)
{
int matrix[81] = {0};
cudaMemcpy(matrix, d_matrix, 81* sizeof(int), cudaMemcpyDeviceToHost);
FILE *fp;
//char temp[10];
fp = fopen(fileName, "w");
for (int i=0; i<9; i++)
{
for (int j=0; j<9; j++)
{
fprintf(fp,"%d",matrix[i*9+j]);
}
fprintf(fp,"\n");
}
fprintf(fp,"\n");
fclose(fp);
}
//Outputing the soduku matrix according to appropriate format
void testRes(int *d_matrix)
{
int matrix[81] = {0};
cudaMemcpy(matrix, d_matrix, 81* sizeof(int), cudaMemcpyDeviceToHost);
int i,j;
for(i=0;i<9;i++)
{
for(j=0;j<9;j++)
{
printf("%d ",matrix[i*9+j]);
}
printf("\n");
}
printf("\n");
}
__device__ void CalSudokuCondense(int i, int j, int *m, int n)
{
int kk;
int breakTrue = false;
// determine row
for (int ii=0; ii<9; ii++)
{
for (kk=1; kk<10; kk++)
{
if (m[ii*90+j*10+kk] == n)
{
m[ii*90+j*10+kk] = 0;
atomicSub(&m[ii*90+j*10],1);
breakTrue = true;
break;
}
}
//if (breakTrue)
//{
// for (int tt = kk; tt<8; tt++)
// {
// m[ii*90+j*9+tt] = m[ii*90+j*9+tt+1];
// }
//}
}
//syncthreads();
//determine column
for (int jj=0; jj<9; jj++)
{
for (kk=1; kk<10; kk++)
{
if (m[i*90+jj*10+kk] == n)
{
m[i*90+jj*10+kk] = 0;
//m[i*90+jj*10] = m[i*90+jj*10]-1;
atomicSub(&m[i*90+jj*10],1);
breakTrue = true;
break;
}
}
//if (breakTrue)
//{
// for (int tt = kk; tt<8; tt++)
// {
// m[i*90+jj*9+tt] = m[i*90+jj*9+tt+1];
// }
//}
}
//syncthreads();
// determine 3*3 grid
int idxx = i/3*3;
int idxy = j/3*3;
for (int ii=idxx; ii<idxx+3; ii++)
{
for (int jj=idxy; jj<idxy+3; jj++)
{
for (kk=1; kk<10; kk++)
{
if (m[ii*90+jj*10+kk] == n)
{
m[ii*90+jj*10+kk] = 0;
//m[ii*90+jj*10] = m[ii*90+jj*10]-1;
atomicSub(&m[ii*90+jj*10],1);
breakTrue = true;
break;
}
}
//if (breakTrue)
//{
// for (int tt = kk; tt<8; tt++)
// {
// m[ii*90+jj*9+tt] = m[ii*90+jj*9+tt+1];
// }
//}
}
}
//syncthreads();
}
__device__ void CalSudoku(int i, int j, int *sudoku_matrix, int mArray[10])
{
int availableTemp[9]={1,2,3,4,5,6,7,8,9};
if(0==sudoku_matrix[i*9+j])
{
//the empty grids to fill
//Finding out the eligible numbers, and locating them into array available[]
//row(i,matrix,available);
for (int k=0; k<9; k++)
{
if (sudoku_matrix[i*9+k] !=0)
{
availableTemp[sudoku_matrix[i*9+k]-1] = 0;
}
}
//column(j,matrix,available);
for (int k=0; k<9; k++)
{
if (sudoku_matrix[k*9+j] !=0)
{
availableTemp[sudoku_matrix[k*9+j]-1] = 0;
}
}
//block(i,j,matrix,available);
// determine 3*3 grid
int idxx = i/3*3;
int idxy = j/3*3;
for (int ii=idxx; ii<idxx+3; ii++)
{
for (int jj=idxy; jj<idxy+3; jj++)
{
if (sudoku_matrix[ii*9+jj] !=0)
{
availableTemp[sudoku_matrix[ii*9+jj]-1] = 0;
}
}
}
//condense(available);
int tt=1;
for (int k=0; k<9; k++)
{
if (availableTemp[k] != 0)
{
mArray[tt]= availableTemp[k];
tt++;
}
}
mArray[0]=tt-1;
}
}
__global__ void CheckEndCUDA(int *sudokuMatrix, int *Res)
{
int i = blockIdx.x;
//int i = threadIdx.y;
int j = threadIdx.x;
Res[0] = 1;
//int sum=0;
if (sudokuMatrix[i*9+j] == 0)
{
Res[0] = 0;
}
return;
}
__global__ void SudokuCUDA(int *sudokuMatrix, int *m)
{
int i = blockIdx.x;
//int i = threadIdx.y;
int j = threadIdx.x;
CalSudoku(i, j, sudokuMatrix, m+(i*90+j*10));
}
__global__ void SudokuCondenseCUDA(int *sudokuMatrix, int *m)
{
int i = blockIdx.x;
//int i = threadIdx.y;
int j = threadIdx.x;
//if (m[i*90+j*9] != 0 && m[i*90+j*9+1] == 0)
//{
// sudokuMatrix[i*9+j] = m[i*90+j*9];
// m[i*90+j*9] = 0;
// //printf("%d",sudokuMatrix[i*9+j])
// CalSudokuCondense(i, j, m,sudokuMatrix[i*9+j]);
//}
//int count=0;
//int dataTemp = 0;
//for (int k=1; k<10;k++)
//{
// if (m[i*90+j*10+k] != 0)
// {
// count++;
// dataTemp = m[i*90+j*10+k];
// }
//}
if (m[i*90+j*10] == 1)
//if(count == 1)
{
sudokuMatrix[i*9+j] = m[i*90+j*10+1];
//CalSudokuCondense(i, j, m,sudokuMatrix[i*9+j]);
}
}
void TestM(char *fileName, int *m)
{
FILE *fp;
//char temp[10];
fp = fopen(fileName, "w");
for (int iii=0; iii<9;iii++)
{
for (int jjj=0; jjj<9; jjj++)
{
for (int kkk=0; kkk<10; kkk++)
{
fprintf(fp, "%d ", m[iii*90+jjj*10+kkk]);
}
fprintf(fp,"\n");
}
}
fclose(fp);
}
int main ()
{
//the matrix for sudoku
//Items 0 represent the empty grids
int sudoku_matrix[81]={0};
ReadInputData("sudokusolver.txt", sudoku_matrix); // read input data
int *dev_sudoku_matrix;
int memSizeMatirx = sizeof(int)*81;
cudaMalloc((void**)&dev_sudoku_matrix, memSizeMatirx); // GPU buffer store computing array
int *dev_m;
int memSizeM = sizeof(int)*9*9*10;
cudaMalloc((void**)&dev_m, memSizeM);// GPU buffer store the process computing array
int ResEnd = 0; // determine whether the computing is completed
int *dev_ResEnd;
cudaMalloc((void**)&dev_ResEnd, sizeof(int));
cudaMemcpy(dev_ResEnd,&ResEnd,sizeof(int),cudaMemcpyHostToDevice);
cudaMemcpy(dev_sudoku_matrix,sudoku_matrix,memSizeMatirx,cudaMemcpyHostToDevice); // copy data to GPU
cudaMemset(dev_m,0,memSizeM); //clear processing array
//computing the runtime
//float elapsedTimeInMs = 0.0f;
//cudaEvent_t start, stop;
//cudaEventCreate(&start);
//cudaEventCreate(&stop);
//cudaEventRecord(start, 0);
int threadDim = 9;
int blockDim = 9;
//int matrix[90*9] = {0};
while(!ResEnd)
{
SudokuCUDA<<<blockDim, threadDim>>>(dev_sudoku_matrix, dev_m); // computing all possible answers
//cudaMemcpy(matrix, dev_m, memSizeM, cudaMemcpyDeviceToHost);
//TestM("M1.txt", matrix);
SudokuCondenseCUDA<<<blockDim, threadDim>>>(dev_sudoku_matrix, dev_m); // simplify the function according to the only computed value
CheckEndCUDA<<<blockDim, threadDim>>>(dev_sudoku_matrix, dev_ResEnd); // check whether it is completed
cudaMemcpy(&ResEnd, dev_ResEnd, sizeof(int), cudaMemcpyDeviceToHost);
}
//computing the runtime
//cudaEventRecord(stop, 0);
//cudaDeviceSynchronize();
//cudaEventElapsedTime(&elapsedTimeInMs, start, stop);
//printf("GPU used %fms\n", elapsedTimeInMs);
OutputResultData("sudokusolver.sol", dev_sudoku_matrix);
//testRes(dev_sudoku_matrix);
cudaFree(dev_sudoku_matrix);
}
|
10,645 | #include <stdio.h>
#include <math.h>
#include <time.h>
#include <sys/time.h>
#include <iostream>
#include <cuda_runtime_api.h>
#define NSTEPS 8388600
#define NITER 8388600
#define P_START 0
#define P_END 10
struct timeval startTime;
struct timeval finishTime;
double timeIntervalLength;
__global__
void rect(int n, double h, double *area)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
double p_current;
double f_result;
for (int i = index; i < n; i += stride) {
p_current = i*h;
f_result = cos(p_current);
area[i] += f_result*h;
}
}
int main(int argc, char* argv[])
{
int i;
int blockSize, numBlocks;
double h;
double area_cpu;
double area_gpu;
double *area_gpu_device;
double *area_gpu_host;
double p_current = P_START;
double f_result;
cudaMalloc((void **) &area_gpu_device, NITER * sizeof *area_gpu_device);
area_gpu_host = (double*) malloc(NITER * sizeof(double));
//
//I N I T I A L I Z A T I O N S
//
h = (double)(P_END-P_START)/NSTEPS;
p_current = P_START;
area_cpu=0.0;
//Get the start time
gettimeofday(&startTime, NULL);
for(i = 0; i < NITER; i++)
{
p_current = i*h;
f_result = cos(p_current);
area_cpu += f_result*h;
p_current += h;
}
//Get the end time
gettimeofday(&finishTime, NULL); /* after time */
//Calculate the interval length
timeIntervalLength = (double)(finishTime.tv_sec-startTime.tv_sec) * 1000000
+ (double)(finishTime.tv_usec-startTime.tv_usec);
timeIntervalLength=timeIntervalLength/1000;
//Print the interval lenght
printf("Interval length on CPU: %g msec.\n", timeIntervalLength);
printf("Result on GPU: %.2lf \n",area_cpu);
blockSize = 256;
numBlocks = (NITER + blockSize - 1) / blockSize;
//Get the start time
gettimeofday(&startTime, NULL);
rect<<<numBlocks, blockSize>>>(NITER, h, area_gpu_device);
cudaThreadSynchronize();
cudaMemcpy(area_gpu_host, area_gpu_device, NITER * sizeof *area_gpu_device, cudaMemcpyDeviceToHost);
for (int i = 0; i < NITER; ++i)
area_gpu += area_gpu_host[i];
//Get the end time
gettimeofday(&finishTime, NULL); /* after time */
//Calculate the interval length
timeIntervalLength = (double)(finishTime.tv_sec-startTime.tv_sec) * 1000000
+ (double)(finishTime.tv_usec-startTime.tv_usec);
timeIntervalLength=timeIntervalLength/1000;
//Print the interval lenght
printf("Interval length on GPU: %g msec.\n", timeIntervalLength);
printf("Result on GPU: %.2lf \n",area_gpu);
return 0;
}
|
10,646 | //menyelesaikan persamaan eliptik 2D untuk persegi (X = Y = N)
#include <stdio.h>
#include <time.h>
#include <math.h>
void writeToFile(int X, int Y, int length, char* filename1, float* arr) {
//print ke file
FILE * pFile = fopen(filename1,"w");
fprintf(pFile, "%d\n%d\n",X,Y);
for (int i=0;i<Y;i++) {
for(int j=0;j<X;j++) {
fprintf(pFile,"%.2f ",arr[(i*X)+j]);
}
fprintf(pFile, "\n");
}
fclose(pFile);
}
float absol(float input) {
if(input < 0) {
return (-input);
}
else {
return input;
}
}
void cpuJacobi (float * input, float * output, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N, int jumlahElemen) {
for (int i = 0; i < jumlahElemen; i++) {
//float A = input[i], B = input[i], C = input[i], D = input[i];
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if(i == 0 || i == N-1 || ((i / N == 0) && (i % N == N-1)) || ((i % N == 0) && (i / N == N-1))) {
output[i] = input[i];
}
else if(poisson[i]) {
output[i] = input[i];
}
else if((i%N == 0) || (i/N == 0) || (i%N == N-1) || (i/N == N-1)) {
if(i/N == 0 && ad_n) {
output[i] = (input[i+1] + input[i+N] + input[i-1])/3;
}
else if(i%N == N-1 && ad_e) {
output[i] = (input[i-1] + input[i+N] + input[i-N])/3;
}
else if(i/N == N-1 && ad_s) {
output[i] = (input[i+1] + input[i-1] + input[i-N])/3;
}
else if(i%N == 0 && ad_w){
output[i] = (input[i+1] + input[i+N] + input[i-N])/3;
}
else{
output[i] = input[i];
}
}
else { //tidak pada batas
//lakukan perhitungan Jacobi
output[i] = (input[i-1] + input[i+1] + input[i-N] + input[i+N])/(4);
}
//hitung error
error[i] = output[i]-input[i];
//input[i] = output[i];
}
for(int i = 0; i< jumlahElemen; i++) {
input[i] = output[i];
}
}
void cpuPGS(float * input, float * output, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N, int jumlahElemen) {
//float A = 0, B = 0, C = 0, D = 0;
for (int i = 0; i < jumlahElemen; i++) {
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if(i == 0 || i == N-1 || ((i / N == 0) && (i % N == N-1)) || ((i % N == 0) && (i / N == N-1))) {
output[i] = input[i];
}
else if(poisson[i]) {
output[i] = input[i];
}
else if((i%N == 0) || (i/N == 0) || (i%N == N-1) || (i/N == N-1)) {
if(i/N == 0 && ad_n) {
output[i] = (input[i+1] + input[i+N] + input[i-1])/3;
}
else if(i%N == N-1 && ad_e) {
output[i] = (input[i-1] + input[i+N] + input[i-N])/3;
}
else if(i/N == N-1 && ad_s) {
output[i] = (input[i+1] + input[i-1] + input[i-N])/3;
}
else if(i%N == 0 && ad_w){
output[i] = (input[i+1] + input[i+N] + input[i-N])/3;
}
else{
output[i] = input[i];
}
}
else { //tidak pada batas
//lakukan perhitungan PGS
output[i] = (output[i-1] + input[i+1] + output[i-N] + input[i+N])/(4);
}
//hitung error
error[i] = output[i]-input[i];
//input[i] = output[i];
}
for(int i = 0; i< jumlahElemen; i++) {
input[i] = output[i];
}
}
void cpuPSOR(float * input, float * output, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N, int jumlahElemen, float omega) {
//float A = 0, B = 0, C = 0, D = 0;
for (int i = 0; i < jumlahElemen; i++) {
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if(i == 0 || i == N-1 || ((i / N == 0) && (i % N == N-1)) || ((i % N == 0) && (i / N == N-1))) {
output[i] = input[i];
}
else if(poisson[i]) {
output[i] = input[i];
}
else if((i%N == 0) || (i/N == 0) || (i%N == N-1) || (i/N == N-1)) {
if(i/N == 0 && ad_n) {
output[i] = (input[i+1] + input[i+N] + input[i-1])/3;
}
else if(i%N == N-1 && ad_e) {
output[i] = (input[i-1] + input[i+N] + input[i-N])/3;
}
else if(i/N == N-1 && ad_s) {
output[i] = (input[i+1] + input[i-1] + input[i-N])/3;
}
else if(i%N == 0 && ad_w){
output[i] = (input[i+1] + input[i+N] + input[i-N])/3;
}
else{
output[i] = input[i];
}
}
else { //tidak pada batas
//lakukan perhitungan PSOR
output[i] = input[i] + ((output[i-1] + input[i+1] + output[i-N] + input[i+N] - (input[i]*4))*omega/(4));
}
//hitung error
error[i] = output[i]-input[i];
//input[i] = output[i];
}
for(int i = 0; i< jumlahElemen; i++) {
input[i] = output[i];
}
}
__global__ void gpuJacobi (float * input, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N) {
//thread ID
int tidx = (blockIdx.x * blockDim.x) + threadIdx.x;
int tidy = (blockIdx.y * blockDim.y) + threadIdx.y;
int tid = (tidy * N) + tidx;
if(tid<(N*N)) {
float hasil = 0;
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if(tid == 0 || tid == N-1 || ((tid / N == 0) && (tid % N == N-1)) || ((tid % N == 0) && (tid / N == N-1))) {
hasil = input[tid];
}
else if (poisson[tid]) {
hasil = input[tid];
}
else if((tid%N == 0) || (tid/N == 0) || (tid%N == N-1) || (tid/N == N-1)) {
if(tid/N == 0 && ad_n) {
hasil = (input[tid+1] + input[tid+N] + input[tid-1])/3;
}
else if(tid%N == N-1 && ad_e) {
hasil = (input[tid-1] + input[tid+N] + input[tid-N])/3;
}
else if(tid/N == N-1 && ad_s) {
hasil = (input[tid+1] + input[tid-1] + input[tid-N])/3;
}
else if(tid%N == 0 && ad_w){
hasil = (input[tid+1] + input[tid+N] + input[tid-N])/3;
}
else{
hasil = input[tid];
}
}
else { //tidak pada batas
//lakukan perhitungan Jacobi
hasil = (input[tid-1] + input[tid+1] + input[tid-N] + input[tid+N])/(4);
error[tid] = hasil-input[tid];
}
input[tid] = hasil;
}
}
__global__ void gpuPGSRed (float * input, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N) {
float hasil = 0;
//thread ID
int tidx = (blockIdx.x * blockDim.x) + threadIdx.x;
int tidy = (blockIdx.y * blockDim.y) + threadIdx.y;
int tid = (tidy * N) + tidx;
if(tid<(N*N)) {
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if (tid%2 != (tid/N)%2) {
if(tid == 0 || tid == N-1 || ((tid / N == 0) && (tid % N == N-1)) || ((tid % N == 0) && (tid / N == N-1))) {
hasil = input[tid];
}
else if (poisson[tid]) {
hasil = input[tid];
}
else if((tid%N == 0) || (tid/N == 0) || (tid%N == N-1) || (tid/N == N-1)) {
if(tid/N == 0 && ad_n) {
hasil = (input[tid+1] + input[tid+N] + input[tid-1])/3;
}
else if(tid%N == N-1 && ad_e) {
hasil = (input[tid-1] + input[tid+N] + input[tid-N])/3;
}
else if(tid/N == N-1 && ad_s) {
hasil = (input[tid+1] + input[tid-1] + input[tid-N])/3;
}
else if(tid%N == 0 && ad_w){
hasil = (input[tid+1] + input[tid+N] + input[tid-N])/3;
}
else{
hasil = input[tid];
}
}
else { //tidak pada batas
//lakukan perhitungan PGS
hasil = (input[tid-1] + input[tid+1] + input[tid-N] + input[tid+N])/(4);
error[tid] = hasil-input[tid];
}
input[tid] = hasil;
}
}
}
__global__ void gpuPGSBlack (float * input, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N) {
float hasil = 0;
//thread ID
int tidx = (blockIdx.x * blockDim.x) + threadIdx.x;
int tidy = (blockIdx.y * blockDim.y) + threadIdx.y;
int tid = (tidy * N) + tidx;
if(tid<(N*N)) {
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if (tid%2 == (tid/N)%2) {
if(tid == 0 || tid == N-1 || ((tid / N == 0) && (tid % N == N-1)) || ((tid % N == 0) && (tid / N == N-1))) {
hasil = input[tid];
}
else if (poisson[tid]) {
hasil = input[tid];
}
else if((tid%N == 0) || (tid/N == 0) || (tid%N == N-1) || (tid/N == N-1)) {
if(tid/N == 0 && ad_n) {
hasil = (input[tid+1] + input[tid+N] + input[tid-1])/3;
}
else if(tid%N == N-1 && ad_e) {
hasil = (input[tid-1] + input[tid+N] + input[tid-N])/3;
}
else if(tid/N == N-1 && ad_s) {
hasil = (input[tid+1] + input[tid-1] + input[tid-N])/3;
}
else if(tid%N == 0 && ad_w){
hasil = (input[tid+1] + input[tid+N] + input[tid-N])/3;
}
else{
hasil = input[tid];
}
}
else { //tidak pada batas
//lakukan perhitungan PGS
hasil = (input[tid-1] + input[tid+1] + input[tid-N] + input[tid+N])/(4);
error[tid] = hasil-input[tid];
}
input[tid] = hasil;
}
}
}
__global__ void gpuPSORRed (float * input, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N, float omega) {
float hasil = 0;
//thread ID
int tidx = (blockIdx.x * blockDim.x) + threadIdx.x;
int tidy = (blockIdx.y * blockDim.y) + threadIdx.y;
int tid = (tidy * N) + tidx;
if(tid<(N*N)) {
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if (tid%2 != (tid/N)%2) {
if(tid == 0 || tid == N-1 || ((tid / N == 0) && (tid % N == N-1)) || ((tid % N == 0) && (tid / N == N-1))) {
hasil = input[tid];
}
else if(poisson[tid]) {
hasil = input[tid];
}
else if((tid%N == 0) || (tid/N == 0) || (tid%N == N-1) || (tid/N == N-1)) {
if(tid/N == 0 && ad_n) {
hasil = (input[tid+1] + input[tid+N] + input[tid-1])/3;
}
else if(tid%N == N-1 && ad_e) {
hasil = (input[tid-1] + input[tid+N] + input[tid-N])/3;
}
else if(tid/N == N-1 && ad_s) {
hasil = (input[tid+1] + input[tid-1] + input[tid-N])/3;
}
else if(tid%N == 0 && ad_w){
hasil = (input[tid+1] + input[tid+N] + input[tid-N])/3;
}
else{
hasil = input[tid];
}
}
else { //tidak pada batas
//lakukan perhitungan PSOR
hasil = input[tid] + ((input[tid-1] + input[tid+1] + input[tid-N] + input[tid+N] - (input[tid]*4))*(omega/4));
error[tid] = hasil-input[tid];
}
input[tid] = hasil;
}
}
}
__global__ void gpuPSORBlack (float * input, float * error, char * poisson, char ad_n, char ad_e, char ad_s, char ad_w, int N, float omega) {
float hasil = 0;
//thread ID
int tidx = (blockIdx.x * blockDim.x) + threadIdx.x;
int tidy = (blockIdx.y * blockDim.y) + threadIdx.y;
int tid = (tidy * N) + tidx;
if(tid<(N*N)) {
//tetap pada batas:
//y = 0 --> i % N = 0
//x = 0 --> i / N = 0
//y = y --> i % N = N-1
//x = x --> i / N = N-1
if (tid%2 == (tid/N)%2) {
if(tid == 0 || tid == N-1 || ((tid / N == 0) && (tid % N == N-1)) || ((tid % N == 0) && (tid / N == N-1))) {
hasil = input[tid];
}
else if (poisson[tid]) {
hasil = input[tid];
}
else if((tid%N == 0) || (tid/N == 0) || (tid%N == N-1) || (tid/N == N-1)) {
if(tid/N == 0 && ad_n) {
hasil = (input[tid+1] + input[tid+N] + input[tid-1])/3;
}
else if(tid%N == N-1 && ad_e) {
hasil = (input[tid-1] + input[tid+N] + input[tid-N])/3;
}
else if(tid/N == N-1 && ad_s) {
hasil = (input[tid+1] + input[tid-1] + input[tid-N])/3;
}
else if(tid%N == 0 && ad_w){
hasil = (input[tid+1] + input[tid+N] + input[tid-N])/3;
}
else{
hasil = input[tid];
}
}
else { //tidak pada batas
//lakukan perhitungan PSOR
hasil = input[tid] + ((input[tid-1] + input[tid+1] + input[tid-N] + input[tid+N] - (input[tid]*4))*(omega/4));
error[tid] = hasil-input[tid];
}
input[tid] = hasil;
}
}
}
int main (int argc, char** argv) {
int SISI_MATRIKS = 64;
int totalElemen = SISI_MATRIKS*SISI_MATRIKS;
//float pi = 3.14159265;
char ad_n = 0, ad_e = 0, ad_s = 0, ad_w = 0;
char *awal_poi;
char *host_poi;
char *dev_poi;
float omega = 1.1;
float *awal_in, *awal_out, *awal_err;
float *host_in, *host_out, *host_err;
float *dev_in, *dev_err;
float total_error = -1;
float MAX_ERROR = 0.005;
int CPUIter = 0, CPUIter2 = 0, CPUIter3 = 0, GPUIter = 0, GPUIter2 = 0, GPUIter3 = 0;
float CPUTime = 0, CPUTime2 = 0, CPUTime3 = 0, GPUTime = 0, GPUTime2 = 0, GPUTime3 = 0;
dim3 jumlahBlock, threadPerBlock;
clock_t t1;
clock_t t2;
//inialisasi
awal_poi = (char *)malloc(sizeof(char) * totalElemen);
awal_in = (float *)malloc(sizeof(float) * totalElemen);
awal_out = (float *)malloc(sizeof(float) * totalElemen);
awal_err = (float *)malloc(sizeof(float) * totalElemen);
//instansiasi
printf("generating domain matrix...");
for(int i = 0; i<totalElemen; i++) {
//kondisi batas
if(i==((SISI_MATRIKS*SISI_MATRIKS/2) + SISI_MATRIKS/2)) {
awal_in[i] = 100;
}
else {
awal_in[i] = 0;
}
awal_poi = 0;
awal_out[i] = awal_in[i];
awal_err[i] = 0;
}
printf("done\n");
printf("\nMulai pengujian...\n------------------------------------------\n");
printf("CPU\n");
//pengujian
printf("Metode Jacobi.....");
//---CPU Jacobi---
//alokasi memori domain
host_poi = (char *)malloc(sizeof(float) * totalElemen);
host_in = (float *)malloc(sizeof(float) * totalElemen);
host_out = (float *)malloc(sizeof(float) * totalElemen);
host_err = (float *)malloc(sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
for(int i = 0; i<totalElemen; i++) {
host_poi[i] = awal_poi[i];
host_in[i] = awal_in[i];
host_out[i] = awal_out[i];
host_err[i] = awal_err[i];
}
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
cpuJacobi(host_in, host_out, host_err, host_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, totalElemen);
//hitung error
total_error = 0;
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
CPUIter++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
CPUTime = t2-t1;
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "CPUJacobi.hasil", host_out);
//bebaskan memori
free(host_in);
free(host_out);
free(host_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", CPUIter, CPUTime);
//---CPU PGS---
printf("\n");
printf("Metode Point Gauss-Seidel.....");
//alokasi memori domain
host_in = (float *)malloc(sizeof(float) * totalElemen);
host_out = (float *)malloc(sizeof(float) * totalElemen);
host_err = (float *)malloc(sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
for(int i = 0; i<totalElemen; i++) {
host_in[i] = awal_in[i];
host_out[i] = awal_out[i];
host_err[i] = awal_err[i];
}
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
cpuPGS(host_in, host_out, host_err, host_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, totalElemen);
//hitung error
total_error = 0;
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
CPUIter2++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
CPUTime2 = t2-t1;
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "CPUPGS.hasil", host_out);
//bebaskan memori
free(host_in);
free(host_out);
free(host_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", CPUIter2, CPUTime2);
//---CPU PSOR---
printf("\n");
printf("Metode Point Successive-Over-Relaxation.....");
//alokasi memori domain
host_in = (float *)malloc(sizeof(float) * totalElemen);
host_out = (float *)malloc(sizeof(float) * totalElemen);
host_err = (float *)malloc(sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
for(int i = 0; i<totalElemen; i++) {
host_in[i] = awal_in[i];
host_out[i] = awal_out[i];
host_err[i] = awal_err[i];
}
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
cpuPSOR(host_in, host_out, host_err, host_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, totalElemen, omega);
//hitung error
total_error = 0;
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
CPUIter3++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
CPUTime3 = t2-t1;
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "CPUPSOR.hasil", host_out);
//bebaskan memori
free(host_in);
free(host_poi);
//free(host_out);
//free(host_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", CPUIter3, CPUTime3);
printf("\n----\n");
printf("\nGPU: 1x1");
//sebelum GPU:inisialisasi dimensi matriks
int threadx = 1, thready = 1;
int blockx, blocky;
blockx = (SISI_MATRIKS/threadx)+1;
blocky = (SISI_MATRIKS/thready)+1;
jumlahBlock = dim3(blockx, blocky);
threadPerBlock = dim3(threadx,thready);
//---GPU Jacobi---
printf("\n");
printf("GPU Jacobi....");
//alokasi memori domain
cudaMalloc( (void **)&dev_poi, sizeof(char) * totalElemen);
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_poi, awal_poi, totalElemen*sizeof(char), cudaMemcpyHostToDevice);
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuJacobi<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUJacobi1x1.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter, GPUTime);
//---GPU PGS---
printf("\n");
printf("GPU PGS....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPGSRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
gpuPGSBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter2++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime2 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPGS1x1.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter2, GPUTime2);
//---GPU PSOR---
printf("\n");
printf("GPU PSOR....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPSORRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
gpuPSORBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",GPUIter3,total_error);
GPUIter3++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime3 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPSOR1x1.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter3, GPUTime3);
printf("\n----\n");
printf("\nGPU: 2x2");
//sebelum GPU:inisialisasi dimensi matriks
threadx = 2;
thready = 2;
blockx = (SISI_MATRIKS/threadx)+1;
blocky = (SISI_MATRIKS/thready)+1;
jumlahBlock = dim3(blockx, blocky);
threadPerBlock = dim3(threadx,thready);
GPUIter = 0; GPUIter2 = 0; GPUIter3 = 0;
GPUTime = 0; GPUTime2 = 0; GPUTime3 = 0;
//---GPU Jacobi---
printf("\n");
printf("GPU Jacobi....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuJacobi<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUJacobi2x2.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter, GPUTime);
//---GPU PGS---
printf("\n");
printf("GPU PGS....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPGSRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
gpuPGSBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter2++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime2 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPGS2x2.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter2, GPUTime2);
//---GPU PSOR---
printf("\n");
printf("GPU PSOR....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPSORRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
gpuPSORBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter3++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime3 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPSOR2x2.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter3, GPUTime3);
printf("\n----\n");
printf("\nGPU: 4x4");
//sebelum GPU:inisialisasi dimensi matriks
threadx = 4;
thready = 4;
blockx = (SISI_MATRIKS/threadx)+1;
blocky = (SISI_MATRIKS/thready)+1;
jumlahBlock = dim3(blockx, blocky);
threadPerBlock = dim3(threadx,thready);
GPUIter = 0; GPUIter2 = 0; GPUIter3 = 0;
GPUTime = 0; GPUTime2 = 0; GPUTime3 = 0;
//---GPU Jacobi---
printf("\n");
printf("GPU Jacobi....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuJacobi<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUJacobi4x4.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter, GPUTime);
//---GPU PGS---
printf("\n");
printf("GPU PGS....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPGSRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
gpuPGSBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter2++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime2 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPGS4x4.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter2, GPUTime2);
//---GPU PSOR---
printf("\n");
printf("GPU PSOR....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPSORRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
gpuPSORBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter3++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime3 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPSOR4x4.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter3, GPUTime3);
printf("\n----\n");
printf("\nGPU: 8x8");
//sebelum GPU:inisialisasi dimensi matriks
threadx = 8;
thready = 8;
blockx = (SISI_MATRIKS/threadx)+1;
blocky = (SISI_MATRIKS/thready)+1;
jumlahBlock = dim3(blockx, blocky);
threadPerBlock = dim3(threadx,thready);
GPUIter = 0; GPUIter2 = 0; GPUIter3 = 0;
GPUTime = 0; GPUTime2 = 0; GPUTime3 = 0;
//---GPU Jacobi---
printf("\n");
printf("GPU Jacobi....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuJacobi<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUJacobi8x8.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter, GPUTime);
//---GPU PGS---
printf("\n");
printf("GPU PGS....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPGSRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
gpuPGSBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter2++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime2 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPGS8x8.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter2, GPUTime2);
//---GPU PSOR---
printf("\n");
printf("GPU PSOR....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPSORRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
gpuPSORBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter3++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime3 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPSOR8x8.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter3, GPUTime3);
printf("\n----\n");
printf("\nGPU: 16x16");
//sebelum GPU:inisialisasi dimensi matriks
threadx = 16;
thready = 16;
blockx = (SISI_MATRIKS/threadx)+1;
blocky = (SISI_MATRIKS/thready)+1;
jumlahBlock = dim3(blockx, blocky);
threadPerBlock = dim3(threadx,thready);
GPUIter = 0; GPUIter2 = 0; GPUIter3 = 0;
GPUTime = 0; GPUTime2 = 0; GPUTime3 = 0;
//---GPU Jacobi---
printf("\n");
printf("GPU Jacobi....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuJacobi<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUJacobi16x16.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter, GPUTime);
//---GPU PGS---
printf("\n");
printf("GPU PGS....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPGSRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
gpuPGSBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter2++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime2 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPGS16x16.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter2, GPUTime2);
//---GPU PSOR---
printf("\n");
printf("GPU PSOR....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPSORRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
gpuPSORBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter3++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime3 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPSOR16x16.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter3, GPUTime3);
printf("\n----\n");
printf("\nGPU: 32x32");
//sebelum GPU:inisialisasi dimensi matriks
threadx = 32;
thready = 32;
blockx = (SISI_MATRIKS/threadx)+1;
blocky = (SISI_MATRIKS/thready)+1;
jumlahBlock = dim3(blockx, blocky);
threadPerBlock = dim3(threadx,thready);
GPUIter = 0; GPUIter2 = 0; GPUIter3 = 0;
GPUTime = 0; GPUTime2 = 0; GPUTime3 = 0;
//---GPU Jacobi---
printf("\n");
printf("GPU Jacobi....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuJacobi<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUJacobi32x32.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter, GPUTime);
//---GPU PGS---
printf("\n");
printf("GPU PGS....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPGSRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
gpuPGSBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter2++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime2 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPGS32x32.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter2, GPUTime2);
//---GPU PSOR---
printf("\n");
printf("GPU PSOR....");
//alokasi memori domain
cudaMalloc( (void **)&dev_in, sizeof(float) * totalElemen) ;
cudaMalloc( (void **)&dev_err, sizeof(float) * totalElemen);
//copy data dari matriks awal ke domain
cudaMemcpy(dev_in, awal_in, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_err, awal_err, totalElemen*sizeof(float), cudaMemcpyHostToDevice);
//mulai komputasi
t1 = clock() / (CLOCKS_PER_SEC / 1000);
total_error = -1;
while ((total_error == -1) || (total_error > MAX_ERROR)) {
gpuPSORRed<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
gpuPSORBlack<<<jumlahBlock, threadPerBlock>>>(dev_in, dev_err, dev_poi, ad_n, ad_e, ad_s, ad_w, SISI_MATRIKS, omega);
//hitung error
total_error = 0;
cudaMemcpy(host_err, dev_err, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
for(int i=0; i<totalElemen; i++) {
total_error += absol(host_err[i]);
}
//printf("%d, %f\n",CPUIter,total_error);
GPUIter3++;
}
t2 = clock() / (CLOCKS_PER_SEC / 1000);
GPUTime3 = t2-t1;
cudaMemcpy(host_out, dev_in, totalElemen*sizeof(float), cudaMemcpyDeviceToHost);
writeToFile(SISI_MATRIKS, SISI_MATRIKS, totalElemen, "GPUPSOR32x32.hasil", host_out);
//bebaskan memori
cudaFree(dev_in);
cudaFree(dev_err);
cudaFree(dev_poi);
printf("done\n");
printf("Banyak iterasi = %d\nWaktu komputasi = %f\n", GPUIter3, GPUTime3);
getchar();
return 0;
}
|
10,647 | #include <stdio.h>
#include <cuda_runtime.h>
// #include <helper_cuda.h>
#define N 1000
__global__ void initializeElementsTo(int *a, int value){
int i = threadIdx.x + blockIdx.x * blockDim.x;
if (i < N)
a[i] = value;
}
int main(void){
cudaError_t err = cudaSuccess;
size_t size = N * sizeof(int);
int *a;
cudaMallocManaged(&a, size);
size_t threads_per_block = 256;
size_t number_of_blocks = (N + threads_per_block - 1) / threads_per_block;
initializeElementsTo<<<number_of_blocks, threads_per_block>>>(a, 123);
if ((err = cudaGetLastError()) != cudaSuccess){
fprintf(stderr, "Failed to launch kernel: %s\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
cudaDeviceSynchronize();
for(int i = 0; i < N; i++)
if(a[i] != 123){
printf("Failed\n");
break;
}
printf("Done\n");
cudaFree(a);
return 0;
} |
10,648 |
__global__ void gpuFunc_copiarLayer(float *layer, float *layer_copy)
{
/*Formula para calcular la posicion*/
int bloqueId = blockIdx.x + blockIdx.y * gridDim.x;
int globalId = bloqueId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;
layer_copy[globalId]=layer[globalId];
}
__global__ void gpuFunc_actualiza(float *layer, int posicion, float energia)
{
/*Formula para calcular la posicion*/
int bloqueId = blockIdx.x + blockIdx.y * gridDim.x;
int globalId = bloqueId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;
/* Función actualiza */
int distancia = posicion - globalId;
if ( distancia < 0 ) distancia = - distancia;
/* 2. El punto de impacto tiene distancia 1 */
distancia = distancia + 1;
/* 3. Raiz cuadrada de la distancia */
//float atenuacion = (float)distancia*distancia;
//float atenuacion = (float)distancia / PI;
float atenuacion = sqrtf( (float)distancia );
/* 4. Calcular energia atenuada */
float energia_k = energia / atenuacion;
/* 5. No sumar si el valor absoluto es menor que umbral */
if ( energia_k >= 0.001f || energia_k <= -0.001f )
layer[globalId] = layer[globalId] + energia_k;
}
__global__ void gpuFunc_extremos(float *layer, float *layer_copy, int layer_size)
{
/*Formula para calcular la posicion*/
int bloqueId = blockIdx.x + blockIdx.y * gridDim.x;
int globalId = bloqueId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;
if(globalId > 0 && globalId < layer_size-1){
layer[globalId] = ( layer_copy[globalId-1] + layer_copy[globalId] + layer_copy[globalId+1] ) / 3;
}
}
__global__ void gpuFunc_maximos(float *layer, int *posiciones, float *maximos, int layer_size, int i)
{
/*Formula para calcular la posicion*/
int bloqueId = blockIdx.x + blockIdx.y * gridDim.x;
int globalId = bloqueId * (blockDim.x * blockDim.y) + (threadIdx.y * blockDim.x) + threadIdx.x;
if(globalId > 0 && globalId < layer_size){
if ( layer[globalId] > layer[globalId-1] && layer[globalId] > layer[globalId+1] ) {
if ( layer[globalId] > maximos[i] ) {
maximos[i] = layer[globalId];
posiciones[i] = globalId;
}
}
}
}
|
10,649 | // Mike Hagenow
// ME759 Final Project - Kernel to iteratively solve Laplacian
#include "harmonickernel.cuh"
#include <stdio.h>
// Dirichlet CUDA kernel for the harmonic potential fields using Shared Memory and Increased Arithmetic Density
// The approach is similar to the shared memory, but with multiple loops through the shared memory
// to promote faster diffusion! It also uses a harmonic mean. Unfortunately, none of these things actually worked.
__global__ void harmonic_kernel_tiled_2(unsigned long long int* A, unsigned long long int* B, const unsigned long long int* Mask, size_t num_rows, size_t num_cols, unsigned long long int delta_threshold, int* delta_count){
extern __shared__ unsigned long long int A_tile_sh[];
int BLOCK_SIZE = 32;
int buffer_size = (BLOCK_SIZE+2)*(BLOCK_SIZE+2);
/////////////////////////////
// Loading the tile //
/////////////////////////////
// Each thread will load an element of the tile and TODO (max 1) an element from the edges
int ty = threadIdx.y; //the row index in the sub-block
int tx = threadIdx.x; //the column index in the sub-block
int whichRow = blockIdx.y*BLOCK_SIZE+ty;
int whichCol = blockIdx.x*BLOCK_SIZE+tx;
// Log the 32x32 grid of interest
if(whichCol<num_cols && whichRow<num_rows)
{
// Logging is done with an offset from (1,1) since the first row and column are the extra padding for calculations
A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)] = A[whichRow*num_cols+whichCol];
}
else{
A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)] = (ULLONG_MAX/4); //treat as collision if outside bounds of array
}
// Load the edges of the shared memory array!!
int threadId = ty*BLOCK_SIZE+tx;
if(threadId<(BLOCK_SIZE+2)){
// for each check that row and column are ok (including both ends for the threadID sweep direction)
// Row on top of tile
if((blockIdx.y*BLOCK_SIZE)>=1 && (blockIdx.x*BLOCK_SIZE-1+threadId)<num_cols && (blockIdx.x*BLOCK_SIZE+threadId)>=1){
// [0][threadId]
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = A[(blockIdx.y*BLOCK_SIZE-1)*num_cols+blockIdx.x*BLOCK_SIZE-1+threadId];
}
else{
// A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
// Row below tile
if(((blockIdx.y+1)*BLOCK_SIZE)<num_rows && (blockIdx.x*BLOCK_SIZE-1+threadId)<num_cols && (blockIdx.x*BLOCK_SIZE+threadId)>=1){
// A_tile_sh[BLOCK_SIZE+2][threadId] = A[(blockIdx.y+1)*BLOCK_SIZE+2)*num_cols+threadId];
A_tile_sh[(BLOCK_SIZE+1)*(BLOCK_SIZE+2)+threadId] = A[(blockIdx.y+1)*BLOCK_SIZE*num_cols+blockIdx.x*BLOCK_SIZE-1+threadId];
}
else{
// A_tile_sh[(BLOCK_SIZE+1)*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
// Col to left of tile
// check col and row
if((blockIdx.x*BLOCK_SIZE)>=1 && (blockIdx.y*BLOCK_SIZE-1+threadId)<num_rows && (blockIdx.y*BLOCK_SIZE+threadId)>=1){
// A_tile_sh[threadId][0] = A[(blockIdx.y+1)*BLOCK_SIZE+2)*num_cols+threadId];
A_tile_sh[threadId*(BLOCK_SIZE+2)+0] = A[(blockIdx.y*BLOCK_SIZE-1+threadId)*num_cols+blockIdx.x*BLOCK_SIZE-1];
}
else{
// A_tile_sh[threadId*(BLOCK_SIZE+2)+0] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
// Col to right of tile
if((blockIdx.x+1)*BLOCK_SIZE<num_cols && (blockIdx.y*BLOCK_SIZE-1+threadId)<num_rows && (blockIdx.y*BLOCK_SIZE+threadId)>=1){
// A_tile_sh[threadId][BLOCK_SIZE+2] = A[(blockIdx.y+1)*BLOCK_SIZE+2)*num_cols+threadId];
A_tile_sh[threadId*(BLOCK_SIZE+2)+(BLOCK_SIZE+1)] = A[(blockIdx.y*BLOCK_SIZE-1+threadId)*num_cols+(blockIdx.x+1)*BLOCK_SIZE];
}
else{
// A_tile_sh[threadId*(BLOCK_SIZE+2)+(BLOCK_SIZE+1)] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
}
__syncthreads();
//////////////////////////////////////////////
// Calculate the new tile and store in B //
//////////////////////////////////////////////
if(whichRow<num_rows && whichCol<num_cols){
int num_subloops = 1;
int pout = 0, pin = 1;
for(int i=0;i<num_subloops;i++){
pout = 1 - pout; // swap double buffer indices
pin = 1 - pout;
// One store into the global memory (or enforce boundary condition)
if(Mask[whichRow*num_cols+whichCol]==(ULLONG_MAX/4)/2){
// shared memory already accounts for bounds so no checks are necessary
// left, right, up, down
float new_val = 0;
new_val+=1.0/float(A_tile_sh[pin*buffer_size+(ty+1)*(BLOCK_SIZE+2)+(tx+1)-1]);
new_val+=1.0/float(A_tile_sh[pin*buffer_size+(ty+1)*(BLOCK_SIZE+2)+(tx+1)+1]);
new_val+=1.0/float(A_tile_sh[pin*buffer_size+(ty+1-1)*(BLOCK_SIZE+2)+(tx+1)]);
new_val+=1.0/float(A_tile_sh[pin*buffer_size+(ty+1+1)*(BLOCK_SIZE+2)+(tx+1)]);
unsigned long long int temp = (long long int)(4.0/new_val);
A_tile_sh[pout*buffer_size+(ty+1)*(BLOCK_SIZE+2)+(tx+1)]=temp;
// B[whichRow*num_cols+whichCol] = temp;
if(i==0 && (unsigned long long int)abs((long long int)(temp)-(long long int)A_tile_sh[pin*buffer_size+(ty+1)*(BLOCK_SIZE+2)+(tx+1)])>delta_threshold){
atomicAdd(delta_count,1);
}
}
else{
A_tile_sh[pout*buffer_size+(ty+1)*(BLOCK_SIZE+2)+(tx+1)]=Mask[whichRow*num_cols+whichCol];
// B[whichRow*num_cols+whichCol] = Mask[whichRow*num_cols+whichCol];
}
__syncthreads();
}
// Store the final result back in global memory
B[whichRow*num_cols+whichCol]=A_tile_sh[pout*buffer_size+(ty+1)*(BLOCK_SIZE+2)+(tx+1)];
}
}
// Dirichlet CUDA kernel for the harmonic potential fields using Shared Memory
// The approach is similar to matrix multiplication except that there are padding requirements and each block
// in the final matrix only requires the equivalent tile in the original matrix.
__global__ void harmonic_kernel_tiled(unsigned long long int* A, unsigned long long int* B, const unsigned long long int* Mask, size_t num_rows, size_t num_cols, unsigned long long int delta_threshold, int* delta_count){
extern __shared__ unsigned long long int A_tile_sh[];
int BLOCK_SIZE = 32;
/////////////////////////////
// Loading the tile //
/////////////////////////////
// Each thread will load an element of the tile and TODO (max 1) an element from the edges
int ty = threadIdx.y; //the row index in the sub-block
int tx = threadIdx.x; //the column index in the sub-block
int whichRow = blockIdx.y*BLOCK_SIZE+ty;
int whichCol = blockIdx.x*BLOCK_SIZE+tx;
// Log the 32x32 grid of interest
if(whichCol<num_cols && whichRow<num_rows)
{
// Logging is done with an offset from (1,1) since the first row and column are the extra padding for calculations
A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)] = A[whichRow*num_cols+whichCol];
}
else{
A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)] = (ULLONG_MAX/4); //treat as collision if outside bounds of array
}
// Load the edges of the shared memory array!!
int threadId = ty*BLOCK_SIZE+tx;
if(threadId<(BLOCK_SIZE+2)){
// for each check that row and column are ok (including both ends for the threadID sweep direction)
// Row on top of tile
if((blockIdx.y*BLOCK_SIZE)>=1 && (blockIdx.x*BLOCK_SIZE-1+threadId)<num_cols && (blockIdx.x*BLOCK_SIZE+threadId)>=1){
// [0][threadId]
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = A[(blockIdx.y*BLOCK_SIZE-1)*num_cols+blockIdx.x*BLOCK_SIZE-1+threadId];
}
else{
// A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
// Row below tile
if(((blockIdx.y+1)*BLOCK_SIZE)<num_rows && (blockIdx.x*BLOCK_SIZE-1+threadId)<num_cols && (blockIdx.x*BLOCK_SIZE+threadId)>=1){
// A_tile_sh[BLOCK_SIZE+2][threadId] = A[(blockIdx.y+1)*BLOCK_SIZE+2)*num_cols+threadId];
A_tile_sh[(BLOCK_SIZE+1)*(BLOCK_SIZE+2)+threadId] = A[(blockIdx.y+1)*BLOCK_SIZE*num_cols+blockIdx.x*BLOCK_SIZE-1+threadId];
}
else{
// A_tile_sh[(BLOCK_SIZE+1)*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
// Col to left of tile
// check col and row
if((blockIdx.x*BLOCK_SIZE)>=1 && (blockIdx.y*BLOCK_SIZE-1+threadId)<num_rows && (blockIdx.y*BLOCK_SIZE+threadId)>=1){
// A_tile_sh[threadId][0] = A[(blockIdx.y+1)*BLOCK_SIZE+2)*num_cols+threadId];
A_tile_sh[threadId*(BLOCK_SIZE+2)+0] = A[(blockIdx.y*BLOCK_SIZE-1+threadId)*num_cols+blockIdx.x*BLOCK_SIZE-1];
}
else{
// A_tile_sh[threadId*(BLOCK_SIZE+2)+0] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
// Col to right of tile
if((blockIdx.x+1)*BLOCK_SIZE<num_cols && (blockIdx.y*BLOCK_SIZE-1+threadId)<num_rows && (blockIdx.y*BLOCK_SIZE+threadId)>=1){
// A_tile_sh[threadId][BLOCK_SIZE+2] = A[(blockIdx.y+1)*BLOCK_SIZE+2)*num_cols+threadId];
A_tile_sh[threadId*(BLOCK_SIZE+2)+(BLOCK_SIZE+1)] = A[(blockIdx.y*BLOCK_SIZE-1+threadId)*num_cols+(blockIdx.x+1)*BLOCK_SIZE];
}
else{
// A_tile_sh[threadId*(BLOCK_SIZE+2)+(BLOCK_SIZE+1)] = (ULLONG_MAX/4); //treat as collision if outside bounds
A_tile_sh[0*(BLOCK_SIZE+2)+threadId] = (ULLONG_MAX/4); //treat as collision if outside bounds
}
}
__syncthreads();
//////////////////////////////////////////////
// Calculate the new tile and store in B //
//////////////////////////////////////////////
if(whichRow<num_rows && whichCol<num_cols){
unsigned long long int new_val = 0;
// shared memory already accounts for bounds so no checks are necessary
// left, right, up, down
new_val+=A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)-1];
new_val+=A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)+1];
new_val+=A_tile_sh[(ty+1-1)*(BLOCK_SIZE+2)+(tx+1)];
new_val+=A_tile_sh[(ty+1+1)*(BLOCK_SIZE+2)+(tx+1)];
// One store into the global memory (or enforce boundary condition)
if(Mask[whichRow*num_cols+whichCol]==(ULLONG_MAX/4)/2){
unsigned long long int temp = (new_val/4);
// unsigned long long int temp = (new_val/4)/100*85+(A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)])/100*15;
B[whichRow*num_cols+whichCol] = temp;
if((unsigned long long int)abs((long long int)(temp)-(long long int)A_tile_sh[(ty+1)*(BLOCK_SIZE+2)+(tx+1)])>delta_threshold){
atomicAdd(delta_count,1);
// (*delta_count)+=1;
}
}
else{
B[whichRow*num_cols+whichCol] = Mask[whichRow*num_cols+whichCol];
}
}
}
// Neumann CUDA kernel for the harmonic potential fields
// Also uses global memory access and enforces Neumann boundary via three-point derivative (i.e., sets current point to make derivative 0)
// To enforce 2d laplacian, the values of the current point to create a zero-partial in each direction are averaged.
__global__ void global_neumann_harmonic_kernel(unsigned long long int* A, unsigned long long int* B, const unsigned long long int* Mask, size_t num_rows, size_t num_cols, unsigned long long int delta_threshold, int* delta_count){
int index = threadIdx.x+ blockIdx.x* blockDim.x;
int whichRow = index/num_cols;
int whichCol = index%num_cols;
if(index<num_rows*num_cols){
unsigned long long int new_val = 0;
// 4 Checks - Left, right, up, and down (each has different issues)
// If not passed, don't add anything
// Left (need a column to left)
if(whichCol>=1){
new_val+=A[whichRow*num_cols+whichCol-1];
}
// Right (need a column to right)
if((whichCol+1)<num_cols){
new_val+=A[whichRow*num_cols+whichCol+1];
}
// Up (need a previous row)
if(whichRow>=1){
new_val+=A[(whichRow-1)*num_cols+whichCol];
}
// Down (need a row after)
if((whichRow+1)<num_rows){
new_val+=A[(whichRow+1)*num_cols+whichCol];
}
// printf(" ** %u %u\n",new_val,new_val/4);
// One store into the global memory (or enforce boundary condition)
if(Mask[index]==(ULLONG_MAX/4)/2){
B[index] = new_val/4;
// printf(" %llu \n",(unsigned long long int)abs((long long int)(new_val/4)-(long long int)A[index]));
if((unsigned long long int)abs((long long int)(new_val/4)-(long long int)A[index])>delta_threshold){
atomicAdd(delta_count,1);
// (*delta_count)+=1;
}
}
else{ // For the neumann, we will update left, right, above, below (i.e., central difference for velocity)
if(Mask[index]==0)
{
B[index]=0;
}
else{
unsigned long long int left = A[index];
unsigned long long int right = left;
unsigned long long int up = left;
unsigned long long int down = left;
if(whichCol>=1){
left=A[whichRow*num_cols+whichCol-1];
}
// Right (need a column to right)
if((whichCol+1)<num_cols){
right=A[whichRow*num_cols+whichCol+1];
}
// Up (need a previous row)
if(whichRow>=1){
up=A[(whichRow-1)*num_cols+whichCol];
}
// Down (need a row after)
if((whichRow+1)<num_rows){
down=A[(whichRow+1)*num_cols+whichCol];
}
B[index] = (3*left+right)/8+(3*up+down)/8;
}
}
}
}
// Baseline CUDA kernel for the harmonic potential fields
// Uses global memory access and Dirichlet boundary conditions (i.e., all obstacles are repulsive fields)
__global__ void global_harmonic_kernel(unsigned long long int* A, unsigned long long int* B, const unsigned long long int* Mask, size_t num_rows, size_t num_cols, unsigned long long int delta_threshold, int* delta_count){
int index = threadIdx.x+ blockIdx.x* blockDim.x;
int whichRow = index/num_cols;
int whichCol = index%num_cols;
if(index<num_rows*num_cols){
unsigned long long int new_val = 0;
// 4 Checks - Left, right, up, and down (each has different issues)
// If not passed, don't add anything
// Left (need a column to left)
if(whichCol>=1){
new_val+=A[whichRow*num_cols+whichCol-1];
}
// Right (need a column to right)
if((whichCol+1)<num_cols){
new_val+=A[whichRow*num_cols+whichCol+1];
}
// Up (need a previous row)
if(whichRow>=1){
new_val+=A[(whichRow-1)*num_cols+whichCol];
}
// Down (need a row after)
if((whichRow+1)<num_rows){
new_val+=A[(whichRow+1)*num_cols+whichCol];
}
// printf(" ** %u %u\n",new_val,new_val/4);
// One store into the global memory (or enforce boundary condition)
if(Mask[index]==(ULLONG_MAX/4)/2){
B[index] = new_val/4;
// printf(" %llu \n",(unsigned long long int)abs((long long int)(new_val/4)-(long long int)A[index]));
if((unsigned long long int)abs((long long int)(new_val/4)-(long long int)A[index])>delta_threshold){
atomicAdd(delta_count,1);
// (*delta_count)+=1;
}
}
else{
B[index] = Mask[index];
}
}
}
// Harmonic function is responsible for launching the successive kernel
// calls and setting up appropriate dimensions for each kernel (e.g., 2d vs 1d)
void harmonic(unsigned long long int** A, unsigned long long int** B, const unsigned long long int* Mask, size_t num_rows, size_t num_cols, unsigned int threads_per_block, unsigned int max_iters){
// Determine appropriate number of blocks (n^2 operations for row-col combinations)
// code asks for 1D kernel configuration
const unsigned int blocks_per_grid = ((num_rows*num_cols)+threads_per_block-1)/threads_per_block;
// For the tiling, need to compute a tile and the appopriate grid of tiles
unsigned int block_dim = 32;
unsigned int grid_dim = ((num_rows) + block_dim - 1) / block_dim; // Assume rows and columns are the same
// Printing iterations
unsigned iter_print = 1;
if(max_iters>20){
iter_print = max_iters/20;
}
unsigned long long int* temp_switch;
int *d_delta_count;
int delta_count;
cudaMalloc((void **)&d_delta_count, sizeof(int));
// unsigned long long int delta_threshold=ULLONG_MAX/4*0.00000000000001;
unsigned long long int delta_threshold=0;
printf("Delta Threshold: %llu\n",delta_threshold);
// TODO: multiple runs within a kernel call to try and improve arithmetic intensity?
unsigned int i=0;
bool converged = false;
while(i<max_iters && !converged) {
cudaMemset(d_delta_count, 0, sizeof(int));
/////////////////////
// Kernel Options //
////////////////////
// global_harmonic_kernel<<<blocks_per_grid, threads_per_block>>>(*A,*B,Mask,num_rows,num_cols,delta_threshold,d_delta_count);
harmonic_kernel_tiled<<<dim3(grid_dim,grid_dim),dim3(block_dim,block_dim),(block_dim+2)*(block_dim+2)*sizeof(unsigned long long int)>>>(*A,*B,Mask,num_rows,num_cols,delta_threshold,d_delta_count);
// harmonic_kernel_tiled_2<<<dim3(grid_dim,grid_dim),dim3(block_dim,block_dim),2*(block_dim+2)*(block_dim+2)*sizeof(unsigned long long int)>>>(*A,*B,Mask,num_rows,num_cols,delta_threshold,d_delta_count);
// global_neumann_harmonic_kernel<<<blocks_per_grid, threads_per_block>>>(*A,*B,Mask,num_rows,num_cols,delta_threshold,d_delta_count);
cudaDeviceSynchronize();
cudaMemcpy(&delta_count, d_delta_count, sizeof(int), cudaMemcpyDeviceToHost);
if(i%(iter_print)==0){
printf("Run %d of Max %d\n",i,max_iters);
printf(" DC: %d\n",delta_count);
}
i++;
// if(delta_count<(num_rows*num_cols*0.00001)){
if(delta_count==0){
converged=true;
printf("Iterations to finish: %u\n",i);
}
//switch A and B for next iteration: todo if another run -- otherwise, B has result
else if(i<(max_iters-1)){
temp_switch = *A;
*A = *B;
*B = temp_switch;
}
}
cudaDeviceSynchronize();
cudaFree(d_delta_count);
// printf("Cuda Error Check: %s\n",cudaGetErrorString(cudaGetLastError()));
}
|
10,650 | //kernel
__global__ void BuildBx( double * Bx, const int * frombus, const int * tobus, const int * BranchStatus, const double * xline, const int numline, const int Mat4_Width)
{
int Row = blockIdx.y*blockDim.y+threadIdx.y;
int Col = blockIdx.x*blockDim.x+threadIdx.x;
if(Row == frombus[Row] && Col == tobus[Row])
{
Bx[frombus[Row]*Mat4_Width + tobus[Row]] = 1/xline[Row];
}
}
|
10,651 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "curand.h"
#include "curand_kernel.h"
#include <curand.h>
#include <ctime>
#include <cstdio>
#include <iostream>
using namespace std;
__global__ void addTen(float* d, int count) {
int threadsPerBlock = blockDim.x * blockDim.y * blockDim.z;
int threadPosInBlock = threadIdx.x + blockDim.x * threadIdx.y + blockDim.x * blockDim.y * threadIdx.z;
int blockPosInGrid = blockIdx.x + gridDim.x * blockIdx.y + gridDim.x * gridDim.y * blockIdx.z;
int tid = blockPosInGrid * threadsPerBlock + threadPosInBlock;
if(tid < count) {
d[tid] = d[tid] + 10;
}
}
int main() {
curandGenerator_t gen;
curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_MTGP32);
curandSetPseudoRandomGeneratorSeed(gen, time(0));
cudaError_t status;
const int count = 123456;
const int size = count * sizeof(float);
float* d;
float h[count];
cudaMalloc(&d, size);
curandGenerateUniform(gen, d, count);
dim3 block(8, 8, 8);
dim3 grid(16, 16);
addTen<<<grid, block>>>(d, count);
status = cudaMemcpy(h, d, size, cudaMemcpyDeviceToHost);
cudaFree(d);
for (int i = 0; i < 100; i++) {
cout << h[i] << '\t';
}
return 0;
}
|
10,652 | /*
This is by far the fastest time of all systems.
1000 bodies for 10 min
real 0m6.662s
user 0m4.099s
sys 0m2.546s
8000 bodies for 10 min
real 2m3.830s
user 1m16.900s
sys 0m46.798s
*/
#include<stdlib.h>
#include<stdio.h>
#include<math.h>
#define G 6.673e-11
#define MOON_MASS 7.34e22
#define MOON_RADIUS 1.7371e6
__global__
void update_velocity(int n, float *position, float *velocity, float *mass, float duration) {
float r1, r2, r3, rsquared, accel, normal, dvx = 0, dvy = 0, dvz = 0;
int j, i = blockIdx.x * blockDim.x + threadIdx.x;
if(i < n) {
#define LOOP \
/* compute the force of gravity */ \
r1 = (position[j*3 + 0] - position[i*3 + 0]); \
r2 = (position[j*3 + 1] - position[i*3 + 1]); \
r3 = (position[j*3 + 2] - position[i*3 + 2]); \
rsquared = r1*r1 + r2*r2 + r3*r3; \
accel = (G * mass[j]) / rsquared; \
\
/* compute the normal vector pointing from i to j */ \
normal = 1 / sqrtf(rsquared); \
\
/* now update the velocity */ \
dvx += r1 * normal * accel * duration; \
dvy += r2 * normal * accel * duration; \
dvz += r3 * normal * accel * duration; \
for(j = 0; j < i; j++) {
LOOP
}
for(j = i + 1; j < n; j++) {
LOOP
}
velocity[i*3 + 0] += dvx;
velocity[i*3 + 1] += dvy;
velocity[i*3 + 2] += dvz;
}
}
__global__
void update_position(int n, float *position, float *velocity, float duration) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if(i < n) {
position[i*3 + 0] += velocity[i*3 + 0] * duration;
position[i*3 + 1] += velocity[i*3 + 1] * duration;
position[i*3 + 2] += velocity[i*3 + 2] * duration;
}
}
/* This simulates a 1000 Kg body orbiting the moon at at distance of about 10 kilometers */
void two_body_test() {
int i, n = 2;
float position[6] =
{ 0 , 0, 0,
1.75e6, 0, 0 };
float velocity[6] =
{ 0, 0 , 0,
0, 1.673e3, 0 };
float mass[2] = {MOON_MASS, 1e3};
float *position_d, *velocity_d, *mass_d;
float alt = 0;
cudaMalloc(&position_d, 6*sizeof(float));
cudaMalloc(&velocity_d, 6*sizeof(float));
cudaMalloc(&mass_d, 2*sizeof(float));
cudaMemcpy(position_d, position, 6*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(velocity_d, velocity, 6*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(mass_d, mass, 2*sizeof(float), cudaMemcpyHostToDevice);
while(1) {
for(i = 0; i < 6000; i++) {
update_velocity<<<2, 2>>>(n, position_d, velocity_d, mass_d, 0.1);
update_position<<<2, 2>>>(n, position_d, velocity_d, 0.1);
}
cudaMemcpy(&position, position_d, 6*sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(&velocity, velocity_d, 6*sizeof(float), cudaMemcpyDeviceToHost);
alt = sqrt(powf((position[0*3 + 0] - position[1*3 + 0]), 2) +
powf((position[0*3 + 1] - position[1*3 + 1]), 2) +
powf((position[0*3 + 2] - position[1*3 + 2]), 2));
printf("alt %f v %f,%f,%f\n", alt - MOON_RADIUS,
velocity[3+0], velocity[3+1], velocity[3+2]);
}
}
float random_float() {
float n = (float) rand();
return (n / (log10f(n) + 1)); /* between 0 and 1 */
}
void many_body_test() {
int i, n = 8000;
float position[n*3];
float velocity[n*3];
float mass[n];
float *position_d, *velocity_d, *mass_d;
float alt;
cudaMalloc(&position_d, n*3*sizeof(float));
cudaMalloc(&velocity_d, n*3*sizeof(float));
cudaMalloc(&mass_d, n*sizeof(float));
srand(1232);
for(i = 0; i < n; i++) {
position[i*3 + 0] = random_float() * 1e9;
position[i*3 + 1] = random_float() * 1e9;
position[i*3 + 2] = random_float() * 1e9;
velocity[i*3 + 0] = random_float() * 5e2;
velocity[i*3 + 1] = random_float() * 5e2;
velocity[i*3 + 2] = random_float() * 5e2;
mass[i] = random_float() * 1e22;
}
cudaMemcpy(position_d, position, n*3*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(velocity_d, velocity, n*3*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(mass_d, mass, n*sizeof(float), cudaMemcpyHostToDevice);
#define B 16
for(i = 0; i < 6000; i++) {
update_velocity<<<(n+(B-1))/B, B>>>(n, position_d, velocity_d, mass_d, 0.1);
update_position<<<(n+(B-1))/B, B>>>(n, position_d, velocity_d, 0.1);
}
cudaMemcpy(&position, position_d, n*3*sizeof(float), cudaMemcpyDeviceToHost);
cudaMemcpy(&velocity, velocity_d, n*3*sizeof(float), cudaMemcpyDeviceToHost);
alt = sqrt(powf((position[(n-1)*3 + 0] - position[1*3 + 0]), 2) +
powf((position[(n-1)*3 + 1] - position[1*3 + 1]), 2) +
powf((position[(n-1)*3 + 2] - position[1*3 + 2]), 2));
printf("alt %f v %f,%f,%f\n", alt,
velocity[3+0], velocity[3+1], velocity[3+2]);
}
int main() {
many_body_test();
return 0;
}
|
10,653 | #include <cuda_runtime.h>
#include <cstddef>
#include <sys/time.h>
#include <iostream>
#include <vector>
void checkError(cudaError_t err)
{
if (err != cudaSuccess)
{
std::cout << cudaGetErrorString(err) << std::endl;
exit(-1);
}
}
__global__ void reduce_neighbored_global(int* A, int* B, const int N)
{
// reduction with global memory
for (int loop=0; loop < 1000; ++loop)
{
const int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int gridStride = blockDim.x * gridDim.x;
int temp = idx + gridStride;
while (temp < N)
{
A[idx] += A[temp];
temp += gridStride;
}
__syncthreads();
for (int s = 1; s < blockDim.x; s *= 2)
{
if (tid % (2*s) == 0)
{
A[idx] += A[idx + s];
}
__syncthreads();
}
if (tid == 0)
B[blockIdx.x] = A[idx];
}
}
__global__ void reduce_neighbored_conflict_divergent(int* A, int* B, const int N)
{
//reduction with shared memory
for (int loop=0; loop < 1000; ++loop)
{
const int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int gridStride = blockDim.x * gridDim.x;
__shared__ int buf[1024];
buf[tid] = 0;
while (idx < N)
{
buf[tid] += A[idx];
idx += gridStride;
}
__syncthreads();
for (int s = 1; s < blockDim.x; s *= 2)
{
if (tid % (2*s) == 0)
{
buf[tid] += buf[tid + s];
}
__syncthreads();
}
if (tid == 0)
B[blockIdx.x] = buf[0];
}
}
__global__ void reduce_neighbored_conflict_nondivergent(int* A, int* B, const int N)
{
//remove warp divergence
for (int loop=0; loop < 1000; ++loop)
{
const int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int gridStride = blockDim.x * gridDim.x;
__shared__ int buf[1024];
buf[tid] = 0;
while (idx < N)
{
buf[tid] += A[idx];
idx += gridStride;
}
__syncthreads();
for (int s = 1; s < blockDim.x; s *= 2)
{
const int index = 2 * s * tid;
if (index < blockDim.x)
{
buf[index] += buf[index + s];
}
__syncthreads();
}
if (tid == 0)
B[blockIdx.x] = buf[0];
}
}
__global__ void reduce_interleaved_noconflict_nondivergent(int* A, int* B, const int N)
{
// remove bank conflicts
for (int loop=0; loop < 1000; ++loop)
{
const int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int gridStride = blockDim.x * gridDim.x;
__shared__ int buf[1024];
buf[tid] = 0;
while (idx < N)
{
buf[tid] += A[idx];
idx += gridStride;
}
__syncthreads();
for (int s = blockDim.x/2; s > 0; s >>= 1)
{
if (tid < s)
{
buf[tid] += buf[tid + s];
}
__syncthreads();
}
if (tid == 0)
B[blockIdx.x] = buf[0];
}
}
__device__ void unroll(volatile int* buf, int tid)
{
if (tid < 32)
{
buf[tid] += buf[tid + 32];
buf[tid] += buf[tid + 16];
buf[tid] += buf[tid + 8];
buf[tid] += buf[tid + 4];
buf[tid] += buf[tid + 2];
buf[tid] += buf[tid + 1];
}
}
__global__ void reduce_interleaved_noconflict_nondivergent_unrolled(int* A, int* B, const int N)
{
// unroll last loops
for (int loop=0; loop < 1000; ++loop)
{
const int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int gridStride = blockDim.x * gridDim.x;
__shared__ int buf[1024];
buf[tid] = 0;
while (idx < N)
{
buf[tid] += A[idx];
idx += gridStride;
}
__syncthreads();
for (int s = blockDim.x/2; s > 32; s >>= 1)
{
if (tid < s)
{
buf[tid] += buf[tid + s];
}
__syncthreads();
}
unroll(buf, tid);
if (tid == 0)
B[blockIdx.x] = buf[0];
}
}
template <unsigned int blockSize>
__global__ void reduce_interleaved_noconflict_nondivergent_completelyunrolled(int* A, int* B, const int N)
{
for (int loop=0; loop < 1000; ++loop)
{
const int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int gridStride = blockDim.x * gridDim.x;
__shared__ int buf[1024];
buf[tid] = 0;
while (idx < N)
{
buf[tid] += A[idx];
idx += gridStride;
}
__syncthreads();
if (blockSize>=1024 && tid < 512)
buf[tid] += buf[tid + 512];
__syncthreads();
if (blockSize>=512 && tid < 256)
buf[tid] += buf[tid + 256];
__syncthreads();
if (blockSize>=256 && tid < 128)
buf[tid] += buf[tid + 128];
__syncthreads();
if (blockSize>=128 && tid < 64)
buf[tid] += buf[tid + 64];
__syncthreads();
unroll(buf, tid);
if (tid == 0)
B[blockIdx.x] = buf[0];
}
}
int main()
{
const int nElem = 1024*2048;
std::vector<int> A(nElem, 1);
std::vector<int> B(1024, 1);
const int nBytes = nElem * sizeof(int);
std::cout << nBytes * 1e-6 << std::endl;
int* d_A;
int* d_B;
checkError(cudaMalloc(&d_A, nBytes));
checkError(cudaMalloc(&d_B, 1024 * sizeof(int)));
//warmup
checkError( cudaMemcpy(d_A, &A[0], nBytes, cudaMemcpyHostToDevice) );
checkError( cudaDeviceSynchronize() );
reduce_neighbored_conflict_divergent <<< 1024, 512 >>> (d_A, d_B, nElem);
checkError( cudaPeekAtLastError() );
checkError( cudaDeviceSynchronize() );
checkError( cudaMemcpy(d_A, &A[0], nBytes, cudaMemcpyHostToDevice) );
checkError( cudaDeviceSynchronize() );
reduce_neighbored_global <<< 1024, 512 >>> (d_A, d_B, nElem);
checkError( cudaPeekAtLastError() );
checkError( cudaDeviceSynchronize() );
checkError( cudaMemcpy(d_A, &A[0], nBytes, cudaMemcpyHostToDevice) );
checkError( cudaDeviceSynchronize() );
reduce_neighbored_conflict_divergent <<< 1024, 512 >>> (d_A, d_B, nElem);
checkError( cudaPeekAtLastError() );
checkError( cudaDeviceSynchronize() );
checkError( cudaMemcpy(d_A, &A[0], nBytes, cudaMemcpyHostToDevice) );
checkError( cudaDeviceSynchronize() );
reduce_neighbored_conflict_nondivergent <<< 1024, 512 >>> (d_A, d_B, nElem);
checkError( cudaPeekAtLastError() );
checkError( cudaDeviceSynchronize() );
checkError( cudaMemcpy(d_A, &A[0], nBytes, cudaMemcpyHostToDevice) );
checkError( cudaDeviceSynchronize() );
reduce_interleaved_noconflict_nondivergent <<< 1024, 512 >>> (d_A, d_B, nElem);
checkError( cudaPeekAtLastError() );
checkError( cudaDeviceSynchronize() );
checkError( cudaMemcpy(d_A, &A[0], nBytes, cudaMemcpyHostToDevice) );
checkError( cudaDeviceSynchronize() );
reduce_interleaved_noconflict_nondivergent_unrolled <<< 1024, 512 >>> (d_A, d_B, nElem);
checkError( cudaPeekAtLastError() );
checkError( cudaDeviceSynchronize() );
checkError( cudaMemcpy(d_A, &A[0], nBytes, cudaMemcpyHostToDevice) );
checkError( cudaDeviceSynchronize() );
reduce_interleaved_noconflict_nondivergent_completelyunrolled<512> <<< 1024, 512 >>> (d_A, d_B, nElem);
checkError( cudaPeekAtLastError() );
checkError( cudaDeviceSynchronize() );
checkError(cudaMemcpy(&B[0], d_B, 1024 * sizeof(int), cudaMemcpyDeviceToHost));
for (long long i = 0; i < 1024; ++i)
{
if (B.at(i) != 2048)
{
std::cout << "error: " << i << " "
<< B.at(i) << std::endl;
exit(-1);
}
}
checkError(cudaFree(d_A));
checkError(cudaFree(d_B));
}
|
10,654 | #include "includes.h"
/*
:copyright:
William B. Frank and Eric Beauce
:license:
GNU General Public License, Version 3
(https://www.gnu.org/licenses/gpl-3.0.en.html)
*/
__global__ void network_corr(float *templates, float *sum_square_template, int *moveout, float *data, float *weights, size_t step, size_t n_samples_template, size_t n_samples_data, size_t n_stations, size_t n_components, int chunk_offset, int chunk_size, float *cc_mat) {
// each thread matches the template to one time in the data
int idx, first_sample_block, first_sample_trace, last_sample_trace; // sample's index
int i, s, c; // counters
int data_offset, templates_offset, sum_square_template_offset, cc_mat_offset;
float numerator, denominator, sum_square_data;
float data_sample;
int t_idx;
//------------------------------------------------
int count_template = (n_samples_template / WARPSIZE + 1) * WARPSIZE;
extern __shared__ float shared[];
float *ss_template = &shared[0];
float *templates_s = &shared[sizeof(float)];
float *data_s = &shared[count_template+sizeof(float)];
// 1 block processes one channel to blockDim.x / step different positions in time
idx = blockIdx.x/n_stations * blockDim.x + chunk_offset;
first_sample_block = idx * step;
s = blockIdx.x % n_stations;
for (c = 0; c < n_components; c++){
if (weights[s * n_components + c] != 0.){
// compute offsets for input variables
cc_mat_offset = (first_sample_block / step + threadIdx.x - chunk_offset) * n_stations * n_components + s * n_components + c;
templates_offset = s * n_samples_template * n_components + c * n_samples_template;
sum_square_template_offset = s * n_components + c;
first_sample_trace = first_sample_block + moveout[s * n_components + c];
last_sample_trace = first_sample_trace + n_samples_template + threadIdx.x * step;
data_offset = s * n_samples_data * n_components + c * n_samples_data + first_sample_trace;
// initialize sums
sum_square_data = 0.0f;
numerator = 0.0f;
// load template and data into shared memory
t_idx = threadIdx.x;
if (t_idx == 0){
ss_template[0] = sum_square_template[sum_square_template_offset];
}
while(t_idx < n_samples_template) {
templates_s[t_idx] = templates[templates_offset + t_idx];
if ((first_sample_trace + t_idx) < n_samples_data) data_s[t_idx] = data[data_offset + t_idx];
t_idx += blockDim.x;
}
while(t_idx < (blockDim.x * step + n_samples_template)){
if ((first_sample_trace + t_idx) < n_samples_data) data_s[t_idx] = data[data_offset + t_idx];
t_idx += blockDim.x;
}
__syncthreads(); // make sure the waveforms are read before keep going
// calculate correlation coefficient
if (last_sample_trace < n_samples_data){
// if not, corresponds to an ill-defined CC with some samples out of the bounds
for(i = 0; i < n_samples_template; i++) {
data_sample = data_s[i + threadIdx.x * step];
numerator += data_sample * templates_s[i];
sum_square_data += data_sample * data_sample;
}
//denominator = sum_square_data * sum_square_template[sum_square_template_offset];
denominator = sum_square_data * ss_template[0];
if (cc_mat_offset < (chunk_size * n_stations * n_components)){
// check that this thread is not ouf of the chunk's bounds
if (denominator > STABILITY_THRESHOLD) cc_mat[cc_mat_offset] = numerator * rsqrtf(denominator);
}
}
}
__syncthreads(); // wait for every thread to finish before leaving the kernel
}
} |
10,655 | #include "includes.h"
__global__ void combine_im_kernel(const float *A, const float *B, float *C, int numElements)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
/*
combines images for a joint histogram computation with the formula:
comb_im = B1*(im1 + im2*(B2-1))/(B1*B2 - 1)
for a joint histogram of 256: B1*B2 must equal 256
--> choose B1=B2=16
*/
float B1 = 16.0;
float B2 = 16.0;
if (i < numElements)
{
C[i] = B1*(A[i] + B[i] * (B2 - 1)) / (B1*B2 - 1);
}
} |
10,656 | #include "includes.h"
__global__ void warp_kernel(float* out, const float* in, const int* index,const float* weights,const int npixels,const int nchannels){
int pixel = blockIdx.x * blockDim.x + threadIdx.x;
int channel = blockIdx.y * blockDim.y + threadIdx.y;
if( channel >= nchannels||pixel >= npixels)
return;
out[nchannels*pixel+channel]=in[nchannels*index[4*pixel]+channel]*weights[4*pixel]
+in[nchannels*index[4*pixel+1]+channel]*weights[4*pixel+1]
+in[nchannels*index[4*pixel+2]+channel]*weights[4*pixel+2]
+in[nchannels*index[4*pixel+3]+channel]*weights[4*pixel+3];
} |
10,657 | #include <cuda.h>
#include <stdio.h>
__global__ void matMulKernel(float* M, float* N, float* P, int width){
int col = blockDim.x*blockIdx.x + threadIdx.x;
int row = blockDim.y*blockIdx.y + threadIdx.y;
if ((row < width) && (col < width)) {
float Pvalue = 0;
for (int k = 0; k < width; ++k){
Pvalue += M[k*width+col]*N[row*width+k];
}
P[row*width+col] = Pvalue;
}
}
void matMul(float* A, float* B, float* C, int width)
{
int size = width * width * sizeof(float);
static float *d_A, *d_B, *d_C;
cudaMalloc((void **) &d_A, size);
cudaMemcpy(d_A, A, size, cudaMemcpyHostToDevice);
cudaMalloc((void **) &d_B, size);
cudaMemcpy(d_B, B, size, cudaMemcpyHostToDevice);
cudaMalloc((void **) &d_C, size);
dim3 dimGrid(2, 2, 1);
dim3 dimBlock(2, 2, 1);
matMulKernel<<<dimGrid, dimBlock>>>(d_A, d_B, d_C, width);
cudaMemcpy(C, d_C, size, cudaMemcpyDeviceToHost);
printf("\nA: \n");
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++){
printf("%2.0f ", A[i + j*width]);
}
printf("\n");
}
printf("\nB: \n");
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++){
printf("%2.0f ", B[i + j*width]);
}
printf("\n");
}
printf("\n-------------------------------------");
printf("\nC: \n");
for (int i = 0; i < 4; i++) {
for (int j = 0; j < 4; j++){
printf("%2.0f ", C[i + j*width]);
}
printf("\n");
}
printf("\n-------------------------------------\n");
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
}
int main() {
int width = 4;
static float h_A[16];
static float h_B[16];
static float h_C[16];
for (int i = 0; i < width; i++) {
for (int j = 0; j < width; j++) {
h_A[i + j*width] = (i+j)%2;
h_B[i + j*width] = (i+j)%3;
}
}
matMul(h_A, h_B, h_C, width);
} |
10,658 | #include <cuda_runtime.h>
#include <stdio.h>
__global__ void printWhoAmIKernel() {
int bx = blockIdx.x;
int tx = threadIdx.x;
printf("Hello CUDA! I'm thread %d from block %d.\n", tx, bx);
}
int main(int argc, char **argv) {
dim3 BLOCK_SIZE(8, 1, 1);
dim3 GRID_SIZE(4, 1, 1);
printWhoAmIKernel<<<GRID_SIZE, BLOCK_SIZE>>>();
return 0;
}
|
10,659 | #include <stdio.h>
#include <stdlib.h>
#define SIZE 1024
#define TILE_WIDTH 32
__global__ void Multiply(float* A, float* B, float* C, int width)
{
int row = blockDim.y * TILE_WIDTH + threadIdx.y;
int col = blockDim.x * TILE_WIDTH + threadIdx.x;
__shared__ float sA[TILE_WIDTH][TILE_WIDTH];
__shared__ float sB[TILE_WIDTH][TILE_WIDTH];
int tx = threadIdx.x,ty = threadIdx.y;
float s = 0;
for (int i = 0; i < width/TILE_WIDTH; i++) {
sA[ty][tx] = A[row*width + (i*TILE_WIDTH + tx)];
sB[ty][tx] = B[col + (i*TILE_WIDTH + ty)*width];
__syncthreads();
for (int j = 0; j < TILE_WIDTH; j++)
s += sA[ty][j] * sB[j][tx];
__syncthreads();
}
C[row*width+col] = s;
}
int main(){
float A[SIZE*SIZE];
float B[SIZE*SIZE];
float C[SIZE*SIZE];
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
float * d_A, * d_B, *d_C;
int BLOCKSIZE;
printf("Choose block size : ");
scanf("%d",&BLOCKSIZE);
int GRIDSIZE = int(SIZE/BLOCKSIZE);
dim3 dimBlock(BLOCKSIZE,BLOCKSIZE); // 20
dim3 dimGrid(GRIDSIZE,GRIDSIZE);
size_t size = SIZE*SIZE*sizeof(float);
for(int i = 0; i < SIZE; i++){
for(int j = 0; j < SIZE; j++){
A[i*SIZE+j] = rand()%10;
B[i*SIZE+j] = rand()%10;
C[i*SIZE+j] = 0;
} // 30
}
cudaEventRecord(start);
cudaMalloc((void **)&d_A, size);
cudaMemcpy(d_A,A,size,cudaMemcpyHostToDevice);
cudaMalloc((void **)&d_B, size);
cudaMemcpy(d_B,B,size,cudaMemcpyHostToDevice);
cudaMalloc((void **)&d_C, size);
cudaMemcpy(d_C,C,size,cudaMemcpyHostToDevice);
Multiply<<<dimGrid,dimBlock>>>(d_A, d_B,d_C,64);
cudaEventRecord(stop);
cudaMemcpy(C, d_C, size, cudaMemcpyDeviceToHost);
cudaEventSynchronize(stop);
float elapsed = 0;
cudaEventElapsedTime(&elapsed, start, stop);
printf("Using Grid Size [%d, %d] and Block Size [%d, %d]..\n", dimGrid.x, dimGrid.y,dimBlock.x, dimBlock.y);
printf("Execution time : %f miliseconds\n", elapsed);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
} |
10,660 | #include <iostream>
#include <stdio.h>
#include <stdlib.h>
#define BLOCK_SIZE 32
__global__ void reduce(int *g_idata, int *g_odata,int num_vec) {
__shared__ int sdata[BLOCK_SIZE];
// each thread loads one element from global to shared mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*blockDim.x + threadIdx.x;
if(i<num_vec)
sdata[tid] = g_idata[i];
else
sdata[tid]=0;
__syncthreads();
// do reduction in shared mem
for(unsigned int s=blockDim.x/2; s > 0; s >>= 1) {
if(tid < s){
sdata[tid] += sdata[tid + s];
}
__syncthreads();
}
// write result for this block to global mem
if (tid == 0) g_odata[blockIdx.x] = sdata[0];
}
void sumS(int *A,int N ,int *r){
int value=0;
for(int i=0;i<N;i++)
value+=A[i];
*r=value;
}
void llenar(int *A,int N,int a){
for(int i = 0; i <N; i++ )
A[i] = a;
}
void imprimir(int *A,int N){
for(int i = 0; i <N; i++)
printf("%d ",A[i]);
printf("\n");
}
int main(){
int N=1000000;
int s;
int bytes=(N)*sizeof(int);
int *A=(int*)malloc(bytes);
int *R=(int*)malloc(bytes);
//Lleno las matrices.
llenar(A,N,1);
llenar(R,N,0);
//////////////////Algoritmo secuencial///////////////////////
clock_t start = clock();
sumS(A,N,&s);
clock_t end= clock();
double elapsed_seconds=end-start;
printf("Tiempo transcurrido Secuencial: %lf\n", (elapsed_seconds / CLOCKS_PER_SEC));
/////////////////////////////////////////////////////////////
////////////////////////Algoritmo Paralelo///////////////////
int *d_A=(int*)malloc(bytes);
int *d_R=(int*)malloc(bytes);
cudaMalloc((void**)&d_A,bytes);
cudaMalloc((void**)&d_R,bytes);
clock_t start2 = clock();
cudaMemcpy(d_A, A, bytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_R, R, bytes, cudaMemcpyHostToDevice);
float blocksize=BLOCK_SIZE;
int i=N;
while(i>1){
dim3 dimBlock(BLOCK_SIZE,1,1);
int grid=ceil(i/blocksize);
dim3 dimGrid(grid,1,1);
reduce<<<dimGrid,dimBlock>>>(d_A,d_R,i);
cudaDeviceSynchronize();
cudaMemcpy(d_A, d_R, bytes, cudaMemcpyDeviceToDevice);
i=ceil(i/blocksize);
}
cudaMemcpy(R, d_R, bytes, cudaMemcpyDeviceToHost);
clock_t end2= clock();
double elapsed_seconds2=end2-start2;
printf("Tiempo transcurrido Paralelo Reduccion: %lf\n", (elapsed_seconds2 / CLOCKS_PER_SEC));
/////////////////////////////////////////////////////////////
if(s==R[0])
printf("Las sumatorias son iguales: %d %d \n",s,R[0]);
else
printf("Las sumatorias no son iguales: %d %d \n",s,R[0]);
printf("Aceleración: %lf",elapsed_seconds/elapsed_seconds2);
free(A);
free(R);
cudaFree(d_A);
cudaFree(d_R);
return 0;
}
|
10,661 | #include<stdio.h>
#include<stdlib.h>
#define N 512
void host_add(int *a, int *b, int *c) {
for(int idx=0;idx<N;idx++)
c[idx] = a[idx] + b[idx];
}
__global__ void device_add(int *a, int *b, int *c) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
c[index] = a[index] + b[index];
}
//basically just fills the array with index.
void fill_array(int *data) {
for(int idx=0;idx<N;idx++)
data[idx] = idx;
}
void print_output(int *a, int *b, int*c) {
for(int idx=0;idx<N;idx++)
printf("\n %d + %d = %d", a[idx] , b[idx], c[idx]);
}
int main(void) {
int *a, *b, *c;
int *d_a, *d_b, *d_c; // device copies of a, b, c
int threads_per_block=0, no_of_blocks=0;
int size = N * sizeof(int);
// Alloc space for host copies of a, b, c and setup input values
a = (int *)malloc(size); fill_array(a);
b = (int *)malloc(size); fill_array(b);
c = (int *)malloc(size);
// Alloc space for device copies of a, b, c
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
// Copy inputs to device
cudaMemcpy(d_a, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b, size, cudaMemcpyHostToDevice);
threads_per_block = 4;
no_of_blocks = N/threads_per_block;
device_add<<<no_of_blocks,threads_per_block>>>(d_a,d_b,d_c);
// Copy result back to host
cudaMemcpy(c, d_c, size, cudaMemcpyDeviceToHost);
print_output(a,b,c);
free(a); free(b); free(c);
cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
return 0;
}
|
10,662 | #include "includes.h"
#define SIZE 1024
__global__ void vectorAdd(int *a, int *b, int *c, int n)
{
int i = threadIdx.x;
if(i<n)
c[i]=a[i]+b[i];
} |
10,663 | /* Voxel sampling GPU implementation
* Author Zhaoyu SU
* All Rights Reserved. Sep., 2019.
*/
#include <stdio.h>
#include <iostream>
#include <float.h> // import FLT_EPSILON
__global__ void voxel2col_gpu_kernel(int input_num, int channels, int input_voxel_size,
int output_voxel_size, int kernel_size,
const float* input_voxels,
float* output_voxels,
int* output_idx) {
const int input_voxel_num = input_voxel_size * input_voxel_size * input_voxel_size;
const int output_voxel_num = output_voxel_size * output_voxel_size * output_voxel_size;
const int kernel_num = kernel_size * kernel_size * kernel_size;
int thread_id = threadIdx.x + blockIdx.x * blockDim.x;
if (thread_id < input_num * output_voxel_num * kernel_num) {
output_idx[thread_id] = -1;
}
__syncthreads();
if (thread_id < input_num * output_voxel_num * kernel_num) {
int current_id = thread_id / (output_voxel_num * kernel_num);
int output_voxel_id = thread_id % (output_voxel_num * kernel_num) / kernel_num;
int kernel_id = thread_id % kernel_num;
int kernel_x = kernel_id / (kernel_size * kernel_size);
int kernel_y = kernel_id % (kernel_size * kernel_size) / kernel_size;
int kernel_z = kernel_id % kernel_size;
int output_voxel_coor_x = output_voxel_id / (output_voxel_size * output_voxel_size);
int output_voxel_coor_y = output_voxel_id % (output_voxel_size * output_voxel_size) / output_voxel_size;
int output_voxel_coor_z = output_voxel_id % output_voxel_size;
/*
input_voxel_coor = output_voxel_coor + 1;
kernel_coor = input_voxel_coor + [kernel_x/y/z] - 1, for kernel_x/y/z in [0, 1, 2];
so:
kernel_coor = output_voxel_coor + [kernel_x/y/z], for kernel_x/y/z in [0, 1, 2];
*/
int kernel_coor_x = output_voxel_coor_x + kernel_x;
int kernel_coor_y = output_voxel_coor_y + kernel_y;
int kernel_coor_z = output_voxel_coor_z + kernel_z;
int input_voxel_id = current_id * input_voxel_num + \
kernel_coor_x * input_voxel_size * input_voxel_size + \
kernel_coor_y * input_voxel_size + \
kernel_coor_z;
output_idx[thread_id] = input_voxel_id;
for (int c=0; c<channels; c++) {
output_voxels[thread_id * channels + c] = input_voxels[input_voxel_id * channels + c];
}
}
}
__global__ void voxel2col_grad_gpu_kernel(int input_num, int output_voxel_num, int kernel_num, int channels,
const float* input_voxels,
const int* output_idx,
const float* output_voxels_grad,
float* input_voxels_grad) {
int thread_id = threadIdx.x + blockIdx.x * blockDim.x;
if (thread_id < input_num * output_voxel_num * kernel_num) {
int input_voxel_id = output_idx[thread_id];
if (input_voxel_id >= 0) {
for (int c=0; c<channels; c++) {
atomicAdd(&input_voxels_grad[input_voxel_id*channels + c], output_voxels_grad[thread_id*channels + c]);
}
}
}
}
void voxel2col_gpu_launcher(int input_num, int channels, int input_voxel_size,
int output_voxel_size, int kernel_size,
const float* input_voxels,
float* output_voxels,
int* output_idx) {
if (channels * input_voxel_size * output_voxel_size * kernel_size <= 0) {
printf("Voxel2ColOp ERROR: Invalid CUDA input dimensions.\n");
return;
}
if (input_num <=0) {
return;
}
const int output_voxel_num = output_voxel_size * output_voxel_size * output_voxel_size;
const int kernel_num = kernel_size * kernel_size * kernel_size;
int blockSize; // The launch configurator returned block size
int minGridSize; // The minimum grid size needed to achieve the maximum occupancy for a full device launch
int gridSize; // The actual grid size needed, based on input size
cudaOccupancyMaxPotentialBlockSize(&minGridSize, &blockSize, voxel2col_gpu_kernel, 0, input_num * output_voxel_num * kernel_num);
gridSize = (input_num * output_voxel_num * kernel_num + blockSize - 1) / blockSize;
voxel2col_gpu_kernel<<<gridSize, blockSize>>>(input_num, channels, input_voxel_size,
output_voxel_size, kernel_size,
input_voxels,
output_voxels,
output_idx);
}
void voxel2col_grad_gpu_launcher(int input_num, int output_voxel_num, int kernel_num, int channels,
const float* input_voxels,
const int* output_idx,
const float* output_voxels_grad,
float* input_voxels_grad) {
if (channels * output_voxel_num * kernel_num <= 0) {
printf("Voxel2ColGradOp ERROR: Invalid CUDA input dimensions.\n");
return;
}
if (input_num <=0) {
return;
}
int blockSize; // The launch configurator returned block size
int minGridSize; // The minimum grid size needed to achieve the maximum occupancy for a full device launch
int gridSize; // The actual grid size needed, based on input size
cudaOccupancyMaxPotentialBlockSize(&minGridSize, &blockSize, voxel2col_gpu_kernel, 0, input_num * output_voxel_num * kernel_num);
gridSize = (input_num * output_voxel_num * kernel_num + blockSize - 1) / blockSize;
voxel2col_grad_gpu_kernel<<<gridSize, blockSize>>>(input_num, output_voxel_num, kernel_num, channels,
input_voxels,
output_idx,
output_voxels_grad,
input_voxels_grad);
} |
10,664 | #include<stdio.h>
#include<cuda.h>
int main(void){
int deviceCount;
char deviceName[256];
CUdevice device;
size_t szMem; int szProc;
cuInit(0);
cuDeviceGetCount(&deviceCount);
cuDeviceGet(&device,0);
cuDeviceGetName(deviceName,255,device);
cuDeviceTotalMem(&szMem,device);
cuDeviceGetAttribute(&szProc,CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT,device);
printf("There are %d devices detected\n",deviceCount);
printf("Device %s has %f GB of global memory\n",
deviceName,szMem/pow(1024.0,3));
printf("Device multiprocessor count: %d\n",szProc);
}
|
10,665 |
#include <stdio.h> //manipulacion de ficheros, lectura-escritura ficheros, scandf-printf
#include <stdlib.h> //Conversion de tipos de datos, memoria dinamica, abs
#include <string.h> //Uso de memcpy principalmente
#include <math.h> //funciones matemáticas
#define TILE_WIDTH_X 32
#define TILE_WIDTH_Y 32
#define PI 3.141592653589793
#define left ((ix-1) + Nx*iz) //Izquierda
#define top (ix + Nx*(iz-1)) //Arriba
#define center (ix + Nx*iz) //Centro
#define bottom (ix + Nx*(iz+1)) //Abajo
#define right ((ix+1) + Nx*iz) //Derecha
// A = Presente
// B = Pasado
// C = Futuro
//DEVICE CODE - Kernel1
__global__ void kernel_lap(float *lap, float *A, int nx, int ny, float dh){
int ix=threadIdx.x + blockIdx.x*blockDim.x;
int iy=threadIdx.y + blockIdx.y*blockDim.y;
int tid = ix + iy*nx;
if(ix > 3 && ix < nx-4 && iy > 3 && iy< ny-4){
lap[tid] =((-1./560)*(A[ tid - 4 ] + A[tid + 4] + A[tid + 4*nx ] + A[tid -4*nx]) +
(8./315)*(A[ tid - 3 ] + A[tid + 3] + A[tid + 3*nx ] + A[tid -3*nx]) +
(-1./5)*(A[ tid - 2 ] + A[tid + 2] + A[tid + 2*nx ] + A[tid -2*nx]) +
(8./5)*(A[ tid - 1 ] + A[tid + 1] + A[tid + 1*nx ] + A[tid -1*nx]) +
(-205./36)*(A[tid]))/(dh*dh);
__syncthreads();
}
}
__global__
void get_CPML_x(float *a_x, float *b_x, int CPMLimit, float R, float VelMax, int Nx, float dt, float dh, float frec){
int ix = threadIdx.x + blockDim.x * blockIdx.x; // Indice vector
float Lx = CPMLimit*dh;
float d0 = -3*log(R)/(2*Lx);
a_x[ix] = 0;
b_x[ix] = 0;
if (ix<CPMLimit+1) //Left CPML
{
b_x[ix] = exp(-( (d0 * VelMax * (((CPMLimit-ix)*dh)/Lx) * (((CPMLimit-ix)*dh)/Lx)) + (PI * frec * (Lx - ((CPMLimit-ix)*dh))/Lx))*dt);
__syncthreads();
a_x[ix] = (d0 * VelMax * (((CPMLimit-ix)*dh)/Lx) * (((CPMLimit-ix)*dh)/Lx)) * ( b_x[ix] - 1 ) / ( (d0 * VelMax * (((CPMLimit-ix)*dh)/Lx) * (((CPMLimit-ix)*dh)/Lx)) + (PI * frec * (Lx - ((CPMLimit-ix)*dh))/Lx));
__syncthreads();
__syncthreads();
}
if (ix>(Nx-CPMLimit-1) && ix<Nx) //Right CPML
{
b_x[ix] = exp(-( (d0 * VelMax * (((ix-Nx+CPMLimit+1)*dh)/Lx) * (((ix-Nx+CPMLimit+1)*dh)/Lx)) + (PI * frec * (Lx - ((ix-Nx+CPMLimit+1)*dh))/Lx) )*dt);
__syncthreads();
a_x[ix] = (d0 * VelMax * (((ix-Nx+CPMLimit+1)*dh)/Lx) * (((ix-Nx+CPMLimit+1)*dh)/Lx)) * ( b_x[ix] - 1 ) / ( (d0 * VelMax * (((ix-Nx+CPMLimit+1)*dh)/Lx) * (((ix-Nx+CPMLimit+1)*dh)/Lx)) + (PI * frec * (Lx - ((ix-Nx+CPMLimit+1)*dh))/Lx) );
__syncthreads();
__syncthreads();
}
}
__global__
void get_CPML_z(float *a_z, float *b_z, int CPMLimit, float R, float VelMax, int Nz, float dt, float dh, float frec){
//dh = dx; Notacion
int iz = threadIdx.x + blockDim.x * blockIdx.x; // Indice vector
float Lz = CPMLimit*dh;
float d0 = -3*log(R)/(2*Lz);
//Inicializando valores de CPML
a_z[iz] = 0;
b_z[iz] = 0;
if (iz>(Nz-CPMLimit-1) && iz<Nz) //bottom CPML
{
b_z[iz] = exp(-( (d0 * VelMax * (((iz-Nz+CPMLimit+1)*dh)/Lz) * (((iz-Nz+CPMLimit+1)*dh)/Lz)) + (PI * frec * (Lz - ((iz-Nz+CPMLimit+1)*dh))/Lz) )*dt);
__syncthreads();
a_z[iz] = (d0 * VelMax * (((iz-Nz+CPMLimit+1)*dh)/Lz) * (((iz-Nz+CPMLimit+1)*dh)/Lz)) * ( b_z[iz] - 1 ) / ( (d0 * VelMax * (((iz-Nz+CPMLimit+1)*dh)/Lz) * (((iz-Nz+CPMLimit+1)*dh)/Lz)) + (PI * frec * (Lz - ((iz-Nz+CPMLimit+1)*dh))/Lz) );
__syncthreads();
}
}
__global__
void PSI(float *A, float *a_x, float *b_x, float *a_z, float *b_z, float *Psi_x, float *Psi_z, int CPMLimit, int Nx, int Nz, float dh){
int ix = threadIdx.x + blockDim.x * blockIdx.x; // Row of the A matrix
int iz = threadIdx.y + blockDim.y * blockIdx.y; // Column of the A matrix
int tid = ix + iz*Nx;
if(ix > 3 && ix < Nx-4 && iz > 3 && iz< Nz-4)
{
// Primera derivada de segundo orden centrada
/*
Psi_x[tid] = Psi_x[tid]*b_x[ix] + a_x[ix]*( (-1./2)*A[tid-1] + (1./2)*A[tid+1])/(2*dh);
Psi_z[tid] = Psi_z[tid]*b_z[iz] + a_z[iz]*( (-1./2)*A[tid-Nx] + (1./2)*A[tid+Nx])/(2*dh);
*/
// Primera derivada de octavo orden centrada
Psi_x[tid] = Psi_x[tid]*b_x[ix] + a_x[ix]*( (1./280.)*A[tid-4] - (4./105.)*A[tid-3] + (1./5.)*A[tid-2] - (4./5.)*A[tid-1] + (4./5.)*A[tid+1] + (-1./5.)*A[tid+2] + (4./105.)*A[tid+3] + (-1./280.)*A[tid+4] )/(dh);
__syncthreads();
Psi_z[tid] = Psi_z[tid]*b_z[iz] + a_z[iz]*( (1./280.)*A[tid-4*Nx] + (-4./105.)*A[tid-3*Nx] + (1./5.)*A[tid-2*Nx] + (-4./5.)*A[tid-1*Nx] + (4./5.)*A[tid+1*Nx] + (-1./5.)*A[tid+2*Nx] + (4./105.)*A[tid+3*Nx] + (-1./280.)*A[tid+4*Nx] )/(dh);
__syncthreads();
}
}
__global__
void kernel_propaga(float *lap, float *A, float *B, int nx, int nz, float *source, float *c, float *traza, float *P, float *dP, int it, int sx, int sz, int borde, float dt, float *a_z, float *a_x, float *b_z, float *b_x, float *Psi_x, float *Psi_z, float *Z_x, float *Z_z, float *temp, float dh, int modo){
int ix=threadIdx.x + blockIdx.x*blockDim.x;
int iy=threadIdx.y + blockIdx.y*blockDim.y;
int tid = ix + iy*nx;
float G = c[tid]*c[tid]*dt*dt;
if(ix > 3 && ix < nx-4 && iy > 3 && iy< nz-4){
// Calculo de las derivadas de psi
// Primera derivada de segundo orden central
//temp[tid]=((-1./2)*Psi_x[tid-1] + (1./2)*Psi_x[tid+1])/(2*dh) + ((-1./2)*Psi_z[tid-nx] + (1./2)*Psi_z[tid+nx])/(2*dh);
// Primera derivada octavo orden central
temp[tid]=( (1./280)*Psi_x[tid-4] + (-4./105)*Psi_x[tid-3] + (1./5)*Psi_x[tid-2] + (-4./5)*Psi_x[tid-1]
+ (4./5)*Psi_x[tid+1] + (-1./5)*Psi_x[tid+2] + (4./105)*Psi_x[tid+3] + (-1./280)*Psi_x[tid+4] )/(dh)
+ ( (1./280)*Psi_z[tid-4*nx] + (-4./105)*Psi_z[tid-3*nx] + (1./5)*Psi_z[tid-2*nx] + (-4./5)*Psi_z[tid-1*nx]
+ (4./5)*Psi_z[tid+1*nx] + (-1./5)*Psi_z[tid+2*nx] + (4./105)*Psi_z[tid+3*nx] + (-1./280)*Psi_z[tid+4*nx] )/(dh);
__syncthreads();
// Calculo de los zetas
// Derivadas de segundo orden
// Z_x[tid]=b_x[ix]*Z_x[tid] + a_x[ix]*( (A[tid+1] -2*A[tid] + A[tid-1])/(dh*dh) + ((-1./2)*Psi_x[tid-1] + (1./2)*Psi_x[tid+1])/dh);
// Z_z[tid]=b_z[iy]*Z_z[tid] + a_z[iy]*( (A[tid-nx] -2*A[tid] + A[tid+nx])/(dh*dh) + ((-1./2)*Psi_z[tid-nx] + (1./2)*Psi_z[tid+nx])/dh);
// Derivadas de octavo orden
Z_x[tid]=b_x[ix]*Z_x[tid] + a_x[ix]*( ( (-1./560)*A[tid-4] + (8./315)*A[tid-3] + (-1./5)*A[tid-2] + (8./5)*A[tid-1] + (-205./72)*A[tid] + (8./5)*A[tid+1] + (-1./5)*A[tid+2] + (8./315)*A[tid+3] + (-1./560)*A[tid+4] )/(dh*dh) + ((1./280)*Psi_x[tid-4] + (-4./105)*Psi_x[tid-3] + (1./5)*Psi_x[tid-2] + (-4./5)*Psi_x[tid-1] + (4./5)*Psi_x[tid+1] + (-1./5)*Psi_x[tid+2] + (4./105)*Psi_x[tid+3] + (-1./280)*Psi_x[tid+4] )/(dh*dh));
__syncthreads();
Z_z[tid]=b_z[iy]*Z_z[tid] + a_z[iy]*( ( (-1./560)*A[tid-4*nx] + (8./315)*A[tid-3*nx] + (-1./5)*A[tid-2*nx] + (8./5)*A[tid-1*nx] + (-205./72)*A[tid] + (8./5)*A[tid+1*nx] + (-1./5)*A[tid+2*nx] + (8./315)*A[tid+3*nx] + (-1./560)*A[tid+4*nx] )/(dh*dh) + ((1./280)*Psi_z[tid-4*nx] + (-4./105)*Psi_z[tid-3*nx] + (1./5)*Psi_z[tid-2*nx] + (-4./5)*Psi_z[tid-1*nx] + (4./5)*Psi_z[tid+1*nx] + (-1./5)*Psi_z[tid+2*nx] + (4./105)*Psi_z[tid+3*nx] + (-1./280)*Psi_z[tid+4*nx] )/(dh*dh));
__syncthreads();
}
//if(ix<nx-1 && iy<nz-1){
if(ix > 3 && ix < nx-4 && iy > 3 && iy< nz-4 ){
B[tid]=2*A[tid] - B[tid] + G*( lap[tid] + Z_x[tid] + Z_z[tid] + temp[tid]);
__syncthreads();
}
if(ix < nx && iy< nz){
// if(it<Nt){
if(ix==(sx-1+4) && iy==(sz-1+4)){
A[tid] = A[tid] + source[it];
__syncthreads();
}
// }
if(modo==1 || modo==3 ){
P[tid+nx*nz*it]=B[tid];
__syncthreads();
}
if(modo==2 || modo==3 ){
dP[tid+nx*nz*it]=c[tid]*c[tid]*(lap[tid] + Z_x[tid] + Z_z[tid] + temp[tid]);
__syncthreads();
}
if(ix>=borde && ix< nx-borde && iy==sz-1+4){
traza[(ix-borde)+it*(nx-2*borde)]=B[tid];
__syncthreads();
}
}
__syncthreads();
}
// A = Presente
// B = Pasado
// C = Futuro
//HOST CODE
int main(){
cudaDeviceReset();
//variables host
int borde=20;
int nx=210;
int ny=71;
int modo=1;
int it;
int nt;
int sx = ceil(nx/2);
int sz = 6;
float VelMax=4700;
float R=100e-6;
float frec=3;
float tend=1.0;
float dt=0.001;
float dh=25;
float *A_d, *B_d, *Pt_d, *Pt_h, *P_d, *P_h, *dP_d, *dP_h, *s_h, *s_d, *v_d, *v_h, *A_x, *A_z, *B_x, *B_z, *lap, *temp, *temp1, *Psi_x, *Psi_z, *Z_x, *Z_z;
FILE *source, *model_ori;
nt= ceil(tend/dt);
Pt_h = (float *)calloc((nx-2*borde)*nt,sizeof(float));
v_h = (float *)calloc(nx*ny,sizeof(float));
s_h = (float *)calloc(nt,sizeof(float));
P_h = (float *)calloc(nx*ny*nt,sizeof(float));
dP_h = (float *)calloc(nx*ny*nt,sizeof(float));
//variables y memory allocation en device
cudaMalloc(&Pt_d,(nx-2*borde)*nt*sizeof(float));
cudaMalloc(&A_d, nx*ny*sizeof(float));
cudaMalloc(&B_d, nx*ny*sizeof(float));
cudaMalloc(&P_d, nx*ny*nt*sizeof(float));
cudaMalloc(&dP_d, nx*ny*nt*sizeof(float));
cudaMalloc(&s_d, nt*sizeof(float));
cudaMalloc(&v_d, nx*ny*sizeof(float));
cudaMalloc(&lap, nx*ny*sizeof(float));
cudaMalloc(&Psi_x, nx*ny*sizeof(float));
cudaMalloc(&Psi_z, nx*ny*sizeof(float));
cudaMalloc(&Z_x, nx*ny*sizeof(float));
cudaMalloc(&Z_z, nx*ny*sizeof(float));
cudaMalloc(&temp1, nx*ny*sizeof(float));
cudaMalloc(&A_x, nx*sizeof(float));
cudaMalloc(&A_z, ny*sizeof(float));
cudaMalloc(&B_x, nx*sizeof(float));
cudaMalloc(&B_z, ny*sizeof(float));
cudaMemset(A_d,0,nx*ny*sizeof(float));
cudaMemset(B_d,0,nx*ny*sizeof(float));
cudaMemset(lap,0,nx*ny*sizeof(float));
cudaMemset(Psi_x,0,nx*ny*sizeof(float));
cudaMemset(Psi_z,0,nx*ny*sizeof(float));
cudaMemset(Z_x,0,nx*ny*sizeof(float));
cudaMemset(Z_z,0,nx*ny*sizeof(float));
cudaMemset(temp1,0,nx*ny*sizeof(float));
cudaMemset(A_x,0,nx*sizeof(float));
cudaMemset(A_z,0,ny*sizeof(float));
cudaMemset(B_x,0,nx*sizeof(float));
cudaMemset(B_z,0,ny*sizeof(float));
//Leer y condicionar archivos fuente
source = fopen ("Fuente.bin","rb");
fread(s_h,nt*sizeof(float),1,source);
printf("\nDatos de fuente cargados...\n");
fclose(source);
//Leer y condicionar archivos modelo original
model_ori = fopen ("Modelo_ori.bin","rb");
fread(v_h,nx*ny*sizeof(float),1,model_ori);
printf("\nDatos de modelo original cargados...\n");
fclose(model_ori);
//Enviando informacion necesaria a la GPU
cudaMemcpy(v_d, v_h, nx*ny*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(s_d, s_h, nt*sizeof(float), cudaMemcpyHostToDevice);
//ejecución Kernel
dim3 dimGrid_M(ceil((nx) / (float)TILE_WIDTH_X) , ceil((ny) / (float)TILE_WIDTH_Y));
dim3 dimBlock_M(TILE_WIDTH_X , TILE_WIDTH_Y);
dim3 dimGrid_Vx(ceil((nx) / (float)TILE_WIDTH_X));
dim3 dimBlock_Vx(TILE_WIDTH_X);
dim3 dimGrid_Vz(ceil((ny) / (float)TILE_WIDTH_Y));
dim3 dimBlock_Vz(TILE_WIDTH_Y);
get_CPML_x<<<dimGrid_Vx, dimBlock_Vx>>>(A_x, B_x, borde, R, VelMax, nx, dt, dh, frec);
get_CPML_z<<<dimGrid_Vz, dimBlock_Vz>>>(A_z, B_z, borde, R, VelMax, ny, dt, dh, frec);
for (it=0;it<nt;it++){
printf("Voy en el paso temporal %d \n",it+1);
PSI<<<dimGrid_M, dimBlock_M>>>(A_d, A_x, B_x, A_z, B_z, Psi_x, Psi_z, borde, nx, ny, dh);
kernel_lap<<<dimGrid_M, dimBlock_M>>>(lap,A_d,nx,ny,dh); //#blocks=dimGrid, #threads=dimBlock
kernel_propaga<<<dimGrid_M, dimBlock_M>>>(lap,A_d,B_d,nx,ny,s_d,v_d,Pt_d,P_d,dP_d,it,sx,sz,borde,dt,A_z,A_x,B_z,B_x,Psi_x,Psi_z,Z_x,Z_z,temp1,dh,modo); //#blocks=dimGrid,
temp = A_d;
A_d = B_d;
B_d = temp;
}
if(modo==1 || modo==3){
cudaMemcpy(P_h, P_d, nx*ny*nt*sizeof(float), cudaMemcpyDeviceToHost);
}
if(modo==2 || modo==3){
cudaMemcpy(dP_h, dP_d, nx*ny*nt*sizeof(float), cudaMemcpyDeviceToHost);
}
cudaMemcpy(Pt_h, Pt_d, (nx-2*borde)*nt*sizeof(float), cudaMemcpyDeviceToHost);
//---------------------------------------
// Guarda informacion -----------------------
// Trazas
source=fopen("Trazas.bin","wb");
fwrite(Pt_h,sizeof(float),(nx-2*borde)*nt,source);
fclose(source);
free(Pt_h);
cudaFree(Pt_d);
// Frente de Onda
source=fopen("Frentedeonda.bin","wb");
fwrite(P_h,sizeof(float),nx*ny*nt,source);
fclose(source);
free(P_h);
cudaFree(P_d);
// Derivad Frente de Onda
source=fopen("DerivadaFrentedeOnda.bin","wb");
fwrite(dP_h,sizeof(float),nx*ny*nt,source);
fclose(source);
free(dP_h);
cudaFree(dP_d);
// Liberamos resto de punteros
// Host
free(s_h);
free(v_h);
// Device
cudaFree(A_d);
cudaFree(B_d);
cudaFree(lap);
cudaFree(s_d);
cudaFree(v_d);
cudaFree(A_x);
cudaFree(A_z);
cudaFree(B_x);
cudaFree(B_z);
cudaFree(Psi_x);
cudaFree(Psi_z);
cudaFree(Z_x);
cudaFree(Z_z);
printf("\n Creo que Termine ....\n");
cudaDeviceReset();
return 0;
}
|
10,666 | #include <stdio.h>
#include <iostream>
#include <cuda.h>
#include <chrono>
#include <random>
using namespace std;
int random_in_range( int minimum, int maximum )
{
thread_local std::ranlux48 rng(
std::chrono::system_clock::now().time_since_epoch().count() );
return std::uniform_int_distribution <int> ( minimum, maximum )( rng );
}
__global__
void matrixVector(int *vec, int *mat, int *result, int n, int m)
{
int tid = blockIdx.x*blockDim.x + threadIdx.x;
int sum=0;
if(tid <= n) {
for(int i=0; i<n; i++) {
sum += vec[i]*mat[(i*m) + tid];
}
result[tid] = sum;
}
}
void maxtrixVector_cpu(int *vec, int *mat, int *result, int n, int m) {
for(int i = 0 ; i < n ; i++) {
long sum = 0;
for(int j = 0 ; j < m ; j++) {
sum = sum + mat[j*m+i] * vec[j];
}
result[i] = sum;
}
}
void init_array(int *a, int n) {
for(int i=0; i<n; i++)
a[i] = random_in_range(10,40);
}
void init_matrix(int *a, int n, int m) {
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++) {
a[i*m + j] = random_in_range(10, 40);
}
}
}
void print_array(int *a, int n) {
for(int i=0; i<n; i++) {
cout<<a[i]<<" ";
}
cout<<endl;
}
void print_matrix(int *a, int n, int m) {
for(int i=0; i<n; i++) {
for(int j=0; j<m; j++)
cout<<" "<<a[i*m + j];
cout<<endl;
}
}
int main() {
int *a, *b, *c;
int *a_dev, *b_dev, *c_dev;
int n = 100;
int m = 100;
a = new int[n];
b = new int[n*m];
c = new int[m];
init_array(a, n);
init_matrix(b, n, m);
cout<<"Initial vector array : "<<endl;
print_array(a, n);
cout<<endl;
cout<<"Initial matrix : "<<endl;
print_matrix(b, n, m);
cout<<endl;
cudaMalloc(&a_dev, sizeof(int)*n);
cudaMalloc(&b_dev, sizeof(int)*n*m);
cudaMalloc(&c_dev, sizeof(int)*m);
cudaMemcpy(a_dev, a, sizeof(int)*n, cudaMemcpyHostToDevice);
cudaMemcpy(b_dev, b, sizeof(int)*n*m, cudaMemcpyHostToDevice);
float gpu_elapsed_time;
cudaEvent_t gpu_start,gpu_stop;
cudaEventCreate(&gpu_start);
cudaEventCreate(&gpu_stop);
cudaEventRecord(gpu_start,0);
matrixVector<<<m, 1>>>(a_dev, b_dev, c_dev, n, m);
cudaEventRecord(gpu_stop, 0);
cudaEventSynchronize(gpu_stop);
cudaEventElapsedTime(&gpu_elapsed_time, gpu_start, gpu_stop);
cudaEventDestroy(gpu_start);
cudaEventDestroy(gpu_stop);
cout<<"GPU Elapsed time is: "<<gpu_elapsed_time<<" milliseconds"<<endl;
cudaMemcpy(c, c_dev, sizeof(int)*m, cudaMemcpyDeviceToHost);
cout<<"GPU Resultant vector : ";
print_array(c, m);
cout<<endl;
cudaEventCreate(&gpu_start);
cudaEventCreate(&gpu_stop);
cudaEventRecord(gpu_start,0);
maxtrixVector_cpu(a, b, c, n, m);
cudaEventRecord(gpu_stop, 0);
cudaEventSynchronize(gpu_stop);
cudaEventElapsedTime(&gpu_elapsed_time, gpu_start, gpu_stop);
cudaEventDestroy(gpu_start);
cudaEventDestroy(gpu_stop);
cout<<"CPU Elapsed time is: "<<gpu_elapsed_time<<" milliseconds"<<endl;
cout<<"CPU Resultant vector : ";
for(int i = 0 ; i < n ; i++) {
cout<<c[i]<<" ";
}
cout<<endl;
cudaFree(a_dev);
cudaFree(b_dev);
cudaFree(c_dev);
delete[] a;
delete[] b;
delete[] c;
return 0;
}
|
10,667 | #include "includes.h"
__global__ void cudaComputeXGradient(int* x_gradient, unsigned char* channel, int image_width, int image_height, int chunk_size_per_thread) {
int x_kernel[3][3] = { { 1, 0, -1 }, { 2, 0, -2 }, { 1, 0, -1 } };
int index = blockIdx.x * blockDim.x + threadIdx.x;
for (int i = index * chunk_size_per_thread; i < (index + 1) * chunk_size_per_thread - 1; i++) {
if (i + 2 * image_width + 1 < image_width * image_height) {
if (i == 0 && blockIdx.x == 0 && threadIdx.x == 0) {
continue;
} else {
x_gradient[i] =
x_kernel[0][0] * channel[i - 1] +
x_kernel[1][0] * channel[i] +
x_kernel[2][0] * channel[i + 1] +
x_kernel[0][1] * channel[i + image_width - 1] +
x_kernel[1][1] * channel[i + image_width] +
x_kernel[2][1] * channel[i + image_width + 1] +
x_kernel[0][2] * channel[i + 2 * image_width - 1] +
x_kernel[1][2] * channel[i + 2 * image_width] +
x_kernel[2][2] * channel[i + 2 * image_width + 1];
}
}
}
return;
} |
10,668 | // #include "kernel.h"
// #include <math.h>
// #include <stdio.h>
// const int INF = 999999;
/////////////////////////////////////////////////////////////////////////////
// Minimal Time
/////////////////////////////////////////////////////////////////////////////
// __global__ void MinTime(float* ignTime, float* rothData, float* times,
// float* L_n, int size, int rowSize, int colSize){
// __global__ void MT(){
// // Get thread id + stride
// /* neighbor's address*/ /* N NE E SE S SW W NW NNW NNE NEE SEE SSE SSW SWW NWW*/
// int nCol[16] = { 0, 1, 1, 1, 0, -1, -1, -1, -1, 1, 2, 2, 1, -1, -2, -2};
// int nRow[16] = { 1, 1, 0, -1, -1, -1, 0, 1, 2, 2, 1, -1, -2, -2, -1, 1};
// int cell = blockIdx.x * blockDim.x + threadIdx.x;
// int ncell, nrow, ncol, row, col;
// float ignCell, ignCellN, timeNext, timeNow, ROS;
// // timeNext = INF;
// // get timenext and timenow from global memory
// timeNow = times[1]; // timeNow = timeNext
// timeNext = INF;
// while(cell < size){
// row = cell / rowSize;
// col = cell - rowSize*row;
// // Load ignition cell to register
// ignCell = ignTime[cell];
// // Do atomic update of TimeNext Var (atomicMin)
// if(timeNext > ignTime[cell] && ignTime[cell] > timeNow){
// atomicExch(×[1], ignCell);
// timeNext = ignCell;
// }
// else if(ignCell == timeNow){
// // Find burning cells
// for(int n = 0; n < 16; n++){
// // Propagate from burning cells
// nrow = row + nRow[n];
// ncol = col + nCol[n];
// if ( nrow<0 || nrow>= rowSize || ncol<0 || ncol>= colSize )
// continue;
// ncell = ncol + nrow*colSize;
// ignCellN = ignTime[ncell];
// // printf("%f ", ignCellN);
// // If neighbor is unburned
// if(ignCellN > timeNow){
// // compute ignition time
// ROS = rothData[3*cell + 0] * (1.0 - rothData[3*cell + 1]) /
// (1.0 - rothData[3*cell + 1] * cos(rothData[3*cell + 2] * 3.14159/180));
// // ROS = 10.0 * (1.0 - 0.321) / (1.0 - 0.321 * cos(30.f));
// // ROS = 0.5;
// float ignTimeNew = timeNow + L_n[n] / ROS;
// // printf("%f, %f, %f, %f \n", timeNow, L_n[n], ROS, ignTimeNew);
// if(ignTimeNew < ignCellN){
// // printf("%f ", ignTime[ncell]);
// // ignTime[ncell] = ignTimeNew; // This could cause a race cond.
// // atomicMin(&ignTime[ncell], ignTimeNew);
// if(ignTimeNew < ignTime[ncell])
// ignTime[ncell] = ignTimeNew;
// // float tmp = atomicExch(&ignTime[ncell], ignTimeNew);
// // if(tmp < ignTimeNew)
// // atomicExch(&ignTime[ncell], ignTimeNew);
// // printf("%f \n",ignTime[ncell]);
// }
// if(ignTimeNew < timeNext){
// // timeNext = ignTimeNew;
// // printf("%f, %f \n", times[1], ignTimeNew);
// atomicExch(×[1], ignTimeNew);
// // printf("%f, %f \n \n", times[1], ignTimeNew);
// }
// }
// }
// }
// // Do striding
// cell += blockDim.x * gridDim.x;
// // printf("%d \n", cell);
// }
// // printf("%f\n", timeNext);
// if(timeNext == INF){
// // printf("BLAH");
// end = 1;
// printf("Kernel: %d\n", end);
// // }
// }
// #endif
// #if IMT
// #endif
// #if BURNDIST
// #endif |
10,669 | __global__ void reduce_add_kernel(const float *d_in, float *d_out, int input_size) {
extern __shared__ float sdata[];
int index = threadIdx.x + blockDim.x * blockIdx.x;
int myId = threadIdx.x;
// Put whole block in shared memory
sdata[myId] = (index < input_size) ? d_in[index] : 0;
__syncthreads();
for (int i = blockDim.x / 2; i > 0; i>>=1) {
if (myId < i) {
sdata[myId] = sdata[myId] + sdata[myId+i];
}
__syncthreads();
}
if (myId == 0) {
d_out[blockIdx.x] = sdata[0];
}
}
int next_power_of_two(int number) {
int result = 1;
while(result < number) {
result <<= 1;
}
return result;
}
void primitive_reduce_add(float *d_input, float *d_result, int input_size) {
const int SIZE_AS_POT = next_power_of_two(input_size);
int shared_size = sizeof(float) * SIZE_AS_POT;
reduce_add_kernel<<<1, SIZE_AS_POT, shared_size>>>(d_input, d_result, input_size);
}
|
10,670 | #include <cuda_runtime.h>
#include <stdio.h>
void Generate(int* M, int width, int height)
{
for(int r = 0; r < height; r ++)
{
for(int c = 0;c< width; c++)
{
*(M + (r * width) + c) = r*width +c;
}
}
}
__global__ void MatrixAdd(int* A, int * B, int* C, int width, int height)
{
int r = threadIdx.x + blockIdx.x * blockDim.x;
int c = threadIdx.y + blockIdx.y * blockDim.y;
if(r >= height || c >= width)
return;
C[r * width + c] = A[r * width + c] + B[r * width + c];
printf("row: %d, col: %d, sum = %d\n", r,c,C[r * width + c]);
}
void MatrixAddOnDevice(int width, int height)
{
int nBytes = sizeof(int) * width * height;
int* A = (int*)malloc(nBytes);
int* B = (int*)malloc(nBytes);
int* C = (int*)malloc(nBytes);
Generate(A, width, height);
Generate(B, width, height);
int* DA;
int* DB;
int* DC;
cudaMalloc((int**)&DA, nBytes);
cudaMalloc((int**)&DB, nBytes);
cudaMalloc((int**)&DC, nBytes);
cudaMemcpy(DA, A, nBytes, cudaMemcpyHostToDevice);
cudaMemcpy(DB, B, nBytes, cudaMemcpyHostToDevice);
dim3 block(32,32);
dim3 grid((width + block.x - 1) / block.x,(height + block.y -1)/ block.y);
MatrixAdd<<<grid,block>>>(DA,DB,DC,width,height);
cudaMemcpy(C,DC,nBytes,cudaMemcpyDeviceToHost);
for(int r = 0; r < width; ++r)
{
for(int c= 0 ; c < height; ++c)
{
printf("%d ", C[r * width + c]);
}
printf("\n");
}
cudaFree(DA);
cudaFree(DB);
cudaFree(DC);
free(A);
free(B);
free(C);
} |
10,671 | #include <iostream>
#include <stdlib.h>
#include <cuda.h>
#include <cuComplex.h>
using namespace std;
__global__ void __sumcomplex(cuDoubleComplex *g_idata, cuDoubleComplex *g_odata) {
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
g_odata[i] = make_cuDoubleComplex(g_idata[i].x, g_idata[i].y);
__syncthreads();
for (unsigned int s=blockDim.x/2; s>0; s>>=1) {
if (tid < s) {
g_odata[i] = make_cuDoubleComplex(g_odata[i].x+g_odata[i + s].x, g_odata[i].y+g_odata[i + s].y);
}
__syncthreads();
}
}
__global__ void __sum_inplace(cuDoubleComplex *g_idata) {
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
__syncthreads();
for (unsigned int s=blockDim.x/2; s>0; s>>=1) {
if (tid < s) {
g_idata[i] = make_cuDoubleComplex(g_idata[i].x+g_idata[i + s].x, 0);
}
__syncthreads();
}
}
int main() {
const int m=16, n=8;
cuDoubleComplex *h_data = new cuDoubleComplex[m*n];
cuDoubleComplex *h_sum = new cuDoubleComplex[m*n];
cuDoubleComplex *d_data, *d_sum;
cudaMalloc(&d_data, m*n*sizeof(cuDoubleComplex));
cudaMalloc(&d_sum, m*n*sizeof(cuDoubleComplex));
for (int i=0; i<m; i++) {
for (int j=0; j<n; j++) {
h_data[i*n+j] = make_cuDoubleComplex(i+j, 0);
}
}
cout << "in:" << endl;
for (int i=0; i<m; i++) {
for (int j=0; j<n; j++) {
cout << "(" << h_data[i*n+j].x << "," << h_data[i*n+j].y << ") ";
}
cout << endl;
}
cudaMemcpy(d_data, h_data, m*n*sizeof(cuDoubleComplex), cudaMemcpyHostToDevice);
__sum_inplace<<<m,n>>>(d_data);
cudaMemcpy(h_data, d_data, m*n*sizeof(cuDoubleComplex), cudaMemcpyDeviceToHost);
cout << "out:" << endl;
for (int i=0; i<m; i++) {
for (int j=0; j<n; j++) {
cout << "(" << h_data[i*n+j].x << "," << h_data[i*n+j].y << ") ";
}
cout << endl;
}
return 0;
}
|
10,672 | /*
**********************************************
* CS314 Principles of Programming Languages *
* Spring 2020 *
**********************************************
*/
#include <stdio.h>
#include <stdlib.h>
__global__ void collateSegments_gpu(int * src, int * scanResult, int * output, int numEdges) {
/*YOUR CODE HERE*/
int tid = blockIdx.x * blockDim.x + threadIdx.x;//this is the thread id and will be used as index
while( tid < numEdges )
{
if( tid == (numEdges - 1) )//last element in the array
{
output[src[tid]] = scanResult[tid];
}
else if( src[tid] != src[tid+1] )
{
output[src[tid]] = scanResult[tid];
}
tid += ( blockDim.x * gridDim.x );
}
return;
}
|
10,673 |
extern "C" {
__host__ __device__ __forceinline__ int
index_strides(const int i, const int j, const int k, const dim3 strides) {
return i * strides.x + j * strides.y + k * strides.z;
}
__global__ void laplace3d_strides(double *d, double *n, int Nx, int Ny, int Nz, int istride, int jstride, int kstride) {
dim3 sizes(Nx,Ny,Nz);
dim3 strides(istride,jstride,kstride);
int i = threadIdx.x + blockIdx.x * blockDim.x;
int j = threadIdx.y + blockIdx.y * blockDim.y;
int k = threadIdx.z + blockIdx.z * blockDim.z;
if (i > 0 && i < sizes.x - 1)
if (j > 0 && j < sizes.y - 1)
if (k > 0 && k < sizes.z - 1)
d[index_strides(i, j, k, strides)] =
1. / 2. *
( //
__ldg(&n[index_strides(i - 1, j, k, strides)]) //
+ __ldg(&n[index_strides(i + 1, j, k, strides)]) //
+ __ldg(&n[index_strides(i, j - 1, k, strides)]) //
+ __ldg(&n[index_strides(i, j + 1, k, strides)]) //
+ __ldg(&n[index_strides(i, j, k - 1, strides)]) //
+ __ldg(&n[index_strides(i, j, k + 1, strides)]) //
- 6. * __ldg(&n[index_strides(i, j, k, strides)]));
}
__global__ void set_val(double *d) {
int index = threadIdx.x + blockIdx.x * blockDim.x + threadIdx.y + blockIdx.y * blockDim.y + threadIdx.z + blockIdx.z * blockDim.z;
d[index] = 123.;
}
}
|
10,674 | #include <stdio.h>
__global__ void reduce0(int *g_idata, int *g_odata) {
extern __shared__ int sdata[];
// each thread loads one element from global to shared mem
unsigned int tid = threadIdx.x;
unsigned int i = blockIdx.x*blockDim.x + threadIdx.x;
sdata[tid] = g_idata[i];
__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[0];
}
int main(void)
{
long int size = 1 << 20;
long int s;
int sizeByte = size*sizeof(int);
int* h_data = (int*) malloc(sizeByte);
for(int i = 0; i < size; i++) {
// h_data[i] = rand() & 0xFF;
h_data[i] = i % 10;
}
long long int sum = 0;
for(int i = 0; i < size; i++) sum += h_data[i];
printf("CPU results = %lld \n", sum);
int* d_idata = NULL;
int* d_odata = NULL;
cudaMalloc(&d_idata, sizeByte);
cudaMalloc(&d_odata, sizeByte);
cudaMemcpy(d_idata, h_data, sizeByte, cudaMemcpyHostToDevice);
s = size;
int blocks = (s+512-1)/512;
reduce0<<<blocks, 512, 512*sizeof(int)>>>(d_idata, d_odata);
cudaDeviceSynchronize();
printf("The size of array is %ld and it is processedon # of Blocks: %d \n", s, blocks);
s = blocks;
blocks = (s+512-1)/512;
reduce0<<<blocks, 512, 512*sizeof(int)>>>(d_odata, d_idata);
cudaDeviceSynchronize();
printf("The size of array is %ld and it is processedon # of Blocks: %d \n", s, blocks);
// Now # of blocks is 4 --> It is better to do computation on CPU.
cudaMemcpy(h_data, d_idata, blocks*sizeof(int), cudaMemcpyDeviceToHost);
sum = 0;
for(int i = 0; i < blocks; i++) {
printf("%d ", h_data[i]);
sum += h_data[i];
}
printf("\n");
printf("GPU results = %lld \n", sum);
cudaFree(d_idata);
cudaFree(d_odata);
free(h_data);
}
|
10,675 | #include <stdio.h>
// __global__ tell the compiler that the function will be called from CPU and executed from GPU
__global__ void helloFromGPU(void) {
int idx = threadIdx.x;
printf("GPU hello world on thread # %d\n", idx);
}
int main(void) {
helloFromGPU <<<1, 10>>>(); // 10 threads will execute the kernel
cudaDeviceReset(); // clean up all resources associated with the current device
return 0;
} |
10,676 | #include "includes.h"
// create an image buffer. return host ptr, pass out device pointer through pointer to pointer
__global__ void boxfilter_kernel(int iw, int ih, unsigned char *source, unsigned char *dest, int bw, int bh)
{
// Calculate our pixel's location
int x = (blockIdx.x * blockDim.x) + threadIdx.x;
int y = (blockIdx.y * blockDim.y) + threadIdx.y;
// Variables to store the sum
int count = 0;
float sum = 0.0;
// Do the blur operation by summing the surround pixels
for(int j = -(bh/2); j <= (bh/2); j++)
{
for(int i = -(bw/2); i <= (bw/2); i++)
{
// Verify that this offset is within the image boundaries
if((x+i) < iw && (x+i) >= 0 && (y+j) < ih && (y+j) >= 0)
{
sum += (float) source[((y+j) * iw) + (x+i)];
count++;
}
}
}
// Average the sum
sum /= (float) count;
dest[(y * iw) + x] = (unsigned char) sum;
} |
10,677 | #include <stdio.h>
#include <time.h>
const int M = 1024 * 1024;
const int n_thread = 512;
//define the timer for GPU
#define time_record_begin(start){ \
cudaEventCreate(&start); \
cudaEventRecord(start, 0); \
}
#define time_record_end(start, stop, time){ \
cudaEventCreate(&stop); \
cudaEventRecord(stop, 0); \
cudaEventSynchronize(stop); \
cudaEventElapsedTime(&time, start, stop);\
}
__global__ void findMin(int *A){
int tid = threadIdx.x;
int idx = blockIdx.x * (blockDim.x * 2) + tid;
__shared__ int s_data[512];
if(A[idx] < A[idx + blockDim.x]){
s_data[tid] = A[idx];
}
else{
s_data[tid] = A[idx + blockDim.x];
}
__syncthreads();
for(int k = 256; k > 32; k /= 2){
if(tid < k){
if(s_data[tid] > s_data[tid + k])
s_data[tid] = s_data[tid + k];
}
__syncthreads();
}
// threads within one warp can work in completely parallel and __syncthreads() is not needed
if(tid < 32){
if(blockDim.x > 32)
if(s_data[tid] > s_data[tid + 32]) s_data[tid] = s_data[tid + 32];
if(blockDim.x > 16)
if(s_data[tid] > s_data[tid + 16]) s_data[tid] = s_data[tid + 16];
if(blockDim.x > 8)
if(s_data[tid] > s_data[tid + 8]) s_data[tid] = s_data[tid + 8];
if(blockDim.x > 4)
if(s_data[tid] > s_data[tid + 4]) s_data[tid] = s_data[tid + 4];
if(blockDim.x > 2)
if(s_data[tid] > s_data[tid + 2]) s_data[tid] = s_data[tid + 2];
if(blockDim.x > 1)
if(s_data[tid] > s_data[tid + 1]) s_data[tid] = s_data[tid + 1];
}
if(tid == 0)
A[blockIdx.x] = s_data[0];
}
__global__ void findMax(int *A){
int tid = threadIdx.x;
int idx = blockIdx.x * (blockDim.x * 2) + tid;
__shared__ int s_data[512];
if(A[idx] > A[idx + blockDim.x]){
s_data[tid] = A[idx];
}
else{
s_data[tid] = A[idx + blockDim.x];
}
__syncthreads();
for(int k = 256; k > 32; k /= 2){
if(tid < k){
if(s_data[tid] < s_data[tid + k])
s_data[tid] = s_data[tid + k];
}
__syncthreads();
}
// threads within one warp can work in completely parallel and __syncthreads() is not needed
if(tid < 32){
if(blockDim.x > 32)
if(s_data[tid] < s_data[tid + 32]) s_data[tid] = s_data[tid + 32];
if(blockDim.x > 16)
if(s_data[tid] < s_data[tid + 16]) s_data[tid] = s_data[tid + 16];
if(blockDim.x > 8)
if(s_data[tid] < s_data[tid + 8]) s_data[tid] = s_data[tid + 8];
if(blockDim.x > 4)
if(s_data[tid] < s_data[tid + 4]) s_data[tid] = s_data[tid + 4];
if(blockDim.x > 2)
if(s_data[tid] < s_data[tid + 2]) s_data[tid] = s_data[tid + 2];
if(blockDim.x > 1)
if(s_data[tid] < s_data[tid + 1]) s_data[tid] = s_data[tid + 1];
}
if(tid == 0)
A[blockIdx.x] = s_data[0];
}
void random_number_generator(int *A, int size){
time_t t;
srand((unsigned) time(&t));
for(int i = 0; i < size; i++){
A[i] = rand();
}
}
int findMax_cpu(int* A, int n_Elements){
int max = INT_MIN;
for(int i = 0; i < n_Elements; i++){
if(A[i] > max)
max = A[i];
}
return max;
}
int findMin_cpu(int* A, int n_Elements){
int min = INT_MAX;
for(int i = 0; i < n_Elements; i++){
if(A[i] < min)
min = A[i];
}
return min;
}
void findMinMax(int s, int* SIZE){
int *A;
int *d_A_MAX, *d_A_MIN;
int max_gpu, min_gpu;
int max_cpu, min_cpu;
int size = SIZE[s] * M;
A = (int*) malloc(size * sizeof(int));
random_number_generator(A, size);
//difine the grid size and block size for three kernel calls
dim3 dimGrid[3] = {size/n_thread/2, size/n_thread/n_thread/4, 1};
dim3 dimBlock[3] = {n_thread, n_thread, size/n_thread/n_thread/8};
// Timer initialize
float max_average_cpu = 0;
float min_average_cpu = 0;
cudaEvent_t start,stop;
float time_findMax, time_findMin;
float max_average_gpu = 0;
float min_average_gpu = 0;
// Loop for 10 times
for(int j = 0; j < 10; j++){
// Find MIN and MAX on CPU
clock_t tiem_start, time_end;
tiem_start = clock();
max_cpu = findMax_cpu(A, size);
time_end = clock();
max_average_cpu += (float)(time_end - tiem_start) / 1000000; // find_max_cpu_time in seconds
tiem_start = clock();
min_cpu = findMin_cpu(A, size);
time_end = clock();
min_average_cpu += (float)(time_end - tiem_start) / 1000000; // find_min_cpu_time in seconds
// Find MIN and MAX on GPU
// Allocate memory on gpu and copy data from host to device
cudaMalloc((void**)&d_A_MAX, size * sizeof(int));
cudaMemcpy(d_A_MAX, A, size * sizeof(int), cudaMemcpyHostToDevice);
cudaMalloc((void**)&d_A_MIN, size * sizeof(int));
cudaMemcpy(d_A_MIN, A, size * sizeof(int), cudaMemcpyHostToDevice);
// find min and max by 3 kernel calls
time_record_begin(start);
for(int k = 0; k < 3; k++){
findMax<<<dimGrid[k], dimBlock[k]>>>(d_A_MAX);
}
time_record_end(start, stop, time_findMax);
max_average_gpu += time_findMax / 1000; // find_max_gpu_time in seconds
cudaMemcpy(&max_gpu, d_A_MAX, sizeof(int), cudaMemcpyDeviceToHost);
time_record_begin(start);
for(int k = 0; k < 3; k++){
findMin<<<dimGrid[k], dimBlock[k]>>>(d_A_MIN);
}
time_record_end(start, stop, time_findMin);
min_average_gpu += time_findMin / 1000; // find_min_gpu_time in seconds
cudaMemcpy(&min_gpu, d_A_MIN, sizeof(int), cudaMemcpyDeviceToHost);
// free memomy
cudaFree(d_A_MAX);
cudaFree(d_A_MIN);
}
// Calculate the average time in ten runs and the speedup for GPU
max_average_cpu /= 10;
min_average_cpu /= 10;
max_average_gpu /= 10;
min_average_gpu /= 10;
float speedup_max = max_average_cpu / max_average_gpu;
float speedup_min = min_average_cpu / min_average_gpu;
printf("N:%dM GPUmax:%d CPUmax:%d GPUtime:%f CPUtime:%f GPUSpeedup:%f N:%dM GPUmin:%d CPUmin:%d GPUtime:%f CPUtime:%f GPUSpeedup:%f\n",
SIZE[s], max_gpu, max_cpu, max_average_gpu, max_average_cpu, speedup_max, SIZE[s], min_gpu, min_cpu, min_average_gpu, min_average_cpu, speedup_min);
free(A);
}
int main(int argc, char *argv[]){
int SIZE[3] = {2, 8, 32};
printf("FIRSTNAME: Xi\nLASTNAME: Chen\n");
for(int i = 0; i < 3; i++){
findMinMax(i, SIZE);
}
}
|
10,678 | #include "nnkernels.cuh"
/**
* [addKernel description]
* @param c [description]
* @param a [description]
* @param b [description]
*/
__global__
void addKernel(int *c, const int *a, const int *b)
{
int i = threadIdx.x;
c[i] = a[i] + b[i];
}
/**
* [hadamard description]
* @param a [description]
* @param b [description]
*/
__global__
void hadamard(double *a, double *b)
{
int i = threadIdx.x;
a[i] = a[i] * b[i];
}
/**
* [sigmoid description]
* @param zVec [description]
* @param activations [description]
* @param sps [description]
*/
__global__
void sigmoid(double *zVec, double *activations, double *sps)
{
int i = threadIdx.x;
double sig = 1.0 / (1.0 + exp(-zVec[i]));
activations[i] = sig;
sps[i] = sig * (1.0 - sig);
}
/**
* [had description]
* @param a [description]
* @param b [description]
* @param numElements [description]
*/
void had(double *a, double *b, int numElements)
{
hadamard <<< 1, numElements >>> (a, b);
return;
}
/**
* [sigmoids description]
* @param zVec [description]
* @param activations [description]
* @param sps [description]
* @param numElements [description]
*/
void sigmoids(double *zVec, double *activations, double *sps, int numElements)
{
sigmoid <<< 1, numElements >>> (zVec, activations, sps);
return;
} |
10,679 | #include "includes.h"
#define INF 2147483647
extern "C" {
}
__global__ void init(int * tab, int len) {
for(int i = threadIdx.x + len*blockIdx.x; i < len*blockIdx.x + len; i += 1024) {
tab[i] = INF;
}
} |
10,680 | float h_A[]= {
0.5893382358257286, 0.7305768009524473, 0.7029599525902375, 0.9462849507172565, 0.6151901814435743, 0.662629573310543, 0.5393775836173019, 0.7984611517739488, 0.7675309967000936, 0.5954109184468228, 0.6083790178112334, 0.8211317575619363, 0.8720796389237023, 0.802159349083113, 0.6397155580208536, 0.838485321074216, 0.7537195848241327, 0.5228923204906728, 0.9661956522428224, 0.5855060846228767, 0.7649958667076365, 0.9098787446342858, 0.789976731178249, 0.6015761574029779, 0.9577607920476534, 0.7772747601438319, 0.6632999690806447, 0.9666650979778181, 0.9662710384806265, 0.5039750566367993, 0.7794818851543224, 0.7049856669632117, 0.6632973319222195, 0.9692517196082764, 0.9148969148058458, 0.5047763854630621, 0.6843479589214276, 0.7317361578420822, 0.6928288509809912, 0.8921236900186176, 0.5140772036200765, 0.8220532612249687, 0.7885255423419144, 0.5287650047668673, 0.877281661021537, 0.5144176222414031, 0.8645519765595431, 0.540117480355933, 0.629413493256787, 0.9896257742226705, 0.9045900154602725, 0.6515733152924655, 0.9456887256477369, 0.9109790194084239, 0.7962053720909563, 0.6874029650957747, 0.8267642986897942, 0.5710990153983302, 0.508588313575392, 0.910766876899865, 0.6021236887980765, 0.9855266584426944, 0.8614223315667155, 0.6292172629305184, 0.5918562708436981, 0.6012134730656564, 0.7161219682057269, 0.5781613642532926, 0.7225289017374285, 0.6768965901344617, 0.7970317400529243, 0.9144517053710093, 0.923585836529741, 0.6002923427873118, 0.9150032688424232, 0.5877985725923593, 0.858652473875714, 0.5751315531860423, 0.8392835213313004, 0.5091859746759376, 0.945140653753745, 0.6187006463256514, 0.5671626536739506, 0.6290631940373286, 0.9586380203300613, 0.7466618685068207, 0.9433551609473974, 0.8959308774545613, 0.7037161809842094, 0.9150322629407472, 0.8933934164084376, 0.7849251267047703, 0.7058920528687074, 0.8837100678212806, 0.7847357997997377, 0.9521228469515008, 0.9419577050820431, 0.5262089773771459, 0.9649672252550128, 0.9020725081932415, 0.6080653192546328, 0.9964762345225173, 0.6213097049278695, 0.7259729624274849, 0.7938746083924998, 0.5718555840678705, 0.7458606258820204, 0.7486081521652624, 0.9356245505380474, 0.9393050097007867, 0.548138339620927, 0.5485457988638103, 0.7660388477286213, 0.6808526664167524, 0.9092439052944228, 0.6333701563016338, 0.8447712628201687, 0.6267125345837226, 0.7655963697591339, 0.6547036286034961, 0.5668425827876641, 0.8384517193608503, 0.6981372587656447, 0.9201182306317892, 0.5279205814009467, 0.8346020982157649, 0.8555914693653086, 0.7815419532769456, 0.7035119082448724, 0.9507478349376103, 0.6100810306530933, 0.5206286245658294, 0.9199122392296853, 0.8254729437748185, 0.5808517981996648, 0.6471661270579843, 0.8339192313081407, 0.8647123230608023, 0.5394036661771251, 0.6079447603301464, 0.8432235918185823, 0.7393535011105352, 0.5305639733558378, 0.6128129490009857, 0.8678491871871495, 0.8566147395058494, 0.6066906042811331, 0.5550553339447002, 0.7607134320404505, 0.5007000598323366, 0.5495732649614802, 0.6067340621598885, 0.998940269331166, 0.8767711208054099, 0.9067839147456508, 0.5710267024705795, 0.9881690004775344, 0.6876634975249968, 0.718124679333348, 0.884984646786181, 0.7452574803624468, 0.6145267049848666, 0.8070946215423696, 0.7620455154249078, 0.6301491912512961, 0.6953656407268964, 0.6686145735903769, 0.9867484326277527, 0.8498698133927072, 0.5049859844132921, 0.6056832714888503, 0.8292072300110652, 0.9136256752849521, 0.8748368017900698, 0.6351554899786411, 0.7212657425716305, 0.5905006860368152, 0.8016827219430213, 0.5413740050886533, 0.5437393175794054, 0.5508483619006197, 0.8624964760887452, 0.7902544315894037, 0.8681872603613274, 0.5775351244243192, 0.6156111176319001, 0.8426983097835877, 0.8042107641451426, 0.8040053993012022, 0.5783883480428859, 0.7916772869640131, 0.6957578130653812, 0.5395537525818856, 0.8417761227426367, 0.984559268544063, 0.8959925071068124, 0.8091222602670253, 0.9724441813816465, 0.8661314376975242, 0.6959909676225701, 0.6516390995257879, 0.8342066952369257, 0.8419805909656386, 0.7216640385993216, 0.9603828687297876, 0.7335312821753794, 0.8424798878659405, 0.8290409165862731, 0.7374950917269636, 0.6684091242104236, 0.9465951671655567, 0.6995614389377383, 0.9566895408580346, 0.7171136154688564, 0.9107181198310004, 0.6326630640115618, 0.6015283457584584, 0.5915630940901204, 0.6901426087596783, 0.8510282120031911, 0.5282197753066491, 0.8360904879803149, 0.5864587895744875, 0.8984005409487145, 0.9107235211972481, 0.5995403053348349, 0.7750180072605417, 0.6521625180043629, 0.8757113728194035, 0.5600356822887408, 0.8032717415110032, 0.8430411050397907, 0.6027982174276192, 0.6305730697176126, 0.8865277110759454, 0.9217467710183479, 0.5553579857534584, 0.7597961764041139, 0.9099428334955724, 0.782206295608364, 0.977025308763431, 0.7144587479026607, 0.6367496421191298, 0.6720259030943637, 0.974593338786485, 0.8947667936610502, 0.6080137401478667, 0.75829482634701, 0.6824740232808579, 0.5200449972050232, 0.6706426345911212, 0.7708242751394888, 0.9358643175542828, 0.6103057193071277, 0.944409875312803, 0.6964439983444448, 0.6270179659084281, 0.6889325333103207, 0.7221280847476251, 0.8658889328559778, 0.8992959963919513, 0.8199674879213954, 0.5750293362448153, 0.6384595852557808, 0.7341702028505346, 0.6728742852740259, 0.6874902993810548, 0.5947230101502652, 0.909499507776238, 0.7618805587124287, 0.8711850938260446, 0.5802133534036376, 0.9192923251443358, 0.5531768388741944, 0.5260477863959645, 0.520709821116805, 0.8240916281420785, 0.6365385750663817, 0.5968498979140155, 0.6501811539363236, 0.7801033533629537, 0.7281697817484615, 0.9134408803064231, 0.6285268902091661, 0.8071848583093695, 0.7545975603201337, 0.7489616112979438, 0.6466375023521349, 0.8211141964191078, 0.9160499353879838, 0.7030643978407949, 0.7606929083402174, 0.5163404766463359, 0.7228173560066975, 0.5468383003621238, 0.5073356447330192, 0.5567178473827532, 0.5293237121283803, 0.7409970304823064, 0.9822367173633135, 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0.8720881732604848, 0.9704566392441216, 0.8287588277672462, 0.7875386791230214, 0.6085752427902759, 0.5320775775697135, 0.5129996225515405, 0.5478774155728279, 0.8236380123831213, 0.9879124687694134, 0.5686762685560987, 0.9331710578366169, 0.8756817533928724, 0.831229711126001, 0.758670740677884, 0.9802541666931233, 0.8574781200105402, 0.9618971758891659, 0.9485858425302691, 0.8409117529972486, 0.8240420867574441, 0.9299306405966923, 0.7089437009873656, 0.5652370239403481, 0.6059744248726806, 0.615728445970926, 0.7769603736354365, 0.9738241804767238, 0.8722800303601954, 0.6353026896929163, 0.6824877051498613, 0.9259970362323535, 0.9312105500900935, 0.7162970546179463, 0.9832983771864156, 0.5601301052428921, 0.7103388047976414, 0.9505731954587922, 0.7478634349393054, 0.7370550826792092, 0.7180129394780785, 0.7233938465895711, 0.7058430633162353, 0.7063306095246537, 0.8602805079789039, 0.9673526109315246, 0.93287152061402, 0.8280530588544914, 0.5154397823077719, 0.648485173060698, 0.6401526375259337, 0.783796996437538, 0.6243594761438626, 0.6418371773892075, 0.7593954842299713, 0.7515649876816453, 0.9402785676774862, 0.5097351566480839, 0.6674865829730974, 0.7749782975513293, 0.852787650289226, 0.7086707918646176, 0.654720859590121, 0.8765872308252158, 0.5516675404655829, 0.8146208338255316, 0.8845858217220453, 0.9586442079890973, 0.7867886228941448, 0.9806780802752477, 0.8842697607050357, 0.8848239451538539, 0.5601893745408278, 0.8720548379141964, 0.825500916843453, 0.9387781291086659, 0.915879668978542, 0.8669198254876156, 0.565849200127437, 0.6410789493765122, 0.5957282391969396, 0.5970105248995763, 0.5003730718168559, 0.8985066426345851, 0.8389997940018934, 0.7619244777158284, 0.8773079886878031, 0.7367950005872992, 0.9609551651016146, 0.63176648558255, 0.7766933224656067, 0.5650037525494503, 0.9753449647710255, 0.9587582801175162, 0.7880831091827734, 0.9673112944377227, 0.9867282824576908, 0.9210448233287956, 0.7819936482189467, 0.7618646794835091, 0.6785642048659356, 0.907247557384437, 0.5839191133047944, 0.617421307421284, 0.5079367238929182, 0.8208334957928245, 0.8426483331351771, 0.6896705950268688, 0.9323729707576951, 0.5374728039460315, 0.9319643550585697, 0.9154234625055715, 0.976798527597405, 0.9383258490723045, 0.7029225932728871, 0.6522692290810816, 0.7048788194989155, 0.670219776394455, 0.7997369292407873, 0.6425077629568556, 0.7787703091765503, 0.9945199479686525, 0.8371708872025003, 0.7713303794355377, 0.8436545129804787, 0.8583640156034628, 0.9543591982969881, 0.5585001273307909, 0.9865253207823794, 0.8574082695627632, 0.9173145394084132, 0.5687768576198358, 0.9978625782328984, 0.937937932093323, 0.65977255613353, 0.9240484424462967, 0.6334796761527546, 0.8162437055321932, 0.8611802609160019, 0.5075325776610138, 0.8491592441920276, 0.7656571736597264, 0.8407246954665342, 0.5366044768857857, 0.5285661869416471, 0.6701002315195177, 0.762703420896456, 0.625621174879651, 0.7820308608839046, 0.5459410075374638, 0.7325319830941381, 0.8858174121912382, 0.8105556842926354, 0.8879104665866746, 0.8083948384842874, 0.5417019094671282, 0.8987201200499582, 0.5850578943675381, 0.5101148931832873, 0.7184516542181257, 0.8448044581506899, 0.8264809555318411, 0.8857880465429182, 0.8383584712887264, 0.9605707954204943, 0.538431383108456, 0.6264635304228607, 0.9609268039398992, 0.5250617942384029, 0.7519149677701736, 0.6009556066931732, 0.9930236326890383, 0.6538530400725363, 0.5886990662838458, 0.6420145809814772, 0.5929442507437808, 0.8635349722871313, 0.9566986842614359, 0.8748959739034581, 0.5280043671634702, 0.716610918779163, 0.8968164672679737, 0.6159338644183724, 0.5570957996635828, 0.8452044458829571, 0.6553912255089662, 0.5152724800315256, 0.954021300531574, 0.670899716766109, 0.6544939662863132, 0.9044253008150067, 0.5302420955751581, 0.8460551961945677, 0.9543609870320773, 0.9108985962011515, 0.8861894490908677, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
0, 2, 4, 6, 8, 10, 12, 14, 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, 2182, 2184, 2186, 2188, 2190, 2192, 2194, 2196, 2198, 2200, 2202, 2204, 2206, 2208, 2210, 2212, 2214, 2216, 2218, 2220, 2222, 2224, 2226, 2228, 2230, 2232, 2234, 2236, 2238, 2240, 2242, 2244, 2246, 2248, 2250, 2252, 2254, 2256, 2258, 2260, 2262, 2264, 2266, 2268, 2271, 2273, 2275, 2277, 2280, 2282, 2284, 2286, 2288, 2290, 2292, 2294, 2296, 2298, 2300, 2302, 2304, 2306, 2308, 2310, 2312, 2314, 2316, 2318, 2320, 2322, 2324, 2326, 2328, 2330, 2332, 2334, 2336, 2338, 2340, 2342, 2344, 2346, 2348, 2350, 2352, 2354, 2356, 2358, 2360, 2362, 2364, 2366, 2368, 2370, 2372, 2374, 2376, 2378, 2380, 2382, 2384, 2386, 2388, 2390, 2392, 2394, 2396, 2398, 2400, 2402, 2404, 2406, 2408, 2410, 2412, 2414, 2416, 2418, 2420, 2422, 2424, 2426, 2428, 2430, 2432, 2434, 2436, 2438, 2440, 2442, 2444, 2446, 2448, 2450, 2452, 2454, 2456, 2458, 2460, 2462, 2464, 2466, 2468, 2470, 2472, 2474, 2476, 2478, 2480, 2482, 2484, 2486, 2488, 2490, 2492, 2494, 2496, 2498, 2500, 2502, 2504, 2506, 2508, 2510, 2512, 2514, 2516, 2518, 2520, 2522, 2524, 2526, 2528, 2530, 2532, 2534, 2536, 2538, 2540, 2542, 2544, 2546, 2548, 2550, 2552, 2554, 2556, 2558, 2560, 2562, 2564, 2566, 2568, 2570, 2572, 2574, 2576, 2578, 2580, 2582, 2584, 2586, 2588, 2590, 2592, 2594, 2596, 2598, 2600, 2602, 2604, 2606, 2608, 2610, 2612, 2614, 2616, 2618, 2620, 2622, 2624, 2626, 2628, 2630, 2632, 2634, 2636, 2638, 2640, 2642, 2644, 2646, 2648, 2650, 2652, 2654, 2656, 2658, 2660, 2662, 2664, 2666, 2668, 2670, 2672, 2674, 2676, 2678, 2680, 2682, 2684, 2686, 2688, 2690, 2692, 2694, 2696, 2698, 2700, 2702, 2704, 2706, 2708, 2710, 2712, 2714, 2716, 2718, 2720, 2722, 2724, 2726, 2728, 2730, 2732, 2734, 2736, 2738, 2740, 2742, 2744, 2746, 2748, 2750, 2752, 2754, 2756, 2758, 2760, 2762, 2764, 2766, 2768, 2770, 2772, 2774, 2776, 2778, 2780, 2782, 2784, 2786, 2788, 2790, 2792, 2794, 2796, 2798, 2800, 2802, 2804, 2806, 2808, 2810, 2812, 2814, 2816, 2818, 2820, 2822, 2824, 2826, 2828, 2830, 2832, 2834, 2836, 2838, 2840, 2842, 2844, 2846, 2848, 2850, 2852, 2854, 2856, 2858, 2860, 2862, 2864, 2866, 2868, 2870, 2872, 2874, 2876, 2878, 2880, 2882, 2884, 2886, 2888, 2890, 2892, 2894, 2896, 2898, 2900, 2902, 2904, 2906, 2908, 2910, 2912, 2914, 2916, 2918, 2920, 2922, 2924, 2926, 2928, 2930, 2932, 2934, 2936, 2938, 2940, 2942, 2944, 2946, 2948, 2950, 2952, 2954, 2956, 2958, 2960, 2962, 2964, 2966, 2968, 2970, 2972, 2974, 2976, 2978, 2980, 2982, 2984, 2986, 2988, 2990, 2992, 2994, 2996, 2998, 3000, 3002, 3004, 3006, 3008, 3010, 3012, 3014, 3016, 3018, 3020, 3022, 3024, 3026, 3028, 3030, 3032, 3034, 3036, 3038, 3040, 3042, 3044, 3046, 3048, 3050, 3052, 3054, 3056, 3058, 3060, 3062, 3064, 3066, 3068, 3070, 3072, 3074, 3076, 3078, 3080, 3082, 3084, 3086, 3088, 3090, 3092, 3094, 3096, 3098, 3100, 3102, 3104, 3106, 3108, 3110, 3112, 3114, 3116, 3118, 3120, 3122, 3124, 3126, 3128, 3130, 3132, 3134, 3136, 3138, 3140, 3142, 3144, 3146, 3148, 3150, 3152, 3154, 3156, 3158, 3160, 3162, 3164, 3166, 3168, 3170, 3172, 3174, 3176, 3178, 3180, 3182, 3184, 3186, 3188, 3190, 3192, 3194, 3196, 3198, 3200, 3202, 3204, 3206, 3208, 3210, 3212, 3214, 3216, 3218, 3220, 3222, 3224, 3226, 3228, 3230, 3232, 3234, 3236, 3238, 3240, 3242, 3244, 3246, 3248, 3250, 3252, 3254, 3256, 3258, 3260, 3262, 3264, 3266, 3268, 3270, 3272, 3274, 3276, 3278, 3280, 3282, 3284, 3286, 3288, 3290, 3292, 3294, 3296, 3298, 3300, 3302, 3304, 3306, 3308, 3310, 3312, 3314, 3316, 3318, 3320, 3322, 3324, 3326, 3328, 3330, 3332, 3334, 3336, 3338, 3340, 3342, 3344, 3346, 3348, 3350, 3352, 3354, 3356, 3358, 3360, 3362, 3364, 3366, 3368, 3370, 3372, 3374, 3376, 3378, 3380, 3382, 3384, 3386, 3388, 3390, 3392, 3394, 3396, 3398, 3400, 3402, 3404, 3406, 3408, 3410, 3412, 3414, 3416, 3418, 3420, 3422, 3424, 3426, 3428, 3430, 3432, 3434, 3436, 3438, 3440, 3442, 3444, 3446, 3448, 3450, 3452, 3454, 3456, 3458, 3460, 3462, 3464, 3466, 3468, 3470, 3472, 3474, 3476, 3478, 3480, 3482, 3484, 3486, 3488, 3490, 3492, 3494, 3496, 3498, 3500, 3502, 3504, 3506, 3508, 3510, 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35718, 35716, 36704, 36705, 35729, 36706, 36707, 36708, 35739, 36709, 36710, 36711, 36712, 35745, 36714, 36715, 36717, 36718, 36727, 36728, 36735, 36737, 36738, 36739, 36740, 36806, 36845, 36847, 36848, 36850, 36852, 36854, 36855, 36856, 36859, 36426, 36861, 36862, 36865, 36433, 36867, 36869, 36871, 36872, 36887, 36892, 36894, 36895, 36898, 36470, 36900, 36901, 36903, 36904, 36938, 36947, 36948, 36949, 36950, 36953, 36534, 36955, 36957, 36959, 36961, 36964, 36543, 36966, 36967, 36969, 36970, 36984, 36993, 36995, 36584, 36997, 36998, 37001, 37002, 37004, 37005, 37007, 37008, 37017, 37025, 37030, 37035, 37036, 37038, 37039, 37042, 36640, 37052, 37085, 37086, 37087, 37090, 36699, 37093, 36702, 37095, 37096, 37097, 37099, 37101, 37103, 37105, 37108, 36713, 37110, 37111, 36805, 36803, 36801, 36810, 36808, 35759, 36815, 36813, 36823, 36819, 36821, 36817, 35761, 36832, 36830, 36828, 36826, 35763, 36837, 36835, 35764, 36844, 36842, 36840, 36445, 36443, 36194, 36878, 35774, 36881, 35776, 36886, 36884, 36891, 36889, 36907, 35055, 36912, 36486, 36484, 35787, 36495, 36493, 36491, 35057, 36921, 36919, 35789, 36928, 36926, 36924, 35790, 36937, 36935, 36933, 36931, 36946, 36944, 36942, 36940, 36556, 36554, 35797, 36978, 36976, 35799, 36983, 36981, 36574, 36572, 36570, 36239, 36992, 36990, 37011, 35810, 37016, 37014, 37019, 35813, 37024, 37022, 37029, 37027, 36628, 36626, 36258, 37051, 37049, 37047, 37045, 37058, 37056, 37054, 35825, 37061, 35106, 37066, 37064, 35827, 36675, 36673, 36671, 35108, 37075, 36680, 36678, 35830, 37084, 37082, 37080, 37078, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 36846, 37124, 37126, 36858, 36864, 37135, 36893, 36897, 37151, 36952, 37156, 37158, 36963, 36994, 37000, 37034, 37183, 37041, 37187, 37089, 37092, 37195, 37198, 37200, 37107, 37206, 37207, 37208, 35021, 37209, 37210, 37211, 37212, 37213, 37214, 37215, 37216, 37217, 37218, 37219, 37220, 37221, 37222, 37223, 37224, 37225, 37226, 37227, 37228, 37229, 35026, 37132, 37138, 37230, 37231, 37232, 37233, 37234, 37235, 37236, 37237, 37238, 35777, 37239, 37240, 35778, 37146, 37148, 37241, 37242, 37243, 37244, 37245, 37246, 37247, 37248, 37249, 37250, 37251, 37252, 37253, 37254, 37255, 37256, 37257, 37258, 37259, 37260, 37261, 35791, 37262, 37263, 37264, 37265, 35792, 37163, 37165, 37266, 37267, 37268, 37269, 37270, 37271, 37272, 37273, 35800, 37274, 37275, 37276, 37277, 37278, 37279, 35803, 37171, 37175, 37177, 37280, 37281, 37282, 37283, 35811, 37284, 37285, 37286, 37287, 35814, 37288, 37289, 35815, 37290, 37291, 37292, 37293, 37294, 37295, 37296, 37297, 37298, 37299, 37300, 37301, 37302, 37303, 37304, 37305, 37306, 37307, 37308, 37309, 37310, 37311, 37312, 37313, 37314, 37315, 37316, 37317, 35831, 37190, 37205, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37401, 37404, 37405, 37408, 37410, 37412, 37415, 37417, 37420, 37423, 37426, 37123, 37125, 37128, 37130, 37427, 37134, 37136, 37428, 37429, 37436, 37438, 37439, 37441, 37142, 37144, 37442, 37443, 37446, 37450, 37454, 37457, 37461, 37463, 37465, 37466, 37468, 37470, 37153, 37155, 37157, 37159, 37161, 37471, 37472, 37473, 37476, 37479, 37481, 37482, 37486, 37488, 37169, 37489, 37173, 37490, 37491, 37494, 37496, 37499, 37501, 37502, 37504, 37505, 37182, 37184, 37186, 37508, 37510, 35824, 37512, 37518, 37521, 37525, 37529, 37531, 37533, 37534, 37192, 37194, 37197, 37199, 37201, 37203, 37535, 37435, 37433, 37445, 37498, 37493, 37517, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37568, 37571, 37574, 37577, 37579, 37580, 37581, 37582, 37584, 37585, 37592, 37593, 37596, 37597, 37599, 37600, 37603, 37606, 37607, 37608, 37609, 37610, 37617, 37620, 37622, 37632, 37633, 37634, 37635, 37637, 37638, 37640, 37641, 37642, 37646, 37647, 37648, 37649, 37650, 37651, 37422, 37407, 36723, 36726, 37431, 37591, 37653, 37589, 37654, 36730, 36729, 37655, 37456, 36307, 36306, 37616, 37619, 37478, 37475, 36310, 36309, 36736, 37628, 37656, 37507, 37657, 37626, 37630, 37658, 37520, 36319, 36311, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37697, 37419, 37736, 37737, 37569, 37578, 37738, 36722, 36725, 36720, 36719, 36721, 37739, 36724, 37740, 37741, 37743, 37745, 37746, 37114, 37113, 37453, 37460, 37449, 37602, 37605, 37748, 36733, 36732, 37749, 36731, 37750, 36302, 36734, 37485, 37751, 37752, 37753, 37754, 37755, 37756, 37757, 37115, 37116, 37758, 37760, 37762, 37763, 37119, 37118, 37117, 37725, 37515, 37528, 37765, 37644, 37524, 36315, 37766, 36743, 36741, 36744, 37767, 36742, 36745, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37825, 37828, 37829, 37414, 37831, 37832, 37833, 37834, 37835, 37837, 37838, 37742, 37843, 37844, 37841, 37845, 37846, 37847, 37848, 37849, 37851, 37852, 37854, 37856, 37857, 37858, 37860, 37866, 37867, 37863, 37868, 37869, 37870, 37872, 37873, 37874, 37875, 37876, 37877, 37879, 37880, 37881, 37883, 37884, 37885, 37887, 37888, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37955, 37952, 37827, 37830, 37957, 37959, 37836, 37962, 37964, 37967, 37969, 37747, 37972, 37853, 37855, 37977, 37865, 37982, 37985, 37988, 37990, 37878, 37993, 37994, 37996, 37997, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37954, 38017, 38019, 38021, 38023, 37966, 38025, 38027, 38028, 38030, 38031, 37981, 38033, 38034, 38035, 38037, 38038, 38040, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38081, 38082, 38086, 38088, 38090, 38091, 38094, 38096, 38085, 38093, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38149, 38152, 38147, 38151, 38145, 38153, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38208, 38210, 38211, 38212, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38272, 38273, 38275, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38336, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38400, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
int h_C[]= {
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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37057, 37055, 37053, 37059, 37060, 37062, 37065, 37063, 37067, 37070, 37069, 37068, 37071, 37074, 37073, 37072, 37076, 37083, 37081, 37079, 37077, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37122, 36851, 37127, 37129, 37133, 36868, 37141, 37143, 37152, 37154, 36956, 36960, 37160, 37168, 37172, 37181, 36636, 37185, 36652, 37191, 37193, 37196, 37100, 37104, 37202, 14320, 14321, 14322, 37120, 14324, 14325, 14326, 14327, 14328, 14329, 14330, 14331, 14332, 14333, 14334, 14335, 14336, 14337, 14338, 14339, 14340, 14341, 14342, 14343, 14344, 37121, 37131, 37137, 14364, 14365, 14366, 14367, 14368, 14369, 14370, 14371, 14372, 37139, 14374, 14375, 37140, 37145, 37147, 14385, 14386, 14387, 14388, 14389, 14390, 14391, 14392, 14393, 14394, 14395, 14396, 14397, 14398, 14399, 14400, 14401, 14402, 14403, 14404, 14405, 37149, 14407, 14408, 14409, 14410, 37150, 37162, 37164, 14427, 14428, 14429, 14430, 14431, 14432, 14433, 14434, 37166, 14436, 14437, 14438, 14439, 14440, 14441, 37167, 37170, 37174, 37176, 14453, 14454, 14455, 14456, 37178, 14458, 14459, 14460, 14461, 37179, 14463, 14464, 37180, 14466, 14467, 14468, 14475, 14476, 14477, 14478, 14480, 14481, 14482, 14483, 14484, 14485, 14486, 14487, 14488, 14489, 14490, 14491, 14492, 14493, 14494, 14495, 14496, 14497, 14498, 14499, 14500, 37188, 37189, 37204, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37402, 14323, 37406, 37409, 37411, 37413, 37416, 37418, 37421, 37424, 14345, 37376, 37377, 37378, 37379, 14355, 37380, 37381, 14362, 37430, 37437, 14373, 37440, 14376, 37382, 37383, 14381, 14383, 37447, 37451, 37455, 37458, 37462, 37464, 14406, 37467, 37469, 14411, 37384, 37385, 37386, 37387, 37388, 14423, 14425, 37474, 37477, 37480, 14435, 37483, 37487, 14442, 37389, 14445, 37390, 14449, 14451, 37495, 14457, 37500, 14462, 37503, 14465, 37506, 37391, 37392, 37393, 37509, 37511, 37394, 37513, 37519, 37522, 37526, 37530, 37532, 14501, 14502, 37395, 37396, 37397, 37398, 37399, 37400, 14517, 37434, 37432, 37444, 37497, 37492, 37516, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37403, 37572, 37575, 37425, 14346, 14348, 14351, 14353, 14357, 14359, 14377, 14379, 37448, 37452, 37459, 37601, 37604, 14412, 14414, 14416, 14418, 14421, 37484, 14443, 14447, 14469, 14471, 14473, 37636, 14479, 37514, 37523, 37527, 37643, 14504, 14506, 14508, 14510, 14512, 14515, 37576, 37570, 37583, 37586, 37587, 37590, 14535, 37588, 14537, 37595, 37594, 14546, 37598, 37612, 37611, 37615, 37618, 37614, 37613, 37624, 37623, 37621, 37627, 14567, 37631, 14569, 37625, 37629, 14578, 37639, 37652, 37645, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 37573, 37698, 14520, 14521, 37696, 37699, 14525, 37703, 37705, 37701, 37700, 37702, 14531, 37704, 14533, 14534, 14536, 14538, 14539, 37707, 37706, 37709, 37710, 37708, 37711, 37712, 14548, 37716, 37714, 14551, 37713, 14553, 37715, 37717, 37718, 14557, 14558, 14559, 14560, 14561, 14562, 14563, 37719, 37720, 14566, 14568, 14570, 14571, 37723, 37722, 37721, 37724, 37726, 37728, 14579, 37729, 37727, 37733, 14583, 37732, 37730, 37734, 14587, 37731, 37735, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 14519, 14522, 14523, 37824, 14526, 14527, 14528, 14529, 14530, 14532, 37839, 37840, 14540, 14541, 37842, 14542, 14543, 14544, 14545, 14547, 14549, 14550, 14552, 14554, 14555, 14556, 37861, 14564, 14565, 37864, 37759, 37761, 37871, 14572, 14573, 14574, 14575, 14576, 14577, 14580, 14581, 14582, 14584, 14585, 14586, 14588, 14589, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 14524, 37826, 37953, 37956, 37958, 37960, 37961, 37963, 37965, 37968, 37970, 37971, 37973, 37974, 37975, 37859, 37979, 37983, 37986, 37989, 37764, 37991, 37882, 37995, 37886, 37998, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38016, 38018, 38020, 38022, 37744, 38024, 38026, 37850, 38029, 37976, 37978, 38032, 37984, 37987, 38036, 37992, 38039, 38041, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38080, 38083, 38087, 38089, 37862, 37980, 38095, 38097, 38084, 38092, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38148, 14591, 38146, 38150, 38144, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 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, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38209, 38274, 38213, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38337, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 38338, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63};
bool h_Op[]= {
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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#define THREADS_PER_BLOCK 64
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 14656
#define SIZE_OF_AC 23872
__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[602*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];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 229*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 + 230*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 + 231*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 + 232*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 + 233*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 + 234*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 + 235*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 + 236*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 + 237*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 + 238*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 + 239*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 + 240*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 + 241*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 + 242*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 + 243*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 + 244*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 + 245*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 + 246*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 + 247*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 + 248*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 + 249*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 + 250*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 + 251*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 + 252*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 + 253*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 + 254*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 + 255*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 + 256*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 + 257*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 + 258*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 + 259*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 + 260*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 + 261*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 + 262*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 + 263*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 + 264*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 + 265*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 + 266*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 + 267*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 + 268*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 + 269*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 + 270*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 + 271*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 + 272*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 + 273*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 + 274*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 + 275*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 + 276*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 + 277*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 + 278*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 + 279*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 + 280*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 + 281*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 + 282*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 + 283*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 + 284*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 + 285*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 + 286*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 + 287*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 + 288*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 + 289*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 + 290*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 + 291*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 + 292*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 + 293*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 + 294*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 + 295*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 + 296*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 + 297*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 + 298*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 + 299*t] = Op[i + 70*t] ? R[B[i + 70*t]] * R[C[i + 70*t]] : R[B[i + 70*t]] + R[C[i + 70*t]];
__syncthreads();
R[i + 300*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 + 301*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 + 302*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 + 303*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 + 304*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 + 305*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 + 306*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 + 307*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 + 308*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 + 309*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 + 310*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 + 311*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 + 312*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 + 313*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 + 314*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 + 315*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 + 316*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 + 317*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 + 318*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 + 319*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 + 320*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 + 321*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 + 322*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 + 323*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 + 324*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 + 325*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 + 326*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 + 327*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 + 328*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 + 329*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 + 330*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 + 331*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 + 332*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 + 333*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 + 334*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 + 335*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 + 336*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 + 337*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 + 338*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 + 339*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 + 340*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]];
__syncthreads();
R[i + 341*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 + 342*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 + 343*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 + 344*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 + 345*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 + 346*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 + 347*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 + 348*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 + 349*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 + 350*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 + 351*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 + 352*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 + 353*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 + 354*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 + 355*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 + 356*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 + 357*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 + 358*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 + 359*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 + 360*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 + 361*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 + 362*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 + 363*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 + 364*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 + 365*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 + 366*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 + 367*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 + 368*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 + 369*t] = Op[i + 140*t] ? R[B[i + 140*t]] * R[C[i + 140*t]] : R[B[i + 140*t]] + R[C[i + 140*t]];
R[i + 370*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 + 371*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 + 372*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 + 373*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 + 374*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 + 375*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 + 376*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]];
__syncthreads();
R[i + 377*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 + 378*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 + 379*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 + 380*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 + 381*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 + 382*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 + 383*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 + 384*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 + 385*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 + 386*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 + 387*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 + 388*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 + 389*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 + 390*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 + 391*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 + 392*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 + 393*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 + 394*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 + 395*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 + 396*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 + 397*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 + 398*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 + 399*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 + 400*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 + 401*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 + 402*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 + 403*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 + 404*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 + 405*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 + 406*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 + 407*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 + 408*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 + 409*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 + 410*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 + 411*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 + 412*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]];
__syncthreads();
R[i + 413*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 + 414*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 + 415*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 + 416*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 + 417*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 + 418*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 + 419*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 + 420*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 + 421*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 + 422*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 + 423*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 + 424*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 + 425*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 + 426*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 + 427*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 + 428*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 + 429*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 + 430*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 + 431*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 + 432*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 + 433*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 + 434*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 + 435*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]];
__syncthreads();
R[i + 436*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 + 437*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 + 438*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 + 439*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 + 440*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 + 441*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 + 442*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 + 443*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 + 444*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 + 445*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 + 446*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 + 447*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 + 448*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 + 449*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 + 450*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 + 451*t] = Op[i + 222*t] ? R[B[i + 222*t]] * R[C[i + 222*t]] : R[B[i + 222*t]] + R[C[i + 222*t]];
R[i + 452*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 + 453*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 + 454*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 + 455*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 + 456*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 + 457*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 + 458*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 + 459*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 + 460*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 + 461*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 + 462*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 + 463*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 + 464*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 + 465*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 + 466*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 + 467*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 + 468*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 + 469*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 + 470*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 + 471*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 + 472*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 + 473*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]];
__syncthreads();
R[i + 474*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 + 475*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 + 476*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 + 477*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 + 478*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 + 479*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 + 480*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 + 481*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 + 482*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 + 483*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 + 484*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 + 485*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 + 486*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 + 487*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 + 488*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 + 489*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 + 490*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 + 491*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 + 492*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 + 493*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 + 494*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 + 495*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 + 496*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 + 497*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 + 498*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 + 499*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 + 500*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 + 501*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 + 502*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 + 503*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 + 504*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 + 505*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 + 506*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 + 507*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]];
__syncthreads();
R[i + 508*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 + 509*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 + 510*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 + 511*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 + 512*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 + 513*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 + 514*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 + 515*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 + 516*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 + 517*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 + 518*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 + 519*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 + 520*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 + 521*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 + 522*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 + 523*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 + 524*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 + 525*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 + 526*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 + 527*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 + 528*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 + 529*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 + 530*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 + 531*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 + 532*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 + 533*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 + 534*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]];
__syncthreads();
R[i + 535*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 + 536*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 + 537*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 + 538*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 + 539*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 + 540*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 + 541*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 + 542*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 + 543*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 + 544*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 + 545*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 + 546*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 + 547*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 + 548*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]];
__syncthreads();
R[i + 549*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 + 550*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 + 551*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 + 552*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 + 553*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 + 554*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 + 555*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 + 556*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 + 557*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 + 558*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 + 559*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 + 560*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]];
__syncthreads();
R[i + 561*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 + 562*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 + 563*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 + 564*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 + 565*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 + 566*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 + 567*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]];
__syncthreads();
R[i + 568*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 + 569*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 + 570*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 + 571*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 + 572*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 + 573*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 + 574*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]];
__syncthreads();
R[i + 575*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 + 576*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 + 577*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 + 578*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 + 579*t] = Op[i + 350*t] ? R[B[i + 350*t]] * R[C[i + 350*t]] : R[B[i + 350*t]] + R[C[i + 350*t]];
__syncthreads();
R[i + 580*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 + 581*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 + 582*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 + 583*t] = Op[i + 354*t] ? R[B[i + 354*t]] * R[C[i + 354*t]] : R[B[i + 354*t]] + R[C[i + 354*t]];
__syncthreads();
R[i + 584*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 + 585*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 + 586*t] = Op[i + 357*t] ? R[B[i + 357*t]] * R[C[i + 357*t]] : R[B[i + 357*t]] + R[C[i + 357*t]];
__syncthreads();
R[i + 587*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 + 588*t] = Op[i + 359*t] ? R[B[i + 359*t]] * R[C[i + 359*t]] : R[B[i + 359*t]] + R[C[i + 359*t]];
__syncthreads();
R[i + 589*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 + 590*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]];
__syncthreads();
R[i + 591*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 + 592*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]];
__syncthreads();
R[i + 593*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]];
__syncthreads();
R[i + 594*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]];
__syncthreads();
R[i + 595*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 + 596*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]];
__syncthreads();
R[i + 597*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]];
__syncthreads();
R[i + 598*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]];
__syncthreads();
R[i + 599*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]];
__syncthreads();
R[i + 600*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]];
__syncthreads();
R[i + 601*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]];
if (i==0) { final += R[601*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
10,681 | #include <stdio.h>
#include <math.h>
// Kernel function to add the elements of two arrays
#include <curand_kernel.h>
//#include <pycuda-complex.hpp>
#include <stdio.h>
#include <fstream>
#include <iostream>
#include <ctime>
#define X_MIN -1.5f
#define X_MAX 1.5f
#define Y_MIN -3.2f
#define Y_MAX 2.0f
#define X_DIM 1440
#define Y_DIM 2560
#define ITERS 20
#define N (1<<16)
//typedef pycuda::complex<float> cmplx;
__device__ curandState_t* states[N];
__global__
void init_kernel(int seed) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < N) {
curandState_t* s = new curandState_t;
if (s != 0) {
curand_init(seed, idx, 0, s);
}
states[idx] = s;
} else {
printf("forbidden memory access %%d/%%d\\n", idx, N);
}
}
__device__
void to_pixel(float &px, float &py, int &ix, int &iy) {
px -= X_MIN;
py -= Y_MIN;
px /= X_MAX - X_MIN;
py /= Y_MAX - Y_MIN;
px *= X_DIM;
py *= Y_DIM;
ix = __float2int_rd(px);
iy = __float2int_rd(py);
}
__device__
void write_pixel(int idx, float px, float py, int ix, int iy,
float4 *z, unsigned int *canvas) {
px = z[idx].y;
py = z[idx].x;
to_pixel(px, py, ix, iy);
if (0 <= ix & ix < X_DIM & 0 <= iy & iy < Y_DIM) {
canvas[iy*X_DIM + ix] += 1;
}
}
__device__
void generate_random_complex(float real, float imag, int idx,
float4 *z, float *dists, unsigned int *counts) {
real *= X_MAX-X_MIN+3;
real += X_MIN-2;
imag *= Y_MAX-Y_MIN+0;
imag += Y_MIN-0;
z[idx].x = real;
z[idx].y = imag;
z[idx].z = real;
z[idx].w = imag;
dists[idx] = 0;
counts[idx] = 0;
}
__global__
void buddha_kernel(unsigned int *counts, float4 *z,
float *dists, unsigned int *canvas) {
int idx = threadIdx.x + blockIdx.x * blockDim.x;
int i, j, ix, iy;
float real, imag;//, temp0, temp1;
if (idx < N) {
curandState_t s = *states[idx];
#pragma unroll 4
for(i = 0; i < 10000; i++) {
real = curand_uniform(&s);
imag = curand_uniform(&s);
generate_random_complex(real, imag, idx, z, dists, counts);
to_pixel(real, imag, ix, iy);
while (counts[idx] < ITERS & dists[idx] < 25) {
counts[idx]++;
real = z[idx].x*z[idx].x - z[idx].y*z[idx].y + z[idx].z;
imag = 2*z[idx].x*z[idx].y + z[idx].w;
z[idx].x = real;
z[idx].y = imag;
dists[idx] = z[idx].x*z[idx].x + z[idx].y*z[idx].y;
}
if (dists[idx] > 25) {
z[idx].x = 0;
z[idx].y = 0;
for (j = 0; j < counts[idx]+1; j++) {
real = z[idx].x*z[idx].x - z[idx].y*z[idx].y + z[idx].z;
imag = 2*z[idx].x*z[idx].y + z[idx].w;
z[idx].x = real;
z[idx].y = imag;
write_pixel(idx, real, imag, ix, iy, z, canvas);
}
}
}
*states[idx] = s;
} else {
printf("forbidden memory access %%d/%%d\\n", idx, N);
}
}
int writeFile (unsigned int *canvas, double elapsed) {
std::ofstream myfile;
myfile.open ("example.txt");
double sum = 0;
// myfile << "Writing this to a file.\n";
for (int i = 0; i < Y_DIM; i++) {
for (int j = 0; j < X_DIM-1; j++) {
myfile << canvas[i*X_DIM + j] << ",";
sum += canvas[i*X_DIM + j];
}
myfile << canvas[(i+1)*X_DIM-1];
sum += canvas[(i+1)*X_DIM - 1];
if (i < Y_DIM-1) {
myfile << "\n";
}
}
printf("\tTotal iterations: %.2e\n", sum);
printf("\tIterations per second: %.2e\n", (sum/elapsed));
myfile.close();
return 0;
}
int main(void) {
unsigned int *counts, *canvas;
float4 *z;
float *dists;
// Allocate Unified Memory – accessible from CPU or GPU
cudaMallocManaged(&counts, N*sizeof(unsigned int));
cudaMallocManaged(&canvas, X_DIM*Y_DIM*sizeof(unsigned int));
cudaMallocManaged(&z, N*sizeof(float4));
cudaMallocManaged(&dists, N*sizeof(float));
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
init_kernel<<<numBlocks, blockSize>>>(123);
std::clock_t begin = std::clock();
buddha_kernel<<<numBlocks, blockSize>>>(counts, z, dists, canvas);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
std::clock_t end = std::clock();
double elapsed_secs = double(end - begin) / CLOCKS_PER_SEC;
printf("\tTotal time: %.2fs\n", elapsed_secs);
writeFile(canvas, elapsed_secs);
// Free memory
cudaFree(counts);
cudaFree(canvas);
cudaFree(z);
cudaFree(dists);
printf("Buddhabrot generated!\n");
return 0;
} |
10,682 | // MIT License
// Copyright (c) 2019 Edward Liu
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
// SOFTWARE.
#include <stdio.h>
int main(int argc, char **argv) {
cudaDeviceProp dP;
float min_cc = 3.0;
int rc = cudaGetDeviceProperties(&dP, 0);
if (rc != cudaSuccess) {
cudaError_t error = cudaGetLastError();
printf("CUDA error: %s", cudaGetErrorString(error));
return rc; /* Failure */
}
if ((dP.major + (dP.minor / 10)) < min_cc) {
printf(
"Min Compute Capability of %2.1f required: %d.%d found\n Not Building "
"CUDA Code",
min_cc, dP.major, dP.minor);
return 1; /* Failure */
} else {
printf("-arch=sm_%d%d", dP.major, dP.minor);
return 0; /* Success */
}
}
|
10,683 | #include <stdio.h>
#include <stdlib.h>
//uses shared memory, in a single block
//limited to n<= 4000 if only 16K shared memory
__device__ void initsp( int *sprimes, int n, int nth, int me){
int chunk, startsetsp, endsetsp, val, i;
sprimes[2] = 1;
chunk = (n-1)/ nth;
startsetsp = 2 +me*chunk;
if(me<nth -1) endsetsp = startsetsp + chunk-1;
else endsetsp = n;
val = startsetsp%2;
for(i = startsetsp; i<=endsetsp; i++){
sprimes[i] = val;
val = 1 - val;
}
__syncthreads();
}
__device__ void cpytoglb(int *dprimes, int *sprimes, int n, int nth, int me){
int startcpy, endcpy, chunk, i;
chunk = (n-1)/nth;
startcpy = 2 + me*chunk;
if(me<nth-1) endcpy = startcpy + chunk -1;
else endcpy = n;
for(i = startcpy; i<= endcpy; i++) dprimes[i] = sprimes[i];
__syncthreads();
}
__global__ void sieve(int *dprimes, int n, int nth){
extern __shared__ int sprimes[];
int me = threadIdx.x;
int nthl = nth -1;
initsp(sprimes, n ,nth, me);
int maxmult, m, startmult, endmult, chunk, i;
for(m = 3; m*m <= n; m++){
if(sprimes != 0){
maxmult = n/m;
chunk = (maxmult -1)/nth;
startmult = 2 + me*chunk;
if(me<nthl) endmult = startmult + chunk -1;
else endmult = maxmult;
}
for(i = startmult; i <= endmult; i++) sprimes[i*m] = 0;
}
__syncthreads();
cpytoglb(dprimes,sprimes,n,nth,me);
}
int main(){
int n = 10, nth = 16;
int *hprimes, *dprimes;
int psize = (n+1)*sizeof(int);
hprimes = (int *) malloc(psize);
printf("before cuda Malloc\n");
cudaMalloc((void **) &dprimes, psize);
cudaError_t err = cudaGetLastError();
if(err != cudaSuccess) printf("fail\n");
printf("after cudamalloc\n");
dim3 dimGrid(1,1);
dim3 dimBlock(nth, 1, 1);
sieve<<<dimGrid, dimBlock, psize>>>(dprimes, n ,nth);
err = cudaGetLastError();
if(err != cudaSuccess) printf("fail\n");
cudaThreadSynchronize();
cudaMemcpy(hprimes, dprimes, psize, cudaMemcpyDeviceToHost);
int i;
for(i = 2; i<=n; i++){
if(hprimes[i] == 1){
printf("%d\n", i);
}
}
return 0;
} |
10,684 | #include <cuda.h>
#include <math.h>
#include <iostream>
#include <vector>
static const size_t N = 102400;
__global__ void kernel(const float* A, const float* B, float* C, int N)
{
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid < N) {
C[tid] = A[tid] + B[tid];
}
}
int main() {
std::vector<float> h_A, h_B, h_C;
h_A.resize(N);
h_B.resize(N);
h_C.resize(N);
for (int i = 0; i < N; i++) {
h_A[i] = i;
h_B[i] = 0.5f * i - 2;
}
float *d_A, *d_B, *d_C;
cudaMalloc(&d_A, sizeof(float) * N);
cudaMalloc(&d_B, sizeof(float) * N);
cudaMalloc(&d_C, sizeof(float) * N);
cudaMemcpy(d_A, &h_A[0], sizeof(float) * N, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, &h_B[0], sizeof(float) * N, cudaMemcpyHostToDevice);
kernel<<<ceil(double(N) / 512), 512>>>(d_A, d_B, d_C, N);
cudaMemcpy(&h_C[0], d_C, sizeof(float) * N, cudaMemcpyDeviceToHost);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
double err = 0;
for (int i = 0; i < N; i++) {
err += (h_A[i] + h_B[i]) - h_C[i];
}
std::cout << "Cum error: " << sqrt(err) << std::endl;
return 0;
}
|
10,685 | #include "includes.h"
__global__ void iKernel(float *src, float *dst)
{
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
dst[idx] = src[idx] * 2.0f;
} |
10,686 | #include <cuda.h>
#include <cuda_runtime.h>
#include <iostream>
using namespace std;
//DEVICE
__global__ void kernelVector_suma_constante(float* array, int _size, int _constant){
int idx= blockIdx.x * blockDim.x + threadIdx.x;
if(idx < _size){
array[idx] = array[idx]+_constant;
}
}
//HOST
int main(){
int size = 1000000;
float* arr = new float[size];
float* arr_DEVICE= NULL;
for (int index = 0; index < size; index++){
arr[index] = index;
}
cudaMalloc((void**)&arr_DEVICE,size * sizeof(float));
cudaMemcpy(arr_DEVICE, arr,size * sizeof(float), cudaMemcpyHostToDevice);
kernelVector_suma_constante <<< ceil(size/512),512>>>(arr_DEVICE,size,65);
cudaMemcpy(arr,arr_DEVICE,size * sizeof (float), cudaMemcpyDeviceToHost);
for ( int index = 0; index<100; index++){
cout<<arr[index]<<endl;
}
cudaFree(arr_DEVICE);
delete[] arr;
} |
10,687 | #define TILE_DIM 32
template<typename T>
__device__ void getColumn(const T* matrix, const int col, T* result,
const int rows, const int cols) {
int bx = blockIdx.x;
int tx = threadIdx.x;
int index = bx * blockDim.x + tx;
result[index] = matrix[index * cols + col];
}
template<typename T>
__device__ void updateColumn(T* matrix, const int col, const T* column,
const int rows, const int cols) {
int bx = blockIdx.x;
int tx = threadIdx.x;
int index = bx * blockDim.x + tx;
matrix[index * cols + col] = column[index];
}
template<typename T>
__device__ void matrixAddColumn(const T* matrix, const T* column, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = matrix[row * cols + col] + tile[ty];
result[row * cols + col] = value;
}
}
template<typename T>
__device__ void columnAddMatrix(const T* column, const T* matrix, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = tile[ty] + matrix[row * cols + col];
result[row * cols + col] = value;
}
}
template<typename T>
__device__ void matrixSubColumn(const T* matrix, const T* column, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = matrix[row * cols + col] - tile[ty];
result[row * cols + col] = value;
}
}
template<typename T>
__device__ void columnSubMatrix(const T* column, const T* matrix, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = tile[ty] - matrix[row * cols + col];
result[row * cols + col] = value;
}
}
template<typename T>
__device__ void matrixTimesColumn(const T* matrix, const T* column, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = matrix[row * cols + col] * tile[ty];
result[row * cols + col] = value;
}
}
template<typename T>
__device__ void columnTimesMatrix(const T* column, const T* matrix, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = tile[ty] * matrix[row * cols + col];
result[row * cols + col] = value;
}
}
template<typename T>
__device__ void matrixDivColumn(const T* matrix, const T* column, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = matrix[row * cols + col] / tile[ty];
result[row * cols + col] = value;
}
}
template<typename T>
__device__ void columnDivMatrix(const T* column, const T* matrix, T* result,
const int rows, const int cols) {
__shared__ T tile[TILE_DIM];
int bx = blockIdx.x;
int by = blockIdx.y;
int tx = threadIdx.x;
int ty = threadIdx.y;
int row = by * blockDim.y + ty;
int col = bx * blockDim.x + tx;
if (ty == 0) {
int r = row + tx;
if (r < rows) {
tile[tx] = column[r];
} else {
tile[tx] = 0;
}
}
__syncthreads();
if (row < rows && col < cols) {
T value = tile[ty] / matrix[row * cols + col];
result[row * cols + col] = value;
}
}
|
10,688 | #include "./BlockSync.cu"
extern "C"
__global__ void TestBlockSync(unsigned int *output) {
int block_size = blockDim.x * blockDim.y;
int block_index = blockIdx.x + blockIdx.y * gridDim.x;
int local_index = threadIdx.x + threadIdx.y * blockDim.x;
int global_index = block_index * block_size + local_index;
if (global_index == 0)
atomicCAS((unsigned int *)&QUANT_SEMAPHORE, 0, QUANT_SEMAPHORE);
// Sync all threads
while(QUANT_SEMAPHORE > 0);
__syncthreads();
BlockSyncTest(output);
} |
10,689 | #include <stdio.h>
#include <math.h>
#include <unistd.h>
#include <cuda_runtime_api.h>
#include <time.h>
#include <errno.h>
/*****************************************************************************
*
*
*
*
*
* Compile with:
* nvcc -o lr_cuda lr_cuda.cu
*
* Dr Kevan Buckley, University of Wolverhampton, 2018
****************************************************************************/
typedef struct point_t {
double x;
double y;
} point_t;
int n_data = 1000;
__device__ int d_n_data = 1000;
point_t data[] = {
{67.98,72.86},{72.02,121.49},{82.57,126.11},{73.94,130.07},
{83.66,115.76},{84.13,109.93},{79.89,115.22},{80.17,118.88},
{72.60,98.79},{69.11,93.40},{82.51,108.77},{66.85,92.86},
{69.28,93.71},{69.62,120.06},{83.03,96.81},{76.56,107.48},
{69.85,114.10},{89.46,123.00},{54.70,78.18},{58.08,91.85},
{57.56,90.21},{72.68,112.10},{33.61,63.88},{40.83,71.19},
{15.18,29.73},{81.49,114.72},{51.74,83.70},{70.91,92.98},
{30.08,73.13},{77.69,111.95},{21.50,52.81},{75.95,93.88},
{85.06,105.59},{20.51,43.20},{39.04,60.39},{37.18,50.60},
{19.21,54.82},{68.45,105.10},{ 0.11,52.19},{ 8.39,41.88},
{33.74,74.63},{76.85,99.26},{42.68,63.27},{69.41,104.25},
{74.85,117.93},{85.45,112.78},{79.19,125.00},{48.46,79.10},
{25.90,56.04},{92.02,112.20},{74.13,108.95},{96.93,125.88},
{15.03,31.67},{40.21,74.85},{38.55,70.50},{20.08,43.84},
{49.03,88.26},{66.04,94.67},{82.49,122.35},{ 8.75,49.95},
{94.69,134.32},{78.20,115.08},{53.80,70.65},{56.31,99.57},
{65.08,75.93},{88.99,143.82},{77.72,124.76},{19.19,51.43},
{17.87,42.23},{31.07,51.48},{12.70,51.82},{84.63,127.68},
{ 9.09,38.36},{71.34,117.87},{ 8.72,33.16},{65.33,91.08},
{71.24,125.14},{ 6.11,29.05},{37.55,65.63},{88.29,127.64},
{45.14,81.26},{51.19,79.63},{86.16,118.06},{92.61,133.30},
{14.97,58.65},{34.30,44.22},{70.26,87.77},{43.75,71.15},
{47.97,67.58},{93.06,137.73},{ 2.93,32.64},{80.56,124.36},
{43.15,74.38},{93.15,122.12},{85.61,107.78},{81.12,116.04},
{70.10,100.47},{38.00,67.49},{65.09,89.42},{89.95,128.91},
{64.18,86.63},{12.44,39.02},{99.41,128.49},{33.93,76.66},
{32.45,53.71},{21.12,52.13},{91.86,132.82},{17.41,37.59},
{25.94,58.45},{86.53,108.19},{49.07,91.20},{ 6.57,26.70},
{72.03,110.77},{32.88,77.61},{56.60,89.05},{53.04,68.41},
{36.18,68.32},{74.34,110.02},{35.96,65.69},{91.34,121.61},
{95.86,117.85},{96.15,147.12},{87.52,126.28},{96.74,134.54},
{17.35,26.70},{12.58,18.78},{28.53,70.44},{18.60,44.67},
{61.69,84.49},{28.85,40.73},{22.52,53.34},{54.82,97.34},
{20.34,49.79},{15.70,33.76},{63.45,91.61},{47.25,69.81},
{19.11,45.60},{31.02,68.30},{36.26,64.80},{60.44,95.08},
{ 8.99,28.88},{84.84,129.88},{ 5.09,30.21},{66.13,100.26},
{63.62,82.43},{10.40,33.62},{95.10,146.64},{ 7.57,50.98},
{92.10,138.76},{67.18,104.50},{10.02,37.03},{15.37,36.20},
{43.27,71.96},{83.69,91.26},{73.16,95.39},{ 8.57,40.71},
{85.73,110.46},{62.25,77.14},{98.10,158.89},{83.64,118.39},
{98.21,132.31},{70.32,95.66},{71.27,96.05},{53.62,80.59},
{79.48,108.01},{20.83,46.06},{33.81,59.00},{52.05,83.03},
{39.84,46.24},{79.97,114.69},{19.50,33.45},{86.84,112.48},
{27.50,66.83},{68.06,97.16},{94.50,140.40},{70.06,97.66},
{ 9.51,55.46},{62.06,83.11},{29.50,61.84},{35.75,47.26},
{69.00,92.12},{46.96,66.46},{74.23,115.20},{64.61,105.51},
{ 6.18,29.26},{22.66,22.54},{98.97,145.01},{69.66,102.09},
{26.16,49.32},{22.25,37.34},{ 2.50,17.40},{22.13,40.23},
{ 6.73,37.72},{99.41,137.47},{46.27,86.47},{93.52,129.57},
{28.65,56.58},{24.26,46.85},{25.75,56.05},{18.62,59.32},
{71.49,114.00},{88.43,112.19},{12.99,32.69},{52.75,78.17},
{16.92,38.47},{42.19,83.91},{79.37,120.75},{82.45,117.44},
{87.49,115.25},{94.21,118.79},{85.80,127.54},{22.24,42.54},
{15.85,38.33},{18.57,48.92},{63.38,93.61},{42.73,73.51},
{32.93,57.20},{26.18,44.49},{76.10,110.75},{31.24,56.19},
{29.02,54.23},{98.35,127.20},{ 3.15,19.47},{76.05,95.41},
{23.49,46.25},{90.18,122.46},{48.79,68.21},{65.09,85.25},
{ 1.31,23.86},{ 9.44,44.97},{81.25,107.59},{72.02,102.95},
{ 4.74,20.90},{97.75,129.72},{ 9.57,42.75},{45.74,95.22},
{28.02,69.54},{69.27,87.04},{69.28,114.49},{18.20,37.12},
{54.60,65.86},{63.45,101.49},{88.94,126.19},{30.14,70.99},
{93.73,115.72},{80.98,103.65},{37.37,71.24},{22.64,52.21},
{13.08,37.40},{87.23,116.35},{ 7.16,46.47},{ 0.09,28.01},
{64.94,105.79},{35.73,86.41},{67.14,111.06},{32.01,66.54},
{90.01,133.67},{98.42,127.59},{48.98,85.46},{ 2.12,28.16},
{ 4.36,54.51},{64.39,101.65},{48.04,82.59},{75.03,102.60},
{47.97,66.81},{20.24,33.85},{11.87,40.27},{94.56,123.17},
{85.45,118.35},{24.05,45.80},{76.07,100.69},{ 3.13,28.05},
{62.56,93.74},{78.29,104.46},{59.34,89.12},{48.39,73.15},
{20.47,51.63},{80.97,114.58},{93.33,135.74},{ 1.79,33.00},
{20.12,37.59},{37.14,79.35},{71.07,108.55},{45.29,72.51},
{12.42,28.89},{49.30,78.97},{45.50,64.44},{20.31,63.73},
{74.58,107.24},{13.26,34.52},{61.57,86.12},{77.35,96.35},
{71.91,102.66},{64.69,98.29},{91.35,129.32},{97.95,126.95},
{30.75,48.84},{41.43,84.83},{63.43,85.08},{98.99,134.50},
{44.73,75.10},{22.76,51.85},{42.36,67.84},{84.28,126.36},
{17.60,37.14},{27.02,47.41},{15.40,39.72},{27.34,45.49},
{62.96,90.28},{87.22,116.25},{45.96,82.18},{15.57,45.43},
{92.06,124.36},{41.66,69.65},{47.81,65.15},{27.22,51.25},
{44.21,80.96},{25.17,64.65},{ 7.02,30.35},{98.39,135.29},
{ 9.74,41.30},{ 6.31,43.90},{87.75,116.75},{72.25,112.85},
{25.73,57.79},{63.31,83.32},{64.95,92.98},{97.01,134.91},
{57.89,86.61},{77.52,116.27},{11.23,17.18},{17.20,53.05},
{16.24,46.25},{25.37,77.19},{51.05,83.74},{75.23,119.64},
{61.35,100.17},{65.98,96.23},{25.99,50.50},{ 8.83,36.84},
{73.77,89.95},{56.54,84.52},{43.48,77.55},{81.59,122.18},
{30.30,66.46},{49.72,96.63},{79.97,109.35},{47.88,72.85},
{ 8.16,38.93},{ 9.79,36.77},{49.91,78.85},{14.71,28.84},
{ 8.62,49.10},{26.93,49.27},{71.99,94.09},{43.38,61.76},
{18.60,40.29},{37.03,58.92},{71.31,106.99},{ 3.20, 7.88},
{65.80,78.56},{79.85,115.69},{53.22,86.56},{33.71,62.14},
{26.06,65.68},{78.91,101.78},{74.51,110.85},{63.23,68.61},
{13.20,40.12},{34.81,52.07},{56.12,94.44},{48.15,90.88},
{19.26,43.74},{ 6.85,23.65},{22.89,51.29},{40.36,71.12},
{ 5.32,10.10},{98.57,135.26},{ 5.72,37.58},{35.80,61.85},
{74.35,130.77},{44.65,77.77},{40.33,53.98},{99.66,130.49},
{ 7.47,16.05},{99.99,135.78},{49.96,59.18},{86.11,137.83},
{28.11,61.95},{38.82,58.89},{95.08,120.72},{32.56,60.55},
{13.90,50.06},{17.12,46.71},{94.32,135.38},{47.34,90.08},
{69.43,114.25},{75.57,105.21},{56.99,86.50},{40.44,70.09},
{52.23,75.09},{53.86,74.32},{ 2.21,21.73},{55.72,63.56},
{94.03,125.36},{60.36,80.49},{ 8.90,45.26},{97.06,120.41},
{ 8.31,20.39},{58.60,77.34},{43.23,94.93},{84.25,136.71},
{63.12,88.25},{72.67,110.82},{66.75,110.84},{28.11,65.68},
{25.89,36.51},{85.87,108.23},{65.82,100.72},{67.39,94.30},
{27.43,63.15},{89.69,120.78},{31.63,58.25},{99.43,145.69},
{ 3.12,36.92},{98.74,132.67},{69.48,125.02},{49.71,80.07},
{35.89,70.91},{12.56,41.65},{39.53,81.07},{39.48,67.33},
{13.89,39.06},{77.92,96.14},{73.51,92.46},{66.21,100.32},
{86.15,133.90},{ 7.05,30.80},{16.14,49.83},{52.77,59.63},
{43.98,75.04},{24.03,63.80},{40.26,76.89},{34.98,77.88},
{91.74,132.64},{60.76,93.42},{23.23,53.85},{20.49,54.88},
{12.34,25.36},{28.20,54.04},{53.59,66.83},{38.72,63.98},
{58.56,84.35},{65.38,105.30},{41.18,69.71},{55.98,92.20},
{79.74,128.00},{25.83,60.35},{ 2.12,34.42},{29.76,58.33},
{60.65,86.20},{23.70,49.85},{78.15,122.98},{88.18,114.34},
{16.35,38.11},{60.38,80.90},{73.08,96.10},{98.28,142.95},
{44.63,74.85},{92.38,132.87},{25.57,37.73},{ 0.04,24.13},
{39.33,58.12},{83.95,115.08},{35.50,64.42},{ 0.17, 9.05},
{64.58,94.58},{35.24,70.09},{97.30,135.67},{26.06,45.26},
{ 0.90,30.12},{80.66,106.03},{82.07,122.43},{72.73,120.24},
{ 7.55,30.75},{91.21,120.65},{14.11,42.15},{29.63,45.31},
{20.51,45.83},{38.98,61.72},{29.67,46.13},{37.15,60.72},
{57.10,82.78},{84.33,122.82},{76.45,112.19},{26.85,50.43},
{83.25,125.70},{35.11,67.01},{48.39,67.41},{92.69,117.42},
{99.04,133.07},{16.72,45.04},{65.69,96.08},{44.01,81.74},
{92.73,122.89},{11.55,34.29},{82.97,122.08},{23.38,41.46},
{52.09,100.25},{92.06,130.54},{35.62,59.91},{88.09,120.12},
{16.59,35.88},{47.29,72.47},{31.12,42.42},{31.69,56.24},
{69.76,97.67},{29.68,56.28},{53.28,84.20},{72.66,95.53},
{84.83,124.09},{65.01,114.18},{27.16,51.41},{54.48,90.32},
{ 3.69,30.82},{55.16,68.85},{30.97,64.40},{41.66,55.99},
{17.41,28.20},{31.03,57.96},{99.95,132.54},{54.90,94.98},
{39.56,66.54},{58.22,89.72},{10.70,51.97},{36.87,60.05},
{49.68,77.57},{58.51,88.82},{57.19,95.75},{57.56,84.55},
{27.79,63.65},{56.84,75.63},{55.95,80.88},{ 1.91,25.62},
{43.77,69.59},{ 8.28,30.30},{30.97,44.56},{35.70,48.43},
{35.10,43.36},{45.53,75.09},{33.33,53.23},{95.39,136.26},
{39.03,80.93},{32.37,55.13},{41.52,60.06},{ 8.65,21.60},
{87.60,120.68},{63.20,85.51},{42.86,66.78},{42.44,65.48},
{78.99,121.64},{91.07,124.54},{26.39,65.90},{52.95,82.21},
{52.19,76.07},{94.45,132.48},{ 3.81,22.73},{93.38,120.12},
{67.31,102.54},{ 5.64,31.88},{95.31,121.28},{71.00,101.76},
{56.96,86.59},{51.00,91.45},{85.08,114.82},{76.70,108.14},
{79.64,122.91},{82.79,106.84},{10.66,48.01},{93.14,150.77},
{96.47,138.41},{46.77,69.40},{79.79,109.79},{25.98,67.04},
{88.91,130.04},{16.55,48.93},{65.08,102.75},{73.49,106.25},
{14.06,53.92},{26.60,52.30},{80.98,112.10},{49.58,67.20},
{92.46,118.24},{66.48,105.97},{79.58,125.30},{43.75,78.40},
{ 4.75,29.98},{83.29,126.75},{79.16,135.90},{17.82,53.93},
{77.44,105.97},{84.35,116.54},{62.58,117.87},{14.84,59.66},
{17.08,35.95},{62.65,96.65},{70.39,105.03},{72.52,99.37},
{94.01,120.53},{40.31,70.67},{44.35,74.97},{71.51,111.60},
{54.10,83.47},{41.54,58.50},{37.91,60.92},{72.63,102.90},
{ 8.15,49.42},{91.70,120.00},{41.63,70.14},{47.14,76.74},
{70.47,96.46},{16.61,38.40},{97.68,129.24},{36.24,65.72},
{89.49,136.43},{85.12,127.41},{83.10,119.58},{84.25,122.70},
{56.10,79.87},{19.75,57.34},{88.76,114.02},{97.26,143.19},
{60.96,93.88},{85.88,127.06},{74.62,91.32},{64.27,97.94},
{ 7.57,27.88},{57.09,106.11},{52.10,85.28},{54.30,84.57},
{49.60,83.27},{94.11,120.31},{16.18,35.67},{92.45,138.61},
{67.76,99.96},{10.37,38.68},{16.96,51.66},{99.55,114.71},
{86.72,125.13},{83.26,102.62},{88.81,118.53},{57.69,84.14},
{88.09,119.33},{98.69,123.72},{16.11,43.72},{14.22,58.95},
{71.11,95.86},{ 5.69,41.04},{23.13,59.63},{66.96,67.53},
{49.71,113.24},{53.97,96.50},{43.03,67.66},{73.86,97.76},
{ 3.68,39.48},{73.91,115.59},{95.95,132.37},{80.51,96.37},
{19.52,30.27},{71.32,117.24},{17.74,48.53},{87.40,119.37},
{80.25,120.08},{91.16,126.78},{45.03,82.59},{73.87,104.05},
{28.71,56.65},{ 3.65,35.67},{81.76,117.14},{87.51,119.17},
{79.21,118.84},{66.05,102.02},{22.33,48.93},{70.91,107.70},
{24.36,42.83},{92.73,119.57},{54.84,74.71},{38.90,76.86},
{33.44,56.65},{95.72,146.37},{65.28,96.06},{81.08,117.94},
{51.73,73.85},{67.59,110.14},{59.97,83.39},{ 6.08,38.97},
{72.52,88.07},{72.21,103.74},{29.67,47.92},{56.58,84.41},
{15.84,64.75},{49.00,77.58},{88.50,126.22},{74.48,113.57},
{83.12,110.96},{66.87,102.02},{13.02,43.58},{25.22,50.03},
{85.12,110.70},{29.29,56.18},{86.98,124.53},{42.95,79.25},
{43.60,80.60},{31.58,63.84},{45.41,54.26},{78.18,117.76},
{27.26,42.43},{67.41,87.85},{68.46,109.39},{65.19,105.16},
{26.65,62.28},{60.94,89.46},{20.30,34.62},{96.29,128.44},
{63.40,95.34},{13.00,50.65},{42.83,57.99},{22.71,48.58},
{74.00,104.81},{ 7.19,37.10},{74.73,110.34},{41.60,74.15},
{11.11,29.87},{25.27,39.60},{ 0.26,39.84},{ 1.02,40.25},
{69.39,92.52},{15.11,44.29},{65.59,95.36},{24.05,58.03},
{ 1.08, 7.67},{26.20,48.97},{26.06,57.00},{50.62,74.29},
{17.20,33.93},{24.63,52.43},{74.06,112.59},{13.44,39.45},
{71.89,113.40},{35.81,73.11},{33.43,46.36},{88.86,124.45},
{ 4.79,14.84},{23.85,44.88},{84.21,121.08},{ 1.50,28.35},
{32.56,63.59},{21.93,51.74},{96.05,118.26},{46.41,87.50},
{ 0.87,33.71},{43.96,86.13},{46.06,67.38},{30.50,43.54},
{56.15,87.40},{31.97,74.53},{60.20,88.20},{51.45,75.63},
{74.82,116.52},{88.61,101.98},{84.19,124.24},{70.82,81.55},
{39.91,61.07},{56.55,103.37},{74.79,106.80},{72.80,107.32},
{13.41,27.56},{ 8.20,29.60},{60.66,93.00},{77.08,96.38},
{87.56,123.69},{78.62,84.74},{58.00,90.51},{61.06,116.46},
{46.42,94.08},{91.50,148.64},{32.99,58.75},{39.99,66.52},
{20.11,52.18},{30.99,39.95},{95.25,116.31},{ 6.86,45.91},
{74.66,97.39},{20.88,71.05},{60.10,79.86},{43.04,50.94},
{98.32,134.95},{21.95,38.97},{37.15,77.61},{91.95,118.85},
{96.73,136.03},{31.92,44.71},{33.38,52.72},{61.45,86.33},
{74.14,107.63},{85.30,115.57},{73.79,104.74},{14.07,42.96},
{83.74,117.16},{29.09,71.12},{25.53,56.20},{76.08,90.23},
{72.10,107.27},{76.74,117.82},{27.71,53.82},{39.20,67.29},
{89.57,110.39},{94.19,117.64},{76.30,101.56},{42.31,65.56},
{94.12,128.54},{ 8.87,41.00},{37.69,66.53},{57.07,92.92},
{60.82,97.63},{81.04,114.51},{89.77,115.35},{83.89,114.70},
{79.91,121.23},{ 7.46,21.41},{20.18,57.73},{46.66,94.82},
{66.18,99.52},{83.17,99.83},{27.67,62.05},{ 8.16,46.39},
{73.11,106.17},{31.24,59.63},{ 3.85,37.41},{47.02,83.44},
{37.61,60.89},{80.65,116.35},{33.11,70.04},{14.95,22.74},
{44.61,76.62},{36.80,63.46},{14.91,37.50},{97.75,142.84},
{61.31,85.64},{57.02,87.30},{23.31,24.90},{79.08,115.97},
{67.57,102.44},{43.10,77.76},{60.39,77.98},{37.89,58.26},
{51.20,88.08},{79.04,119.41},{78.89,109.14},{19.70,53.61},
{67.12,96.20},{69.44,91.16},{68.28,89.69},{ 6.07,32.31},
{52.16,88.81},{56.72,89.38},{51.14,88.74},{34.81,83.94},
{26.27,56.50},{93.20,126.51},{92.96,109.11},{58.11,82.08},
{27.00,59.35},{57.21,95.21},{ 0.01,17.53},{25.91,50.11},
{47.62,68.36},{91.63,120.31},{70.58,90.55},{ 6.97,47.61},
{10.60,54.82},{54.12,68.12},{18.61,51.08},{35.15,60.78},
{16.03,49.80},{83.12,142.61},{89.25,126.99},{10.91,45.62},
{62.47,90.43},{25.53,46.29},{58.95,95.45},{44.22,60.56},
{54.83,88.36},{82.10,106.74},{37.63,55.71},{65.69,89.28},
{ 3.98,26.10},{66.15,96.27},{10.60,30.94},{82.86,108.63},
{13.26,63.92},{51.19,91.40},{ 6.41,33.75},{27.01,57.14},
{94.19,125.78},{48.93,69.11},{38.63,55.41},{91.55,119.60},
{38.33,64.35},{72.76,110.46},{88.77,136.89},{48.32,74.45},
{94.28,151.66},{30.46,54.94},{30.33,60.46},{67.87,89.48},
{49.30,68.29},{92.46,132.98},{74.06,113.69},{58.17,78.48},
{15.66,29.92},{27.00,53.51},{40.07,63.86},{34.26,58.71},
{63.83,85.28},{16.95,38.52},{29.48,60.96},{95.89,136.89},
{ 8.33,37.34},{90.00,121.90},{65.20,103.73},{71.02,109.68},
{35.67,72.89},{ 9.50,24.26},{63.87,95.85},{16.50,54.83},
{87.18,118.97},{27.21,30.63},{ 1.27,38.08},{55.29,86.34},
{ 5.65,49.85},{44.45,75.38},{29.88,50.44},{73.42,108.53},
{67.92,100.28},{ 6.45,43.32},{30.87,57.40},{99.59,131.07},
{23.56,24.64},{55.44,82.72},{ 1.20,15.24},{50.30,86.10},
{82.44,119.27},{89.69,130.15},{ 8.88,52.82},{49.95,90.79},
{92.50,136.95},{ 5.99,30.29},{37.76,70.28},{49.27,76.11},
{51.31,71.10},{59.32,98.44},{30.95,68.85},{82.33,116.56},
{76.60,124.68},{58.13,82.84},{24.47,29.99},{90.98,116.41},
{30.86,59.16},{12.70,29.70},{54.46,82.99},{94.70,137.87},
{62.63,100.08},{64.04,83.29},{49.34,49.35},{17.93,48.21},
{65.94,91.39},{80.92,97.54},{92.13,119.57},{61.67,89.38},
{19.57,53.39},{40.01,51.29},{59.93,108.45},{16.73,59.66},
{12.35,24.37},{12.91,31.31},{32.47,76.68},{90.43,133.70},
{16.66,17.51},{18.48,49.77},{28.85,49.66},{91.14,119.52},
{99.84,153.53},{88.39,117.82},{62.14,89.68},{10.62,40.97},
{38.64,73.37},{60.79,110.08},{11.99,48.18},{47.76,68.53}
};
double residual_error (double x , double y , double m , double c){
double e = (m*x) +c - y;
return e * e;
}
__device__ double d_residual_error (double x , double y , double m , double c){
double e = (m*x) +c - y;
return e*e;
}
double rms_error (double m , double c){
int i;
double mean;
double error_sum =0;
for (i=0; i<n_data; i++){
error_sum += residual_error(data[i].x,data [i].y,m,c);
}
mean = error_sum / n_data;
return sqrt (mean);
}
__global__ void d_rms_error (double *m , double *c, double *error_sum_arr, point_t *d_data){
int i = threadIdx.x + blockIdx.x * blockDim.x;
error_sum_arr[i] = d_residual_error(d_data[i].x, d_data[i].y, *m, *c);
}
int time_difference(struct timespec *start, struct timespec *finish,
long long int *difference) {
long long int ds = finish->tv_sec - start->tv_sec;
long long int dn = finish->tv_nsec - start->tv_nsec;
if(dn < 0 ) {
ds--;
dn += 1000000000;
}
*difference = ds * 1000000000 + dn;
return !(*difference > 0);
}
int main() {
int i;
double bm = 1.3;
double bc = 10;
double be;
double dm[8];
double dc[8];
double e[8];
double step = 0.01;
double best_error = 999999999;
int best_error_i;
int minimum_found = 0;
double om[] = {0,1,1, 1, 0,-1,-1,-1};
double oc[] = {1,1,0,-1,-1,-1, 0, 1};
struct timespec start, finish;
long long int time_elapsed;
clock_gettime(CLOCK_MONOTONIC, &start);
cudaError_t error;
//Device variables
double *d_dm;
double *d_dc;
double *d_error_sum_arr;
point_t *d_data;
be = rms_error(bm, bc);
error = cudaMalloc(&d_dm, (sizeof(double) * 8));
if(error){
fprintf(stderr, "cudaMalloc on d_dm returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
//Allocate memory for d_dc
error = cudaMalloc(&d_dc, (sizeof(double) * 8));
if(error){
fprintf(stderr, "cudaMalloc on d_dc returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
error = cudaMalloc(&d_error_sum_arr, (sizeof(double) * 1000));
if(error){
fprintf(stderr, "cudaMalloc on d_error_sum_arr returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
//Allocate memory for d_data
error = cudaMalloc(&d_data, sizeof(data));
if(error){
fprintf(stderr, "cudaMalloc on d_data returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
while(!minimum_found) {
for(i=0;i<8;i++) {
dm[i] = bm + (om[i] * step);
dc[i] = bc + (oc[i] * step);
}
//Copy memory for dm to d_dm
error = cudaMemcpy(d_dm, dm, (sizeof(double) * 8), cudaMemcpyHostToDevice);
if(error){
fprintf(stderr, "cudaMemcpy to d_dm returned %d %s\n", error,
cudaGetErrorString(error));
}
//Copy memory for dc to d_dc
error = cudaMemcpy(d_dc, dc, (sizeof(double) * 8), cudaMemcpyHostToDevice);
if(error){
fprintf(stderr, "cudaMemcpy to d_dc returned %d %s\n", error,
cudaGetErrorString(error));
}
//Copy memory for data to d_data
error = cudaMemcpy(d_data, data, sizeof(data), cudaMemcpyHostToDevice);
if(error){
fprintf(stderr, "cudaMemcpy to d_data returned %d %s\n", error,
cudaGetErrorString(error));
}
for(i=0;i<8;i++) {
//Host variable storing the array returned from the kernel function.
double h_error_sum_arr[1000];
//Stores the total sum of the values from the error sum array.
double error_sum_total;
//Stores the mean of the total sum of the error sums.
double error_sum_mean;
//Call the rms_error function using 100 blocks and 10 threads.
d_rms_error <<<100,10>>>(&d_dm[i], &d_dc[i], d_error_sum_arr, d_data);
cudaThreadSynchronize();
//Copy memory for d_error_sum_arr
error = cudaMemcpy(&h_error_sum_arr, d_error_sum_arr, (sizeof(double) * 1000), cudaMemcpyDeviceToHost);
if(error){
fprintf(stderr, "cudaMemcpy to error_sum returned %d %s\n", error,
cudaGetErrorString(error));
}
//Loop through the error sum array returned from the kernel function
for(int j=0; j<n_data; j++) {
//Add each error sum to the error sum total.
error_sum_total += h_error_sum_arr[j];
}
//Calculate the mean for the error sum.
error_sum_mean = error_sum_total / n_data;
//Calculate the square root for the error sum mean.
e[i] = sqrt(error_sum_mean);
if(e[i] < best_error) {
best_error = e[i];
best_error_i = i;
}
//Reset the error sum total.
error_sum_total = 0;
}
//printf("best m,c is %lf,%lf with error %lf in direction %d\n",
//dm[best_error_i], dc[best_error_i], best_error, best_error_i);
if(best_error < be) {
be = best_error;
bm = dm[best_error_i];
bc = dc[best_error_i];
} else {
minimum_found = 1;
}
}
//Free memory for d_dm
error = cudaFree(d_dm);
if(error){
fprintf(stderr, "cudaFree on d_dm returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
//Free memory for d_dc
error = cudaFree(d_dc);
if(error){
fprintf(stderr, "cudaFree on d_dc returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
//Free memory for d_data
error = cudaFree(d_data);
if(error){
fprintf(stderr, "cudaFree on d_data returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
//Free memory for d_error_sum_arr
error = cudaFree(d_error_sum_arr);
if(error){
fprintf(stderr, "cudaFree on d_error_sum_arr returned %d %s\n", error,
cudaGetErrorString(error));
exit(1);
}
printf("minimum m,c is %lf,%lf with error %lf\n", bm, bc, be);
//Get the system time after we have run the linear regression function.
clock_gettime(CLOCK_MONOTONIC, &finish);
//Calculate the time spent between the start time and end time.
time_difference(&start, &finish, &time_elapsed);
//Output the time spent running the program.
printf("Time elapsed was %lldns or %0.9lfs\n", time_elapsed,
(time_elapsed/1.0e9));
return 0;
}
|
10,690 | /*
I am implement the K7 passive strategy iin to GPU
* Each thread saw a order, and handle its own order
* Market prce are input as integer levels
* Alpha matrix is initially given
* Latency is always 2 ticks
*/
#include <bits/stdc++.h>
#include <thrust/device_vector.h>
#include <thrust/copy.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 def_dvec(t) thrust::device_vector<t>
using namespace std;
const int BLOCK_SIZE = 1024;
const int NUM_INSTANCE = 1024;
const int NUM_LEVELS = 256;
const int LEVEL_LIM = 5;
const int MAX_POSITION = 5;
const int MAX_QTY = 1;
const int LATENCY = 2;
__device__ float getAskBbo(float alpha_price, float beta, float gamma, int pos, int Q){
return alpha_price + beta*pos + gamma*Q;
}
__device__ float getBidBbo(float alpha_price, float beta, float gamma, int pos, int Q){
return alpha_price + beta*pos - gamma*Q;
}
__global__ void gpuPassive(int N, int N_ins, int *ask_size, int *bid_size, int lvl0, float ts, float *alp_prc, float *beta, float *gamma, float *pnl){
int b_sz = blockDim.x, b_id = blockIdx.x, t_id = threadIdx.x;
__shared__ int info[3];
__shared__ float bbo[2];
if(t_id < 3){
info[t_id] = 0;
}
float prc = (lvl0 + t_id) * ts;
int latency = 0, qty = 0, filled_qty = 0, Q = 0, bsz = 0, asz = 0;
bool sell = true, cancel = false, sending = true;
__syncthreads();
for(int i=0; i<N; ++i){
/* compute bbos */
if(t_id < 2){
float tmp = (t_id? -1:1)*gamma[b_id];
bbo[t_id] = getAskBbo(alp_prc[b_id*N + i], beta[b_id], tmp, info[0], info[1]);
info[1] = 0;
}
__syncthreads();
/* Checking filled status */
// If an order latency just arrives, initialize it
asz = ask_size[NUM_LEVELS*i + t_id];
bsz = bid_size[NUM_LEVELS*i + t_id];
if(sending && !latency){
sending = false;
Q = (sell? asz:bsz);
info[0] += (sell? -1:1)*qty;
}
// If the order is ready to fill, fill it
if(!sending && qty){
Q -= (sell? bsz:asz);
filled_qty = min(qty, max(0, -Q));
qty -= filled_qty;
info[2] += (sell? 1:-1)*filled_qty*(t_id + lvl0);
Q = max(Q, 0);
}
/* Checing whether cancelled */
if(cancel && !latency){
cancel = false;
info[0] += (sell? 1:-1)*qty;
qty = 0;
}
// 1. Checking sell order for cancel.
if(qty && sell && (prc < bbo[0] || prc > bbo[0] + LEVEL_LIM*ts)){
cancel = true;
latency = LATENCY - 1;
}
// 2. Checing buy order for cancel
if(qty && !sell && (prc > bbo[1] || prc < bbo[1] - LEVEL_LIM*ts)){
cancel = true;
latency = LATENCY;
}
// When the order is in latency, we cannot sending an order
if(latency) --latency;
/* Checking whether to cancel */
else{
// Sending a sell order
if(!qty && prc >= bbo[0] && prc <= bbo[0] + LEVEL_LIM && info[0]>-MAX_POSITION){
sell = sending = true;
qty = MAX_QTY;
info[0] -= qty;
}
// Sending a buy order
if(!qty && prc >= bbo[0] && prc <= bbo[0] + LEVEL_LIM && info[0]<MAX_POSITION){
sell = false;
sending = true;
qty = MAX_QTY;
info[0] += qty;
}
}
info[1] += Q;
__syncthreads();
}
pnl[b_id] = info[2]*ts;
}
int main(){
int N = 10000;
def_dvec(int) asize(N*NUM_LEVELS, 0), bsize(N*NUM_LEVELS);
float ts = 1.;
def_dvec(float) aprc(N*NUM_INSTANCE, 3128.), gamma(NUM_INSTANCE, 0.), beta(NUM_INSTANCE, 0.),
pnl(NUM_INSTANCE, 0.);
gpuPassive<<<NUM_INSTANCE, NUM_LEVELS>>>(N, NUM_INSTANCE, to_ptr(asize), to_ptr(bsize),
3000, ts, to_ptr(aprc), to_ptr(beta), to_ptr(gamma), to_ptr(pnl));
for(auto k:pnl) cout<<k<<' ';
}
|
10,691 | #include <stdio.h>
#include <stdlib.h>
#include <cuda.h>
//#define N 100
__device__ int gpu_hist[10];
__global__ void gpuhistogram(int *a,int N)
{
int *ptr;
int tid=blockIdx.x*blockDim.x+threadIdx.x;
int numthr=blockDim.x*gridDim.x;
if(tid==0)
for(int i=0;i<10;i++)
gpu_hist[i]=0;
__syncthreads();
while(tid<N)
{
ptr=&gpu_hist[a[tid]];
atomicAdd(ptr,1);
tid+=numthr;
}
}
int main()
{
int B,T;
int *a;
int *deva;
int N;
int hist[10],cist[10];
for(int i=0;i<10;i++)
{cist[i]=0;hist[i]=0;}
printf("Enter the number of elements .\n");
scanf("%d",&N);
printf("Enter the number of Blocks and Threads .\n");
again:;
printf("Blocks:");
scanf("%d",&B);
printf("Threads:\n");
scanf("%d",&T);
if(B*T<N)
{printf("The number of blocks and threads is less please enter again.\n");
goto again;
}
cudaEvent_t start,stop;
float cput,gput;
cudaEventCreate(&start);
cudaEventCreate(&stop);
int size=N*sizeof(int);
a=(int*)malloc(size);
srand(1);
for(int i=0;i<N;i++)
a[i]=rand()%10;
cudaMalloc((void**)&deva,size);
cudaMemcpy(deva,a,size,cudaMemcpyHostToDevice);
cudaEventRecord(start,0);
gpuhistogram<<<B,T>>>(deva,N);
//cudaThreadSynchronize();
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&gput,start,stop);
cudaMemcpyFromSymbol(&hist,"gpu_hist",sizeof(hist),0,cudaMemcpyDeviceToHost);
printf("GPU execution completed.\n");
int l;
for (int i=0;i<10;i++)
{
cist[i]=0;
}
cudaEventRecord(start,0);
for(int i=0;i<N;i++)
{
l=a[i];
cist[l]++;
}
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&cput,start,stop);
for(int i=0;i<10;i++)
{printf("Number of %d's = gpu: %d cpu: %d \n",i,hist[i],cist[i]);
}
free(a);
cudaFree(deva);
printf("CPUtime= %f and GPUtime= %f.\n",cput,gput);
return 0;
/////////
}
|
10,692 | #include <algorithm>
#include <cstdlib>
#include <cstdio>
__global__ static void fill(int a[]) {
unsigned n = blockIdx.x * blockDim.x + threadIdx.x;
a[n] = n;
}
__global__ static void scan(int a[]) {
unsigned n = blockIdx.x * blockDim.x + threadIdx.x;
int sum = 0;
for(unsigned i = 0; i <= n; ++i) {
sum += a[i];
}
__syncthreads();
a[n] = sum;
}
static cudaError_t report_error(void) {
cudaError_t err = cudaGetLastError();
if(err != cudaSuccess) {
std::fprintf(stderr, "CUDA Error: %s\n", cudaGetErrorString(err));
std::abort();
}
return err;
}
int main(int argc, char *argv[]) {
unsigned device_id = 0;
if(argc >= 2 && std::sscanf(argv[1], "-d%u", &device_id) == 1) {
--argc; ++argv;
}
cudaSetDevice(device_id); report_error();
unsigned blk_size = 64;
if(argc >= 2) {
std::sscanf(argv[1], "%u", &blk_size);
blk_size = ((blk_size-1)/4+1)*4;
}
unsigned blk_cnt = 4;
if(argc >= 3) {
std::sscanf(argv[2], "%u", &blk_cnt);
}
unsigned times = 1;
if(argc >= 4) {
std::sscanf(argv[3], "%u", ×);
} else {
std::fprintf(stderr, "Block configuration: %u x %u (%u)\n", blk_size, blk_cnt, blk_size * blk_cnt);
}
int *a;
cudaMalloc(&a, blk_cnt * blk_size * sizeof (int)); report_error();
fill<<<blk_cnt, blk_size>>>(a); report_error();
cudaEvent_t start, end;
cudaEventCreate(&start); report_error();
cudaEventCreate(&end); report_error();
cudaEventRecord(start); report_error();
cudaEventSynchronize(start); report_error();
for(unsigned i = 0; i < times; ++i) {
scan<<<blk_cnt, blk_size>>>(a); report_error();
}
cudaEventRecord(end); report_error();
cudaEventSynchronize(end); report_error();
float elapsed;
cudaEventElapsedTime(&elapsed, start, end); report_error();
cudaEventDestroy(end); report_error();
cudaEventDestroy(start); report_error();
if(argc < 4) {
size_t n = std::min<size_t>(256, blk_size * blk_cnt);
int *h_a = new int[n];
cudaMemcpy(h_a, a, n * sizeof (int), cudaMemcpyDeviceToHost); report_error();
std::printf("[%d", h_a[0]);
for(size_t i = 1; i < n; ++i) {
std::printf(", %u", h_a[i]);
}
std::puts("]");
delete[] h_a;
} else {
std::printf("%.9g", elapsed * 0.001f / times);
}
cudaFree(a); report_error();
return 0;
}
|
10,693 | #include "includes.h"
__global__ void kern_Lbl(float* lbl, float* flo, float* cap, const int size)
{
int idx = CUDASTDOFFSET;
float value1 = cap[idx];
float value2 = flo[idx];
float minVal = (value2 == value1) ? 1.0f : 0.0f;
if( idx < size )
{
lbl[idx] = minVal;
}
} |
10,694 | #include <stdio.h>
#include <time.h>
typedef struct {
long width;
long height;
float *element;
} Matrix;
__global__ void sum(long N, const Matrix A, const Matrix B, Matrix C) {
int i = threadIdx.x;
if (i<N) {
C.element[i] = A.element[i]+B.element[i];
}
}
int main(void) {
long DIM_0 = 1<<10;
long DIM_1 = 1<<10;
// CUDA Performance Variables
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
// Declare initial parameters
long N = DIM_0*DIM_1;
size_t size = N*sizeof(float);
// Create Host variables
Matrix mA = {DIM_0, DIM_1};
Matrix mB = {DIM_0, DIM_1};
Matrix mC = {DIM_0, DIM_1};
// Create Device variables
Matrix d_mA = {DIM_0, DIM_1};
Matrix d_mB = {DIM_0, DIM_1};
Matrix d_mC = {DIM_0, DIM_1};
// Allocate memory for Host
mA.element = (float*)malloc(size);
mB.element = (float*)malloc(size);
mC.element = (float*)malloc(size);
// Set Host values
for (long i=0; i<(DIM_0*DIM_1); i++) {
mA.element[i] = 1;
mB.element[i] = 1;
}
// Allocate memory for Device
cudaMalloc(&d_mA.element, size);
cudaMalloc(&d_mB.element, size);
cudaMalloc(&d_mC.element, size);
// Set Device values
cudaMemcpy(d_mA.element, mA.element, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_mB.element, mB.element, size, cudaMemcpyHostToDevice);
// Send kernel to Device
dim3 threadsPerBlock(256);
dim3 blocksPerGrid((N-1+256)/255);
cudaEventRecord(start);
sum<<<blocksPerGrid, threadsPerBlock>>>(N, d_mA, d_mB, d_mC);
//sum<<<1, 4>>>(N, d_mA, d_mB, d_mC);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
float microseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
microseconds = milliseconds*1000;
// Retrieve Device variables
cudaMemcpy(mC.element, d_mC.element, size, cudaMemcpyDeviceToHost);
// Print results
printf("%f %f\n", mC.element[0], mC.element[1]);
printf("%f %f\n", mC.element[2], mC.element[3]);
printf("Time taken (microseconds): %f\n", microseconds);
free(mA.element);
free(mB.element);
free(mC.element);
cudaEventDestroy(start);
cudaEventDestroy(stop);
return 0;
}
|
10,695 | #include <stdio.h>
#include <stdlib.h>
#define DEBUG
#define RADIUS 2
#define BLOCK_SIZE 10
void checkCudaError(const char* filename, const int linenum)
{
#ifdef DEBUG
cudaThreadSynchronize();
cudaError_t error = cudaGetLastError();
if(error != cudaSuccess){
printf("File: %s, line: %d, CUDA Error: %s\n", filename, linenum, cudaGetErrorString(error));
exit(-1);
}
#endif
}
__global__ void cudaStencil1d(int* in, int* out)
{
__shared__ int tmp[BLOCK_SIZE+2*RADIUS];
int threadIndex = threadIdx.x + blockIdx.x*blockDim.x;
int arrayIndex = threadIdx.x + RADIUS;
tmp[arrayIndex] = in[threadIndex];
if(threadIdx.x < RADIUS){
if(threadIndex-RADIUS > 0)
tmp[arrayIndex-RADIUS] = in[threadIndex-RADIUS];
else
tmp[arrayIndex-RADIUS] = 0;
tmp[arrayIndex+BLOCK_SIZE] = in[threadIndex+BLOCK_SIZE];
}
__syncthreads();
int result = 0;
int offset;
for(offset = -RADIUS; offset <= RADIUS; ++offset)
result += tmp[arrayIndex+offset];
out[threadIndex] = result;
}
void stencil1d(int* in, int* out)
{
int* dev_in;
int* dev_out;
int nByte1 = sizeof(int)*(BLOCK_SIZE+2*RADIUS);
int nByte2 = sizeof(int)*(BLOCK_SIZE);
cudaError_t error = cudaMalloc((void**)&dev_in, nByte1);
if(error != cudaSuccess){
exit(-1);
}
error = cudaMalloc((void**)&dev_out, nByte2);
if(error != cudaSuccess){
exit(-1);
}
error = cudaMemcpy(dev_in, in, nByte1, cudaMemcpyHostToDevice);
if(error != cudaSuccess){
exit(-1);
}
cudaStencil1d<<<1, BLOCK_SIZE>>>(dev_in, dev_out);
checkCudaError(__FILE__, __LINE__);
error = cudaMemcpy(out, dev_out, nByte2, cudaMemcpyDeviceToHost);
if(error != cudaSuccess){
exit(-1);
}
cudaFree(dev_in);
cudaFree(dev_out);
}
void random_int(int* array, int N)
{
if(array){
for(int i = 0; i < N; ++i)
array[i] = rand()%10;
}
}
int main(void)
{
int nByte1 = sizeof(int)*(BLOCK_SIZE+2*RADIUS);
int nByte2 = sizeof(int)*(BLOCK_SIZE);
int* in = (int*)malloc(nByte1);
int* out = (int*)malloc(nByte2);
random_int(in, BLOCK_SIZE+2*RADIUS);
stencil1d(in, out);
for(int i = 0; i < 12; ++i)
printf("%d ", in[i]);
printf("\n");
for(int i = 0; i < 10; ++i)
printf("%d ", out[i]);
printf("\n");
free(in);
free(out);
return 0;
}
|
10,696 | #include <cstdio>
#include <climits>
#include <algorithm>
#define SERIAL_SCALE 3
#define SERIAL_PART (1<<SERIAL_SCALE)
extern "C" {
__global__
void kernelMain(int* input, int* output, int N)
{
//int thid = (blockIdx.x * blockDim.x) + threadIdx.x; // Globalny ID wątku (mało przydatne)
__shared__ int mem[SERIAL_PART * 1024];
for (int i = 0; i < SERIAL_PART; ++i) {
mem[blockDim.x * i + threadIdx.x] = input[N * blockIdx.y + blockDim.x * i + threadIdx.x];
}
__syncthreads();
for (int shift = 1; shift < N; shift *= 2) {
int v[SERIAL_PART];
for (int i = 0; i < SERIAL_PART; ++i) {
v[i] = mem[blockDim.x * i + threadIdx.x];
if (shift <= blockDim.x * i + threadIdx.x)
v[i] += mem[blockDim.x * i + threadIdx.x - shift];
}
__syncthreads();
for (int i = 0; i < SERIAL_PART; ++i) {
mem[blockDim.x * i + threadIdx.x] = v[i];
}
}
for (int i = 0; i < SERIAL_PART; ++i) {
output[(N + 1)*blockIdx.y + blockDim.x * i + threadIdx.x + 1] = mem[blockDim.x * i + threadIdx.x];
}
if (threadIdx.x == 0)
output[(N + 1)*blockIdx.y] = 0;
}
__global__
void findMax(int* prefixSumMatrix, int* output, int N)
{
int x = (blockIdx.x * blockDim.x) + threadIdx.x;
int y = (blockIdx.y * blockDim.y) + threadIdx.y + 1;
__shared__ int mem[1024];
int flatId = blockDim.x * threadIdx.y + threadIdx.x;
if (x >= y) {
mem[flatId] = 0;
} else {
int result = 0;
int current = 0;
for (int i = 0; i < N; ++i) {
current = current + prefixSumMatrix[(N + 1) * i + y] - prefixSumMatrix[(N + 1) * i + x];
if (current < 0)
current = 0;
if (current > result)
result = current;
}
mem[flatId] = result;
}
__syncthreads();
for (int shift = 1; shift < 1024; shift *= 2) {
int v = mem[flatId];
if (flatId >= shift)
if (v < mem[flatId - shift])
v = mem[flatId - shift];
__syncthreads();
mem[flatId] = v;
}
if (flatId == 1023) {
output[gridDim.x * blockIdx.y + blockIdx.x] = mem[flatId];
}
}
}
|
10,697 | #include <stdio.h>
#include <math.h>
#define N (2048*2048)
#define THREAD_PER_BLOCK 512
__global__ void tra( int *a, int *b) {
int i = blockIdx.x/4;
int j = (blockIdx.x%4) * blockDim.x + threadIdx.x;
b[i*2048+j] = a[j*2048+i];
}
void random_ints(int *p, int n) {
int i;
for(i=0; i<n; i++) {
p[i]=rand();
}
}
int main( void ) {
int *a, *b, *c; // host copies of a, b, c
int *dev_a, *dev_b; // device copies of a, b, c
int size = N * sizeof( int ); // we need space for N // integers
int i, j;
// allocate device copies of a, b
cudaMalloc( (void**)&dev_a, size );
cudaMalloc( (void**)&dev_b, size );
a = (int*)malloc( size );
b = (int*)malloc( size );
c = (int*)malloc( size );
random_ints( a, N );
random_ints( b, N );
// copy inputs to device
cudaMemcpy( dev_a, a, size, cudaMemcpyHostToDevice );
cudaMemcpy( dev_b, b, size, cudaMemcpyHostToDevice );
// launch an rev() kernel with N threads
tra<<< N/THREAD_PER_BLOCK, THREAD_PER_BLOCK >>>( dev_a, dev_b);
// copy device result back to host copy of c
cudaMemcpy( b, dev_b, size, cudaMemcpyDeviceToHost );
for(i=0; i<2048; i++) {
for(j=0; j<2048; j++) {
c[i*2048+j] = a[j*2048+i];
if(b[i*2048+j]!=c[i*2048+j]) {
printf("error: expected %d, got %d!\n",c[i*2048+j], b[i*2048+j]);
break;
}
}
}
if(i==N) {printf("correct!\n");}
free( a ); free( b ); free( c );
cudaFree( dev_a );
cudaFree( dev_b );
return 0;
} |
10,698 | #include <iostream>
#include <math.h>
#include <cstdio>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
/*
to be compiled via nvcc ==> nvcc main.cu -o exec
*/
// CUDA Kernel function to add the elements of two arrays on the GPU
bool CUDA_ = false;
// __device__ indicates funtion to be executed by the gpu
__global__
void mykernel(void) {
printf( "hello from CUDA \n ==> saying hello from GPU \n"); // dunno why ==> works only with printf
}
__global__
void
summer_kernel(int* a )
{
*a = *a +1;
}
__global__
void add_tut
(int *a, int *b, int *c)
{
*c = *a + *b;
}
// __global__ functions must be void, are meant ot be called by device(GPU)
__global__
void
add(int N, int *x, int *y, int *add_result)
{
for (int i =0; i< N; ++i)
{
add_result[i] = x[i] + y[i];
}
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i < N) {
add_result[i] = x[i] + y[i];
}
}
/*
__global__
add_smart
{
c[blockIdx.x] = a[blockIdx.x] + b[blockIdx.x];
}
*/
int main(void)
{
int print_N_time_in_parallel = 12;
mykernel<<<print_N_time_in_parallel,1>>>(); // function invoked on GPU
// executing same operation on device:
int execute_N_time_in_parallel = 12;
int a_host;
a_host = 0;
int *a_device;
//(1) alloc memory on GPU
cudaMalloc((void **)&a_device, sizeof(int));
//(2) copy value(s) into GPU variable
cudaMemcpy(a_device, &a_host,sizeof(int),cudaMemcpyHostToDevice);
//(3) execute command
summer_kernel <<< execute_N_time_in_parallel,1 >>>(a_device);
// (4) copy back into original value
int a_result;
cudaMemcpy(&a_result,a_device, sizeof(int), cudaMemcpyDeviceToHost);// returns 1 but execute it 12 times in parallel
std::cout << "EOC, sigle value ==> a_host = "<< a_result <<std::endl;
/*
summing 2 vector on device
*/
const int N = 10;
// host arrays
int x[N] = { 1, 2, 3, 4, 5 };
int y[N] = { 10, 20, 30, 40, 50 };
int z[N] = {0};
// device copies
int size_f = sizeof(int);
int *d_x = 0;
int *d_y = 0;
int *d_z = 0;
// vectors alllocation on GPU
cudaMalloc((void **)&d_x, size_f*N);
cudaMalloc((void **)&d_y, size_f*N);
cudaMalloc((void **)&d_z, size_f*N);
// copy values, this operation maps device-values and host-values
cudaMemcpy(d_x, x, size_f*N, cudaMemcpyHostToDevice);
cudaMemcpy(d_y, y, size_f*N, cudaMemcpyHostToDevice);
//Launch add() kernel on GPU
/*
Launch a kernel on the GPU with one thread for each element.
2 is number of computational blocks and (N + 1) / 2 is a number of threads in a block
*/
add<<<2, (N + 1) / 2>>>(N, d_x, d_y, d_z);
/* cudaDeviceSynchronize waits for the kernel to finish, and returns
any errors encountered during the launch.*/
cudaDeviceSynchronize();
cudaMemcpy(z, d_z, size_f*N, cudaMemcpyDeviceToHost);
printf("{1, 2, 3, 4, 5} + {10, 20, 30, 40, 50} = {%d, %d, %d, %d, %d}\n", z[0], z[1], z[2], z[3], z[4]);
int a, b, c;
int *d_a, *d_b, *d_c;
int size = sizeof(int);
cudaMalloc((void **)&d_a, size);
cudaMalloc((void **)&d_b, size);
cudaMalloc((void **)&d_c, size);
a = 22;
b = 44;
cudaMemcpy(d_a, &a, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, &b, size, cudaMemcpyHostToDevice);
add_tut<<<execute_N_time_in_parallel,1>>>(d_a, d_b, d_c);
cudaMemcpy(&c, d_c, size, cudaMemcpyDeviceToHost);
std::cout << "tutorial==> sum result is \n c = "<< c << std::endl;
/* following line fails ahhahhahha
===>std::cout << "tutorial==> sum result is \n *d_c = "<< *d_c << std::endl; <=== */
// Free memory
//delete [] d_x;
//delete [] d_y;
cudaDeviceReset();
cudaFree(d_x);
cudaFree(d_y);
cudaFree(d_a);
cudaFree(d_b);
return 0;
} |
10,699 | #include "includes.h"
__global__ void applyNormSum(double *dMap,double *dSupFeature, double *dMaxSupFeature, double *dMeanSupFeature, double *dInfFeature, double *dMaxInfFeature, double *dMeanInfFeature, int dSize){
int tid = threadIdx.x + blockIdx.x * blockDim.x;
double SupCoeff = (dMaxSupFeature[0] - dMeanSupFeature[0])*(dMaxSupFeature[0] - dMeanSupFeature[0]);
double InfCoeff = (dMaxInfFeature[0] - dMeanInfFeature[0])*(dMaxInfFeature[0] - dMeanInfFeature[0]);
while (tid < dSize) {
dMap[tid] += dSupFeature[tid]*SupCoeff + dInfFeature[tid]*InfCoeff;
tid += blockDim.x * gridDim.x;
}
} |
10,700 | //Adding vectors in parralel
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define N (2048*2048) //Vectors of length 4 million
#define THREADS_PER_BLOCK 512
void generateRand(int *a);
__global__ void addVectCuda(int *da, int *db, int*dc){
//If threads are used, must index at threadIdx.x
//For fully indexing threads and block index is calculated by:
//index = threadIdx.x + blockIdx.x*(Num Blocks)
//Use blockDim.x for the threads per block
int index = threadIdx.x + blockIdx.x*(blockDim.x);
dc[index] = da[index] + db[index];
printf("i=%d a=%d b=%d c=%d\n", index,da[index], db[index], dc[index]);
}
int main(){
int *a, *b, *c;
int *da, *db, *dc;
int size = N*sizeof(int);
a = (int *)malloc(size);
b = (int *)malloc(size);
c = (int *)malloc(size);
generateRand(a);
generateRand(b);
//Allocate CUDA memory
cudaMalloc((void **)&da, size);
cudaMalloc((void **)&db, size);
cudaMalloc((void **)&dc, size);
//Copy inputs to CUDA memory
cudaMemcpy(da, a, size, cudaMemcpyHostToDevice);
cudaMemcpy(db, b, size, cudaMemcpyHostToDevice);
//X index controls the number of blocks, Y index controls the number of threads
addVectCuda<<<(N + THREADS_PER_BLOCK-1)/THREADS_PER_BLOCK,THREADS_PER_BLOCK>>>(da, db, dc);
cudaMemcpy(c, dc, size, cudaMemcpyDeviceToHost);
//int i = 0;
//for(i; i < N; i++){
// printf("i=%d a=%d b=%d c=%d\n", i,a[i], b[i], c[i]);
//}
free(a);
free(b);
free(c);
cudaFree(da);
cudaFree(db);
cudaFree(dc);
return 0;
}
void generateRand(int *a){
int i = 0;
for(i; i < N; i++){
*(a+i) = rand()%100 + 1;
}
}
|
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