cuda-samples/Samples/conjugateGradientMultiDeviceCG/conjugateGradientMultiDeviceCG.cu

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/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of NVIDIA CORPORATION nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/*
* This sample implements a conjugate gradient solver on multiple GPU using
* Multi Device Cooperative Groups, also uses Unified Memory optimized using
* prefetching and usage hints.
*
*/
// includes, system
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <map>
#include <iostream>
#include <set>
#include <utility>
#include <cuda_runtime.h>
// Utilities and system includes
#include <helper_cuda.h> // helper function CUDA error checking and initialization
#include <helper_functions.h> // helper for shared functions common to CUDA Samples
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
namespace cg = cooperative_groups;
const char *sSDKname = "conjugateGradientMultiDeviceCG";
#define ENABLE_CPU_DEBUG_CODE 0
#define THREADS_PER_BLOCK 512
__device__ double grid_dot_result = 0.0;
/* genTridiag: generate a random tridiagonal symmetric matrix */
void genTridiag(int *I, int *J, float *val, int N, int nz) {
I[0] = 0, J[0] = 0, J[1] = 1;
val[0] = static_cast<float>(rand()) / RAND_MAX + 10.0f;
val[1] = static_cast<float>(rand()) / RAND_MAX;
int start;
for (int i = 1; i < N; i++) {
if (i > 1) {
I[i] = I[i - 1] + 3;
} else {
I[1] = 2;
}
start = (i - 1) * 3 + 2;
J[start] = i - 1;
J[start + 1] = i;
if (i < N - 1) {
J[start + 2] = i + 1;
}
val[start] = val[start - 1];
val[start + 1] = static_cast<float>(rand()) / RAND_MAX + 10.0f;
if (i < N - 1) {
val[start + 2] = static_cast<float>(rand()) / RAND_MAX;
}
}
I[N] = nz;
}
// I - contains location of the given non-zero element in the row of the matrix
// J - contains location of the given non-zero element in the column of the
// matrix val - contains values of the given non-zero elements of the matrix
// inputVecX - input vector to be multiplied
// outputVecY - resultant vector
void cpuSpMV(int *I, int *J, float *val, int nnz, int num_rows, float alpha,
float *inputVecX, float *outputVecY) {
for (int i = 0; i < num_rows; i++) {
int num_elems_this_row = I[i + 1] - I[i];
float output = 0.0;
for (int j = 0; j < num_elems_this_row; j++) {
output += alpha * val[I[i] + j] * inputVecX[J[I[i] + j]];
}
outputVecY[i] = output;
}
return;
}
double dotProduct(float *vecA, float *vecB, int size) {
double result = 0.0;
for (int i = 0; i < size; i++) {
result = result + (vecA[i] * vecB[i]);
}
return result;
}
void scaleVector(float *vec, float alpha, int size) {
for (int i = 0; i < size; i++) {
vec[i] = alpha * vec[i];
}
}
void saxpy(float *x, float *y, float a, int size) {
for (int i = 0; i < size; i++) {
y[i] = a * x[i] + y[i];
}
}
void cpuConjugateGrad(int *I, int *J, float *val, float *x, float *Ax, float *p,
float *r, int nnz, int N, float tol) {
int max_iter = 10000;
float alpha = 1.0;
float alpham1 = -1.0;
float r0 = 0.0, b, a, na;
cpuSpMV(I, J, val, nnz, N, alpha, x, Ax);
saxpy(Ax, r, alpham1, N);
float r1 = dotProduct(r, r, N);
int k = 1;
while (r1 > tol * tol && k <= max_iter) {
if (k > 1) {
b = r1 / r0;
scaleVector(p, b, N);
saxpy(r, p, alpha, N);
} else {
for (int i = 0; i < N; i++) p[i] = r[i];
}
cpuSpMV(I, J, val, nnz, N, alpha, p, Ax);
float dot = dotProduct(p, Ax, N);
a = r1 / dot;
saxpy(p, x, a, N);
na = -a;
saxpy(Ax, r, na, N);
r0 = r1;
r1 = dotProduct(r, r, N);
printf("\nCPU code iteration = %3d, residual = %e\n", k, sqrt(r1));
k++;
}
}
__device__ void gpuSpMV(int *I, int *J, float *val, int nnz, int num_rows,
float alpha, float *inputVecX, float *outputVecY,
cg::thread_block &cta,
const cg::multi_grid_group &multi_grid) {
for (int i = multi_grid.thread_rank(); i < num_rows; i += multi_grid.size()) {
int row_elem = I[i];
int next_row_elem = I[i + 1];
int num_elems_this_row = next_row_elem - row_elem;
float output = 0.0;
for (int j = 0; j < num_elems_this_row; j++) {
output += alpha * val[row_elem + j] * inputVecX[J[row_elem + j]];
}
outputVecY[i] = output;
}
}
__device__ void gpuSaxpy(float *x, float *y, float a, int size,
const cg::multi_grid_group &multi_grid) {
for (int i = multi_grid.thread_rank(); i < size; i += multi_grid.size()) {
y[i] = a * x[i] + y[i];
}
}
__device__ void gpuDotProduct(float *vecA, float *vecB, int size,
const cg::thread_block &cta,
const cg::multi_grid_group &multi_grid) {
extern __shared__ double tmp[];
double temp_sum = 0.0;
for (int i = multi_grid.thread_rank(); i < size; i += multi_grid.size()) {
temp_sum += static_cast<double>(vecA[i] * vecB[i]);
}
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0) {
tmp[tile32.meta_group_rank()] = temp_sum;
}
cg::sync(cta);
if (tile32.meta_group_rank() == 0) {
temp_sum = tile32.thread_rank() < tile32.meta_group_size()
? tmp[tile32.thread_rank()]
: 0.0;
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0) {
atomicAdd(&grid_dot_result, temp_sum);
}
}
}
__device__ void gpuCopyVector(float *srcA, float *destB, int size,
const cg::multi_grid_group &multi_grid) {
for (int i = multi_grid.thread_rank(); i < size; i += multi_grid.size()) {
destB[i] = srcA[i];
}
}
__device__ void gpuScaleVectorAndSaxpy(float *x, float *y, float a, float scale,
int size,
const cg::multi_grid_group &multi_grid) {
for (int i = multi_grid.thread_rank(); i < size; i += multi_grid.size()) {
y[i] = a * x[i] + scale * y[i];
}
}
extern "C" __global__ void multiGpuConjugateGradient(
int *I, int *J, float *val, float *x, float *Ax, float *p, float *r,
double *dot_result, int nnz, int N, float tol) {
cg::thread_block cta = cg::this_thread_block();
cg::grid_group grid = cg::this_grid();
cg::multi_grid_group multi_grid = cg::this_multi_grid();
const int max_iter = 10000;
float alpha = 1.0;
float alpham1 = -1.0;
float r0 = 0.0, r1, b, a, na;
for (int i = multi_grid.thread_rank(); i < N; i += multi_grid.size()) {
r[i] = 1.0;
x[i] = 0.0;
}
cg::sync(grid);
gpuSpMV(I, J, val, nnz, N, alpha, x, Ax, cta, multi_grid);
cg::sync(grid);
gpuSaxpy(Ax, r, alpham1, N, multi_grid);
cg::sync(grid);
gpuDotProduct(r, r, N, cta, multi_grid);
cg::sync(grid);
if (grid.thread_rank() == 0) {
atomicAdd_system(dot_result, grid_dot_result);
grid_dot_result = 0.0;
}
cg::sync(multi_grid);
r1 = *dot_result;
int k = 1;
while (r1 > tol * tol && k <= max_iter) {
if (k > 1) {
b = r1 / r0;
gpuScaleVectorAndSaxpy(r, p, alpha, b, N, multi_grid);
} else {
gpuCopyVector(r, p, N, multi_grid);
}
cg::sync(multi_grid);
gpuSpMV(I, J, val, nnz, N, alpha, p, Ax, cta, multi_grid);
if (multi_grid.thread_rank() == 0) {
*dot_result = 0.0;
}
cg::sync(multi_grid);
gpuDotProduct(p, Ax, N, cta, multi_grid);
cg::sync(grid);
if (grid.thread_rank() == 0) {
atomicAdd_system(dot_result, grid_dot_result);
grid_dot_result = 0.0;
}
cg::sync(multi_grid);
a = r1 / *dot_result;
gpuSaxpy(p, x, a, N, multi_grid);
na = -a;
gpuSaxpy(Ax, r, na, N, multi_grid);
r0 = r1;
cg::sync(multi_grid);
if (multi_grid.thread_rank() == 0) {
*dot_result = 0.0;
}
cg::sync(multi_grid);
gpuDotProduct(r, r, N, cta, multi_grid);
cg::sync(grid);
if (grid.thread_rank() == 0) {
atomicAdd_system(dot_result, grid_dot_result);
grid_dot_result = 0.0;
}
cg::sync(multi_grid);
r1 = *dot_result;
k++;
}
}
// Map of device version to device number
std::multimap<std::pair<int, int>, int> getIdenticalGPUs() {
int numGpus = 0;
checkCudaErrors(cudaGetDeviceCount(&numGpus));
std::multimap<std::pair<int, int>, int> identicalGpus;
for (int i = 0; i < numGpus; i++) {
cudaDeviceProp deviceProp;
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, i));
// Filter unsupported devices
if (deviceProp.cooperativeMultiDeviceLaunch &&
deviceProp.concurrentManagedAccess) {
identicalGpus.emplace(std::make_pair(deviceProp.major, deviceProp.minor),
i);
}
printf("GPU Device %d: \"%s\" with compute capability %d.%d\n", i,
deviceProp.name, deviceProp.major, deviceProp.minor);
}
return identicalGpus;
}
int main(int argc, char **argv) {
constexpr size_t kNumGpusRequired = 2;
int N = 0, nz = 0, *I = NULL, *J = NULL;
float *val = NULL;
const float tol = 1e-5f;
float *x;
float rhs = 1.0;
float r1;
float *r, *p, *Ax;
printf("Starting [%s]...\n", sSDKname);
auto gpusByArch = getIdenticalGPUs();
auto it = gpusByArch.begin();
auto end = gpusByArch.end();
auto bestFit = std::make_pair(it, it);
// use std::distance to find the largest number of GPUs amongst architectures
auto distance = [](decltype(bestFit) p) {
return std::distance(p.first, p.second);
};
// Read each unique key/pair element in order
for (; it != end; it = gpusByArch.upper_bound(it->first)) {
// first and second are iterators bounded within the architecture group
auto testFit = gpusByArch.equal_range(it->first);
// Always use devices with highest architecture version or whichever has the
// most devices available
if (distance(bestFit) <= distance(testFit)) bestFit = testFit;
}
if (distance(bestFit) < kNumGpusRequired) {
printf(
"No Two or more GPUs with same architecture capable of "
"cooperativeMultiDeviceLaunch & concurrentManagedAccess found. "
"\nWaiving the sample\n");
exit(EXIT_WAIVED);
}
std::set<int> bestFitDeviceIds;
// check & select peer-to-peer access capable GPU devices as enabling p2p
// access between participating
// GPUs gives better performance for multi_grid sync.
for (auto itr = bestFit.first; itr != bestFit.second; itr++) {
int deviceId = itr->second;
checkCudaErrors(cudaSetDevice(deviceId));
std::for_each(itr, bestFit.second, [&deviceId, &bestFitDeviceIds,
&kNumGpusRequired](
decltype(*itr) mapPair) {
if (deviceId != mapPair.second) {
int access = 0;
checkCudaErrors(
cudaDeviceCanAccessPeer(&access, deviceId, mapPair.second));
printf("Device=%d %s Access Peer Device=%d\n", deviceId,
access ? "CAN" : "CANNOT", mapPair.second);
if (access && bestFitDeviceIds.size() < kNumGpusRequired) {
bestFitDeviceIds.emplace(deviceId);
bestFitDeviceIds.emplace(mapPair.second);
} else {
printf("Ignoring device %i (max devices exceeded)\n", mapPair.second);
}
}
});
if (bestFitDeviceIds.size() >= kNumGpusRequired) {
printf("Selected p2p capable devices - ");
for (auto devicesItr = bestFitDeviceIds.begin();
devicesItr != bestFitDeviceIds.end(); devicesItr++) {
printf("deviceId = %d ", *devicesItr);
}
printf("\n");
break;
}
}
// if bestFitDeviceIds.size() == 0 it means the GPUs in system are not p2p
// capable,
// hence we add it without p2p capability check.
if (!bestFitDeviceIds.size()) {
printf("Devices involved are not p2p capable.. selecting %zu of them\n",
kNumGpusRequired);
std::for_each(bestFit.first, bestFit.second,
[&bestFitDeviceIds,
&kNumGpusRequired](decltype(*bestFit.first) mapPair) {
if (bestFitDeviceIds.size() < kNumGpusRequired) {
bestFitDeviceIds.emplace(mapPair.second);
} else {
printf("Ignoring device %i (max devices exceeded)\n",
mapPair.second);
}
// Insert the sequence into the deviceIds set
});
} else {
// perform cudaDeviceEnablePeerAccess in both directions for all
// participating devices of a cudaLaunchCooperativeKernelMultiDevice call
// this gives better performance for multi_grid sync.
for (auto p1_itr = bestFitDeviceIds.begin();
p1_itr != bestFitDeviceIds.end(); p1_itr++) {
checkCudaErrors(cudaSetDevice(*p1_itr));
for (auto p2_itr = bestFitDeviceIds.begin();
p2_itr != bestFitDeviceIds.end(); p2_itr++) {
if (*p1_itr != *p2_itr) {
checkCudaErrors(cudaDeviceEnablePeerAccess(*p2_itr, 0));
checkCudaErrors(cudaSetDevice(*p1_itr));
}
}
}
}
/* Generate a random tridiagonal symmetric matrix in CSR format */
N = 10485760 * 2;
nz = (N - 2) * 3 + 4;
checkCudaErrors(
cudaMallocManaged(reinterpret_cast<void **>(&I), sizeof(int) * (N + 1)));
checkCudaErrors(
cudaMallocManaged(reinterpret_cast<void **>(&J), sizeof(int) * nz));
checkCudaErrors(
cudaMallocManaged(reinterpret_cast<void **>(&val), sizeof(float) * nz));
float *val_cpu = reinterpret_cast<float *>(malloc(sizeof(float) * nz));
genTridiag(I, J, val_cpu, N, nz);
memcpy(val, val_cpu, sizeof(float) * nz);
checkCudaErrors(
cudaMemAdvise(I, sizeof(int) * (N + 1), cudaMemAdviseSetReadMostly, 0));
checkCudaErrors(
cudaMemAdvise(J, sizeof(int) * nz, cudaMemAdviseSetReadMostly, 0));
checkCudaErrors(
cudaMemAdvise(val, sizeof(float) * nz, cudaMemAdviseSetReadMostly, 0));
checkCudaErrors(
cudaMallocManaged(reinterpret_cast<void **>(&x), sizeof(float) * N));
double *dot_result;
checkCudaErrors(cudaMallocManaged(reinterpret_cast<void **>(&dot_result),
sizeof(double)));
checkCudaErrors(cudaMemset(dot_result, 0.0, sizeof(double)));
// temp memory for ConjugateGradient
checkCudaErrors(
cudaMallocManaged(reinterpret_cast<void **>(&r), N * sizeof(float)));
checkCudaErrors(
cudaMallocManaged(reinterpret_cast<void **>(&p), N * sizeof(float)));
checkCudaErrors(
cudaMallocManaged(reinterpret_cast<void **>(&Ax), N * sizeof(float)));
std::cout << "\nRunning on GPUs = " << kNumGpusRequired << std::endl;
cudaStream_t nStreams[kNumGpusRequired];
int sMemSize = sizeof(double) * ((THREADS_PER_BLOCK / 32) + 1);
int numBlocksPerSm = INT_MAX;
int numThreads = THREADS_PER_BLOCK;
int numSms = INT_MAX;
auto deviceId = bestFitDeviceIds.begin();
// set numSms & numBlocksPerSm to be lowest of 2 devices
while (deviceId != bestFitDeviceIds.end()) {
cudaDeviceProp deviceProp;
checkCudaErrors(cudaSetDevice(*deviceId));
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, *deviceId));
int numBlocksPerSm_current = 0;
checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&numBlocksPerSm_current, multiGpuConjugateGradient, numThreads,
sMemSize));
if (numBlocksPerSm > numBlocksPerSm_current) {
numBlocksPerSm = numBlocksPerSm_current;
}
if (numSms > deviceProp.multiProcessorCount) {
numSms = deviceProp.multiProcessorCount;
}
deviceId++;
}
if (!numBlocksPerSm) {
printf(
"Max active blocks per SM is returned as 0.\n Hence, Waiving the "
"sample\n");
exit(EXIT_WAIVED);
}
int device_count = 0;
int totalThreadsPerGPU = numSms * numBlocksPerSm * THREADS_PER_BLOCK;
deviceId = bestFitDeviceIds.begin();
while (deviceId != bestFitDeviceIds.end()) {
checkCudaErrors(cudaSetDevice(*deviceId));
checkCudaErrors(cudaStreamCreate(&nStreams[device_count]));
int perGPUIter = N / (totalThreadsPerGPU * kNumGpusRequired);
int offset_Ax = device_count * totalThreadsPerGPU;
int offset_r = device_count * totalThreadsPerGPU;
int offset_p = device_count * totalThreadsPerGPU;
int offset_x = device_count * totalThreadsPerGPU;
checkCudaErrors(cudaMemPrefetchAsync(I, sizeof(int) * N, *deviceId,
nStreams[device_count]));
checkCudaErrors(cudaMemPrefetchAsync(val, sizeof(float) * nz, *deviceId,
nStreams[device_count]));
checkCudaErrors(cudaMemPrefetchAsync(J, sizeof(float) * nz, *deviceId,
nStreams[device_count]));
if (offset_Ax <= N) {
for (int i = 0; i < perGPUIter; i++) {
cudaMemAdvise(Ax + offset_Ax, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(r + offset_r, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(x + offset_x, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(p + offset_p, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetPreferredLocation, *deviceId);
cudaMemAdvise(Ax + offset_Ax, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
cudaMemAdvise(r + offset_r, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
cudaMemAdvise(p + offset_p, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
cudaMemAdvise(x + offset_x, sizeof(float) * totalThreadsPerGPU,
cudaMemAdviseSetAccessedBy, *deviceId);
offset_Ax += totalThreadsPerGPU * kNumGpusRequired;
offset_r += totalThreadsPerGPU * kNumGpusRequired;
offset_p += totalThreadsPerGPU * kNumGpusRequired;
offset_x += totalThreadsPerGPU * kNumGpusRequired;
if (offset_Ax >= N) {
break;
}
}
}
device_count++;
deviceId++;
}
#if ENABLE_CPU_DEBUG_CODE
float *Ax_cpu = reinterpret_cast<float *>(malloc(sizeof(float) * N));
float *r_cpu = reinterpret_cast<float *>(malloc(sizeof(float) * N));
float *p_cpu = reinterpret_cast<float *>(malloc(sizeof(float) * N));
float *x_cpu = reinterpret_cast<float *>(malloc(sizeof(float) * N));
for (int i = 0; i < N; i++) {
r_cpu[i] = 1.0;
Ax_cpu[i] = x_cpu[i] = 0.0;
}
#endif
printf("Total threads per GPU = %d numBlocksPerSm = %d\n",
numSms * numBlocksPerSm * THREADS_PER_BLOCK, numBlocksPerSm);
dim3 dimGrid(numSms * numBlocksPerSm, 1, 1),
dimBlock(THREADS_PER_BLOCK, 1, 1);
void *kernelArgs[] = {
(void *)&I, (void *)&J, (void *)&val, (void *)&x,
(void *)&Ax, (void *)&p, (void *)&r, (void *)&dot_result,
(void *)&nz, (void *)&N, (void *)&tol,
};
cudaLaunchParams *launchParamsList =
(cudaLaunchParams *)malloc(sizeof(cudaLaunchParams) * kNumGpusRequired);
for (int i = 0; i < kNumGpusRequired; i++) {
launchParamsList[i].func = (void *)multiGpuConjugateGradient;
launchParamsList[i].gridDim = dimGrid;
launchParamsList[i].blockDim = dimBlock;
launchParamsList[i].sharedMem = sMemSize;
launchParamsList[i].stream = nStreams[i];
launchParamsList[i].args = kernelArgs;
}
printf("Launching kernel\n");
checkCudaErrors(cudaLaunchCooperativeKernelMultiDevice(
launchParamsList, kNumGpusRequired,
cudaCooperativeLaunchMultiDeviceNoPreSync |
cudaCooperativeLaunchMultiDeviceNoPostSync));
checkCudaErrors(cudaMemPrefetchAsync(x, sizeof(float) * N, cudaCpuDeviceId));
checkCudaErrors(
cudaMemPrefetchAsync(dot_result, sizeof(double), cudaCpuDeviceId));
deviceId = bestFitDeviceIds.begin();
device_count = 0;
while (deviceId != bestFitDeviceIds.end()) {
checkCudaErrors(cudaSetDevice(*deviceId));
checkCudaErrors(cudaStreamSynchronize(nStreams[device_count++]));
deviceId++;
}
r1 = (float)*dot_result;
printf("GPU Final, residual = %e \n ", sqrt(r1));
#if ENABLE_CPU_DEBUG_CODE
cpuConjugateGrad(I, J, val, x_cpu, Ax_cpu, p_cpu, r_cpu, nz, N, tol);
#endif
float rsum, diff, err = 0.0;
for (int i = 0; i < N; i++) {
rsum = 0.0;
for (int j = I[i]; j < I[i + 1]; j++) {
rsum += val_cpu[j] * x[J[j]];
}
diff = fabs(rsum - rhs);
if (diff > err) {
err = diff;
}
}
checkCudaErrors(cudaFree(I));
checkCudaErrors(cudaFree(J));
checkCudaErrors(cudaFree(val));
checkCudaErrors(cudaFree(x));
checkCudaErrors(cudaFree(r));
checkCudaErrors(cudaFree(p));
checkCudaErrors(cudaFree(Ax));
checkCudaErrors(cudaFree(dot_result));
free(val_cpu);
#if ENABLE_CPU_DEBUG_CODE
free(Ax_cpu);
free(r_cpu);
free(p_cpu);
free(x_cpu);
#endif
printf("Test Summary: Error amount = %f \n", err);
fprintf(stdout, "&&&& conjugateGradientMultiDeviceCG %s\n",
(sqrt(r1) < tol) ? "PASSED" : "FAILED");
exit((sqrt(r1) < tol) ? EXIT_SUCCESS : EXIT_FAILURE);
}