mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-11-24 18:49:18 +08:00
715 lines
22 KiB
Plaintext
715 lines
22 KiB
Plaintext
/* 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);
|
|
}
|