cuda-samples/Samples/0_Introduction/simpleMultiGPU/simpleMultiGPU.cu

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2022-01-13 14:05:24 +08:00
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
2021-10-21 19:04:49 +08:00
*
* 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 application demonstrates how to use the CUDA API to use multiple GPUs,
* with an emphasis on simple illustration of the techniques (not on
* performance).
*
* Note that in order to detect multiple GPUs in your system you have to disable
* SLI in the nvidia control panel. Otherwise only one GPU is visible to the
* application. On the other side, you can still extend your desktop to screens
* attached to both GPUs.
*/
// System includes
#include <stdio.h>
#include <assert.h>
// CUDA runtime
#include <cuda_runtime.h>
// helper functions and utilities to work with CUDA
#include <helper_functions.h>
#include <helper_cuda.h>
#ifndef MAX
#define MAX(a, b) (a > b ? a : b)
#endif
#include "simpleMultiGPU.h"
////////////////////////////////////////////////////////////////////////////////
// Data configuration
////////////////////////////////////////////////////////////////////////////////
const int MAX_GPU_COUNT = 32;
const int DATA_N = 1048576 * 32;
////////////////////////////////////////////////////////////////////////////////
// Simple reduction kernel.
// Refer to the 'reduction' CUDA Sample describing
// reduction optimization strategies
////////////////////////////////////////////////////////////////////////////////
__global__ static void reduceKernel(float *d_Result, float *d_Input, int N) {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
const int threadN = gridDim.x * blockDim.x;
float sum = 0;
for (int pos = tid; pos < N; pos += threadN) sum += d_Input[pos];
d_Result[tid] = sum;
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
// Solver config
TGPUplan plan[MAX_GPU_COUNT];
// GPU reduction results
float h_SumGPU[MAX_GPU_COUNT];
float sumGPU;
double sumCPU, diff;
int i, j, gpuBase, GPU_N;
const int BLOCK_N = 32;
const int THREAD_N = 256;
const int ACCUM_N = BLOCK_N * THREAD_N;
printf("Starting simpleMultiGPU\n");
checkCudaErrors(cudaGetDeviceCount(&GPU_N));
if (GPU_N > MAX_GPU_COUNT) {
GPU_N = MAX_GPU_COUNT;
}
printf("CUDA-capable device count: %i\n", GPU_N);
printf("Generating input data...\n\n");
// Subdividing input data across GPUs
// Get data sizes for each GPU
for (i = 0; i < GPU_N; i++) {
plan[i].dataN = DATA_N / GPU_N;
}
// Take into account "odd" data sizes
for (i = 0; i < DATA_N % GPU_N; i++) {
plan[i].dataN++;
}
// Assign data ranges to GPUs
gpuBase = 0;
for (i = 0; i < GPU_N; i++) {
plan[i].h_Sum = h_SumGPU + i;
gpuBase += plan[i].dataN;
}
// Create streams for issuing GPU command asynchronously and allocate memory
// (GPU and System page-locked)
for (i = 0; i < GPU_N; i++) {
checkCudaErrors(cudaSetDevice(i));
checkCudaErrors(cudaStreamCreate(&plan[i].stream));
// Allocate memory
checkCudaErrors(
cudaMalloc((void **)&plan[i].d_Data, plan[i].dataN * sizeof(float)));
checkCudaErrors(
cudaMalloc((void **)&plan[i].d_Sum, ACCUM_N * sizeof(float)));
checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Sum_from_device,
ACCUM_N * sizeof(float)));
checkCudaErrors(cudaMallocHost((void **)&plan[i].h_Data,
plan[i].dataN * sizeof(float)));
for (j = 0; j < plan[i].dataN; j++) {
plan[i].h_Data[j] = (float)rand() / (float)RAND_MAX;
}
}
// Start timing and compute on GPU(s)
printf("Computing with %d GPUs...\n", GPU_N);
// create and start timer
StopWatchInterface *timer = NULL;
sdkCreateTimer(&timer);
// start the timer
sdkStartTimer(&timer);
// Copy data to GPU, launch the kernel and copy data back. All asynchronously
for (i = 0; i < GPU_N; i++) {
// Set device
checkCudaErrors(cudaSetDevice(i));
// Copy input data from CPU
checkCudaErrors(cudaMemcpyAsync(plan[i].d_Data, plan[i].h_Data,
plan[i].dataN * sizeof(float),
cudaMemcpyHostToDevice, plan[i].stream));
// Perform GPU computations
reduceKernel<<<BLOCK_N, THREAD_N, 0, plan[i].stream>>>(
plan[i].d_Sum, plan[i].d_Data, plan[i].dataN);
getLastCudaError("reduceKernel() execution failed.\n");
// Read back GPU results
checkCudaErrors(cudaMemcpyAsync(plan[i].h_Sum_from_device, plan[i].d_Sum,
ACCUM_N * sizeof(float),
cudaMemcpyDeviceToHost, plan[i].stream));
}
// Process GPU results
for (i = 0; i < GPU_N; i++) {
float sum;
// Set device
checkCudaErrors(cudaSetDevice(i));
// Wait for all operations to finish
cudaStreamSynchronize(plan[i].stream);
// Finalize GPU reduction for current subvector
sum = 0;
for (j = 0; j < ACCUM_N; j++) {
sum += plan[i].h_Sum_from_device[j];
}
*(plan[i].h_Sum) = (float)sum;
// Shut down this GPU
checkCudaErrors(cudaFreeHost(plan[i].h_Sum_from_device));
checkCudaErrors(cudaFree(plan[i].d_Sum));
checkCudaErrors(cudaFree(plan[i].d_Data));
checkCudaErrors(cudaStreamDestroy(plan[i].stream));
}
sumGPU = 0;
for (i = 0; i < GPU_N; i++) {
sumGPU += h_SumGPU[i];
}
sdkStopTimer(&timer);
printf(" GPU Processing time: %f (ms)\n\n", sdkGetTimerValue(&timer));
sdkDeleteTimer(&timer);
// Compute on Host CPU
printf("Computing with Host CPU...\n\n");
sumCPU = 0;
for (i = 0; i < GPU_N; i++) {
for (j = 0; j < plan[i].dataN; j++) {
sumCPU += plan[i].h_Data[j];
}
}
// Compare GPU and CPU results
printf("Comparing GPU and Host CPU results...\n");
diff = fabs(sumCPU - sumGPU) / fabs(sumCPU);
printf(" GPU sum: %f\n CPU sum: %f\n", sumGPU, sumCPU);
printf(" Relative difference: %E \n\n", diff);
// Cleanup and shutdown
for (i = 0; i < GPU_N; i++) {
checkCudaErrors(cudaSetDevice(i));
checkCudaErrors(cudaFreeHost(plan[i].h_Data));
}
exit((diff < 1e-5) ? EXIT_SUCCESS : EXIT_FAILURE);
}