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103 lines
3.7 KiB
Plaintext
103 lines
3.7 KiB
Plaintext
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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* * Neither the name of NVIDIA CORPORATION nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
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* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
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* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
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* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/* Simple example demonstrating how to use MPI with CUDA
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*
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* Generate some random numbers on one node.
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* Dispatch them to all nodes.
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* Compute their square root on each node's GPU.
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* Compute the average of the results using MPI.
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*
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* simpleMPI.cu: GPU part, compiled with nvcc
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*/
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#include <iostream>
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using std::cerr;
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using std::endl;
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#include "simpleMPI.h"
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// Error handling macro
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#define CUDA_CHECK(call) \
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if ((call) != cudaSuccess) { \
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cudaError_t err = cudaGetLastError(); \
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cerr << "CUDA error calling \"" #call "\", code is " << err << endl; \
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my_abort(err); \
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}
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// Device code
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// Very simple GPU Kernel that computes square roots of input numbers
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__global__ void simpleMPIKernel(float *input, float *output) {
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int tid = blockIdx.x * blockDim.x + threadIdx.x;
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output[tid] = sqrt(input[tid]);
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}
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// Initialize an array with random data (between 0 and 1)
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void initData(float *data, int dataSize) {
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for (int i = 0; i < dataSize; i++) {
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data[i] = (float)rand() / RAND_MAX;
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}
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}
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// CUDA computation on each node
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// No MPI here, only CUDA
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void computeGPU(float *hostData, int blockSize, int gridSize) {
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int dataSize = blockSize * gridSize;
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// Allocate data on GPU memory
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float *deviceInputData = NULL;
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CUDA_CHECK(cudaMalloc((void **)&deviceInputData, dataSize * sizeof(float)));
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float *deviceOutputData = NULL;
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CUDA_CHECK(cudaMalloc((void **)&deviceOutputData, dataSize * sizeof(float)));
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// Copy to GPU memory
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CUDA_CHECK(cudaMemcpy(deviceInputData, hostData, dataSize * sizeof(float),
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cudaMemcpyHostToDevice));
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// Run kernel
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simpleMPIKernel<<<gridSize, blockSize>>>(deviceInputData, deviceOutputData);
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// Copy data back to CPU memory
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CUDA_CHECK(cudaMemcpy(hostData, deviceOutputData, dataSize * sizeof(float),
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cudaMemcpyDeviceToHost));
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// Free GPU memory
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CUDA_CHECK(cudaFree(deviceInputData));
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CUDA_CHECK(cudaFree(deviceOutputData));
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}
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float sum(float *data, int size) {
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float accum = 0.f;
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for (int i = 0; i < size; i++) {
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accum += data[i];
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}
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return accum;
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}
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