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/* Copyright (c) 2022, 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.
*/
/* Simple example demonstrating how to use MPI with CUDA
*
* Generate some random numbers on one node.
* Dispatch them to all nodes.
* Compute their square root on each node's GPU.
* Compute the average of the results using MPI.
*
* simpleMPI.cu: GPU part, compiled with nvcc
*/
#include <iostream>
using std::cerr;
using std::endl;
#include "simpleMPI.h"
// Error handling macro
#define CUDA_CHECK(call) \
if ((call) != cudaSuccess) { \
cudaError_t err = cudaGetLastError(); \
cerr << "CUDA error calling \"" #call "\", code is " << err << endl; \
my_abort(err); \
}
// Device code
// Very simple GPU Kernel that computes square roots of input numbers
__global__ void simpleMPIKernel(float *input, float *output) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
output[tid] = sqrt(input[tid]);
}
// Initialize an array with random data (between 0 and 1)
void initData(float *data, int dataSize) {
for (int i = 0; i < dataSize; i++) {
data[i] = (float)rand() / RAND_MAX;
}
}
// CUDA computation on each node
// No MPI here, only CUDA
void computeGPU(float *hostData, int blockSize, int gridSize) {
int dataSize = blockSize * gridSize;
// Allocate data on GPU memory
float *deviceInputData = NULL;
CUDA_CHECK(cudaMalloc((void **)&deviceInputData, dataSize * sizeof(float)));
float *deviceOutputData = NULL;
CUDA_CHECK(cudaMalloc((void **)&deviceOutputData, dataSize * sizeof(float)));
// Copy to GPU memory
CUDA_CHECK(cudaMemcpy(deviceInputData, hostData, dataSize * sizeof(float),
cudaMemcpyHostToDevice));
// Run kernel
simpleMPIKernel<<<gridSize, blockSize>>>(deviceInputData, deviceOutputData);
// Copy data back to CPU memory
CUDA_CHECK(cudaMemcpy(hostData, deviceOutputData, dataSize * sizeof(float),
cudaMemcpyDeviceToHost));
// Free GPU memory
CUDA_CHECK(cudaFree(deviceInputData));
CUDA_CHECK(cudaFree(deviceOutputData));
}
float sum(float *data, int size) {
float accum = 0.f;
for (int i = 0; i < size; i++) {
accum += data[i];
}
return accum;
}
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