349 lines
10 KiB
Plaintext
349 lines
10 KiB
Plaintext
#include "convolution.h"
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#include "util.h"
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#include <mpi.h>
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#include <stdio.h>
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#include <cstdio>
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#include <cuda_runtime.h>
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#define CUDA_CALL(f) \
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{ \
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cudaError_t err = (f); \
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if (err != cudaSuccess) { \
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fprintf(stderr, "CUDA error at [%s:%d] %d %s\n", __FILE__, __LINE__, \
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err, cudaGetErrorString(err)); \
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exit(1); \
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} \
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}
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#define MAX_NUM_GPU 4
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static float *input, *output, *filter;
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static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *fil_d[MAX_NUM_GPU];
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static int N, C, H, W;
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static int K, R, S;
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static int OH, OW;
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static int pad;
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static int dilation;
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static int stride;
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static int mpi_rank, mpi_world_size;
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//int num_devices = 0; // ORG
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static int num_devices = 1; // DEBUG
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static int Nsizei[MAX_NUM_GPU]; // Number of Nsize for each GPU[i]
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static int Nodesize[2]; // Number of size for each node
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/*
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// Array of device (GPU) pointers
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static float *input[MAX_NUM_GPU];
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static float *filter[MAX_NUM_GPU];
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static float *output[MAX_NUM_GPU];
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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*/
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// 4279 GFLOPS (TS = 4)
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// 4887 GFLOPS (TS = 8)
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// 3303 GFLOPS (TS = 16)
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//#define TS 4
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#define TS 8
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//#define TS 16
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__global__ void conv_kernel(
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float *_input, float *_output, float *_filter,
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int _N, int _C, int _H, int _W,
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int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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const int globalRow = blockDim.x * blockIdx.x + threadIdx.x;
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const int globalCol = blockDim.y * blockIdx.y + threadIdx.y;
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int OH, OW;
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OH = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
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OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;
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int n, k, w;
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w=globalCol;
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n=w / (_K * OW);
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w = w - n * (_K * OW);
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k = w / OW;
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w = w - k * OW;
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int col = w;
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int row = globalRow;
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if( (globalRow >= OH) || (globalCol >= _N* _K * OW)) return;
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int start_row = row * _stride - _pad;
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int start_col = col * _stride - _pad;
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float o = 0.f;
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for (int c = 0; c < _C; ++c) {
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for (int r = 0; r < _R; ++r) {
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for (int s = 0; s < _S; ++s) {
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//int h = oh * stride - pad + r * dilation;
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//int w = ow * stride - pad + s * dilation;
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int h = start_row + r * _dilation;
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int w = start_col + s * _dilation;
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if (h < 0 || h >= _H || w < 0 || w >= _W) continue;
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float i = _input[n * _C * _H * _W + c * _H * _W + h * _W + w];
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float f = _filter[k * _C * _R * _S + c * _R * _S + r * _S + s];
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o += i * f;
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} // for s
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} // for r
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} // for c
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//_output[n * _K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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_output[n * _K * OH * OW + k * OH * OW + row * OW + col] = o;
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}
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void convolution(
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float *_input, float *_output, float *_filter,
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int _N, int _C, int _H, int _W,
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int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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int offset = 0;
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MPI_Request request;
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MPI_Status status;
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input = _input;
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output = _output;
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filter = _filter;
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// printf("DEBUG: mpi_world_size = %d\n", mpi_world_size);
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/*
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// for two processors
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if(mpi_world_size == 2) Nodesize[1] = _N / 2;
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else Nodesize[1] = 0;
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Nodesize[0] = _N - Nodesize[1];
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OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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*/
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// For two nodes, separate the jobs
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if(mpi_rank == 0 && mpi_world_size == 2 && Nodesize[1] != 0) { // for the first node
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MPI_Isend(&input[Nodesize[0]*C*H*W], Nodesize[1]*C*H*W, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
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MPI_Isend(filter, _K*_C*_R*_S, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
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if(Nodesize[mpi_rank] < MAX_NUM_GPU) { // smaller number of size than MAX_NUM_GPU
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num_devices = Nodesize[mpi_rank];
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}
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}
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else if(mpi_rank == 1 && Nodesize[mpi_rank] != 0) { // for the second node
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alloc_tensor(&input, Nodesize[1], C, H, W);
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alloc_tensor(&output, Nodesize[1], K, OH, OW);
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alloc_tensor(&filter, _K, _C, _R, _S);
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MPI_Recv(input, Nodesize[1]*C*H*W, MPI_FLOAT,0,0,MPI_COMM_WORLD, &status);
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MPI_Recv(filter, _K*_C*_R*_S, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
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if(Nodesize[mpi_rank] < MAX_NUM_GPU) {
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num_devices = Nodesize[mpi_rank];
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}
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}
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offset = 0;
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for(int i=0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(in_d[i], input + offset, Nsizei[i]*C*H*W*sizeof(float), cudaMemcpyHostToDevice));
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CUDA_CALL( cudaMemcpy(fil_d[i], filter, K*C*R*S*sizeof(float), cudaMemcpyHostToDevice));
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offset += Nsizei[i] * C * H * W;
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}
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for(int i=0; i < num_devices; i++) {
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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for(int i=0; i < num_devices; i++) {
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dim3 gridDim ( (OH+TS-1)/TS, (Nsizei[i]*K*OW + TS -1)/TS, 1);
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dim3 blockDim(TS, TS, 1);
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CUDA_CALL(cudaSetDevice(i));
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conv_kernel<<<gridDim, blockDim>>>(in_d[i], out_d[i], fil_d[i], Nsizei[i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride);
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}
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// DO NOT REMOVE: NEEDED FOR TIME MEASURE
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for(int i=0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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// gather for the same node
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offset = 0;
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for(int i=0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMemcpy(output + offset, out_d[i], Nsizei[i]*K*OH*OW *sizeof(float), cudaMemcpyDeviceToHost) );
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offset += Nsizei[i]*K*OH*OW;
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}
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for(int i=0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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// gather for the different node
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if(mpi_rank==0 && mpi_world_size == 2 && Nodesize[1] != 0) {
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MPI_Recv(&output[Nodesize[0]*K*OH*OW], Nodesize[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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}
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else if(mpi_rank==1 && Nodesize[1] != 0) {
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MPI_Isend(output, Nodesize[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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}
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}
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/*
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// if (mpi_rank == 0) {
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//#pragma omp parallel for schedule(dynamic) num_threads(num_threads)
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//#pragma omp parallel for schedule(dynamic)
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//#pragma omp parallel for schedule(dynamic) num_threads(80)
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#pragma omp parallel for schedule(dynamic) num_threads(80) collapse(3)
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//#pragma omp parallel for schedule(dynamic) num_threads(80) collapse(2)
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//for (int n = 0; n < N; ++n) {
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for (int n = 0; n < Nodesize[mpi_rank]; ++n) {
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for (int k = 0; k < K; ++k) {
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for (int oh = 0; oh < OH; ++oh) {
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for (int ow = 0; ow < OW; ++ow) {
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float o = 0.f;
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for (int c = 0; c < C; ++c) {
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for (int r = 0; r < R; ++r) {
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for (int s = 0; s < S; ++s) {
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int h = oh * stride - pad + r * dilation;
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int w = ow * stride - pad + s * dilation;
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if (h < 0 || h >= H || w < 0 || w >= W) continue;
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float i = input[n * C * H * W + c * H * W + h * W + w];
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float f = filter[k * C * R * S + c * R * S + r * S + s];
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o += i * f;
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} // for s
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} // for r
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} // for c
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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} // for ow
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} // for oh
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} // for k
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} // for n
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// }
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if(mpi_rank==0 && mpi_world_size == 2) {
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MPI_Recv(&output[Nodesize[0]*K*OH*OW], Nodesize[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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}
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else if(mpi_world_size == 2) {
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MPI_Isend(output, Nodesize[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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}
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}
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*/
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void convolution_init(
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int _N, int _C, int _H, int _W,
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int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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N = _N; C = _C; H = _H; W = _W;
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K = _K; R = _R; S = _S;
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pad = _pad;
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dilation = _dilation;
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stride = _stride;
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MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
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MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size);
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// set initial value for the cuda and its kernels
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OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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// seperate the jobs for two nodes
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if(mpi_world_size == 2 && _N > 4) Nodesize[1] = _N/2; // two mpi_rank
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else Nodesize[1] = 0;
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Nodesize[0] = N - Nodesize[1]; // allocate more jobs for the node0 (mpi_rank=0)
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// set more Nodesize for mpi_rank=0
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if(Nodesize[mpi_rank] < MAX_NUM_GPU) {
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num_devices = Nodesize[mpi_rank];
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for(int i=0; i < Nodesize[mpi_rank]; i++) Nsizei[i] = 1;
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}
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else {
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num_devices = MAX_NUM_GPU;
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int Nodesize_per_GPU = Nodesize[mpi_rank] / MAX_NUM_GPU;
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int remainder= Nodesize[mpi_rank] % MAX_NUM_GPU;
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for(int i=0; i < MAX_NUM_GPU; i++) {
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Nsizei[i] = Nodesize_per_GPU;
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if(i < remainder) Nsizei[i]++; // increase the more Nsizei for the last term
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}
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}
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// Allocate device memory for each GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&in_d[i], Nsizei[i]*C*H*W* sizeof(float)) );
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CUDA_CALL( cudaMalloc(&out_d[i], Nsizei[i]*K*OH*OW * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&fil_d[i], K*C*R*S* sizeof(float)) );
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}
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}
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/*
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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printf("Using %d devices\n", num_devices);
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for (int i = 0; i < num_devices; i++) {
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cudaDeviceProp prop;
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CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
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// Try printing more detailed information here
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printf("[GPU %d] %s\n", i, prop.name);
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}
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if (num_devices <= 0) {
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printf("No CUDA device found. Aborting\n");
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exit(1);
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}
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// Setup problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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Mbegin[i] = (M / num_devices) * i;
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Mend[i] = (M / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = M;
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// Allocate device memory for each GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&in_d[i], (Mend[i] - Mbegin[i]) * K * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&out_d[i], K * N * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&fil_d[i], (Mend[i] - Mbegin[i]) * N * sizeof(float)) );
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}
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}
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*/
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void convolution_final(
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int _N, int _C, int _H, int _W,
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int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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}
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