285 lines
7.9 KiB
Plaintext
285 lines
7.9 KiB
Plaintext
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#include "convolution.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 TS 32
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#define MAX_NUM_GPU 4
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int num_devices = 0;
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static float *input, *output, *filter;
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static int N, C, H, W;
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static int N_mpi_aware;
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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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static float * input_d[MAX_NUM_GPU];
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static float * filter_d[MAX_NUM_GPU];
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static float * output_d[MAX_NUM_GPU];
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static int Nbegin[MAX_NUM_GPU];
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static int Nend[MAX_NUM_GPU];
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static int input_size;
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static int input_middle;
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static int filter_size;
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static int output_size;
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static int output_middle;
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__global__ void conv(
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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 OH, int OW) {
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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 = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
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//int 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 = 0.0f;
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for(int c = 0; c < _C; c++){
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for(int i = 0; i <_R; i++){
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for(int j = 0; j < _S; j++){
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int h = start_row + i * _dilation;
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int w = start_col + j * _dilation;
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if(h < 0 || w < 0 || h >= _H || w >= _W) continue;
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float in = _input[n*_C*_W*_H + c*_W*_H + h*_W + w];
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float fil = _filter[k*_C*_R*_S + c*_R*_S + i*_S + j];
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o += in * fil;
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}
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}
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}
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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_gpu(
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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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output = _output;
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//printf("\n\n\n\nhead of convolution_gpu %d\n\n\n\n\n", mpi_rank);
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//int OH = (_H + 2 * _pad - _dilation * (_R - 1) - 1) / _stride + 1;
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//int OW = (_W + 2 * _pad - _dilation * (_S - 1) - 1) / _stride + 1;
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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(input_d[i], _input + Nbegin[i]*_C*_H*_W, (Nend[i] - Nbegin[i])*_C*_H*_W* sizeof(float), cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(filter_d[i], _filter, _K*_C*_R*_S*sizeof(float), cudaMemcpyHostToDevice) );
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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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dim3 blockDim(TS, TS, 1);
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for(int i = 0; i < num_devices; i++){
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dim3 gridDim((OH + TS + 1)/TS, ((Nend[i] - Nbegin[i])*_K*OW + TS - 1)/TS, 1);
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CUDA_CALL( cudaSetDevice(i) );
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conv<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], Nend[i] - Nbegin[i], _C, _H, _W, _K ,_R, _S, _pad, _dilation, _stride, OH, OW);
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}
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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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MPI_Request mpi_request;
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MPI_Status mpi_status;
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//N_mpi_aware = N;
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if(mpi_rank == 0){
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input = _input;
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output = _output;
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filter = _filter;
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if(mpi_world_size == 2){
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MPI_Isend(input + input_middle, input_size - input_middle, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &mpi_request);
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MPI_Isend(filter, filter_size, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &mpi_request);
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//N_mpi_aware = N/2;
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//printf("0 N_size : %d\n", N_size);
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}
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}
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else{
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input = (float *) aligned_alloc(32, sizeof(float) * (input_size - input_middle));
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filter = (float *) aligned_alloc(32, sizeof(float) * filter_size);
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output = (float *) aligned_alloc(32, sizeof(float) * (output_size - output_middle));
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MPI_Recv(input, input_size - input_middle, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &mpi_status);
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MPI_Recv(filter, filter_size, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &mpi_status);
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//N_mpi_aware = N - N/2;
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//printf("1 N_size : %d\n", N_size);
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}
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//printf("call core : %d\n", N_size);
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convolution_gpu( input, output, filter,
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N_mpi_aware, C, H, W,
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K, R, S,
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pad, dilation, stride);
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//printf("core finished\n");
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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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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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input_size = N * C * H * W;
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input_middle = (N/2) * C * H * W;
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filter_size = K * C * R * S;
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output_size = N * K * OH * OW;
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output_middle = (N/2) * K * OH * OW;
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CUDA_CALL( cudaGetDeviceCount(&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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}
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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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if(mpi_world_size == 2){
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if(mpi_rank == 0){
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N_mpi_aware = N/2;
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// printf("N_mpi_aware %d : %d\n", mpi_rank, N_mpi_aware);
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}
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else{
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N_mpi_aware = N - N/2;
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// printf("N_mpi_aware %d : %d\n", mpi_rank, N_mpi_aware);
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}
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}
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else{
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N_mpi_aware = N;
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//printf("mpi_world_size 1, N_mpi_aware %d : %d\n", mpi_rank, N_mpi_aware);
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}
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if(num_devices > N_mpi_aware){
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num_devices = 1;
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Nbegin[0] = 0;
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Nend[0] = N_mpi_aware;
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}
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else{
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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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Nbegin[i] = (N_mpi_aware / num_devices) * i;
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Nend[i] = (N_mpi_aware / num_devices) * (i + 1);
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}
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Nend[num_devices - 1] = N_mpi_aware;
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}
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//// debug print
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//for(int i = 0; i < num_devices; i++){
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// printf("%d : Nbegin[%d] == %d / Nend[%d] == %d\n", mpi_rank, i, Nbegin[i], i, Nend[i]);
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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(&input_d[i], (Nend[i] - Nbegin[i])*C*H*W* sizeof(float)) );
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CUDA_CALL( cudaMalloc(&filter_d[i], filter_size*sizeof(float)) );
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CUDA_CALL( cudaMalloc(&output_d[i], (Nend[i] - Nbegin[i])*K*OH*OW*sizeof(float)) );
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//printf("%d : cuda malloc %d\n", mpi_rank, i);
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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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}
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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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//if(mpi_rank == 1) return;
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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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MPI_Request mpi_request;
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MPI_Status mpi_status;
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for(int i = 0; i < num_devices; i++){
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CUDA_CALL( cudaMemcpy(output + Nbegin[i]*K*OH*OW, output_d[i], (Nend[i] - Nbegin[i])*K*OH*OW*sizeof(float), cudaMemcpyDeviceToHost) );
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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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if(mpi_world_size == 2){
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if(mpi_rank == 0){
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//printf("rank 0 recv : %d\n", output_size - output_middle);
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MPI_Recv(output + output_middle, output_size - output_middle, MPI_FLOAT, 1, 1, MPI_COMM_WORLD, &mpi_status);
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}
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else{
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//printf("rank 1 send : %d\n", output_size - output_middle);
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MPI_Isend(output, output_size - output_middle, MPI_FLOAT, 0, 1, MPI_COMM_WORLD, &mpi_request);
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}
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}
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}
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