282 lines
9.4 KiB
Plaintext
282 lines
9.4 KiB
Plaintext
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#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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#define min(a, b) (((a) < (b)) ? (a) : (b))
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#define max(a, b) (((a) > (b)) ? (a) : (b))
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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 8
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#define WPT 16
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#define RTS (TS/WPT)
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#define MAX_NUM_GPU 4
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#define MAX_NUM_MPI 2
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int num_devices = 1;
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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 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 ns[2], ne[2];
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static int Nbegin[MAX_NUM_MPI][MAX_NUM_GPU], Nend[MAX_NUM_MPI][MAX_NUM_GPU];
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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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MPI_Status status;
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MPI_Request request;
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////////////////////////////////// Kernel /////////////////////////////
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__global__ void sgemm(float *_input, float *_filter, float *_output, int _N, int _C, int _H, int _W,
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int _K, int _R, int _S, int _pad, int _dilation, int _stride) {
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//const int row = threadIdx.x; // Local row ID (max: TS/WIDTH)
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//const int col = threadIdx.y; // Local col ID (max: TS)
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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; // 0..N
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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 w = globalCol;
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int n = w / (_K * _OW);
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w = w - n * (_K * _OW);
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int 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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// printf("N :%d, input:0x%X\n", _N, &input);
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/*
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for (int n = 0; n < _N; ++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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*/ 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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//int h = oh * stride - pad + r * dilation;
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for (int s = 0; s < _S; ++s) {
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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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} //s
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} //r
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} //c
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_output[n * _K * _OH * _OW + k * _OH * _OW + row * _OW + col] = o;
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//printf("_output[%d][%d][%d][%d] : %f\n", n, k, row, col, o);
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/* output[n * K * OH * OW + k * OH * OW + oh * OW + ow];
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} //ow
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} //oh
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} //k
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} //n
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*/
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/////////////////////////////////////////////
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// Synchronise before loading the next tile
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__syncthreads();
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// }
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// Store the final results in C
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//for (int w=0; w<WPT; w++) {
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//output[globalRow * N + globalCol] = sum[w];
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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, int _K, int _R, int _S,
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int _pad, int _dilation, int _stride) {
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input = _input;
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output = _output;
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filter = _filter;
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if (mpi_rank == 0) {
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for (int i = 1; i < mpi_world_size; i++) {
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MPI_Isend(&input[ns[i]*C*H*W], (ne[i]-ns[i])*C*H*W, MPI_FLOAT, i, 1, MPI_COMM_WORLD, &request);
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MPI_Isend(filter, K*C*R*S, MPI_FLOAT, i, 2, MPI_COMM_WORLD, &request);
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}
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}
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else {
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alloc_tensor(&input, N, C, H, W);
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alloc_tensor(&filter, K, C, R, S);
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alloc_tensor(&output, N, K, OH, OW);
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MPI_Recv(&input[ns[mpi_rank]*C*H*W], (ne[mpi_rank]-ns[mpi_rank])*C*H*W, MPI_FLOAT, 0, 1, MPI_COMM_WORLD, &status);
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MPI_Recv(filter, K*C*R*S, MPI_FLOAT, 0, 2, MPI_COMM_WORLD, &status);
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}
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// Upload A and B matrix to every GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(input_d[i], input + Nbegin[mpi_rank][i] *C*H*W,
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(Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) *C*H*W * sizeof(float),
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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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// 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( cudaDeviceSynchronize() );
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}
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///////////////// Start Calculation ///////////////////
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// Launch kernel on every GPU
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for (int i = 0; i < num_devices; i++) {
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// dim3 gridDim(((Nend[mpi_rank][i] - Nbegin[mpi_rank][i]+TS-1)/TS), (K+TS-1)/TS, 1);
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dim3 gridDim((OH+TS-1)/TS,((Nend[mpi_rank][i] - Nbegin[mpi_rank][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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sgemm<<<gridDim, blockDim>>>(input_d[i], filter_d[i], output_d[i],
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Nend[mpi_rank][i] - Nbegin[mpi_rank][i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride);
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// printf("[mpi_rank:%d,GPU:%d] Nbegin:%d, Nend:%d\n", mpi_rank, i, Nbegin[mpi_rank][i], Nend[mpi_rank][i]);
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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( cudaDeviceSynchronize() );
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}
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///////////////// End Calculation ///////////////////
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//test
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#if 0
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printf("Print output result\n"); fflush(stdout);
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for (int n = 0; n < N; ++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 = output[n * K * OH * OW + k * OH * OW + oh * OW + ow];
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printf("output[%d][%d][%d][%d] : = %f\n", n, k, oh, ow, o); fflush(stdout);
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}
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}
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}
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}
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#endif
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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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/////////////////////// Init Cuda ///////////////////////
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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if(num_devices > MAX_NUM_GPU) num_devices = MAX_NUM_GPU;
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printf("[MPI:%d] Using %d devices\n", mpi_rank, 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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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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for (int i = 0; i < mpi_world_size; i++) {
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ns[i] = N / mpi_world_size * i;
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ne[i] = N / mpi_world_size * (i + 1);
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// printf("[mpi_rank:%d] ns[%d]:%d, ne[%d]:%d\n", mpi_rank, i, ns[i], i, ne[i]);
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}
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ne[mpi_world_size - 1] = N;
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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[mpi_rank][i] = (ne[mpi_rank]-ns[mpi_rank]) / num_devices * i + ns[mpi_rank];
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Nend[mpi_rank][i] = (ne[mpi_rank]-ns[mpi_rank]) / num_devices * (i + 1) + ns[mpi_rank];
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}
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//if(num_devices == (MAX_NUM_GPU - 1)) Nend[num_devices - 1] = ne[mpi_rank];
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Nend[mpi_rank][num_devices - 1] = ne[mpi_rank];
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/*
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for (int i = 0; i < num_devices; i++) {
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printf("[mpi_rank:%d, GPU:%d] ns[%d]:%d, ne[%d]:%d, Nbegin[%d][%d]:%d, Nend[%d][%d]:%d\n",
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mpi_rank, i, mpi_rank, ns[mpi_rank], mpi_rank, ne[mpi_rank], mpi_rank, i, Nbegin[mpi_rank][i], mpi_rank, i, Nend[mpi_rank][i]);
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fflush(stdout);
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}
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*/
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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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// 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[mpi_rank][i] - Nbegin[mpi_rank][i]) * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&filter_d[i], K * C * R * S * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&output_d[i], (Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) * K * OH * OW * sizeof(float)) );
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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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// Download output matrix from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(output + Nbegin[mpi_rank][i] * K * OH * OW, output_d[i],
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(Nend[mpi_rank][i] - Nbegin[mpi_rank][i]) * K * OH * OW * sizeof(float),
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cudaMemcpyDeviceToHost) );
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}
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if (mpi_rank == 0) {
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for (int i = 1; i < mpi_world_size; i++) {
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MPI_Recv(&output[ns[i]*K*OH*OW], (ne[i]-ns[i])*K*OH*OW, MPI_FLOAT, 1, 1, MPI_COMM_WORLD, &status);
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}
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}
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else {
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MPI_Isend(&output[ns[mpi_rank]*K*OH*OW], (ne[mpi_rank]-ns[mpi_rank])*K*OH*OW, MPI_FLOAT, 0, 1, MPI_COMM_WORLD, &request);
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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( cudaDeviceSynchronize() );
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}
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}
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