202 lines
6.4 KiB
Plaintext
202 lines
6.4 KiB
Plaintext
#include "convolution.h"
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#include <mpi.h>
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#include <stdio.h>
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#include "util.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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#define TS 8
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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, 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 n, k;
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n = globalCol/(_K*OW);
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k = (globalCol-n*(_K*OW))/OW;
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int col = (globalCol-n*(_K*OW))-k*OW;
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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 r = 0; r<_R; r++) {
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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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}
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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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static float *input, *output, *filter;
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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 N, C, H, W;
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static int K, R, S;
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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;
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static int size[2];
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static int Mbegin[MAX_NUM_GPU], NN[MAX_NUM_GPU];
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static int OH, OW;
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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 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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if (size[1] != 0) {
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if (mpi_rank == 0) {
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MPI_Isend(&input[size[0]*C*H*W], size[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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} else {
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alloc_tensor(&input, size[1], C, H, W);
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alloc_tensor(&output, size[1], K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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MPI_Recv(input, size[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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}
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}
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if (size[mpi_rank] != 0) {
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(input_d[i], input + Mbegin[i]*C*H*W,
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NN[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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// Launch kernel on every GPU
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for (int i = 0; i < num_devices; i++) {
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dim3 gridDim((OH+TS-1)/TS, (NN[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<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], NN[i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride, OH, OW);
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}
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// Download C matrix from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(output + Mbegin[i] * K*OH*OW, output_d[i],
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NN[i] * K*OH*OW * sizeof(float),
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cudaMemcpyDeviceToHost) );
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}
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}
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if(size[1] != 0) {
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if (mpi_rank == 0) {
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MPI_Irecv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
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} else {
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MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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}
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MPI_Wait(&request, &status);
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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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if (mpi_world_size == 2 && _N > 4) size[1] = _N / 2;
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else size[1] = 0;
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size[0] = N - size[1];
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if (size[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size[mpi_rank];
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for (int i = 0 ; i < size[mpi_rank] ; i++)
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{
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NN[i] = 1;
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Mbegin[i] = i;
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}
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} else {
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num_devices = MAX_NUM_GPU;
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int q = size[mpi_rank] / MAX_NUM_GPU;
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int r = size[mpi_rank] % MAX_NUM_GPU;
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int sum = 0;
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for (int i = 0 ; i < MAX_NUM_GPU ; i++) {
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NN[i] = q;
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Mbegin[i] = sum;
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if (i == MAX_NUM_GPU - 1) {
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NN[i] += r;
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}
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sum += NN[i];
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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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// possigle to MPI
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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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exit(1);
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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], NN[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], NN[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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}
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