214 lines
6.6 KiB
Plaintext
214 lines
6.6 KiB
Plaintext
#include <stdio.h>
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#include <cuda_runtime.h>
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#include <mpi.h>
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#include "convolution.h"
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#include "util.h"
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// tunable parameter
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#define MAX_GPU_NUMBER 4
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#define TS 8
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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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static float *input, *output, *filter;
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static float *input_d[MAX_GPU_NUMBER], *output_d[MAX_GPU_NUMBER], *filter_d[MAX_GPU_NUMBER];
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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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static int num_devices = 1;
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static int MP_size[2];
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static int N_size[MAX_GPU_NUMBER];
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__global__ void convolution_cuda(
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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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//indexing
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int n, k, w;
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int col = globalCol;
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int row = globalRow;
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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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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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col = w;
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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 out_buf = 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_buf = _input[n *_C * _W * _H + c * _W * _H + h * _W + w];
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float filt_buf = _filter[k *_C * _R * _S + c * _R * _S + i * _S + j];
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out_buf += in_buf * filt_buf;
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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] = out_buf;
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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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N = _N; C = _C; H = _H; W = _W; 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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input = _input;
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output = _output;
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filter = _filter;
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MPI_Request request;
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MPI_Status status;
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if (mpi_rank == 0 && MP_size[1] != 0 && mpi_world_size == 2) {
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MPI_Isend(&input[MP_size[0] * C * H * W], MP_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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if (MP_size[mpi_rank] < MAX_GPU_NUMBER) {
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num_devices = MP_size[mpi_rank];
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}
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}
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else if (mpi_rank == 1 && MP_size[mpi_rank] != 0) {
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alloc_tensor(&input, MP_size[1], C, H, W);
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alloc_tensor(&output, MP_size[1], K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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MPI_Recv(input, MP_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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if (MP_size[mpi_rank] < MAX_GPU_NUMBER) {
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num_devices = MP_size[mpi_rank];
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}
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}
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int offset = 0;
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for (int i = 0 ; i < num_devices ; i++) {
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CUDA_CALL(cudaMemcpy(input_d[i], input + offset, N_size[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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offset += N_size[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, (N_size[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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convolution_cuda<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], N_size[i], C, H, W, K, R, S, pad, dilation, stride);
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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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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, output_d[i], N_size[i] * K * OH * OW * sizeof(float), cudaMemcpyDeviceToHost));
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offset += N_size[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(cudaDeviceSynchronize());
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CUDA_CALL(cudaSetDevice(i));
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}
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if (mpi_rank == 0 && MP_size[1] != 0 && mpi_world_size == 2) {
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MPI_Recv(&output[MP_size[0] * K * OH * OW], MP_size[1] * K * OH * OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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} else if(mpi_rank == 1 && MP_size[1] != 0){
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MPI_Isend(output, MP_size[1] * K * OH * OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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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; 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_size(MPI_COMM_WORLD, &mpi_world_size);
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MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank);
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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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if (mpi_world_size == 2) MP_size[1] = _N / 2;
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MP_size[0] = N - MP_size[1];
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if (MP_size[mpi_rank] < MAX_GPU_NUMBER) {
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num_devices = MP_size[mpi_rank];
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for (int i = 0 ; i < MP_size[mpi_rank] ; i++) {
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N_size[i] = 1;
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}
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}
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else {
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num_devices = MAX_GPU_NUMBER;
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int remainder = MP_size[mpi_rank] % MAX_GPU_NUMBER;
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int quotient = MP_size[mpi_rank] / MAX_GPU_NUMBER;
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for (int i = 0 ; i < MAX_GPU_NUMBER ; i++) {
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N_size[i] = quotient;
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if (i < remainder) N_size[i]++;
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}
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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(cudaMalloc(&input_d[i], N_size[i] * C * H * W * sizeof(float)));
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CUDA_CALL(cudaMalloc(&output_d[i], N_size[i] * K * OH * OW * sizeof(float)));
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CUDA_CALL(cudaMalloc(&filter_d[i], K * C * R * S * 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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