197 lines
6.6 KiB
Plaintext
197 lines
6.6 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 <cuda_runtime.h>
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#include "util.h"
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#define MAX_NUM_GPU 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 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 = 4;
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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, 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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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*OH);
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w = w - n*(_K*OW);
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k = w / OW;
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w = w - k * OH;
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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 = 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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// __syncthreads();
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}
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static float *in_d[MAX_NUM_GPU];
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static float *out_d[MAX_NUM_GPU];
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static float *filter_d[MAX_NUM_GPU];
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static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
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void convolution(float* _input, float* _output, float* _filter, int _N, int _C, int _H, int _W,
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int _K, int _R, int _Rs, int _pad, int _dilation, int _stride){
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int size[2];
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MPI_Request request;
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MPI_Status status;
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if(mpi_world_size == 2) 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 (mpi_rank == 0 && mpi_world_size==2) {
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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*_Rs, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request);
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}
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else if(mpi_world_size==2){
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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, _Rs);
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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*_Rs, MPI_FLOAT, 0,0, 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(in_d[i], _input + (Nbegin[i]-size[1]*mpi_rank) * _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 * _Rs * sizeof(float), cudaMemcpyHostToDevice) );
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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, ((Nend[i]-Nbegin[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>>>(in_d[i], out_d[i], filter_d[i], (Nend[i]-Nbegin[i]), _C, _H, _W, _K, _R, _Rs, _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 ( cudaMemcpy( _output+(Nbegin[i]-size[1]*mpi_rank)* _K * OH * OW, out_d[i], (Nend[i]-Nbegin[i]) * _K * OH * OW*sizeof(float), cudaMemcpyDeviceToHost));
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}
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if(mpi_rank == 0 && mpi_world_size==2){
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MPI_Recv(&_output[size[0]*_K*OH*OW], size[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, size[1]*_K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD,&request);
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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_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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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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if(mpi_rank==0){
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for (int i = 0; i < num_devices; i++) {
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Nbegin[i] = (N / num_devices) * i/mpi_world_size;
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Nend[i] = (N / num_devices) * (i + 1)/mpi_world_size;
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}
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Nend[num_devices - 1] = N/mpi_world_size;
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}
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else {
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for(int i=0;i<num_devices;i++){
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Nbegin[i] = (N / num_devices) * i/mpi_world_size + N/mpi_world_size;
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Nend[i] = (N / num_devices) * (i+1)/mpi_world_size + N/mpi_world_size;
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}
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Nend[num_devices - 1] = N;
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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], (Nend[i]-Nbegin[i]) * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&out_d[i], (Nend[i]-Nbegin[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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// 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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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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