237 lines
7.7 KiB
Plaintext
237 lines
7.7 KiB
Plaintext
#include "util.h"
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#include "convolution.h"
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#include <mpi.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 GPU_NUM 1
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int num_devices = 0;
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#define OH_TILE_WIDTH 16
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#define OW_TILE_WIDTH 16
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static float *i_d[MAX_NUM_GPU];
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static float *f_d[MAX_NUM_GPU];
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static float *o_d[MAX_NUM_GPU];
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static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
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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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static int mpi_chunk[2];
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__global__ void cuconv(
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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, int OH, int OW,
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int pad, int dilation, int stride) {
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__shared__ float tile_filter[OH_TILE_WIDTH][OW_TILE_WIDTH];
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int bx = blockIdx.x, by = blockIdx.y, nk = blockIdx.z;
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int tx = threadIdx.x, ty = threadIdx.y;
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int n = nk/K;
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int k = nk%K;
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int oh = by * OH_TILE_WIDTH + ty;
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int ow = bx * OW_TILE_WIDTH + tx;
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float o = 0.f;
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float Rchunk = (R + OH_TILE_WIDTH - 1)/OH_TILE_WIDTH;
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float Schunk = (S + OW_TILE_WIDTH - 1)/OW_TILE_WIDTH;
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int hpos = oh*stride - pad;
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int wpos = ow*stride - pad;
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int nCHW = n * C * H * W;
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for(int c = 0; c < C; ++c) {
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int cHW = c * H * W;
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for(int rtile = 0; rtile < Rchunk; ++rtile) {
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for(int stile = 0; stile < Schunk; ++stile) {
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int rpos = rtile*OH_TILE_WIDTH+ty;
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int spos = stile*OW_TILE_WIDTH+tx;
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if(rpos < R && spos < S)
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tile_filter[ty][tx] = filter[k*C*R*S + c*R*S + rpos*S + spos];
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else
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tile_filter[ty][tx] = 0;
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__syncthreads();
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int Rbegin = (dilation - hpos - 1)/dilation > rtile*OH_TILE_WIDTH ? (dilation - hpos - 1)/dilation : rtile*OH_TILE_WIDTH;
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int Sbegin = (dilation - wpos - 1)/dilation > stile*OW_TILE_WIDTH ? (dilation - wpos - 1)/dilation : stile*OW_TILE_WIDTH;
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int Rlimit = (H - hpos + dilation - 1)/dilation < R ? (H - hpos + dilation - 1)/dilation : R;
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int Slimit = (W - wpos + dilation - 1)/dilation < S ? (W - wpos + dilation - 1)/dilation : S;
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int Rend = (rtile+1)*OH_TILE_WIDTH < Rlimit ? (rtile+1)*OH_TILE_WIDTH : Rlimit;
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int Send = (stile+1)*OW_TILE_WIDTH < Slimit ? (stile+1)*OW_TILE_WIDTH : Slimit;
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if(oh < OH && ow < OW)
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for(int r = Rbegin; r < Rend; ++r) {
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for(int s = Sbegin; s < Send; ++s) {
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o += input[nCHW + cHW + (hpos + r * dilation) * W + (wpos + s * dilation)] * tile_filter[r-rtile*OH_TILE_WIDTH][s-stile*OW_TILE_WIDTH];
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}
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}
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__syncthreads();
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}
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}
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}
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if(oh < OH && ow < OW)
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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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 request;
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MPI_Status status;
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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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}
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if(mpi_world_size == 2) {
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if (mpi_rank == 0) {
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MPI_Isend(input+mpi_chunk[0]*C*H*W, mpi_chunk[1]*C*H*W, MPI_FLOAT, 1, 1000, MPI_COMM_WORLD, &request);
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MPI_Isend(filter, K*C*R*S, MPI_FLOAT, 1, 1001, MPI_COMM_WORLD, &request);
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} else {
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MPI_Recv(input, mpi_chunk[1]*C*H*W, MPI_FLOAT, 0, 1000, MPI_COMM_WORLD, &status);
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MPI_Recv(filter, K*C*R*S, MPI_FLOAT, 0, 1001, MPI_COMM_WORLD, &status);
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}
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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(i_d[i], input + Nbegin[i]*C*H*W,
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(Nend[i]-Nbegin[i])*C*H*W*sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(f_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 blockDim(OW_TILE_WIDTH, OH_TILE_WIDTH, 1);
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dim3 gridDim((OW+OW_TILE_WIDTH-1)/OW_TILE_WIDTH, (OH+OH_TILE_WIDTH-1)/OH_TILE_WIDTH, K*(Nend[i]-Nbegin[i]));
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CUDA_CALL( cudaSetDevice(i) );
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cuconv<<<gridDim, blockDim>>>(i_d[i], o_d[i], f_d[i],
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Nend[i] - Nbegin[i], C, H, W, K, R, S, OH, OW, pad, dilation, stride);
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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( cudaSetDevice(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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CUDA_CALL( cudaMemcpy(output + Nbegin[i]*K*OH*OW, o_d[i],
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(Nend[i]-Nbegin[i])*K*OH*OW*sizeof(float),
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cudaMemcpyDeviceToHost) );
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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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if(mpi_world_size == 2) {
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if (mpi_rank == 0) {
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MPI_Recv(output+mpi_chunk[0]*K*OH*OW, mpi_chunk[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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} else {
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MPI_Isend(output, mpi_chunk[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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}
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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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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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CUDA_CALL( cudaDeviceSetCacheConfig(cudaFuncCachePreferL1) );
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#if GPU_NUM
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num_devices = GPU_NUM;
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#endif
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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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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) {
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mpi_chunk[0] = (N+1)/2;
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mpi_chunk[1] = N - mpi_chunk[0];
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if (mpi_rank != 0) {
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alloc_tensor(&input, mpi_chunk[mpi_rank], C, H, W);
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alloc_tensor(&output, mpi_chunk[mpi_rank], K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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}
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} else {
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mpi_chunk[0] = N;
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}
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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] = (mpi_chunk[mpi_rank] / num_devices) * i;
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Nend[i] = (mpi_chunk[mpi_rank] / num_devices) * (i + 1);
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}
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Nend[num_devices - 1] = mpi_chunk[mpi_rank];
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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(&i_d[i], (Nend[i]-Nbegin[i])*C*H*W*sizeof(float)) );
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CUDA_CALL( cudaMalloc(&f_d[i], K*C*R*S*sizeof(float)) );
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CUDA_CALL( cudaMalloc(&o_d[i], (Nend[i]-Nbegin[i])*K*OH*OW*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( cudaSetDevice(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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