233 lines
6.9 KiB
Plaintext
233 lines
6.9 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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#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 BLOCK_SIZE (1024)
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int num_devices = 0;
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static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU], Nsize[MAX_NUM_GPU], Nslice;
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// Array of device (GPU) pointers
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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 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 sn, en, firstSize, modN;
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__global__ void convOpt(
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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 = (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 now = blockDim.x * blockIdx.x + threadIdx.x;
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int k = blockIdx.y;
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int oh = blockIdx.z;
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int n = now / OW;
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int ow = now % OW;
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if (oh >= OH || ow >= OW || k >= K || n >= N) return;
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//for (int n = 0; n < N; ++n)
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{
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//for (int k = 0; k < K; ++k)
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{
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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 = oh * stride - pad + r * dilation;
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int w = ow * stride - pad + 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 + oh * OW + ow] = o;
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}
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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,
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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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MPI_Request request[2];
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MPI_Status status[2];
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if (N > 1)
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{
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if (mpi_rank == 0)
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{
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int snTmp = firstSize;
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int enTmp = N;
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MPI_Isend(input+snTmp*C*H*W, (enTmp-snTmp)*C*H*W, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request[0]);
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MPI_Isend(filter, K*C*R*S, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request[1]);
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}
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else
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{
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if (modN > 0)
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{
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alloc_tensor(&input, modN, C, H, W);
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alloc_tensor(&output, modN, K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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}
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MPI_Irecv(input, modN*C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request[0]);
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MPI_Irecv(filter, K*C*R*S, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request[1]);
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}
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MPI_Waitall(2,request, status);
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}
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if (modN > 0) {
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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], Nsize[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], Nsize[i]*K*OH*OW * sizeof(float)) );
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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( cudaSetDevice(i) );
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CUDA_CALL( cudaMemcpy(i_d[i], input + Nbegin[i]*C*H*W,
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Nsize[i]*C*H*W * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(f_d[i], filter,
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K*C*R*S * sizeof(float),
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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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CUDA_CALL( cudaSetDevice(i) );
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dim3 blockDim(BLOCK_SIZE, 1, 1);
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dim3 gridDim((Nsize[i]*OW+BLOCK_SIZE-1)/BLOCK_SIZE, K, OH);
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convOpt<<<gridDim, blockDim>>>(i_d[i], o_d[i], f_d[i], Nsize[i],
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C, H, W, K, R, S,
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pad, dilation, stride);
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}
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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( cudaSetDevice(i) );
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CUDA_CALL( cudaMemcpy(output + Nbegin[i]*K*OH*OW, o_d[i],
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Nsize[i]*K*OH*OW * sizeof(float),
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cudaMemcpyDeviceToHost) );
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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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if (N > 1)
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{
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if (mpi_rank == 0)
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{
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int snTmp = firstSize;
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int enTmp = N;
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MPI_Irecv(output+snTmp*K*OH*OW, (enTmp-snTmp)*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &request[0]);
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}
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else
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{
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MPI_Isend(output, modN*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request[0]);
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}
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MPI_Wait(&request[0], &status[0]);
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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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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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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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firstSize = (N + 1) / mpi_world_size;
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if (mpi_rank == 0)
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{
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sn = 0;
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en = firstSize;
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modN = firstSize;
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}
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else
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{
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sn = firstSize;
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en = N;
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modN = N - firstSize;
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}
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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//printf("Using %d devices\n", num_devices);
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Nslice = (modN + num_devices - 1) / 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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// Setup problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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Nbegin[i] = Nslice * i;
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if (Nbegin[i] + Nslice < modN)
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Nend[i] = Nbegin[i] + Nslice;
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else if (Nbegin[i] < modN)
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Nend[i] = modN;
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else
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Nbegin[i]=0, Nend[i] = 0;
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Nsize[i] = Nend[i] - Nbegin[i];
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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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if (N > 1 && mpi_rank != 0)
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{
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free(input);
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free(output);
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free(filter);
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}
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}
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