237 lines
6.9 KiB
Plaintext
237 lines
6.9 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 "util.h"
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#include <cstdio>
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#include <cuda_runtime.h>
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#define MASTER 0
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#define SLAVE 1
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#define TS 8
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#define MAX_NUM_GPU 4
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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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static int N_Size[MAX_NUM_GPU];
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int num_devices = 1;
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// Array of device (GPU) pointers
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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 *fil_d[MAX_NUM_GPU];
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//static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
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__global__ void conv_kernel(
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float *_input, float *_output, float *_filter,
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int _N, int _C, int _H, int _W, int _K, int _R, int _S, int _pad, int _dilation, int _stride)
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{
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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 OH, OW;
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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 * 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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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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}
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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;
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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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input = _input;
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output = _output;
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filter = _filter;
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MPI_Status status;
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MPI_Request request;
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int size[2];
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int offset;
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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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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(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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N_Size[i]=1;
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}
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}
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else {
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num_devices = MAX_NUM_GPU;
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int qq = size[mpi_rank] / MAX_NUM_GPU;
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int rr = size[mpi_rank] % MAX_NUM_GPU;
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for (int i = 0; i < MAX_NUM_GPU; i++) {
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N_Size[i] = qq;
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if (i < rr) N_Size[i]++;
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}
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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], N_Size[i]*C*H*W*sizeof(float)));
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CUDA_CALL( cudaMalloc(&out_d[i], N_Size[i]*K*OH*OW*sizeof(float)));
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CUDA_CALL( cudaMalloc(&fil_d[i], K*C*R*S*sizeof(float)));
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}
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if (mpi_rank == 0 && mpi_world_size == 2 && size[1] !=0) {
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MPI_Isend(&input[size[0]*C*H*W], size[1]*C*H*W, MPI_FLOAT, SLAVE, 0, MPI_COMM_WORLD, &request);
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MPI_Isend(filter, K*C*R*S, MPI_FLOAT, SLAVE, 0, MPI_COMM_WORLD, &request);
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if(size[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size[mpi_rank];
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}
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}
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else if (mpi_rank == 1 && size[mpi_rank] != 0) {
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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, MASTER, 0, MPI_COMM_WORLD, &status);
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MPI_Recv(filter, K*C*R*S, MPI_FLOAT, MASTER, 0, MPI_COMM_WORLD, &status);
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if(size[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size[mpi_rank];
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}
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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( cudaMemcpy(in_d[i], input + offset, N_Size[i]*C*H*W * sizeof(float), cudaMemcpyHostToDevice));
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CUDA_CALL( cudaMemcpy(fil_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( 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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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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conv_kernel<<<gridDim, blockDim>>>(in_d[i], out_d[i], fil_d[i], N_Size[i], C, H, W, K, R, S, 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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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, out_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( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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if (mpi_rank == 0 && mpi_world_size == 2 && size[1] !=0) {
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MPI_Recv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, SLAVE, 0, MPI_COMM_WORLD, &status);
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}
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else if(mpi_rank == 1 && size[1] !=0) {
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MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, MASTER, 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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{
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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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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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}
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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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}
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