209 lines
6.4 KiB
Plaintext
209 lines
6.4 KiB
Plaintext
#include "util.h"
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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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#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_NODES 2
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#define MAX_NUM_GPU 4
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#define TS 8
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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 num_devices;
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static int Nstart[MAX_NUM_GPU], Nsize[MAX_NUM_GPU];
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static int NN[MAX_NODES];
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MPI_Status status;
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MPI_Request request;
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static float *input_d[MAX_NUM_GPU];
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static float *output_d[MAX_NUM_GPU];
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static float *filter_d[MAX_NUM_GPU];
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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 = (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 n = blockIdx.x;
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int k = blockIdx.y;
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int oh = blockIdx.z;
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int ow = threadIdx.x;
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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 = 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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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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// Scatter Input
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if (mpi_rank == 0 && NN[1] != 0) {
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MPI_Isend(&input[NN[0] * C * H * W], (NN[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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} else if (mpi_rank == 1 && NN[mpi_rank] != 0) {
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alloc_tensor(&input, NN[1], C, H, W);
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alloc_tensor(&output, NN[1], K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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MPI_Recv(input, (NN[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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}
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if(NN[mpi_rank] < MAX_NUM_GPU) num_devices = NN[mpi_rank];
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// Upload input and filter to every GPU
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(input_d[i], input + Nstart[i] * C * H * W, Nsize[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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}
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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(Nsize[i], K, OH);
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dim3 blockDim(OW, 1);
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CUDA_CALL( cudaSetDevice(i) );
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conv<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], Nsize[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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// Download output from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(output + Nstart[i] * K * OH * OW, output_d[i], Nsize[i] * K * OH * OW * sizeof(float), 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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// Gather Output
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if (mpi_rank == 0) {
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MPI_Recv(&output[NN[0] * K * OH * OW], (NN[1] * K * OH * OW), MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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} else {
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MPI_Isend(output, NN[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;
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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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//printf("\nNode[%d] mpi_ramk = %d, mpi_world_size = %d\n", mpi_rank, mpi_rank, 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("Node[%d] [GPU %d] %s\n", mpi_rank, 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_world_size == 2) {
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NN[0] = N - (N / 2);
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NN[1] = N / 2;
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} else {
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NN[0] = N;
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NN[1] = 0;
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}
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// Setup problem size for each GPU
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if(NN[mpi_rank] < MAX_NUM_GPU) {
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num_devices = NN[mpi_rank];
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for(int i = 0; i < NN[mpi_rank]; i++) {
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Nstart[i] = i;
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Nsize[i] = 1;
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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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Nstart[i] = (NN[mpi_rank] / num_devices) * i;
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Nsize[i] = ((NN[mpi_rank] / num_devices) * (i + 1)) - Nstart[i];
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}
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Nsize[num_devices - 1] = NN[mpi_rank] - Nstart[num_devices - 1];
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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(&input_d[i], Nsize[i] * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&filter_d[i], K * C * R * S * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&output_d[i], Nsize[i] * K * OH * OW * 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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