234 lines
6.9 KiB
Plaintext
234 lines
6.9 KiB
Plaintext
#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 <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 TS_X 2
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#define TS_Y 2
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#define TS_Z 32
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#define MAX_NUM_GPU 4
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int num_devices = 0;
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static float *input_d[MAX_NUM_GPU];
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static float *filter_d[MAX_NUM_GPU];
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static float *output_d[MAX_NUM_GPU];
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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void convolution_cuda();
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void convolution_cuda_init(int,int);
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void convolution_cuda_final(int);
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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 stride_N[4];
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static int offset[4];
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MPI_Status status;
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MPI_Request request;
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__global__ void conv_kernel(float *input, float *filter, float *output, int N, int C, int H, int W,
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int K, int R, int S, int OH, int OW, int pad, int dilation, int stride) {
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int k = blockDim.x * blockIdx.x + threadIdx.x;
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int oh = blockDim.y * blockIdx.y + threadIdx.y;
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int ow = blockDim.z * blockIdx.z + threadIdx.z;
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if (k >= K || oh>= OH || ow >= OW) return;
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for(int n = 0; n < N; n++){
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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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}
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void convolution_cuda() {
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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(TS_X, TS_Y, TS_Z);
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dim3 gridDim((K+TS_X-1)/TS_X, (OH+TS_Y-1)/TS_Y, (OW+TS_Z-1)/TS_Z);
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CUDA_CALL( cudaSetDevice(i) );
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conv_kernel<<<gridDim, blockDim>>>(input_d[i], filter_d[i], output_d[i], Mend[i] - Mbegin[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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}
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void convolution_cuda_init() {
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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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// Setup problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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Mbegin[i] = (stride_N[mpi_rank] / num_devices) * i;
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Mend[i] = (stride_N[mpi_rank] / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = stride_N[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(&input_d[i], (Mend[i] - Mbegin[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], (Mend[i] - Mbegin[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( cudaMemcpy(input_d[i], input + Mbegin[i] * C * H * W,
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(Mend[i] - Mbegin[i]) * C * H * W * sizeof(float),
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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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// 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_cuda_final() {
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// Do any post-matmul cleanup work here.
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// Download C matrix from GPUs
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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaMemcpy(output + Mbegin[i] * K * OH * OW, output_d[i],
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(Mend[i] - Mbegin[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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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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int divided_N = N / mpi_world_size;
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int modular_N = N % 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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offset[0] = 0;
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stride_N[0] = divided_N;
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for (int i = 1; i < mpi_world_size; i++) {
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if (i <= modular_N) stride_N[i] = divided_N + 1;
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else stride_N[i] = divided_N;
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offset[1] = divided_N;
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if (i > 1) offset[i] = offset[i-1] + stride_N[i-1];
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}
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if (mpi_rank != 0) {
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alloc_tensor(&input, stride_N[mpi_rank], C, H, W);
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alloc_tensor(&output, stride_N[mpi_rank], K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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}
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MPI_Bcast(filter, K*C*R*S, MPI_FLOAT, 0, MPI_COMM_WORLD);
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if (mpi_rank == 0) {
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for (int i = 1; i < mpi_world_size; i++)
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MPI_Isend(&input[offset[i]*C*H*W], stride_N[i]*C*H*W, MPI_FLOAT, i, 1, MPI_COMM_WORLD, &request);
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}
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else {
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MPI_Recv(input, stride_N[mpi_rank]*C*H*W, MPI_FLOAT, 0, 1, MPI_COMM_WORLD, &status);
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}
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convolution_cuda_init();
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convolution_cuda();
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convolution_cuda_final();
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if (mpi_rank == 0) {
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for (int i = 1; i < mpi_world_size; i++)
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MPI_Recv(&output[offset[i]*K*OH*OW], stride_N[i]*K*OH*OW, MPI_FLOAT, i, 2, MPI_COMM_WORLD, &status);
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}
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else {
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MPI_Send(output, stride_N[mpi_rank]*K*OH*OW, MPI_FLOAT, 0, 2, MPI_COMM_WORLD);
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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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}
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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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