253 lines
7.3 KiB
Plaintext
253 lines
7.3 KiB
Plaintext
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#include "convolution.h"
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#include <mpi.h>
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#include <omp.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 MAX_NUM_NODE 4
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#define THREADS_PER_BLOCK 16
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#define THREADS_DIM 4
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#define WPT 8
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#define RTS 2
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int num_devices = 0;
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__global__ void sgemm(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 OH, int OW,
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int pad, int dilation, int stride) {
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int row = threadIdx.x;
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int col = threadIdx.y;
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int dim = threadIdx.z;
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int oh = blockDim.x * blockIdx.x + threadIdx.x;
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int ow = blockDim.y * blockIdx.y + threadIdx.y;
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int k = blockDim.z * blockIdx.z + threadIdx.z;
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__shared__ float fsub[THREADS_DIM][THREADS_PER_BLOCK][THREADS_PER_BLOCK];
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int r_q = R / THREADS_PER_BLOCK;
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int r_r = R % THREADS_PER_BLOCK;
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if (r_r != 0)
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r_q += 1;
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int s_q = S / THREADS_PER_BLOCK;
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int s_r = S % THREADS_PER_BLOCK;
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if (s_r != 0)
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s_q += 1;
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for (int n = 0; n < N; n++) {
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float temp = 0.0f;
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for (int c = 0; c < C; c++) {
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for (int rr = 0; rr < r_q; rr++) {
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int f_row = THREADS_PER_BLOCK * rr + row;
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for (int ss = 0; ss < s_q; ss++) {
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int f_col = THREADS_PER_BLOCK * ss + col;
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if (f_row >= R || f_col >= S || k >= K)
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fsub[dim][row][col] = 0;
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else
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fsub[dim][row][col] = filter[k * C * R * S + c * R * S + f_row * S + f_col];
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__syncthreads();
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for (int r = 0; r < THREADS_PER_BLOCK; r++) {
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int h = oh * stride - pad + (THREADS_PER_BLOCK * rr + r) * dilation;
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for (int s = 0; s < THREADS_PER_BLOCK; s++) {
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int w = ow * stride - pad + (THREADS_PER_BLOCK * ss + s) * dilation;
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if (oh >= OH || ow >= OW || 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 = fsub[dim][r][s];
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temp += i * f;
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}
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}
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//printf("temp : %f\n", temp);
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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 && k < K)
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = temp;
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}
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}
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// Array of device (GPU) pointers
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static float *a_d[MAX_NUM_GPU];
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static float *b_d[MAX_NUM_GPU];
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static float *c_d[MAX_NUM_GPU];
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static int Mbegin[MAX_NUM_GPU], Mend[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 is[MAX_NUM_NODE], ie[MAX_NUM_NODE];
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int count;
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extern void alloc_tensor(float **t, int D0, int D1, int D2, int D3);
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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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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_Status status;
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for (int i = 0; i < mpi_world_size; i++) {
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is[i] = N / mpi_world_size * i;
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ie[i] = N / mpi_world_size * (i + 1);
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}
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ie[mpi_world_size - 1] = N;
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count = ie[mpi_rank] - is[mpi_rank];
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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_Send(&input[is[i] * C * H * W], (ie[i] - is[i]) * C * H * W, MPI_FLOAT, i, 0, MPI_COMM_WORLD);
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}
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} else {
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alloc_tensor(&input, count, C, H, W);
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alloc_tensor(&output, count, K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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MPI_Recv(input, count * C * H * W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
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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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// 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("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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// Setup problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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Mbegin[i] = (count / num_devices) * i;
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Mend[i] = (count / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = count;
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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(&a_d[i], (Mend[i] - Mbegin[i]) * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b_d[i], K * C * R * S * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c_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(a_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(b_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( cudaDeviceSynchronize() );
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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(THREADS_PER_BLOCK, THREADS_PER_BLOCK, THREADS_DIM);
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dim3 gridDim(((OH + THREADS_PER_BLOCK -1) / THREADS_PER_BLOCK), ((OW + THREADS_PER_BLOCK -1) / THREADS_PER_BLOCK), (K + THREADS_DIM -1) / THREADS_DIM);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm<<<gridDim, blockDim>>>(a_d[i], c_d[i], b_d[i], (Mend[i] - Mbegin[i]), C,
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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( cudaDeviceSynchronize() );
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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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// Do any post-matmul cleanup work here.
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MPI_Status status;
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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, c_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( cudaDeviceSynchronize() );
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}
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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[is[i] * K * OH * OW], (ie[i] - is[i]) * K * OH * OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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}
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}
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else {
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MPI_Send(output, count * K * OH * OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD);
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}
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}
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