343 lines
12 KiB
Plaintext
343 lines
12 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 <omp.h>
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#include <cstdio>
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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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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 thread_num = 20;
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#define MAX_NUM_GPU 4
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int num_devices = 0;
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int num_devices_change = 4;
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// Array of device (GPU) pointers
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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 Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
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#define MASTER_TO_SLAVE_TAG 1 //tag for messages sent from master to slaves
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#define SLAVE_TO_MASTER_TAG 4 //tag for messages sent from slaves to master
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MPI_Request request;
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MPI_Status status;
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__global__ void sgemm_2(float *input2, float *filter2, float *output2,
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int N, int C, int H, int W, int K, int R, int S, int pad, int dilation, int stride, int dev, int start_n_gpu, int end_n_gpu) {
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int i = blockDim.x * blockIdx.x + threadIdx.x; // raw index
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int j = blockDim.y * blockIdx.y + threadIdx.y; // column index
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//if (i >= N || j >= W) return;
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if(i > (end_n_gpu-start_n_gpu)) return;
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//if(i<start_m || i>=end_m) return;
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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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//printf("Dev(%d)(%d/%d) StartN(%d), EndN(%d)\n", dev, i, j, start_n_gpu, end_n_gpu);
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/* int preval1_1= C * H * W;
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int preval1_2 = H * W;
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int preval2_1 = C * R * S;
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int preval2_2 = R * S;
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int preval3_1 = K * OH * OW;
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int preval3_2 = OH * OW;
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*/
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int v;
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for (int n = start_n_gpu; n < end_n_gpu; ++n) {
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v = (n - start_n_gpu);
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for (int k = 0; k < K; ++k) {
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for (int oh = 0; oh < OH; ++oh) {
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for (int ow = 0; ow < OW; ++ow) {
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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 = input2[n * C * H * W + c * H * W + h * W + w];
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float i = input2[v * C * H * W + c * H * W + h * W + w];
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float f = filter2[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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//output2[n * K * OH * OW + k * OH * OW + oh * OW + ow] = o;
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output2[v * 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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}
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//printf("Dev(%d)(%d/%d) StartN(%d), EndN(%d), BDx(%d), BDy(%d), Bx(%d), By(%d), Tx(%d), Ty(%d) \n", dev, i, j, start_n_gpu, end_n_gpu ,
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// blockDim.x, blockDim.y, blockIdx.x, blockIdx.y, threadIdx.x, threadIdx.y);
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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 size_n;
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int start_n;
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int end_n;
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int start_n_no0;
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int end_n_no0;
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if(mpi_rank == 0){
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for(int node=0; node< mpi_world_size; node++){
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size_n = N / (mpi_world_size);
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start_n = (node)*size_n;
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if(node == 0) start_n_no0 = start_n;
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if(((node+1) == mpi_world_size) && ((N%(mpi_world_size)) != 0)){
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end_n = N;
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if(node == 0) end_n_no0 = end_n;
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}
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else{
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end_n = start_n + size_n;
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if(node == 0) end_n_no0 = end_n;
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}
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if(node >= 1){
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MPI_Send(&start_n, 1, MPI_INT, node, MASTER_TO_SLAVE_TAG, MPI_COMM_WORLD);
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//printf("SEND start_n (%d) to (%d)\n", start_n, node);
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MPI_Send(&end_n, 1, MPI_INT, node, MASTER_TO_SLAVE_TAG + 1, MPI_COMM_WORLD);
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//printf("SEND end_n (%d) to (%d)\n", end_n, node);
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int sizeBuf = (end_n - start_n) * C * H * W;
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//printf("SEND input Buff Len : (%d) to (%d)\n", sizeBuf, node);
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MPI_Send(&input[start_n*C * H * W], sizeBuf, MPI_FLOAT, node, MASTER_TO_SLAVE_TAG + 2, MPI_COMM_WORLD);
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//printf("SEND input size (%d) to (%d)\n", sizeBuf, node);
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}
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}
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}
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else{
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alloc_tensor(&input, N, C, H, W);
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MPI_Recv(&start_n, 1, MPI_INT, 0, MASTER_TO_SLAVE_TAG, MPI_COMM_WORLD, &status);
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//printf("RECV start_n (%d) at (%d)\n", start_n, mpi_rank);
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MPI_Recv(&end_n, 1, MPI_INT, 0, MASTER_TO_SLAVE_TAG + 1, MPI_COMM_WORLD, &status);
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//printf("RECV end_n (%d) at (%d)\n", end_n, mpi_rank);
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int sizeBuf = (end_n - start_n) * C * H * W;
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//printf("RECV input Buff Len : %d \n", sizeBuf);
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MPI_Recv(&input[start_n*C * H * W], sizeBuf, MPI_FLOAT, 0, MASTER_TO_SLAVE_TAG + 2, MPI_COMM_WORLD, &status);
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//printf("RECV input size (%d) at (%d)\n", sizeBuf, mpi_rank);
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}
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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(mpi_rank > 0){
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alloc_tensor(&filter, K, C, R, S);
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}
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MPI_Bcast(&filter[0], K*C*R*S, MPI_FLOAT, 0, MPI_COMM_WORLD);
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printf("Bcast filter (%d) to (%d) \n", K*C*R*S, mpi_rank);
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if(mpi_rank > 0){
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alloc_tensor(&output, N, K, OH, OW);
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}
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MPI_Bcast(&output[0], N*K*OH*OW, MPI_FLOAT, 0, MPI_COMM_WORLD);
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printf("Bcast output (%d) to (%d) \n", N*K*OH*OW, mpi_rank);
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MPI_Barrier(MPI_COMM_WORLD);
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if(mpi_rank == 0){
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int size_n_gpu = end_n_no0 - start_n_no0;
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//int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
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// N Devide with GPU
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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] = (size_n_gpu / num_devices) * i;
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Nend[i] = (size_n_gpu / num_devices) * (i + 1);
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}
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int mod = (size_n_gpu % num_devices);
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int small_size = size_n_gpu / num_devices;
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Nend[num_devices - 1] = small_size * num_devices + mod;
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for (int i = 0; i < num_devices; i++) {
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printf("StartN(%d), EndN(%d)\n", Nbegin[i], Nend[i]);
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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], (Nend[i] - Nbegin[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], (Nend[i] - Nbegin[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 + Nbegin[i] * C * H * W ,
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(Nend[i] - Nbegin[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( cudaDeviceSynchronize() );
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}
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//convolution_micro_gpu(input, output, filter, N, C, H, W, K, R, S, pad, dilation, stride, start_n_no0, end_n_no0);
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printf("Start GPU Cal\n");
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for (int i = 0; i < num_devices; i++) {
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printf("StartN(%d), EndN(%d)\n", Nbegin[i], Nend[i]);
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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 gridDim((Nend[i] - Nbegin[i])*C*H*W, 1, 1); // grid : raw (Mend[i] - Mbegin[i]) , column (W)
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//dim3 blockDim(1, 1, 1); // block : 1 x 32
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dim3 gridDim((Nend[i] - Nbegin[i])*C*H*W, 1, 1); // grid : raw (Mend[i] - Mbegin[i]) , column (W)
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dim3 blockDim(1, 1, 1); // block : 1 x 32
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printf("Start %d , %d \n", (Nend[i] - Nbegin[i]), W);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm_2<<<gridDim, blockDim>>>(input_d[i], filter_d[i], output_d[i], N, C, H, W, K, R, S, pad, dilation, stride, i, Nbegin[i], Nend[i]);
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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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printf("End GPU Cal\n");
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// integrate ouput
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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 + Nbegin[i] * K * OH * OW , output_d[i],
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(Nend[i] - Nbegin[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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}
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else{
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//convolution_micro(input, output, filter, N, C, H, W, K, R, S, pad, dilation, stride, start_n, end_n);
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// integrate ouput
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if(mpi_rank >= 1){
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MPI_Send(&start_n, 1, MPI_INT, 0, SLAVE_TO_MASTER_TAG, MPI_COMM_WORLD);
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//printf("SEND output start_n (%d) to (%d)\n", start_n, 0);
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MPI_Send(&end_n, 1, MPI_INT, 0, SLAVE_TO_MASTER_TAG + 1, MPI_COMM_WORLD);
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//printf("SEND output end_n (%d) to (%d)\n", end_n, 0);
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int sizeBuf = (end_n - start_n) * K*OH*OW;
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//printf("SEND output Buff Len : (%d) to (%d)\n", sizeBuf, 0);
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MPI_Send(&output[start_n*K*OH*OW], sizeBuf, MPI_FLOAT, 0, SLAVE_TO_MASTER_TAG + 2, MPI_COMM_WORLD);
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//printf("SEND output size (%d) to (%d)\n", sizeBuf, 0);
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}
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}
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if(mpi_rank == 0){
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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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for (int node = 1; node < mpi_world_size; node++) {// untill all slaves have handed back the processed data
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//receive low bound from a slave
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MPI_Recv(&start_n, 1, MPI_INT, node, SLAVE_TO_MASTER_TAG, MPI_COMM_WORLD, &status);
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//receive upper bound from a slave
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MPI_Recv(&end_n, 1, MPI_INT, node, SLAVE_TO_MASTER_TAG + 1, MPI_COMM_WORLD, &status);
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//receive processed data from a slave
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MPI_Recv(&output[start_n*K*OH*OW], (end_n - start_n) * K*OH*OW, MPI_FLOAT, node, SLAVE_TO_MASTER_TAG + 2, MPI_COMM_WORLD, &status);
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}
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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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// cuda call init
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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printf("Using %d devices\n", num_devices);
|
||
|
|
printf("Change Device Num (%d)->(%d)\n", num_devices, num_devices_change);
|
||
|
|
num_devices = num_devices_change;
|
||
|
|
for (int i = 0; i < num_devices; i++) {
|
||
|
|
cudaDeviceProp prop;
|
||
|
|
CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
|
||
|
|
|
||
|
|
// Try printing more detailed information here
|
||
|
|
printf("[GPU %d] %s\n", i, prop.name);
|
||
|
|
}
|
||
|
|
|
||
|
|
if (num_devices <= 0) {
|
||
|
|
printf("No CUDA device found. Aborting\n");
|
||
|
|
exit(1);
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
void convolution_final(
|
||
|
|
int _N, int _C, int _H, int _W,
|
||
|
|
int _K, int _R, int _S,
|
||
|
|
int _pad, int _dilation, int _stride) {
|
||
|
|
}
|