368 lines
12 KiB
Plaintext
368 lines
12 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 <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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#define MAX_NUM_GPU 4
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#define TS 1
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int num_devices[2] = {0,0};
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__global__ void conv(float *input, float *filter, float *output, int N, int C, int H, int W, int K, int R, int S, 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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if(n >= N || k >= K || oh >= OH || ow >= OW ) return;
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// __syncthreads();
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float tmp = 0.0f;
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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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tmp += i * f;
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}
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}
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}
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// __syncthreads();
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output[n * K * OH * OW + k * OH * OW + oh * OW + ow] = tmp;
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}
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// Array of device (GPU) pointers
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static float *a1_d[MAX_NUM_GPU];
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static float *a2_d[MAX_NUM_GPU];
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static float *b1_d[MAX_NUM_GPU];
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static float *b2_d[MAX_NUM_GPU];
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static float *c1_d[MAX_NUM_GPU];
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static float *c2_d[MAX_NUM_GPU];
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static int Mbegin1[MAX_NUM_GPU], Mend1[MAX_NUM_GPU];
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static int Mbegin2[MAX_NUM_GPU], Mend2[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 portion, low_bound, upper_bound;
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MPI_Status status;
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MPI_Request request;
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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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N = _N, C = _C, H = _H, W = _W;
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K = _K, R = _R, S = _S;
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pad = _pad, dilation = _dilation, 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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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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size_t bytes_nchw = N*C*H*W*sizeof(float);
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size_t bytes_kcrs = K*C*R*S*sizeof(float);
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if (mpi_rank == 0) {
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portion = (N / mpi_world_size);
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for(int i=1; i < mpi_world_size; i++){
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low_bound = i*portion;
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if( ((i+1)==mpi_world_size) && (( N % mpi_world_size) != 0 ) ){
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upper_bound = N;
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}
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else{
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upper_bound = low_bound + portion;
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}
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MPI_Isend(&low_bound, 1, MPI_INT, i, 1, MPI_COMM_WORLD, &request);
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MPI_Isend(&upper_bound, 1, MPI_INT, i, 2, MPI_COMM_WORLD, &request);
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MPI_Isend(&input[low_bound*C*H*W], ( (upper_bound - low_bound)*C*H*W ), MPI_FLOAT, i, 3, MPI_COMM_WORLD, &request);
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MPI_Isend(&filter[0], (K*C*R*S), MPI_FLOAT, i, 4, MPI_COMM_WORLD, &request);
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}
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low_bound = 0;
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upper_bound = portion;
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int n = ((upper_bound-low_bound)/TS)*TS;
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// Setup problem size for each GPU
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for (int i = 0; i < num_devices[0]; i++) {
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Mbegin1[i] = (n / num_devices[0]) * i + low_bound;
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Mend1[i] = (n / num_devices[0]) * (i + 1) + low_bound;
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}
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Mend1[num_devices[0] - 1] = n + low_bound;
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// Upload A and B matrix to every GPU
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for (int i = 0; i < num_devices[0]; i++) {
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CUDA_CALL( cudaMemcpy(a1_d[i], input + Mbegin1[i] * C * H * W ,
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(Mend1[i] - Mbegin1[i]) * C * H * W * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(b1_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[0]; 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[0]; i++) {
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dim3 gridDim( Mend1[i] - Mbegin1[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>>>(a1_d[i], b1_d[i], c1_d[i], Mend1[i]-Mbegin1[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[0]; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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} // mpi_rank == 0
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else{
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MPI_Recv(&low_bound, 1, MPI_INT, 0, 1, MPI_COMM_WORLD, &status);
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MPI_Recv(&upper_bound, 1, MPI_INT, 0, 2, MPI_COMM_WORLD, &status);
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input = (float*)malloc(bytes_nchw);
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filter = (float*)malloc(bytes_kcrs);
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MPI_Recv(&input[low_bound*C*H*W], ( (upper_bound - low_bound)*C*H*W ), MPI_FLOAT, 0, 3, MPI_COMM_WORLD, &status);
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MPI_Recv(&filter[0], (K*C*R*S ), MPI_FLOAT, 0, 4, MPI_COMM_WORLD, &status);
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int n2 = ((upper_bound-low_bound)/TS)*TS;
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// Setup problem size for each GPU
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for (int i = 0; i < num_devices[1]; i++) {
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Mbegin2[i] = (n2 / num_devices[1]) * i;
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Mend2[i] = (n2 / num_devices[1]) * (i + 1);
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}
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Mend2[num_devices[1] - 1] = n2;
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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[1]; i++) {
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CUDA_CALL( cudaMemcpy(a2_d[i], input + (Mbegin2[i] + low_bound )* C * H * W ,
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(Mend2[i] - Mbegin2[i]) * C * H * W * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(b2_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[1]; 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[1]; i++) {
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dim3 gridDim( Mend2[i] - Mbegin2[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>>>(a2_d[i], b2_d[i], c2_d[i], Mend2[i]-Mbegin2[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[1]; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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free(input);
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free(filter);
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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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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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size_t bytes_nchw = N*C*H*W*sizeof(float);
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size_t bytes_kcrs = K*C*R*S*sizeof(float);
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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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if (mpi_rank == 0) {
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portion = (N / mpi_world_size);
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for(int i=1; i < mpi_world_size; i++){
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low_bound = i*portion;
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if( ((i+1)==mpi_world_size) && (( N % mpi_world_size) != 0 ) ){
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upper_bound = N;
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}
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else{
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upper_bound = low_bound + portion;
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}
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MPI_Isend(&low_bound, 1, MPI_INT, i, 1, MPI_COMM_WORLD, &request);
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MPI_Isend(&upper_bound, 1, MPI_INT, i, 2, MPI_COMM_WORLD, &request);
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}
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low_bound = 0;
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upper_bound = portion;
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int n = ((upper_bound-low_bound)/TS)*TS;
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CUDA_CALL( cudaGetDeviceCount(&num_devices[0]) );
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printf("1:Using %d devices\n", num_devices[0]);
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for (int i = 0; i < num_devices[0]; 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("[1:GPU %d] %s\n", i, prop.name);
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}
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if (num_devices[0] <= 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[0]; i++) {
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Mbegin1[i] = (n / num_devices[0]) * i + low_bound;
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Mend1[i] = (n / num_devices[0]) * (i + 1) + low_bound;
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}
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Mend1[num_devices[0] - 1] = n + low_bound;
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// Allocate device memory for each GPU
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for (int i = 0; i < num_devices[0]; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&a1_d[i], (Mend1[i] - Mbegin1[i]) * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b1_d[i], K * C * R * S * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c1_d[i], (Mend1[i] - Mbegin1[i]) * K * OH * OW * sizeof(float)) );
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}
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//
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}
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else{
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MPI_Recv(&low_bound, 1, MPI_INT, 0, 1, MPI_COMM_WORLD, &status);
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MPI_Recv(&upper_bound, 1, MPI_INT, 0, 2, MPI_COMM_WORLD, &status);
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//
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int n2 = ((upper_bound-low_bound)/TS)*TS;
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CUDA_CALL( cudaGetDeviceCount(&num_devices[1]) );
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printf("2:Using %d devices\n", num_devices[1]);
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for (int i = 0; i < num_devices[1]; i++) {
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cudaDeviceProp prop;
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CUDA_CALL( cudaGetDeviceProperties(&prop, i) );
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printf("[2:GPU %d] %s\n", i, prop.name);
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}
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if (num_devices[1] <= 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[1]; i++) {
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Mbegin2[i] = (n2 / num_devices[1]) * i;
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Mend2[i] = (n2 / num_devices[1]) * (i + 1);
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}
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Mend2[num_devices[1] - 1] = n2;
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// Allocate device memory for each GPU
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for (int i = 0; i < num_devices[1]; i++) {
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&a2_d[i], (Mend2[i] - Mbegin2[i]) * C * H * W * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b2_d[i], K * C * R * S * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c2_d[i], (Mend2[i] - Mbegin2[i]) * K * OH * OW * sizeof(float)) );
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}
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//
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} // else
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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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N = _N; C = _C; H = _H; W = _W;
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K = _K; R = _R; S = _S;
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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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OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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||
|
|
OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
|
||
|
|
size_t bytes_nkohow = N*K*OH*OW*sizeof(float);
|
||
|
|
|
||
|
|
if (mpi_rank == 0) {
|
||
|
|
|
||
|
|
for(int i = 1; i < mpi_world_size; i++){
|
||
|
|
|
||
|
|
MPI_Recv(&low_bound, 1, MPI_INT, i, 4, MPI_COMM_WORLD, &status);
|
||
|
|
MPI_Recv(&upper_bound, 1, MPI_INT, i, 5, MPI_COMM_WORLD, &status);
|
||
|
|
MPI_Recv(&output[low_bound*K*OH*OW], ( (upper_bound - low_bound)*K*OH*OW ), MPI_FLOAT, i, 6, MPI_COMM_WORLD, &status);
|
||
|
|
|
||
|
|
}
|
||
|
|
|
||
|
|
for (int i = 0; i < num_devices[0]; i++) {
|
||
|
|
CUDA_CALL( cudaMemcpy( output + Mbegin1[i] * K * OH * OW , c1_d[i],
|
||
|
|
(Mend1[i] - Mbegin1[i]) * K * OH * OW * sizeof(float),
|
||
|
|
cudaMemcpyDeviceToHost) );
|
||
|
|
}
|
||
|
|
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
|
||
|
|
for (int i = 0; i < num_devices[0]; i++) {
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
||
|
|
}
|
||
|
|
|
||
|
|
}
|
||
|
|
else{
|
||
|
|
|
||
|
|
output = (float*)malloc(bytes_nkohow);
|
||
|
|
for (int i = 0; i < num_devices[1]; i++) {
|
||
|
|
CUDA_CALL( cudaMemcpy( output + (Mbegin2[i] + low_bound) * K * OH * OW , c2_d[i],
|
||
|
|
(Mend2[i] - Mbegin2[i]) * K * OH * OW * sizeof(float),
|
||
|
|
cudaMemcpyDeviceToHost) );
|
||
|
|
}
|
||
|
|
// DO NOT REMOVE; NEEDED FOR TIME MEASURE
|
||
|
|
for (int i = 0; i < num_devices[1]; i++) {
|
||
|
|
CUDA_CALL( cudaSetDevice(i) );
|
||
|
|
CUDA_CALL( cudaDeviceSynchronize() );
|
||
|
|
}
|
||
|
|
|
||
|
|
MPI_Isend(&low_bound, 1, MPI_INT, 0, 4, MPI_COMM_WORLD, &request);
|
||
|
|
MPI_Isend(&upper_bound, 1, MPI_INT, 0, 5, MPI_COMM_WORLD, &request);
|
||
|
|
MPI_Isend(&output[low_bound*K*OH*OW], ( (upper_bound - low_bound)*K*OH*OW ), MPI_FLOAT, 0, 6, MPI_COMM_WORLD, &request);
|
||
|
|
free(output);
|
||
|
|
}
|
||
|
|
|
||
|
|
}
|