305 lines
9.1 KiB
Plaintext
305 lines
9.1 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 <cuda_runtime.h>
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#include "util.h"
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#define TS 8
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#define MAX_NODE 2
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#define MAX_NUM_GPU 8
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#define TILE_WIDTH 32
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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 float *in_d[MAX_NUM_GPU];
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static float *out_d[MAX_NUM_GPU];
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static float *fil_d[MAX_NUM_GPU];
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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static int MM[MAX_NUM_GPU];
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int num_devices;
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__global__ void conv(float *_input, float *_output, float *_filter,
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int _N, int _C, int _H, int _W, int _K, int _R, int _S,
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int _pad, int _dilation, int _stride){
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// const int globalRow = blockDim.x * blockIdx.x + threadIdx.x;
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// const int globalCol = blockDim.y * blockIdx.y + threadIdx.y;
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int OH, OW;
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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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// __shared__ float ds_i[TILE_WIDTH][TILE_WIDTH];
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// __shared__ float ds_f[TILE_WIDTH][TILE_WIDTH];
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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;
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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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int size[MAX_NODE];
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input = _input;
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output = _output;
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filter = _filter;
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MPI_Request request;
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MPI_Status status;
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// if(mpi_world_size == 2)
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// size[1] = _N/2;
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// else
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size[1] = 0;
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size[0] = _N - size[1];
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/* if(mpi_world_size == 2){
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for(int i=0; i< num_devices; i++){
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Mbegin[i] = (N/2) /num_devices * i;
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Mend[i] = (N/2) /num_devices*(i+1);
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}
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for(int i=0; i< num_devices; i++){
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Mbegin[i+4] = (N/2)/num_devices * i;
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Mend[i+4] = (N/2)/num_devices*(i+1);
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}
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Mend[num_devices*2-1] = N;
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for(int i=0; i<num_devices*2; i++){
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MM[i] = Mend[i] - Mbegin[i];
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}
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}else{
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*/
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for(int i=0; i< num_devices; i++){
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Mbegin[i] = (N/num_devices) * i;
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Mend[i] = (N/num_devices)*(i+1);
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}
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Mend[num_devices-1] = N;
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for(int i=0; i<num_devices; i++){
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MM[i] = Mend[i] - Mbegin[i];
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}
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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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/*
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if(mpi_rank == 0 && mpi_world_size == 2){
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MPI_Isend(&input[size[0]*_C*_H*_W], size[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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}
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else if(mpi_world_size == 2){
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alloc_tensor(&input, size[1], _C, _H, _W);
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alloc_tensor(&output, size[1], _K, OH, OW);
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alloc_tensor(&filter, _K, _C, _R, _S);
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MPI_Recv(input, size[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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*/
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if(mpi_rank == 0){
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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(&in_d[i], MM[i]*_C*_H*_W*sizeof(float)));
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CUDA_CALL( cudaMalloc(&out_d[i], MM[i]*_K*OH*OW*sizeof(float)));
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CUDA_CALL( cudaMalloc(&fil_d[i], _K*_C*_R*_S*sizeof(float)));
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}
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for(int i=0; i< num_devices; i++){
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CUDA_CALL( cudaMemcpy(in_d[i], _input + Mbegin[i]*_C*_H*_W, MM[i]*_C*_H*_W*sizeof(float), cudaMemcpyHostToDevice));
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CUDA_CALL( cudaMemcpy(fil_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( cudaDeviceSynchronize() );
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}
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//printf("check1\n");
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for(int i=0; i<num_devices; i++){
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dim3 blockDim(OW, 1);
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dim3 gridDim(MM[i],_K,OH);
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CUDA_CALL( cudaSetDevice(i) );
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conv<<<gridDim, blockDim>>>(in_d[i],out_d[i],fil_d[i],MM[i],_C,_H,_W,_K,_R,_S,_pad,_dilation,_stride);
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}
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// printf("check2\n");
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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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/* printf("no 1\n");
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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(&in_d[i+4], MM[i+4]*_C*_H*_W*sizeof(float)));
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CUDA_CALL( cudaMalloc(&out_d[i+4], MM[i+4]*_K*OH*OW*sizeof(float)));
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CUDA_CALL( cudaMalloc(&fil_d[i+4], _K*_C*_R*_S*sizeof(float)));
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}
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printf("no 2\n");
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for(int i=0; i< num_devices; i++){
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CUDA_CALL( cudaMemcpy(in_d[i+4], _input + Mbegin[i+4]*_C*_H*_W, MM[i+4]*_C*_H*_W*sizeof(float), cudaMemcpyHostToDevice));
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CUDA_CALL( cudaMemcpy(fil_d[i+4], _filter, _K*_C*_R*_S*sizeof(float), cudaMemcpyHostToDevice));
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}
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printf("no 3\n");
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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("check1\n");
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for(int i=0; i<num_devices; i++){
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dim3 blockDim(OW, 1);
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dim3 gridDim(MM[i+4],_K,OH);
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CUDA_CALL( cudaSetDevice(i) );
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conv<<<gridDim, blockDim>>>(in_d[i+4],out_d[i+4],fil_d[i+4],MM[i+4],_C,_H,_W,_K,_R,_S,_pad,_dilation,_stride);
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}
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printf("check2\n");
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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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// printf("check3\n");
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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( 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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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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/*
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if(mpi_world_size == 2){
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size[1] = _N/2;
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node_num = 2;
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}
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else {
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size[1] = 0;
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node_num = 1;
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}
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size[0] = N - size[1];
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for(int i=0; i< num_devices; i++){
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Mbegin[i] = (N/num_devices) * i;
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Mend[i] = (N/num_devices)*(i+1);
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}
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Mend[num_devices-1] = N;
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for(int i=0; i<num_devices; i++){
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MM[i] = Mend[i] - Mbegin[i];
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}
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*/
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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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for(int i=0; i< num_devices ; i++){
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CUDA_CALL( cudaSetDevice(i) );
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CUDA_CALL( cudaMalloc(&in_d[i], MM[i]*_C*_H*_W*sizeof(float)));
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CUDA_CALL( cudaMalloc(&out_d[i], MM[i]*_K*OH*OW*sizeof(float)));
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CUDA_CALL( cudaMalloc(&fil_d[i], _K*_C*_R*_S*sizeof(float)));
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}
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for(int i=0; i< num_devices; i++){
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CUDA_CALL( cudaMemcpy(in_d[i], _input + Mbegin[i]*_C*_H*_W, MM[i]*_C*_H*_W*sizeof(float), cudaMemcpyHostToDevice));
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CUDA_CALL( cudaMemcpy(fil_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( cudaDeviceSynchronize() );
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}
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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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/* if(mpi_rank == 0 && mpi_world_size == 2){
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printf("final 1\n");
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for(int i=0; i<num_devices*2; i++){
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CUDA_CALL( cudaMemcpy(output+Mbegin[i]*K*OH*OW, out_d[i], MM[i]*K*OH*OW*sizeof(float), cudaMemcpyDeviceToHost));
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}
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printf("final 2\n");
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for(int i = 0; i < num_devices*2;i++){
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}else if(mpi_rank == 0 && mpi_world_size == 1){
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*/
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if(mpi_rank == 0){
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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, out_d[i], MM[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( cudaDeviceSynchronize() );
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}}
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// }
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//printf("Done\n");
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}
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