327 lines
9.1 KiB
Plaintext
327 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 "util.h"
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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 outH, outW;
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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 num_devices = 0;
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#define MAX_NUM_GPU 4
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#define TILE_WIDTH 8
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static float *input_d[MAX_NUM_GPU];
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static float *output_d[MAX_NUM_GPU];
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static float *filter_d[MAX_NUM_GPU];
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static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
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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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__global__ void conv_cu(
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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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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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float temp =0.0f;
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int n,k,w,OH, OW,col,_row,_col;
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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(globalRow>=OH || globalCol >= _N*_K*OW) return;
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n = globalCol / (_K * OW);
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k= (globalCol- n *(_K * OW))/ OW;
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w= (globalCol- n *(_K * OW))-k*OW;
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col = w;
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_row = globalRow * _stride - _pad;
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_col = col * _stride - _pad;
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for (int c = 0; c<_C;c++){
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for(int i=0; i<_R;i++){
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for(int j=0;j<_S;j++){
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int height = _row + i *_dilation;
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int width = _col + j *_dilation;
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if(height<0|| width<0 || height>=_H || width>=_W) continue;
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float in = _input[n*_C*_W*_H + c*_W*_H + height*_W+width];
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float fil = _filter[k*_C*_R*_S + c*_R*_S + i*_S+j];
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temp += in * fil;
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}
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}
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}
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// printf("%s %d jjlee check output %lf\n",temp);
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_output[n*_K*OH*OW + k*OH*OW + globalRow*OW+col]=temp;
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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 mpi_work[2];
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MPI_Request request;
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MPI_Status status;
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int OH, OW;
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output=_output;
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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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input = _input;
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output = _output;
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filter = _filter;
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if (mpi_world_size == 2) //NODE 2
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{
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//if(_N==1)
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mpi_work[1] = _N / 2;
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mpi_work[0] = _N - (_N / 2);
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}
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else //NODE 1
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{
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mpi_work[0] = N;
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}
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outH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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outW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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if (mpi_world_size == 2) {
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if(mpi_rank == 0 )
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{
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MPI_Isend(&input[mpi_work[0]*C*H*W], mpi_work[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 {
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alloc_tensor(&input, mpi_work[1], C, H, W);
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alloc_tensor(&output, mpi_work[1], K, outH, outW);
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alloc_tensor(&filter, K, C, R, S);
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MPI_Recv(input, mpi_work[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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for (int i = 0; i < num_devices; i++) {
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CUDA_CALL( cudaSetDevice(i) );//jjlee
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// printf("%s %d num_devices %d n (begin %d end %d)%d \n",__func__,__LINE__,num_devices,Nbegin[i],Nend[i],Nend[i] - Nbegin[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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//printf("%s %d \n",__func__,__LINE__);
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for (int i = 0; i < num_devices; i++) {
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dim3 gridDim((OH+TILE_WIDTH-1)/TILE_WIDTH, ( (Nend[i] - Nbegin[i]) *K*OW + TILE_WIDTH - 1)/TILE_WIDTH, 1);
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dim3 blockDim(TILE_WIDTH, TILE_WIDTH, 1);
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CUDA_CALL( cudaSetDevice(i) );
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conv_cu<<<gridDim, blockDim>>>(input_d[i], output_d[i], filter_d[i], (Nend[i] - Nbegin[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; i++) {
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CUDA_CALL( cudaSetDevice(i) );//jjlee
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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#if 0
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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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outH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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outW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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if (mpi_rank == 0) {
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for (int n = 0; n < N; ++n) {
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for (int k = 0; k < K; ++k) {
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for (int oh = 0; oh < outH; ++oh) {
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for (int ow = 0; ow < outW; ++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 = 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 * outH * outW + k * outH * outW + oh * outW + ow] = o;
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}
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}
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}
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}
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}
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}
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#endif
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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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int mpi_work[2];
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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_world_size == 2) //NODE 2
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{
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mpi_work[1] = _N / 2;
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mpi_work[0] = _N - (_N / 2);
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}
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else //NODE 1
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{
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mpi_work[0] = _N;
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}
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N=mpi_work[0];
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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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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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//num_devices=2;
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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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Nbegin[i] = (N / num_devices) * i;
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Nend[i] = (N / num_devices) * (i + 1);
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}
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Nend[num_devices - 1] = N;
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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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// 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_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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int OH, OW;
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int mpi_work[2];
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MPI_Request request;
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MPI_Status status;
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if (mpi_world_size == 2) //NODE 2
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{
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mpi_work[1] = _N / 2;
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mpi_work[0] = _N - (_N / 2);
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}
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else //NODE 1
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{
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mpi_work[0] = N;
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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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// Download C matrix from GPUs
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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( 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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if (mpi_world_size == 2) {
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if(mpi_rank == 0){
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// printf("%s %d IRecv mpi_work[1] %d %d \n",__func__,__LINE__,mpi_work[1],mpi_work[1]*K*outH*outW);
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MPI_Recv(&output[mpi_work[0]*K*outH*outW], mpi_work[1]*K*outH*outW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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}
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else
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{
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// printf("%s %d ISend mpi_work[1] %d %d \n",__func__,__LINE__,mpi_work[1],mpi_work[1]*K*outH*outW);
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MPI_Isend(output, mpi_work[1]*K*outH*outW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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}
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}
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}
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