224 lines
6.7 KiB
Plaintext
224 lines
6.7 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 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 4
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static float *input, *output, *filter;
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static float *in_d[MAX_NUM_GPU], *out_d[MAX_NUM_GPU], *fil_d[MAX_NUM_GPU];
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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 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 size[2];
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static int NN[MAX_NUM_GPU];
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static int OH, OW;
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int num_devices = 1;
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__global__ void conv(
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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, int OH, int OW) {
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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 KK = blockDim.z * blockIdx.z +threadIdx.z;
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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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if (KK >= _K) return;
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int n, w;
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w = globalCol;
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n = w / OW;
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w = w - n * OW;
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int col = w;
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int row = globalRow;
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if (globalRow >= OH || globalCol >= _N*OW) return;
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int start_row = row * _stride - _pad;
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int start_col = col * _stride - _pad;
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float o = 0.0f;
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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 h = start_row + i * _dilation;
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int w = start_col + j * _dilation;
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if (h < 0 || w < 0 || h >= _H || w >= _W) continue;
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float in = _input[n*_C*_W*_H + c*_W*_H + h*_W + w];
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float fil = _filter[KK*_C*_R*_S + c*_R*_S + i*_S + j];
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o += in * fil;
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}
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}
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}
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_output[n*_K*OH*OW + KK*OH*OW + row*OW + col] = 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 offset = 0;
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MPI_Request request;
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MPI_Status status;
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input = _input;
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output = _output;
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filter = _filter;
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if (mpi_rank == 0 && mpi_world_size == 2 && size[1] != 0) {
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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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if(size[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size[mpi_rank];
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}
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}
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else if (mpi_rank == 1 && size[mpi_rank] != 0) {
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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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if(size[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size[mpi_rank];
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}
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}
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offset = 0;
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for(int i = 0; i < num_devices; i++) {
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CUDA_CALL (cudaMemcpy( in_d[i], input + offset, NN[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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offset += NN[i] * C * H * W;
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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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for (int i = 0; i < num_devices; i++) {
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//dim3 gridDim( (OH+TS-1)/TS, (NN[i]*K*OW + TS - 1)/TS, 1);
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//dim3 blockDim( TS, TS, 1);
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dim3 gridDim( (OH+TS-1)/TS, (NN[i]*K*OW + TS - 1)/TS, (K+TS-1)/TS);
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dim3 blockDim( TS, TS, TS);
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CUDA_CALL ( cudaSetDevice(i) );
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conv <<<gridDim, blockDim>>> (in_d[i], out_d[i], fil_d[i], NN[i], _C, _H, _W, _K, _R, _S, _pad, _dilation, _stride, OH, OW);
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}
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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 ( cudaDeviceSynchronize() );
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}
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offset = 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 ( cudaMemcpy(output + offset, out_d[i], NN[i]*K*OH*OW * sizeof(float), cudaMemcpyDeviceToHost) );
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offset += NN[i]*K*OH*OW;
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}
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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 ( cudaDeviceSynchronize() );
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}
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if (mpi_rank == 0 && mpi_world_size == 2 && size[1] != 0) {
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MPI_Recv(&output[size[0]*K*OH*OW], size[1]*K*OH*OW, MPI_FLOAT, 1, 0, MPI_COMM_WORLD, &status);
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}
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else if (mpi_rank == 1 && size[1] != 0) {
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MPI_Isend(output, size[1]*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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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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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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//printf("debug: mpi_world_size=%d\n",mpi_world_size);
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//printf("debug: _N=%d\n",_N);
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if(mpi_world_size == 2 && _N > 4) size[1] = _N / 2;
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else size[1] = 0;
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size[0] = N - size[1];
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//printf("debug: size[0]=%d\n",size[0]);
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//printf("debug: size[1]=%d\n",size[1]);
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if(size[mpi_rank] < MAX_NUM_GPU) {
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num_devices = size[mpi_rank];
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for(int i = 0; i<size[mpi_rank]; i++)
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NN[i] = 1;
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}
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else {
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//num_devices = size[mpi_rank];
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num_devices = 4;
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int quotient = size[mpi_rank] / MAX_NUM_GPU;
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int remain = size[mpi_rank] % MAX_NUM_GPU;
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for (int i=0; i<MAX_NUM_GPU; i++) {
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NN[i] = quotient;
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if(i < remain) NN[i]++;
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}
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}
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//printf("debug: num_devices=%d\n",num_devices);
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for(int i = 0; i < num_devices; i++) {
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// if(i<4) {
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CUDA_CALL ( cudaSetDevice(i) );
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CUDA_CALL ( cudaMalloc(&in_d[i], NN[i]*C*H*W*sizeof(float)) );
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CUDA_CALL ( cudaMalloc(&out_d[i], NN[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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// else {
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// printf("error: i num=%d, num_devices=%d\n",i,num_devices);
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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 ( 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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}
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