211 lines
7.0 KiB
Plaintext
211 lines
7.0 KiB
Plaintext
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#include "convolution.h"
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#include <cstdio>
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#include <cuda_runtime.h>
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#include <mpi.h>
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#include <stdio.h>
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#define MAX_NUM_GPU 4
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#define MAX_NUM_NODE 2
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#define TS 32
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#define WPT 8
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#define RTS TS/WPT
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int num_devices = 0;
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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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void my_alloc_tensor(float **t, int D0, int D1, int D2, int D3) {
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*t = (float *) aligned_alloc(32, sizeof(float) * D0 * D1 * D2 * D3);
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if (*t == NULL) {
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printf("Failed to allocate memory for matrix.\n");
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exit(0);
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}
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}
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__global__ void sgemm(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 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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const int OH = (H + 2 * pad - dilation * (R - 1) - 1) / stride + 1;
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const int OW = (W + 2 * pad - dilation * (S - 1) - 1) / stride + 1;
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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 * OH * OW + k * OH * OW + oh * OW + ow] = o;
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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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// Array of device (GPU) pointers
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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_NODE], Nend[MAX_NUM_NODE];
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static int begin[MAX_NUM_GPU], end[MAX_NUM_GPU];
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//static int split_N[MAX_NUM_GPU];
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void convolution(float *_input, float *_output, float *_filter, int _N, int _C, int _H, int _W, int _K, int _R, int _S, 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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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 >= mpi_world_size) return;
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//if(mpi_rank != 0){
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// my_alloc_tensor(&input, N, C, H, W);
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// my_alloc_tensor(&output, N, K, OH, OW);
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// my_alloc_tensor(&filter, K, C, R, S);
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//}
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if (mpi_rank != 0){
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input = (float *) aligned_alloc(32, sizeof(float) * N*C*H*W);
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filter = (float *) aligned_alloc(32, sizeof(float) * K*C*R*S);
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output = (float *) aligned_alloc(32, sizeof(float) * N*K*OH*OW);
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}
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// Scatter A
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if (mpi_rank == 0) {
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for (int i=1; i<mpi_world_size; i++) {
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MPI_Send(&input[Nbegin[i]*C*H*W], (Nend[i]-Nbegin[i])*C*H*W, MPI_FLOAT, i, 0, MPI_COMM_WORLD);
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}
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}
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else {
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MPI_Recv(&input[Nbegin[mpi_rank]*C*H*W], (Nend[mpi_rank]-Nbegin[mpi_rank])*C*H*W, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, nullptr);
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}
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MPI_Bcast(filter, K*C*R*S, MPI_FLOAT, 0, MPI_COMM_WORLD);
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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+begin[i]*C*H*W, (end[i]-begin[i])*C*H*W*sizeof(float), 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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// Launch kernel on every GPU
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for (int i = 0; i < num_devices; i++) {
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dim3 gridDim(end[i]-begin[i], K, OH);
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dim3 blockDim(OW, 1, 1);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm<<<gridDim, blockDim>>>(input_d[i], filter_d[i], output_d[i], end[i]-begin[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( cudaDeviceSynchronize() );
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}
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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+begin[i]*K*OH*OW, output_d[i], (end[i]-begin[i])*K*OH*OW*sizeof(float), 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_rank == 0){
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for(int i=1; i<mpi_world_size; ++i){
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MPI_Recv(&output[Nbegin[i]*K*OH*OW], (Nend[i]-Nbegin[i])*K*OH*OW, MPI_FLOAT, i, 0, MPI_COMM_WORLD, nullptr);
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}
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}
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else{
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MPI_Send(&output[Nbegin[mpi_rank]*K*OH*OW], (Nend[mpi_rank]-Nbegin[mpi_rank])*K*OH*OW, MPI_FLOAT, 0, 0, MPI_COMM_WORLD);
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}
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}
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void convolution_init(int _N, int _C, int _H, int _W, int _K, int _R, int _S, 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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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 >= mpi_world_size) return;
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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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// 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 < mpi_world_size; i++) {
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Nbegin[i] = (N / mpi_world_size) * i;
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Nend[i] = (N / mpi_world_size) * (i + 1);
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}
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Nend[mpi_world_size - 1] = N;
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// Seupt problem size for each GPU
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for (int i = 0; i < num_devices; i++) {
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begin[i] = Nbegin[mpi_rank] + (Nend[mpi_rank]-Nbegin[mpi_rank])/num_devices*i;
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end[i] = Nbegin[mpi_rank] + (Nend[mpi_rank]-Nbegin[mpi_rank])/num_devices*(i+1);
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}
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end[num_devices - 1] = Nend[mpi_rank];
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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], (end[i]-begin[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], (end[i]-begin[i])*K*OH*OW*sizeof(float)) );
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}
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}
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void convolution_final(int _N, int _C, int _H, int _W, int _K, int _R, int _S, int _pad, int _dilation, int _stride) {
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// Do any post-matmul cleanup work here.
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if (mpi_rank >= mpi_world_size) return;
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}
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