270 lines
8.1 KiB
Plaintext
270 lines
8.1 KiB
Plaintext
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#include "convolution.h"
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#include "util.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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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 int input_line;
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static int filter_size;
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static int output_line;
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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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int num_devices = 0;
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// Array of device (GPU) pointers
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static float *i_d[MAX_NUM_GPU];
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static float *f_d[MAX_NUM_GPU];
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static float *o_d[MAX_NUM_GPU];
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static int Nbegin[MAX_NUM_GPU], Nend[MAX_NUM_GPU];
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__global__ void conv4(float *input, float* filter, float* output,
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int Nsplit, int C, int H, int W, int K, int R, int S, int OH, int OW,
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int pad, int dilation, int stride)
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{
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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.f;
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for (int c = 0; c < C; ++c) { // # of input channel
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for (int r = 0; r < R; ++r) { // filter height (=row)
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for (int s = 0; s < S; s+=4) { // filter width (=column)
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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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int idx_i = (n * C * H * W) + (c * H * W) + (h * W) + w;
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int idx_f = (k * C * R * S) + (c * R * S) + (r * S) + s;
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float4 i = make_float4(input[idx_i + 0], input[idx_i + 1], input[idx_i + 2], input[idx_i + 3]);
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float4 f = reinterpret_cast<float4*>(filter)[idx_f/4];
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o = o
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+ i.x * f.x
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+ i.y * f.y
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+ i.z * f.z
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+ i.w * f.w
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;
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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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__global__ void conv(float *input, float* filter, float* output,
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int Nsplit, int C, int H, int W, int K, int R, int S, int OH, int OW,
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int pad, int dilation, int stride)
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{
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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.f;
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for (int c = 0; c < C; ++c) { // # of input channel
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for (int r = 0; r < R; ++r) { // filter height (=row)
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for (int s = 0; s < S; ++s) { // filter width (=column)
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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 cuda_init(int ns, int ne)
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{
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int N_ = ne - ns;
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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 < num_devices; i++) {
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Nbegin[i] = ns + (N_ / num_devices) * i;
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Nend[i] = ns + (N_ / num_devices) * (i + 1);
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/* debug */// printf("%d: Nbegin[%d] = %d, Nend[%d] = %d\n", mpi_rank, i, Nbegin[i], i, Nend[i]);
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}
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Nend[num_devices - 1] = ns + 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(&i_d[i], (Nend[i] - Nbegin[i]) * input_line * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&f_d[i], filter_size * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&o_d[i], (Nend[i] - Nbegin[i]) * output_line * sizeof(float)) );
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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; i++) {
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CUDA_CALL( cudaMemcpy(i_d[i], input + Nbegin[i] * input_line,
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(Nend[i] - Nbegin[i]) * input_line * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(f_d[i], filter, filter_size * sizeof(float), cudaMemcpyHostToDevice) );
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}
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// DO NOT REMOVE; NEEDED FOR TIME NEASURE
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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 cuda_run()
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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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int Nsplit = Nend[i] - Nbegin[i];
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dim3 blockDim(OW, 1);
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dim3 gridDim(Nsplit, K, OH);
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if (S==16 && R==16 && dilation==1) {
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CUDA_CALL( cudaSetDevice(i) );
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conv4<<<gridDim, blockDim>>>(i_d[i], f_d[i], o_d[i],
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Nsplit, C, H, W, K, R, S, OH, OW,
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pad, dilation, stride);
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} else {
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// printf("\nconv4!\n");
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CUDA_CALL( cudaSetDevice(i) );
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conv<<<gridDim, blockDim>>>(i_d[i], f_d[i], o_d[i],
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Nsplit, C, H, W, K, R, S, OH, OW,
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pad, dilation, stride);
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}
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}
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// DO NOT REMOVE; NEEDED FOR TIME NEASURE
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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 cuda_final()
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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 + Nbegin[i] * output_line, o_d[i],
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(Nend[i] - Nbegin[i]) * output_line * 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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}
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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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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_line = C*H*W;
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filter_size = K*C*R*S;
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output_line = K*OH*OW;
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// Allocate
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if(mpi_rank != 0){
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alloc_tensor(&input, N, C, H, W);
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alloc_tensor(&output, N, K, OH, OW);
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alloc_tensor(&filter, K, C, R, S);
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}
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// N split
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int ns[mpi_world_size], ne[mpi_world_size];
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for (int i = 0; i < mpi_world_size; i++) {
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ns[i] = N / mpi_world_size * i;
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ne[i] = N / mpi_world_size * (i + 1);
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}
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ne[mpi_world_size - 1] = N;
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// Scatter Input
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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 + ns[i] * input_line, (ne[i] - ns[i]) * input_line, MPI_FLOAT, i, 0,
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MPI_COMM_WORLD);
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}
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} else {
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MPI_Recv(input + ns[mpi_rank] * input_line, (ne[mpi_rank] - ns[mpi_rank]) * input_line, MPI_FLOAT,
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0, 0, MPI_COMM_WORLD, nullptr);
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}
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// Broadcast Filter
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MPI_Bcast(filter, filter_size, MPI_FLOAT, 0, MPI_COMM_WORLD);
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cuda_init(ns[mpi_rank], ne[mpi_rank]);
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cuda_run();
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cuda_final();
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// Gather Output
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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 + ns[i] * output_line, (ne[i] - ns[i]) * output_line, MPI_FLOAT, i, 0,
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MPI_COMM_WORLD, nullptr);
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}
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} else {
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MPI_Send(output + ns[mpi_rank] * output_line, (ne[mpi_rank] - ns[mpi_rank]) * output_line, MPI_FLOAT, 0, 0,
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MPI_COMM_WORLD);
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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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}
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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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