257 lines
7.5 KiB
Plaintext
257 lines
7.5 KiB
Plaintext
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#include "mat_mul.h"
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#include "util.h"
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#include <cstdio>
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#include <cuda_runtime.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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int num_devices = 0;
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#define TS 32
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#define WPT 16
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#define RTS TS/WPT
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// step 2: local tiling
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/*
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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const int row = threadIdx.x;
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const int col = threadIdx.y;
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const int global_row = blockDim.x * blockIdx.x + threadIdx.x;
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const int global_col = blockDim.y * blockIdx.y + threadIdx.y;
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__shared__ float Asub[TS*TS];
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__shared__ float Bsub[TS*TS];
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float acc = 0.0f;
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// printf("(info) {row, col, global_row, global_col} = {%2d, %2d, %2d, %2d}\n",
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// row, col, global_row, global_col);
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const int numTiles = K / TS;
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for(int t=0; t<numTiles; ++t)
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{
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// load one tile of A and B into local memory
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const int tile_row = TS * t + row;
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const int tile_col = TS * t + col;
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// Asub[row*TS + col] = A[global_row * K + global_col];
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// Bsub[row*TS + col] = B[global_row * N + global_col];
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Asub[row*TS + col] = A[global_row * K + tile_col];
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Bsub[row*TS + col] = B[tile_row * N + global_col];
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// Asub[row*TS + col] = A[tile_row * K + global_col];
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// Bsub[row*TS + col] = B[global_row * N + tile_col];
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__syncthreads();
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// printf("(info) global (%d, %d), Asub[%d, %d] = %+.3f\n",
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// global_row, global_col, row, col, Asub[row*TS + col]);
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// printf("(info) global (%d, %d), tile (%d, %d, %d) Asub[%d, %d] = %+.3f\n",
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// global_row, global_col, t, tid_row, tid_col, row, col, Asub[row*TS + col]);
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// printf("(info) global (%d, %d), tile (%d, %d, %d) Bsub[%d, %d] = %+.3f\n",
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// global_row, global_col, t, tid_row, tid_col, row, col, Bsub[row*TS + col]);
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for(int k=0; k<TS; ++k)
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{
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acc += Asub[row * TS + k] * Bsub[k * TS + col];
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// printf("(info) Asub[%d, %d] = %+.3f Bsub[%d, %d] = %+.3f\n",
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// row, k, Asub[row*TS + k],
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// k, col, Bsub[k*TS + row]);
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}
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__syncthreads();
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}
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C[global_row * N + global_col] = acc;
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// if( (t == 0) && (row == 0) && (col == 0) )
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// {
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// printf("A = \n");
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// __print_mat(Asub, TS, TS);
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// printf("B = \n");
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// __print_mat(Bsub, TS, TS);
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// }
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}
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*/
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// step 3: tiling + more jobs to threads
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__global__ void sgemm(float *A, float *B, float *C, int M, int N, int K) {
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const int row = threadIdx.x;
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const int col = threadIdx.y;
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// const int blk_row = blockIdx.x;
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// const int blk_col = blockIdx.y;
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const int global_row = (blockDim.x * WPT) * blockIdx.x + threadIdx.x;
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const int global_col = blockDim.y * blockIdx.y + threadIdx.y;
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// printf("(info) blockDim: (%d, %d)\n", blockDim.x, blockDim.y);
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// printf("(info) blockIdx: (%d, %d)\n", blockIdx.x, blockIdx.y);
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// printf("(info) threadDim: (%d, %d)\n", threadIdx.x, threadIdx.y);
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__shared__ float Asub[TS][TS];
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__shared__ float Bsub[TS][TS];
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float acc[WPT];
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for(int w=0; w<WPT; ++w)
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{
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acc[w] = 0.0f;
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}
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const int num_tiles = (int) ((K + TS - 1)/TS);
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for(int t=0; t<num_tiles; ++t)
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{
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for(int w=0; w<WPT; ++w)
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{
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const int t_row = TS * t + row;
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const int t_col = TS * t + col;
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if( (global_row + w*RTS) >= M || (t_col >= K) )
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{
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Asub[row + w*RTS][col] = 0.0f;
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}
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else{
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Asub[row + w*RTS][col] = A[(global_row + w*RTS) * K + t_col];
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}
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if( (t_row + w*RTS >= K) || (global_col >= N) )
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{
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Bsub[row + w*RTS][col] = 0.0f;
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}
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else
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{
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Bsub[row + w*RTS][col] = B[(t_row + w*RTS) * N + global_col];
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}
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}
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__syncthreads();
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for(int k=0; k<TS; ++k)
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{
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for(int w=0; w<WPT; ++w)
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{
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acc[w] += Asub[row + w*RTS][k] * Bsub[k][col];
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}
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}
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__syncthreads();
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}
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for(int w=0; w<WPT; ++w)
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{
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if( (global_row + w*RTS >= M) || (global_col >= N))
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continue;
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C[(global_row + w*RTS) * N + global_col] = acc[w];
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}
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}
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// Array of device (GPU) pointers
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static float *a_d[MAX_NUM_GPU];
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static float *b_d[MAX_NUM_GPU];
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static float *c_d[MAX_NUM_GPU];
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static int M, N, K;
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static int Mbegin[MAX_NUM_GPU], Mend[MAX_NUM_GPU];
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void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K) {
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// printf("\n");
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// printf("(info) A = \n");
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// print_mat(_A, _M, _K);
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// printf("(info) B = \n");
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// print_mat(_B, _K, _N);
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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 blockDim(TS, TS, 1); //
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// dim3 gridDim(((Mend[i] - Mbegin[i])/TS), (N)/TS, 1);
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dim3 blockDim(TS/WPT, TS, 1); //
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dim3 gridDim(((Mend[i] - Mbegin[i]+TS-1)/TS), (N+TS-1)/TS, 1);
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CUDA_CALL( cudaSetDevice(i) );
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sgemm<<<gridDim, blockDim>>>(a_d[i], b_d[i], c_d[i], M, N, K);
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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) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_init(float *A, float *B, float *C, int _M, int _N, int _K) {
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M = _M, N = _N, K = _K;
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CUDA_CALL( cudaGetDeviceCount(&num_devices) );
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// num_devices = 1; // TODO: step-by-step
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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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Mbegin[i] = (M / num_devices) * i;
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Mend[i] = (M / num_devices) * (i + 1);
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}
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Mend[num_devices - 1] = M;
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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(&a_d[i], (Mend[i] - Mbegin[i]) * K * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&b_d[i], K * N * sizeof(float)) );
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CUDA_CALL( cudaMalloc(&c_d[i], (Mend[i] - Mbegin[i]) * N * 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(a_d[i], A + Mbegin[i] * K,
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(Mend[i] - Mbegin[i]) * K * sizeof(float),
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cudaMemcpyHostToDevice) );
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CUDA_CALL( cudaMemcpy(b_d[i], B, K * N * 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( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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void mat_mul_final(float *A, float *B, float *C, int M, int N, int K) {
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// Do any post-matmul cleanup work here.
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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(C + Mbegin[i] * N, c_d[i],
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(Mend[i] - Mbegin[i]) * N * 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( cudaSetDevice(i) );
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CUDA_CALL( cudaDeviceSynchronize() );
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}
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}
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