134 lines
3.6 KiB
C++
134 lines
3.6 KiB
C++
#include "mat_mul.h"
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#include <cstdio>
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#include <cstdlib>
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#include <mpi.h>
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static float *A, *B, *C;
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static int M, N, K;
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static int num_threads;
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static int mpi_rank, mpi_world_size;
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/*
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static void mat_mul_omp() {
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// TODO: parallelize & optimize matrix multiplication
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// Use num_threads per node
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#pragma omp parallel for
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for (int i = 0; i < M; ++i) {
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for (int j = 0; j < N; ++j) {
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for (int k = 0; k < K; ++k) {
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C[i * N + j] += A[i * K + k] * B[k * N + j];
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}
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}
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}
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}
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*/
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#include "util.h"
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#define MAX_RANKS 4
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#define min(x,y) (x) < (y) ? (x) : (y)
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// Since it is very small size data, I store rows and offset, no need to send
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int rows_rank[MAX_RANKS], offset_rank[MAX_RANKS];
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static void mat_mul_omp() {
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int ITILESIZE=32;
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int JTILESIZE=1024;
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int KTILESIZE=1024;
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int is = 0;
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int ie = rows_rank[mpi_rank];
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//#pragma omp parallel for schedule(dynamic)
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// No limit for number of threads: 470 GFLOPS
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#pragma omp parallel for schedule(dynamic) num_threads(num_threads)
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// Limit for number of threads: 320 ~ 350 GFLOPS
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for (int ii = is; ii < ie; ii += ITILESIZE) {
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int min_ii = ((ii + ITILESIZE) < M) ? (ii+ITILESIZE): M;
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for (int jj = 0; jj < N; jj += JTILESIZE) {
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int min_jj = ((jj + JTILESIZE) < N) ? (jj+JTILESIZE): N;
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for (int kk = 0; kk < K; kk += KTILESIZE) {
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int min_kk = ((kk + KTILESIZE) < K) ? (kk+KTILESIZE): K;
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for (int k = kk; k < min_kk; k++) {
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for (int i = ii; i < min_ii; i++) {
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float Aik = A[i * K + k];
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for (int j = jj; j < min_jj; j+=1) {
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C[i * N + j] += Aik * B[k * N + j];
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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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}
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void mat_mul(float *_A, float *_B, float *_C, int _M, int _N, int _K,
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int _num_threads, int _mpi_rank, int _mpi_world_size) {
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A = _A, B = _B, C = _C;
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M = _M, N = _N, K = _K;
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num_threads = _num_threads, mpi_rank = _mpi_rank,
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mpi_world_size = _mpi_world_size;
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// TODO: parallelize & optimize matrix multiplication on multi-node
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// You must allocate & initialize A, B, C for non-root processes
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// FIXME: for now, only root process runs the matrix multiplication.
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MPI_Request request;
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MPI_Status status;
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int working_ranks = mpi_world_size;
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int row_space = M / working_ranks;
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int last_row_space = row_space + M % row_space;
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// Calculate global value rows_rank and offset_rank for the row of matrix A.
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for(int i=0; i < working_ranks; i++) {
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//rows_rank[i] = (i== working_ranks - 1) ? (M - (row_space * (working_ranks -1))) : row_space;
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rows_rank[i] = (i== working_ranks - 1) ? last_row_space : row_space;
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offset_rank[i+1] = offset_rank[i] + rows_rank[i];
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}
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// Matrix allocation for rank 1, 2, 3, ... since there is no Matrix allocation.
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if(mpi_rank != 0) {
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M = rows_rank[mpi_rank]; // Updated the size of the row of matrix A accoring to the rank
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alloc_mat(&A, rows_rank[mpi_rank], K);
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alloc_mat(&B, K, N);
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alloc_mat(&C, rows_rank[mpi_rank], N);
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}
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// Broadcast B since it is wholely used in every working rank.
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MPI_Bcast(B, K*N, MPI_FLOAT, 0, MPI_COMM_WORLD);
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if(mpi_rank == 0) {
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for(int i=1; i < working_ranks; i++)
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MPI_Isend(&A[offset_rank[i]*K], rows_rank[i] * K, MPI_FLOAT, i, 0, MPI_COMM_WORLD, &request);
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}
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else {
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MPI_Recv(A, rows_rank[mpi_rank] * K, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &status);
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}
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// Matrix multiplication for each rank according to its size
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mat_mul_omp();
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// Gather the whole results
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if(mpi_rank == 0) {
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for(int i=1; i < working_ranks; i++)
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MPI_Recv(&C[offset_rank[i]*N], rows_rank[i]*N, MPI_FLOAT, i, 0, MPI_COMM_WORLD, &status);
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}
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else {
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MPI_Isend(C, rows_rank[mpi_rank] * N, MPI_FLOAT, 0, 0, MPI_COMM_WORLD, &request);
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}
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// MPI_Finalize();
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// free(A);
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// free(B);
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// free(C);
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}
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