+++ /dev/null
-//**************************************************************************
-// Multi-threaded Matrix Multiply benchmark
-//--------------------------------------------------------------------------
-// TA : Christopher Celio
-// Student:
-//
-//
-// This benchmark multiplies two 2-D arrays together and writes the results to
-// a third vector. The input data (and reference data) should be generated
-// using the matmul_gendata.pl perl script and dumped to a file named
-// dataset.h.
-
-
-// print out arrays, etc.
-//#define DEBUG
-
-//--------------------------------------------------------------------------
-// Includes
-
-#include <string.h>
-#include <stdlib.h>
-#include <stdio.h>
-
-
-//--------------------------------------------------------------------------
-// Input/Reference Data
-
-typedef float data_t;
-#include "dataset.h"
-
-
-//--------------------------------------------------------------------------
-// Basic Utilities and Multi-thread Support
-
-__thread unsigned long coreid;
-unsigned long ncores;
-
-#include "util.h"
-
-#define stringify_1(s) #s
-#define stringify(s) stringify_1(s)
-#define stats(code) do { \
- unsigned long _c = -rdcycle(), _i = -rdinstret(); \
- code; \
- _c += rdcycle(), _i += rdinstret(); \
- if (coreid == 0) \
- printf("%s: %ld cycles, %ld.%ld cycles/iter, %ld.%ld CPI\n", \
- stringify(code), _c, _c/DIM_SIZE/DIM_SIZE/DIM_SIZE, 10*_c/DIM_SIZE/DIM_SIZE/DIM_SIZE%10, _c/_i, 10*_c/_i%10); \
- } while(0)
-
-
-//--------------------------------------------------------------------------
-// Helper functions
-
-void printArrayMT( char name[], int n, data_t arr[] )
-{
- int i;
- if (coreid != 0)
- return;
-
- printf( " %10s :", name );
- for ( i = 0; i < n; i++ )
- printf( " %3ld ", (long) arr[i] );
- printf( "\n" );
-}
-
-void __attribute__((noinline)) verifyMT(size_t n, const data_t* test, const data_t* correct)
-{
- if (coreid != 0)
- return;
-
- size_t i;
- for (i = 0; i < n; i++)
- {
- if (test[i] != correct[i])
- {
- printf("FAILED test[%d]= %3ld, correct[%d]= %3ld\n",
- i, (long)test[i], i, (long)correct[i]);
- exit(-1);
- }
- }
-
- return;
-}
-
-void matrix_sub(int size, data_t A[], data_t B[], data_t C[]) {
- if (coreid != 0)
- return;
-
- for(int i = 0; i < size; i++){
- C[i] = A[i] + B[i];
- }
-}
-
-void matrix_add(int size, data_t A[], data_t B[], data_t C[]) {
- if (coreid != 0)
- return;
-
- for(int i = 0; i < size; i++){
- C[i] = A[i] - B[i];
- }
-}
-
-void strassen_mult(int dime, const data_t sA[], const data_t sB[], data_t sC[]) {
-
- if (coreid != 0)
- return;
-
- int height, width;
- int sub_size = dime*dime/4;
-
-// data_t A_11[sub_size], B_11[sub_size], C_11[sub_size],
-// A_12[sub_size], B_12[sub_size], C_12[sub_size],
-// A_21[sub_size], B_21[sub_size], C_21[sub_size],
-// A_22[sub_size], B_22[sub_size], C_22[sub_size];
-
- data_t *A_11 = malloc(sub_size*sizeof(data_t));
- data_t *A_12 = malloc(sub_size*sizeof(data_t));
- data_t *A_21 = malloc(sub_size*sizeof(data_t));
- data_t *A_22 = malloc(sub_size*sizeof(data_t));
- data_t *B_11 = malloc(sub_size*sizeof(data_t));
- data_t *B_12 = malloc(sub_size*sizeof(data_t));
- data_t *B_21 = malloc(sub_size*sizeof(data_t));
- data_t *B_22 = malloc(sub_size*sizeof(data_t));
-
- for(height=0; height < dime/2; height++) {
- for(width= 0; width < dime/2; width++) {
- A_11[width+(height*dime/2)] = sA[width + height*dime];
- B_11[width+(height*dime/2)] = sB[width + height*dime];
-
- A_12[width+(height*dime/2)] = sA[dime/2 + width + height*dime];
- B_12[width+(height*dime/2)] = sB[dime/2 + width + height*dime];
-
- A_21[width+(height*dime/2)] = sA[(dime*dime)/2 + width + height*dime];
- B_21[width+(height*dime/2)] = sB[(dime*dime)/2 + width + height*dime];
-
- A_22[width+(height*dime/2)] = sA[(dime*dime)/2 + dime/2 + width + height*dime];
- B_22[width+(height*dime/2)] = sB[(dime*dime)/2 + dime/2 + width + height*dime];
- }
- }
-
-// data_t H_1[sub_size], H_2[sub_size], H_3[sub_size], H_4[sub_size], H_5[sub_size],
-// H_6[sub_size], H_7[sub_size], H_8[sub_size], H_9[sub_size], H_10[sub_size],
-// H_11[sub_size], H_12[sub_size], H_13[sub_size], H_14[sub_size],
-// H_15[sub_size], H_16[sub_size], H_17[sub_size], H_18[sub_size];
-
- data_t *H_1 = malloc(sub_size*sizeof(data_t));
- data_t *H_2 = malloc(sub_size*sizeof(data_t));
- data_t *H_3 = malloc(sub_size*sizeof(data_t));
- data_t *H_4 = malloc(sub_size*sizeof(data_t));
- data_t *H_5 = malloc(sub_size*sizeof(data_t));
- data_t *H_6 = malloc(sub_size*sizeof(data_t));
- data_t *H_7 = malloc(sub_size*sizeof(data_t));
- data_t *H_8 = malloc(sub_size*sizeof(data_t));
- data_t *H_9 = malloc(sub_size*sizeof(data_t));
- data_t *H_10 = malloc(sub_size*sizeof(data_t));
-
- matrix_add(sub_size, A_11, A_22, H_1); //Helper1
- matrix_add(sub_size, B_11, B_22, H_2); //Helper2
- matrix_add(sub_size, A_21, A_22, H_3); //Helper3
- matrix_sub(sub_size, B_12, B_22, H_4); //Helper4
- matrix_sub(sub_size, B_21, B_11, H_5); //Helper5
- matrix_add(sub_size, A_11, A_12, H_6); //Helper6
- matrix_sub(sub_size, A_21, A_11, H_7); //Helper7
- matrix_add(sub_size, B_11, B_12, H_8); //Helper8
- matrix_sub(sub_size, A_12, A_22, H_9); //Helper9
- matrix_add(sub_size, B_21, B_22, H_10); //Helper10
-
- free(A_12);
- free(A_21);
- free(B_12);
- free(B_21);
-
- A_12 = NULL;
- A_21 = NULL;
- B_12 = NULL;
- B_21 = NULL;
-
-// data_t M_1[sub_size], M_2[sub_size], M_3[sub_size], M_4[sub_size],
-// M_5[sub_size], M_6[sub_size], M_7[sub_size];
-
- data_t *M_1 = malloc(sub_size*sizeof(data_t));
- data_t *M_2 = malloc(sub_size*sizeof(data_t));
- data_t *M_3 = malloc(sub_size*sizeof(data_t));
- data_t *M_4 = malloc(sub_size*sizeof(data_t));
- data_t *M_5 = malloc(sub_size*sizeof(data_t));
- data_t *M_6 = malloc(sub_size*sizeof(data_t));
- data_t *M_7 = malloc(sub_size*sizeof(data_t));
-
- if (sub_size == 1) {
- M_1[0] = H_1[0]*H_2[0];
- M_2[0] = H_3[0]*B_11[0];
- M_3[0] = A_11[0]*H_4[0];
- M_4[0] = A_22[0]*H_5[0];
- M_5[0] = H_6[0]*B_22[0];
- M_6[0] = H_7[0]*H_8[0];
- M_7[0] = H_9[0]*H_10[0];
- } else {
- strassen_mult(dime/2, H_1, H_2, M_1);
- strassen_mult(dime/2, H_3, B_11, M_2);
- strassen_mult(dime/2, A_11, H_4, M_3);
- strassen_mult(dime/2, A_22, H_5, M_4);
- strassen_mult(dime/2, H_6, B_22, M_5);
- strassen_mult(dime/2, H_7, H_8, M_6);
- strassen_mult(dime/2, H_9, H_10, M_7);
- }
-
- free(A_11);
- free(A_22);
- free(B_11);
- free(B_22);
-
- A_11 = NULL;
- A_22 = NULL;
- B_11 = NULL;
- B_22 = NULL;
-
- free(H_1);
- free(H_2);
- free(H_3);
- free(H_4);
- free(H_5);
- free(H_6);
- free(H_7);
- free(H_8);
- free(H_9);
- free(H_10);
-
- H_1 = NULL;
- H_2 = NULL;
- H_3 = NULL;
- H_4 = NULL;
- H_5 = NULL;
- H_6 = NULL;
- H_7 = NULL;
- H_8 = NULL;
- H_9 = NULL;
- H_10 = NULL;
-
- data_t *H_11 = malloc(sub_size*sizeof(data_t));
- data_t *H_12 = malloc(sub_size*sizeof(data_t));
- data_t *H_13 = malloc(sub_size*sizeof(data_t));
- data_t *H_14 = malloc(sub_size*sizeof(data_t));
-
- data_t *C_11 = malloc(sub_size*sizeof(data_t));
- data_t *C_12 = malloc(sub_size*sizeof(data_t));
- data_t *C_21 = malloc(sub_size*sizeof(data_t));
- data_t *C_22 = malloc(sub_size*sizeof(data_t));
-
- matrix_add(sub_size, M_1, M_4, H_11);
- matrix_add(sub_size, M_5, M_7, H_12);
- matrix_sub(sub_size, H_11, H_12, C_11);
-
- matrix_add(sub_size, M_3, M_5, C_12);
-
- matrix_add(sub_size, M_2, M_4, C_21);
-
- matrix_sub(sub_size, M_1, M_2, H_13);
- matrix_add(sub_size, M_3, M_6, H_14);
- matrix_add(sub_size, H_13, H_14, C_22);
-
- free(H_11);
- free(H_12);
- free(H_13);
- free(H_14);
-
- H_11 = NULL;
- H_12 = NULL;
- H_13 = NULL;
- H_14 = NULL;
-
-
- for(height=0; height < dime/2; height++) {
- for(width= 0; width < dime/2; width++) {
- sC[width + height*dime] = C_11[width+(height*dime/2)];
- sC[dime/2 + width + height*dime] = C_12[width+(height*dime/2)];
- sC[(dime*dime)/2 + width + height*dime] = C_21[width+(height*dime/2)];
- sC[(dime*dime)/2 + dime/2 + width + height*dime] = C_22[width+(height*dime/2)];
- }
- }
-
- free(C_11);
- free(C_12);
- free(C_21);
- free(C_22);
-
- C_11 = NULL;
- C_12 = NULL;
- C_21 = NULL;
- C_22 = NULL;
-
-}
-
-//--------------------------------------------------------------------------
-// matmul function
-
-// single-thread, naive version
-void __attribute__((noinline)) matmul_naive(const int lda, const data_t A[], const data_t B[], data_t C[] )
-{
- int i, j, k;
-
- if (coreid > 0)
- return;
-
- for ( i = 0; i < lda; i++ )
- for ( j = 0; j < lda; j++ )
- {
- for ( k = 0; k < lda; k++ )
- {
- C[i + j*lda] += A[j*lda + k] * B[k*lda + i];
- }
- }
-
-}
-
-
-
-void __attribute__((noinline)) matmul(const int lda, const data_t A[], const data_t B[], data_t C[] )
-{
-
- // ***************************** //
- // **** ADD YOUR CODE HERE ***** //
- // ***************************** //
- //
- // feel free to make a separate function for MI and MSI versions.
-
- if (coreid > 0)
- return;
-
- strassen_mult(lda, A, B, C);
-
-}
-
-//--------------------------------------------------------------------------
-// Main
-//
-// all threads start executing thread_entry(). Use their "coreid" to
-// differentiate between threads (each thread is running on a separate core).
-
-void thread_entry(int cid, int nc)
-{
- coreid = cid;
- ncores = nc;
-
- // static allocates data in the binary, which is visible to both threads
- static data_t results_data[ARRAY_SIZE];
-
-
-// // Execute the provided, naive matmul
-// barrier(nc);
-// stats(matmul_naive(DIM_SIZE, input1_data, input2_data, results_data); barrier(nc));
-//
-//
-// // verify
-// verifyMT(ARRAY_SIZE, results_data, verify_data);
-//
-// // clear results from the first trial
-// size_t i;
-// if (coreid == 0)
-// for (i=0; i < ARRAY_SIZE; i++)
-// results_data[i] = 0;
-// barrier(nc);
-
-
- // Execute your faster matmul
- barrier(nc);
- stats(matmul(DIM_SIZE, input1_data, input2_data, results_data); barrier(nc));
-
-#ifdef DEBUG
- printArrayMT("results:", ARRAY_SIZE, results_data);
- printArrayMT("verify :", ARRAY_SIZE, verify_data);
-#endif
-
- // verify
- verifyMT(ARRAY_SIZE, results_data, verify_data);
- barrier(nc);
-
- exit(0);
-}
-
projects / riscv-tests.git / blobdiff