+++ /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>
-
-#define REG_I 8
-#define REG_J 2
-#define BLOCK_I 32
-#define BLOCK_J 16
-#define BLOCK_K 16
-#define LDA 32
-#define NCORES 2
-#define MIN(X,Y) (X < Y ? X : Y)
-
-//--------------------------------------------------------------------------
-// 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;
-}
-
-//--------------------------------------------------------------------------
-// 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.
-
- int i, j, k, ri, rj, ii, jj, kk;
- data_t *Aj, *Cj, *Bi;
- data_t c[REG_I][REG_J], a[REG_J], b[REG_I];
- size_t start = coreid * (LDA / NCORES), end = (coreid == NCORES - 1 ? LDA : (coreid + 1) * (LDA / NCORES));
-
- /* if (coreid > 0) { */
- /* return; */
- /* } */
- /* start = 0, end = lda; */
- if (ncores == NCORES && lda == LDA) {
- for (jj = start; jj < end; jj += BLOCK_J) {
- int kk_start= (coreid == 0 ? 0 : LDA/2) ,kk_end = (coreid == 0 ? LDA/2 : LDA);
- for (kk = kk_start; kk < kk_end; kk += BLOCK_K) {
- // for (ii = 0; ii < LDA; ii += BLOCK_I)
- for (j = jj; j < MIN(end, jj + BLOCK_J); j += REG_J) {
- Aj = A + j*LDA;
- Cj = C + j*LDA;
- for (i = 0; i < LDA/*, ii + BLOCK_I)*/; i += REG_I) {
- /* Load C in register blocks. */
- Bi = B + i;
- for (ri = 0; ri < REG_I; ri++) {
- for (rj = 0; rj < REG_J; rj++) {
- c[ri][rj] = Cj[i + ri + ( rj)*LDA];
- }
- }
-
-
- for (k = kk; k < MIN(LDA, kk + BLOCK_K); k++) {
- for (ri = 0; ri < REG_I; ri++) {
- b[ri] = Bi[k*LDA + ri];
- }
- /* Compute C in register blocks. */
- for (rj = 0; rj < REG_J; rj++) {
- a[rj] = Aj[(rj)*LDA + k];
- for (ri = 0; ri < REG_I; ri++) {
- c[ri][rj] += a[rj] * b[ri];
- }
- }
- }
-
- /* store C in register blocks. */
- for (ri = 0; ri < REG_I; ri++) {
- for (rj = 0; rj < REG_J; rj++) {
- Cj[i + ri + ( rj)*LDA] = c[ri][rj];
- }
- }
- }
- }
- /* barrier(nc); */
-
- /* kk_start= (coreid == 1 ? 0 : LDA/2); */
- /* kk_end = (coreid == 1 ? LDA/2 : LDA); */
- /* for (kk = kk_start; kk < kk_end; kk += BLOCK_K) { */
- /* // for (ii = 0; ii < LDA; ii += BLOCK_I) */
- /* for (j = jj; j < MIN(end, jj + BLOCK_J); j += REG_J) { */
- /* Aj = A + j*LDA; */
- /* Cj = C + j*LDA; */
- /* for (i = 0; i < LDA/\*, ii + BLOCK_I)*\/; i += REG_I) { */
- /* /\* Load C in register blocks. *\/ */
- /* Bi = B + i; */
- /* for (ri = 0; ri < REG_I; ri++) { */
- /* for (rj = 0; rj < REG_J; rj++) { */
- /* c[ri][rj] = Cj[i + ri + ( rj)*LDA]; */
- /* } */
- /* } */
-
-
- /* for (k = kk; k < MIN(LDA, kk + BLOCK_K); k++) { */
- /* for (ri = 0; ri < REG_I; ri++) { */
- /* b[ri] = Bi[k*LDA + ri]; */
- /* } */
- /* /\* Compute C in register blocks. *\/ */
- /* for (rj = 0; rj < REG_J; rj++) { */
- /* a[rj] = Aj[(rj)*LDA + k]; */
- /* for (ri = 0; ri < REG_I; ri++) { */
- /* c[ri][rj] += a[rj] * b[ri]; */
- /* } */
- /* } */
- /* } */
-
- /* store C in register blocks. */
- /* for (ri = 0; ri < REG_I; ri++) { */
- /* for (rj = 0; rj < REG_J; rj++) { */
- /* Cj[i + ri + ( rj)*LDA] = c[ri][rj]; */
- /* } */
- /* } */
- /* } */
- /* } */
- }
- }
-
-
- //barrier(nc);
- for (jj = start; jj < end; jj += BLOCK_J) {
- int kk_start= (coreid != 0 ? 0 : LDA/2), kk_end = (coreid != 0 ? LDA/2 : LDA);
- for (kk = kk_start; kk < kk_end; kk += BLOCK_K) {
- // for (ii = 0; ii < LDA; ii += BLOCK_I)
- for (j = jj; j < MIN(end, jj + BLOCK_J); j += REG_J) {
- Aj = A + j*LDA;
- Cj = C + j*LDA;
- for (i = 0; i < LDA/*, ii + BLOCK_I)*/; i += REG_I) {
- /* Load C in register blocks. */
- Bi = B + i;
- for (ri = 0; ri < REG_I; ri++) {
- for (rj = 0; rj < REG_J; rj++) {
- c[ri][rj] = Cj[i + ri + ( rj)*LDA];
- }
- }
-
-
- for (k = kk; k < MIN(LDA, kk + BLOCK_K); k++) {
- for (ri = 0; ri < REG_I; ri++) {
- b[ri] = Bi[k*LDA + ri];
- }
- /* Compute C in register blocks. */
- for (rj = 0; rj < REG_J; rj++) {
- a[rj] = Aj[(rj)*LDA + k];
- for (ri = 0; ri < REG_I; ri++) {
- c[ri][rj] += a[rj] * b[ri];
- }
- }
- }
-
- /* store C in register blocks. */
- for (ri = 0; ri < REG_I; ri++) {
- for (rj = 0; rj < REG_J; rj++) {
- Cj[i + ri + ( rj)*LDA] = c[ri][rj];
- }
- }
- }
- }
- }
- }
- /* We only care about performance for 32x32 matrices and 2 cores. Otherwise just naive mat_mul */
-} else {
- 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];
- }
- }
-
-//--------------------------------------------------------------------------
-// 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);
-}
-