+//**************************************************************************
+// 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 printArray( 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)) verify(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(); */
+
+ /* 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();
+ 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();
+// stats(matmul_naive(DIM_SIZE, input1_data, input2_data, results_data); barrier());
+//
+//
+// // verify
+// verify(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();
+
+
+ // Execute your faster matmul
+ barrier();
+ stats(matmul(DIM_SIZE, input1_data, input2_data, results_data); barrier());
+
+#ifdef DEBUG
+ printArray("results:", ARRAY_SIZE, results_data);
+ printArray("verify :", ARRAY_SIZE, verify_data);
+#endif
+
+ // verify
+ verify(ARRAY_SIZE, results_data, verify_data);
+ barrier();
+
+ exit(0);
+}
+
projects / riscv-tests.git / blobdiff