--- /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 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, n, m;
+
+
+ //matmul_naive(32, input1_data, input2_data, results_data); barrier(): 957424 cycles, 29.2 cycles/iter, 3.6 CPI
+ //matmul(32, input1_data, input2_data, results_data); barrier(): 340408 cycles, 10.3 cycles/iter, 1.8 CPI
+
+ for (n = 0; n < lda; n += 1) {
+ for (m = 0; m < lda; m += 1) {
+ bTranspose[lda*m + n] = B[lda*n + m];
+ bTranspose[lda*n + m] = B[lda*m + n];
+ }
+ }
+ barrier();
+
+ for ( j = coreid; j < lda; j += 2*ncores ) {
+ for ( i = 0; i < lda; i += 1 ){
+ c1 = 0; //global vars c1, c2
+ c2 = 0;
+ for ( k = 0; k < lda; k += 1 ) {
+ c1 += A[j * lda + k] * bTranspose[i*lda + k];
+ c2 += A[(j+2) * lda + k] * bTranspose[i*lda + k];
+
+ //barrier();
+ }
+
+ C[i + j * lda] = c1;
+ C[i + (j+2) * lda] = c2;
+ barrier();
+ }
+ //barrier();
+ }
+
+
+
+
+ //matmul_naive(32, input1_data, input2_data, results_data); barrier(): 983609 cycles, 30.0 cycles/iter, 3.7 CPI
+ //matmul(32, input1_data, input2_data, results_data); barrier(): 389942 cycles, 11.9 cycles/iter, 2.5 CPI
+
+ /*
+ for ( j = coreid; j < lda; j += 2*ncores ) {
+ for ( i = 0; i < lda; i += 1 ){
+ c1 = 0; //global vars c1, c2
+ c2 = 0;
+ for ( k = 0; k < lda; k += 1 ) {
+ c1 += A[j * lda + k] * B[k*lda + i];
+ c2 += A[(j+2) * lda + k] * B[k*lda + i];
+
+ //barrier();
+ }
+
+ C[i + j * lda] = c1;
+ C[i + (j+2) * lda] = c2;
+ barrier();
+ }
+ //barrier();
+ }
+ */
+
+ // matmul_naive(32, input1_data, input2_data, results_data); barrier(): 973781 cycles, 29.7 cycles/iter, 3.7 CPI
+ // matmul(32, input1_data, input2_data, results_data); barrier(): 461066 cycles, 14.0 cycles/iter, 3.5 CPI
+ // for ( k = 0; k < lda; k += 1 ) {
+ // for ( j = coreid; j < lda; j += 2*ncores ) {
+ // for ( i = 0; i < lda; i += 1 ){
+ // C[i + j * lda] += A[j * lda + k] * B[k*lda + i];
+ // C[i + (j+2) * lda] += A[(j+2) * lda + k] * B[k*lda + i];
+ // //barrier();
+ // }
+ // barrier();
+ // }
+ // //barrier();
+ // }
+
+
+ // matmul_naive(32, input1_data, input2_data, results_data); barrier(): 965136 cycles, 29.4 cycles/iter, 3.7 CPI
+ // matmul(32, input1_data, input2_data, results_data); barrier(): 513779 cycles, 15.6 cycles/iter, 3.2 CPI
+
+ // for ( j = coreid; j < lda; j += 2*ncores ) {
+ // for ( i = 0; i < lda; i += 1 ){
+ // for ( k = 0; k < lda; k += 1 ) {
+ // C[i + j * lda] += A[j * lda + k] * B[k*lda + i];
+ // C[i + (j+2) * lda] += A[(j+2) * lda + k] * B[k*lda + i];
+
+ // //barrier();
+ // }
+ // barrier();
+ // }
+ // //barrier();
+ //}
+
+
+ // matmul_naive(32, input1_data, input2_data, results_data); barrier(): 937892 cycles, 28.6 cycles/iter, 3.6 CPI
+ // matmul(32, input1_data, input2_data, results_data); barrier(): 576478 cycles, 17.5 cycles/iter, 3.5 CPI
+
+ // for ( i = 0; i < lda; i += 1 ){
+ // for ( j = coreid; j < lda; j += 2*ncores ) {
+ // for ( k = 0; k < lda; k += 1 ) {
+ // C[i + j * lda] += A[j * lda + k] * B[k*lda + i];
+ // C[i + (j+2) * lda] += A[(j+2) * lda + k] * B[k*lda + i];
+
+ // //barrier();
+ // }
+ // barrier();
+ // }
+ // //barrier();
+ // }
+
+ //for ( i = coreid; i < lda; i += ncores ){
+ // for ( j = coreid; j < lda; j += ncores ) {
+ // for ( k = coreid; k < lda; k += ncores ) {
+ // C[i + j*lda] += A[j*lda + k] * B[k*lda + i];
+ // }
+ //barrier();
+ // }
+ //}
+}
+
+//--------------------------------------------------------------------------
+// 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