--- /dev/null
+//**************************************************************************
+// Multi-threaded Matrix Multiply benchmark
+//--------------------------------------------------------------------------
+// TA : Christopher Celio
+// Student: Anirudh Garg
+//
+//
+// 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;
+
+ /*547287
+ for ( i = coreid*lda/ncores; i < (coreid+1)*lda/ncores; i++ )
+ {
+ for ( j = 0; j < lda; j++ )
+ {
+ int aIndex = j*lda;
+ int cIndex = i + aIndex;
+ C[cIndex] += A[aIndex] * B[i];
+ C[cIndex] += A[aIndex + 1] * B[1*lda + i];
+ C[cIndex] += A[aIndex + 2] * B[2*lda + i];
+ C[cIndex] += A[aIndex + 3] * B[3*lda + i];
+ C[cIndex] += A[aIndex + 4] * B[4*lda + i];
+ C[cIndex] += A[aIndex + 5] * B[5*lda + i];
+ C[cIndex] += A[aIndex + 6] * B[6*lda + i];
+ C[cIndex] += A[aIndex + 7] * B[7*lda + i];
+ C[cIndex] += A[aIndex + 8] * B[8*lda + i];
+ C[cIndex] += A[aIndex + 9] * B[9*lda + i];
+ C[cIndex] += A[aIndex + 10] * B[10*lda + i];
+ C[cIndex] += A[aIndex + 11] * B[11*lda + i];
+ C[cIndex] += A[aIndex + 12] * B[12*lda + i];
+ C[cIndex] += A[aIndex + 13] * B[13*lda + i];
+ C[cIndex] += A[aIndex + 14] * B[14*lda + i];
+ C[cIndex] += A[aIndex + 15] * B[15*lda + i];
+ C[cIndex] += A[aIndex + 16] * B[16*lda + i];
+ C[cIndex] += A[aIndex + 17] * B[17*lda + i];
+ C[cIndex] += A[aIndex + 18] * B[18*lda + i];
+ C[cIndex] += A[aIndex + 19] * B[19*lda + i];
+ C[cIndex] += A[aIndex + 20] * B[20*lda + i];
+ C[cIndex] += A[aIndex + 21] * B[21*lda + i];
+ C[cIndex] += A[aIndex + 22] * B[22*lda + i];
+ C[cIndex] += A[aIndex + 23] * B[23*lda + i];
+ C[cIndex] += A[aIndex + 24] * B[24*lda + i];
+ C[cIndex] += A[aIndex + 25] * B[25*lda + i];
+ C[cIndex] += A[aIndex + 26] * B[26*lda + i];
+ C[cIndex] += A[aIndex + 27] * B[27*lda + i];
+ C[cIndex] += A[aIndex + 28] * B[28*lda + i];
+ C[cIndex] += A[aIndex + 29] * B[29*lda + i];
+ C[cIndex] += A[aIndex + 30] * B[30*lda + i];
+ C[cIndex] += A[aIndex + 31] * B[31*lda + i];
+ }
+ }
+ */
+
+ //492827
+ /* for ( i = coreid*lda/ncores; i < (coreid+1)*lda/ncores; i++ )
+ {
+ for ( j = 0; j < lda; j++ )
+ {
+
+ int aIndex = j*lda;
+ int cIndex = i + aIndex;
+ for ( k = 0; k < lda; k++)
+ {
+ C[cIndex] += A[aIndex + k] * B[k*lda + i];
+ /* C[cIndex] += A[aIndex + k+1] * B[(k+1)*lda + i];
+ C[cIndex] += A[aIndex + k+2] * B[(k+2)*lda + i];
+ C[cIndex] += A[aIndex + k+3] * B[(k+3)*lda + i];
+ C[cIndex] += A[aIndex + k+4] * B[(k+4)*lda + i];
+ C[cIndex] += A[aIndex + k+5] * B[(k+5)*lda + i];
+ C[cIndex] += A[aIndex + k+6] * B[(k+6)*lda + i];
+ C[cIndex] += A[aIndex + k+7] * B[(k+7)*lda + i];
+ C[cIndex] += A[aIndex + k+8] * B[(k+8)*lda + i];
+ C[cIndex] += A[aIndex + k+9] * B[(k+9)*lda + i];
+ C[cIndex] += A[aIndex + k+10] * B[(k+10)*lda + i];
+ C[cIndex] += A[aIndex + k+11] * B[(k+11)*lda + i];
+ C[cIndex] += A[aIndex + k+12] * B[(k+12)*lda + i];
+ C[cIndex] += A[aIndex + k+13] * B[(k+13)*lda + i];
+ C[cIndex] += A[aIndex + k+14] * B[(k+14)*lda + i];
+ C[cIndex] += A[aIndex + k+15] * B[(k+15)*lda + i];*/
+ /* }
+ }
+ }*/
+ /*
+ //326378
+ data_t bTrans[1024];
+
+ for (int counti = 0; counti < 32; counti++) {
+ for (int countj = 0; countj < 32; countj++) {
+ *(bTrans + counti + countj*lda) = *(B + countj + counti*lda);
+ }
+ }
+
+
+ int BLOCKSIZE = 8;
+ for ( i = 0; i < lda; i+=BLOCKSIZE )
+ {
+ for ( int iTemp = i; iTemp < i + BLOCKSIZE; iTemp++ ) {
+ int iFlag = iTemp*lda;
+ for ( j = coreid*lda/ncores; j < (coreid+1)*lda/ncores; j++ ) {
+ int jFlag = j*lda;
+ int cLoc = jFlag+iTemp;
+ for ( k = 0; k < lda; k+=8) {
+ *(C+cLoc) += *(A+jFlag+k) * *(bTrans+iFlag+k);
+ *(C+cLoc) += *(A+jFlag+k+1) * *(bTrans+iFlag+k+1);
+ *(C+cLoc) += *(A+jFlag+k+2) * *(bTrans+iFlag+k+2);
+ *(C+cLoc) += *(A+jFlag+k+3) * *(bTrans+iFlag+k+3);
+ *(C+cLoc) += *(A+jFlag+k+4) * *(bTrans+iFlag+k+4);
+ *(C+cLoc) += *(A+jFlag+k+5) * *(bTrans+iFlag+k+5);
+ *(C+cLoc) += *(A+jFlag+k+6) * *(bTrans+iFlag+k+6);
+ *(C+cLoc) += *(A+jFlag+k+7) * *(bTrans+iFlag+k+7);
+ }
+ }
+ }
+ }*/
+ data_t bTrans[1024];
+
+ for (int counti = coreid*32/ncores; counti < (coreid+1)*lda/ncores; counti++) {
+ for (int countj = 0; countj < 32; countj++) {
+ *(bTrans + counti + countj*lda) = *(B + countj + counti*lda);
+ }
+ }
+
+
+ int BLOCKSIZE = 8;
+ for ( j = 0; j < lda; j++ )
+ {
+ //for ( int jTemp = j; jTemp < j + BLOCKSIZE; jTemp++ ) {
+ int jFlag = j*lda;
+ for ( i = coreid*lda/ncores; i < (coreid+1)*lda/ncores; i+=BLOCKSIZE ) {
+ for ( int iTemp = i; iTemp < i + BLOCKSIZE; iTemp++ ) {
+
+ int iFlag = iTemp*lda;
+ int cLoc = jFlag+iTemp;
+ for ( k = 0; k < lda; k+=16) {
+ *(C+cLoc) += *(A+jFlag+k) * *(bTrans+iFlag+k);
+ *(C+cLoc) += *(A+jFlag+k+1) * *(bTrans+iFlag+k+1);
+ *(C+cLoc) += *(A+jFlag+k+2) * *(bTrans+iFlag+k+2);
+ *(C+cLoc) += *(A+jFlag+k+3) * *(bTrans+iFlag+k+3);
+ *(C+cLoc) += *(A+jFlag+k+4) * *(bTrans+iFlag+k+4);
+ *(C+cLoc) += *(A+jFlag+k+5) * *(bTrans+iFlag+k+5);
+ *(C+cLoc) += *(A+jFlag+k+6) * *(bTrans+iFlag+k+6);
+ *(C+cLoc) += *(A+jFlag+k+7) * *(bTrans+iFlag+k+7);
+ *(C+cLoc) += *(A+jFlag+k+8) * *(bTrans+iFlag+k+8);
+ *(C+cLoc) += *(A+jFlag+k+9) * *(bTrans+iFlag+k+9);
+ *(C+cLoc) += *(A+jFlag+k+10) * *(bTrans+iFlag+k+10);
+ *(C+cLoc) += *(A+jFlag+k+11) * *(bTrans+iFlag+k+11);
+ *(C+cLoc) += *(A+jFlag+k+12) * *(bTrans+iFlag+k+12);
+ *(C+cLoc) += *(A+jFlag+k+13) * *(bTrans+iFlag+k+13);
+ *(C+cLoc) += *(A+jFlag+k+14) * *(bTrans+iFlag+k+14);
+ *(C+cLoc) += *(A+jFlag+k+15) * *(bTrans+iFlag+k+15);
+ }
+ }
+ }
+ //}
+ }
+
+
+}
+
+//--------------------------------------------------------------------------
+// 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