--- /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;
+ int space=lda/ncores;
+ int max= space*coreid+space;
+ static data_t B1[32*32];
+ if (coreid==ncores-1){
+ for (i=0; i<lda*lda/2;i++)
+ {
+ B1[i]=B[i];
+ }
+ }
+ else{
+ for (i=lda*lda/2;i<lda*lda;i++)
+ B1[i]=B[i];
+ }
+ data_t temp=0;
+ data_t temp1=0;
+ data_t temp2=0;
+ data_t temp3=0;
+ data_t tempB=0;
+
+ data_t temp_1=0;
+ data_t temp1_1=0;
+ data_t temp2_1=0;
+ data_t temp3_1=0;
+ data_t tempB_1=0;
+
+ data_t temp_2=0;
+ data_t temp1_2=0;
+ data_t temp2_2=0;
+ data_t temp3_2=0;
+ data_t tempB_2=0;
+
+ data_t temp_3=0;
+ data_t temp1_3=0;
+ data_t temp2_3=0;
+ data_t temp3_3=0;
+ data_t tempB_3=0;
+ barrier();
+ if (coreid!=ncores-1){
+ for (i=space*coreid;i<max/4*4;i+=4)
+ {
+ for(j=0;j<lda/4*4;j+=4)
+ {
+ temp=C[j+i*lda];
+ temp1=C[j+(i+1)*lda];
+ temp2=C[j+(i+2)*lda];
+ temp3=C[j+(i+3)*lda];
+ temp_1=C[j+1+i*lda];
+ temp1_1=C[j+1+(i+1)*lda];
+ temp2_1=C[j+1+(i+2)*lda];
+ temp3_1=C[j+1+(i+3)*lda];
+ temp_2=C[j+2+i*lda];
+ temp1_2=C[j+2+(i+1)*lda];
+ temp2_2=C[j+2+(i+2)*lda];
+ temp3_2=C[j+2+(i+3)*lda];
+ temp_3=C[j+3+i*lda];
+ temp1_3=C[j+3+(i+1)*lda];
+ temp2_3=C[j+3+(i+2)*lda];
+ temp3_3=C[j+3+(i+3)*lda];
+ for (k=0;k<lda;k++)
+ {
+ tempB=B[j+k*lda];
+ temp+=A[k+i*lda]*tempB;
+ temp1+=A[k+(i+1)*lda]*tempB;
+ temp2+=A[k+(i+2)*lda]*tempB;
+ temp3+=A[k+(i+3)*lda]*tempB;
+
+ tempB_1=B[j+1+k*lda];
+ temp_1+=A[k+i*lda]*tempB_1;
+ temp1_1+=A[k+(i+1)*lda]*tempB_1;
+ temp2_1+=A[k+(i+2)*lda]*tempB_1;
+ temp3_1+=A[k+(i+3)*lda]*tempB_1;
+
+ tempB_2=B[j+2+k*lda];
+ temp_2+=A[k+i*lda]*tempB_2;
+ temp1_2+=A[k+(i+1)*lda]*tempB_2;
+ temp2_2+=A[k+(i+2)*lda]*tempB_2;
+ temp3_2+=A[k+(i+3)*lda]*tempB_2;
+
+ tempB_3=B[j+3+k*lda];
+ temp_3+=A[k+i*lda]*tempB_3;
+ temp1_3+=A[k+(i+1)*lda]*tempB_3;
+ temp2_3+=A[k+(i+2)*lda]*tempB_3;
+ temp3_3+=A[k+(i+3)*lda]*tempB_3;
+ }
+ C[j+i*lda]=temp;
+ C[j+(i+1)*lda]=temp1;
+ C[j+(i+2)*lda]=temp2;
+ C[j+(i+3)*lda]=temp3;
+
+ C[j+1+i*lda]=temp_1;
+ C[j+1+(i+1)*lda]=temp1_1;
+ C[j+1+(i+2)*lda]=temp2_1;
+ C[j+1+(i+3)*lda]=temp3_1;
+
+ C[j+2+i*lda]=temp_2;
+ C[j+2+(i+1)*lda]=temp1_2;
+ C[j+2+(i+2)*lda]=temp2_2;
+ C[j+2+(i+3)*lda]=temp3_2;
+
+ C[j+3+i*lda]=temp_3;
+ C[j+3+(i+1)*lda]=temp1_3;
+ C[j+3+(i+2)*lda]=temp2_3;
+ C[j+3+(i+3)*lda]=temp3_3;
+
+ }
+ }
+ }
+ else{
+ for (i=space*coreid;i<lda/4*4;i+=4)
+ {
+ for(j=0;j<lda/4*4;j+=4)
+ {
+ temp=C[j+i*lda];
+ temp1=C[j+(i+1)*lda];
+ temp2=C[j+(i+2)*lda];
+ temp3=C[j+(i+3)*lda];
+ temp_1=C[j+1+i*lda];
+ temp1_1=C[j+1+(i+1)*lda];
+ temp2_1=C[j+1+(i+2)*lda];
+ temp3_1=C[j+1+(i+3)*lda];
+ temp_2=C[j+2+i*lda];
+ temp1_2=C[j+2+(i+1)*lda];
+ temp2_2=C[j+2+(i+2)*lda];
+ temp3_2=C[j+2+(i+3)*lda];
+ temp_3=C[j+3+i*lda];
+ temp1_3=C[j+3+(i+1)*lda];
+ temp2_3=C[j+3+(i+2)*lda];
+ temp3_3=C[j+3+(i+3)*lda];
+ for (k=0;k<lda;k++)
+ {
+ tempB=B1[j+k*lda];
+ temp+=A[k+i*lda]*tempB;
+ temp1+=A[k+(i+1)*lda]*tempB;
+ temp2+=A[k+(i+2)*lda]*tempB;
+ temp3+=A[k+(i+3)*lda]*tempB;
+
+ tempB_1=B1[j+1+k*lda];
+ temp_1+=A[k+i*lda]*tempB_1;
+ temp1_1+=A[k+(i+1)*lda]*tempB_1;
+ temp2_1+=A[k+(i+2)*lda]*tempB_1;
+ temp3_1+=A[k+(i+3)*lda]*tempB_1;
+
+ tempB_2=B1[j+2+k*lda];
+ temp_2+=A[k+i*lda]*tempB_2;
+ temp1_2+=A[k+(i+1)*lda]*tempB_2;
+ temp2_2+=A[k+(i+2)*lda]*tempB_2;
+ temp3_2+=A[k+(i+3)*lda]*tempB_2;
+
+ tempB_3=B1[j+3+k*lda];
+ temp_3+=A[k+i*lda]*tempB_3;
+ temp1_3+=A[k+(i+1)*lda]*tempB_3;
+ temp2_3+=A[k+(i+2)*lda]*tempB_3;
+ temp3_3+=A[k+(i+3)*lda]*tempB_3;
+ }
+ C[j+i*lda]=temp;
+ C[j+(i+1)*lda]=temp1;
+ C[j+(i+2)*lda]=temp2;
+ C[j+(i+3)*lda]=temp3;
+
+ C[j+1+i*lda]=temp_1;
+ C[j+1+(i+1)*lda]=temp1_1;
+ C[j+1+(i+2)*lda]=temp2_1;
+ C[j+1+(i+3)*lda]=temp3_1;
+
+ C[j+2+i*lda]=temp_2;
+ C[j+2+(i+1)*lda]=temp1_2;
+ C[j+2+(i+2)*lda]=temp2_2;
+ C[j+2+(i+3)*lda]=temp3_2;
+
+ C[j+3+i*lda]=temp_3;
+ C[j+3+(i+1)*lda]=temp1_3;
+ C[j+3+(i+2)*lda]=temp2_3;
+ C[j+3+(i+3)*lda]=temp3_3;
+
+ }
+ }
+ }
+
+
+
+
+}
+
+//--------------------------------------------------------------------------
+// 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