--- /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;
+}
+
+data_t mult(data_t x, data_t y)
+{ data_t result = 0;
+ size_t i;
+ for (i=0; i < x; i++) {
+ result += y;
+ }
+ return result;
+}
+//--------------------------------------------------------------------------
+// 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[] )
+{
+ size_t i, j, k, l;
+ int row,row2, row3, row4, column, column2, column3, column4, column5, column6, column7, column8;
+ data_t element, element2, element3, element4, element5, element6, element7, element8;
+ data_t element9, element10, element11, element12, element13, element14, element15, element16;
+ data_t elementB1,elementB2,elementB3,elementB4;
+ data_t temp_mat[128]={0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
+ //data_t temp_mat2[32]={0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0};
+ //for (i=coreid*max_dim/ncores; i<(max_dim/ncores+coreid*max_dim/ncores); i+=8){
+ for (l=coreid*32/ncores; l<32*(1+coreid)/ncores; l+=4){
+ row=l*lda;
+ row2=(l+1)*lda;
+ row3=(l+2)*lda;
+ row4=(l+3)*lda;
+ for (i=0; i<lda; i+=4){
+ element = A[row+i];
+ element2 = A[row+i+1];
+ element3 = A[row+i+2];
+ element4 = A[row+i+3];
+
+ element5 = A[row2+i];
+ element6 = A[row2+i+1];
+ element7 = A[row2+i+2];
+ element8 = A[row2+i+3];
+
+ element9 = A[row3+i];
+ element10 = A[row3+i+1];
+ element11 = A[row3+i+2];
+ element12 = A[row3+i+3];
+
+ element13 = A[row4+i];
+ element14 = A[row4+i+1];
+ element15 = A[row4+i+2];
+ element16 = A[row4+i+3];
+
+ column=i*lda;
+ column2=(i+1)*lda;
+ column3=(i+2)*lda;
+ column4=(i+3)*lda;
+
+
+ for (j=0; j<lda; j+=4){
+
+ temp_mat[j]+=element*B[column+j]+element2*B[column2+j]+element3*B[column3+j]+element4*B[column4+j];
+ temp_mat[j+1]+=element*B[column+j+1]+element2*B[column2+j+1]+element3*B[column3+j+1]+element4*B[column4+j+1];
+ temp_mat[j+2]+=element*B[column+j+2]+element2*B[column2+j+2]+element3*B[column3+j+2]+element4*B[column4+j+2];
+ temp_mat[j+3]+=element*B[column+j+3]+element2*B[column2+j+3]+element3*B[column3+j+3]+element4*B[column4+j+3];
+
+ temp_mat[j+lda]+=element5*B[column+j]+element6*B[column2+j]+element7*B[column3+j]+element8*B[column4+j];
+ temp_mat[j+1+lda]+=element5*B[column+j+1]+element6*B[column2+j+1]+element7*B[column3+j+1]+element8*B[column4+j+1];
+ temp_mat[j+2+lda]+=element5*B[column+j+2]+element6*B[column2+j+2]+element7*B[column3+j+2]+element8*B[column4+j+2];
+ temp_mat[j+3+lda]+=element5*B[column+j+3]+element6*B[column2+j+3]+element7*B[column3+j+3]+element8*B[column4+j+3];
+
+ temp_mat[j+2*lda]+=element9*B[column+j]+element10*B[column2+j]+element11*B[column3+j]+element12*B[column4+j];
+ temp_mat[j+1+2*lda]+=element9*B[column+j+1]+element10*B[column2+j+1]+element11*B[column3+j+1]+element12*B[column4+j+1];
+ temp_mat[j+2+2*lda]+=element9*B[column+j+2]+element10*B[column2+j+2]+element11*B[column3+j+2]+element12*B[column4+j+2];
+ temp_mat[j+3+2*lda]+=element9*B[column+j+3]+element10*B[column2+j+3]+element11*B[column3+j+3]+element12*B[column4+j+3];
+
+ temp_mat[j+3*lda]+=element13*B[column+j]+element14*B[column2+j]+element15*B[column3+j]+element16*B[column4+j];
+ temp_mat[j+1+3*lda]+=element13*B[column+j+1]+element14*B[column2+j+1]+element15*B[column3+j+1]+element16*B[column4+j+1];
+ temp_mat[j+2+3*lda]+=element13*B[column+j+2]+element14*B[column2+j+2]+element15*B[column3+j+2]+element16*B[column4+j+2];
+ temp_mat[j+3+3*lda]+=element13*B[column+j+3]+element14*B[column2+j+3]+element15*B[column3+j+3]+element16*B[column4+j+3];
+
+
+ }
+
+ }
+
+ for(k=0; k<32; k++){
+ C[row+k]=temp_mat[k];
+ temp_mat[k]=0;
+ C[row2+k]=temp_mat[k+lda];
+ temp_mat[k+lda]=0;
+ C[row3+k]=temp_mat[k+2*lda];
+ temp_mat[k+2*lda]=0;
+ C[row4+k]=temp_mat[k+3*lda];
+ temp_mat[k+3*lda]=0;
+
+
+ }
+
+
+ }
+ // ***************************** //
+ // **** ADD YOUR CODE HERE ***** //
+ // ***************************** //
+ //
+ // feel free to make a separate function for MI and MSI versions.
+
+}
+//--------------------------------------------------------------------------
+// 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