--- /dev/null
+//**************************************************************************
+// Multi-threaded Matrix Multiply benchmark
+//--------------------------------------------------------------------------
+// TA : Christopher Celio
+// Student: Benjamin Han
+//
+//
+// 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 j2, i2, k2, j, i, k;
+ int tmpC00, tmpC01, tmpC02, tmpC03, tmpC04, tmpC05, tmpC06, tmpC07;
+ int tmpC10, tmpC11, tmpC12, tmpC13, tmpC14, tmpC15, tmpC16, tmpC17;
+ int jBLOCK = 32;
+ int iBLOCK = 16;
+ int kBLOCK = 32;
+ static __thread int tB[4096]; //__thread
+ int startInd = 0;
+ int endInd = lda >> 1;
+ if (coreid == 1) {
+ startInd = lda >> 1;
+ endInd = lda;
+ }
+
+ //tranpose B (block?)
+ for (i = 0; i < lda; i += 2) {
+ for (j = startInd; j < endInd; j += 2) {
+ tB[j*lda + i] = B[i*lda + j];
+ tB[(j + 1)*lda + i] = B[i*lda + j + 1];
+ tB[j*lda + i + 1] = B[(i + 1)*lda + j];
+ tB[(j + 1)*lda + i + 1] = B[(i + 1)*lda + j + 1];
+ }
+ }
+ barrier();
+
+ // compute C[j*n + i] += A[j*n + k] + Btranspose[i*n + k]
+ for ( j2 = 0; j2 < lda; j2 += jBLOCK )
+ for ( i2 = startInd; i2 < endInd; i2 += iBLOCK )
+ for ( j = j2; j < j2 + jBLOCK; j += 2 )
+ for ( k2 = 0; k2 < lda; k2 += kBLOCK )
+ for ( i = i2; i < i2 + iBLOCK; i += 4) {
+ tmpC00 = C[j*lda + i + 0]; tmpC10 = C[(j + 1)*lda + i + 0];
+ tmpC01 = C[j*lda + i + 1]; tmpC11 = C[(j + 1)*lda + i + 1];
+ tmpC02 = C[j*lda + i + 2]; tmpC12 = C[(j + 1)*lda + i + 2];
+ tmpC03 = C[j*lda + i + 3]; tmpC13 = C[(j + 1)*lda + i + 3];
+ //tmpC04 = C[j*lda + i + 4]; tmpC14 = C[(j + 1)*lda + i + 4];
+ //tmpC05 = C[j*lda + i + 5]; tmpC15 = C[(j + 1)*lda + i + 5];
+ //tmpC06 = C[j*lda + i + 6]; tmpC16 = C[(j + 1)*lda + i + 6];
+ //tmpC07 = C[j*lda + i + 7]; tmpC17 = C[(j + 1)*lda + i + 7];
+ for ( k = k2; k < k2 + kBLOCK; k += 4) {
+ tmpC00 += A[j*lda + k] * tB[(i + 0)*lda + k];
+ tmpC01 += A[j*lda + k] * tB[(i + 1)*lda + k];
+ tmpC02 += A[j*lda + k] * tB[(i + 2)*lda + k];
+ tmpC03 += A[j*lda + k] * tB[(i + 3)*lda + k];
+ //tmpC04 += A[j*lda + k] * tB[(i + 4)*lda + k];
+ //tmpC05 += A[j*lda + k] * tB[(i + 5)*lda + k];
+ //tmpC06 += A[j*lda + k] * tB[(i + 6)*lda + k];
+ //tmpC07 += A[j*lda + k] * tB[(i + 7)*lda + k];
+ tmpC10 += A[(j + 1)*lda + k] * tB[(i + 0)*lda + k];
+ tmpC11 += A[(j + 1)*lda + k] * tB[(i + 1)*lda + k];
+ tmpC12 += A[(j + 1)*lda + k] * tB[(i + 2)*lda + k];
+ tmpC13 += A[(j + 1)*lda + k] * tB[(i + 3)*lda + k];
+ //tmpC14 += A[(j + 1)*lda + k] * tB[(i + 4)*lda + k];
+ //tmpC15 += A[(j + 1)*lda + k] * tB[(i + 5)*lda + k];
+ //tmpC16 += A[(j + 1)*lda + k] * tB[(i + 6)*lda + k];
+ //tmpC17 += A[(j + 1)*lda + k] * tB[(i + 7)*lda + k];
+
+ tmpC00 += A[j*lda + k + 1] * tB[(i + 0)*lda + k + 1];
+ tmpC01 += A[j*lda + k + 1] * tB[(i + 1)*lda + k + 1];
+ tmpC02 += A[j*lda + k + 1] * tB[(i + 2)*lda + k + 1];
+ tmpC03 += A[j*lda + k + 1] * tB[(i + 3)*lda + k + 1];
+ //tmpC04 += A[j*lda + k + 1] * tB[(i + 4)*lda + k + 1];
+ //tmpC05 += A[j*lda + k + 1] * tB[(i + 5)*lda + k + 1];
+ //tmpC06 += A[j*lda + k + 1] * tB[(i + 6)*lda + k + 1];
+ //tmpC07 += A[j*lda + k + 1] * tB[(i + 7)*lda + k + 1];
+ tmpC10 += A[(j + 1)*lda + k + 1] * tB[(i + 0)*lda + k + 1];
+ tmpC11 += A[(j + 1)*lda + k + 1] * tB[(i + 1)*lda + k + 1];
+ tmpC12 += A[(j + 1)*lda + k + 1] * tB[(i + 2)*lda + k + 1];
+ tmpC13 += A[(j + 1)*lda + k + 1] * tB[(i + 3)*lda + k + 1];
+ //tmpC14 += A[(j + 1)*lda + k + 1] * tB[(i + 4)*lda + k + 1];
+ //tmpC15 += A[(j + 1)*lda + k + 1] * tB[(i + 5)*lda + k + 1];
+ //tmpC16 += A[(j + 1)*lda + k + 1] * tB[(i + 6)*lda + k + 1];
+ //tmpC17 += A[(j + 1)*lda + k + 1] * tB[(i + 7)*lda + k + 1];
+
+ tmpC00 += A[j*lda + k + 2] * tB[(i + 0)*lda + k + 2];
+ tmpC01 += A[j*lda + k + 2] * tB[(i + 1)*lda + k + 2];
+ tmpC02 += A[j*lda + k + 2] * tB[(i + 2)*lda + k + 2];
+ tmpC03 += A[j*lda + k + 2] * tB[(i + 3)*lda + k + 2];
+ //tmpC04 += A[j*lda + k + 2] * tB[(i + 4)*lda + k + 2];
+ //tmpC05 += A[j*lda + k + 2] * tB[(i + 5)*lda + k + 2];
+ //tmpC06 += A[j*lda + k + 2] * tB[(i + 6)*lda + k + 2];
+ //tmpC07 += A[j*lda + k + 2] * tB[(i + 7)*lda + k + 2];
+ tmpC10 += A[(j + 1)*lda + k + 2] * tB[(i + 0)*lda + k + 2];
+ tmpC11 += A[(j + 1)*lda + k + 2] * tB[(i + 1)*lda + k + 2];
+ tmpC12 += A[(j + 1)*lda + k + 2] * tB[(i + 2)*lda + k + 2];
+ tmpC13 += A[(j + 1)*lda + k + 2] * tB[(i + 3)*lda + k + 2];
+ //tmpC14 += A[(j + 1)*lda + k + 2] * tB[(i + 4)*lda + k + 2];
+ //tmpC15 += A[(j + 1)*lda + k + 2] * tB[(i + 5)*lda + k + 2];
+ //tmpC16 += A[(j + 1)*lda + k + 2] * tB[(i + 6)*lda + k + 2];
+ //tmpC17 += A[(j + 1)*lda + k + 2] * tB[(i + 7)*lda + k + 2];
+
+ tmpC00 += A[j*lda + k + 3] * tB[(i + 0)*lda + k + 3];
+ tmpC01 += A[j*lda + k + 3] * tB[(i + 1)*lda + k + 3];
+ tmpC02 += A[j*lda + k + 3] * tB[(i + 2)*lda + k + 3];
+ tmpC03 += A[j*lda + k + 3] * tB[(i + 3)*lda + k + 3];
+ //tmpC04 += A[j*lda + k + 3] * tB[(i + 4)*lda + k + 3];
+ //tmpC05 += A[j*lda + k + 3] * tB[(i + 5)*lda + k + 3];
+ //tmpC06 += A[j*lda + k + 3] * tB[(i + 6)*lda + k + 3];
+ //tmpC07 += A[j*lda + k + 3] * tB[(i + 7)*lda + k + 3];
+ tmpC10 += A[(j + 1)*lda + k + 3] * tB[(i + 0)*lda + k + 3];
+ tmpC11 += A[(j + 1)*lda + k + 3] * tB[(i + 1)*lda + k + 3];
+ tmpC12 += A[(j + 1)*lda + k + 3] * tB[(i + 2)*lda + k + 3];
+ tmpC13 += A[(j + 1)*lda + k + 3] * tB[(i + 3)*lda + k + 3];
+ //tmpC14 += A[(j + 1)*lda + k + 3] * tB[(i + 4)*lda + k + 3];
+ //tmpC15 += A[(j + 1)*lda + k + 3] * tB[(i + 5)*lda + k + 3];
+ //tmpC16 += A[(j + 1)*lda + k + 3] * tB[(i + 6)*lda + k + 3];
+ //tmpC17 += A[(j + 1)*lda + k + 3] * tB[(i + 7)*lda + k + 3];
+ }
+ C[j*lda + i + 0] = tmpC00; C[(j + 1)*lda + i + 0] = tmpC10;
+ C[j*lda + i + 1] = tmpC01; C[(j + 1)*lda + i + 1] = tmpC11;
+ C[j*lda + i + 2] = tmpC02; C[(j + 1)*lda + i + 2] = tmpC12;
+ C[j*lda + i + 3] = tmpC03; C[(j + 1)*lda + i + 3] = tmpC13;
+ //C[j*lda + i + 4] = tmpC04; C[(j + 1)*lda + i + 4] = tmpC14;
+ //C[j*lda + i + 5] = tmpC05; C[(j + 1)*lda + i + 5] = tmpC15;
+ //C[j*lda + i + 6] = tmpC06; C[(j + 1)*lda + i + 6] = tmpC16;
+ //C[j*lda + i + 7] = tmpC07; C[(j + 1)*lda + i + 7] = tmpC17;
+ }
+}
+
+//--------------------------------------------------------------------------
+// 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