X-Git-Url: https://git.libre-soc.org/?a=blobdiff_plain;f=mt%2Fas_matmul%2Fas_matmul.c;fp=mt%2Fas_matmul%2Fas_matmul.c;h=d98da8ef9f2791c1165d36b05ae5a98c9d075fc5;hb=60f056880ec6929c5f23af4d66aea0f0cb7b0245;hp=0000000000000000000000000000000000000000;hpb=4412b96c81ca09dcce6305579dd86d4bf3b808da;p=riscv-tests.git diff --git a/mt/as_matmul/as_matmul.c b/mt/as_matmul/as_matmul.c new file mode 100755 index 0000000..d98da8e --- /dev/null +++ b/mt/as_matmul/as_matmul.c @@ -0,0 +1,281 @@ +//************************************************************************** +// 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 +#include +#include + + +//-------------------------------------------------------------------------- +// 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); +} +