X-Git-Url: https://git.libre-soc.org/?p=riscv-tests.git;a=blobdiff_plain;f=mt%2Fbp_matmul%2Fbp_matmul.c;fp=mt%2Fbp_matmul%2Fbp_matmul.c;h=de964db65bd787decd0347880d7ae67e8af19d15;hp=0000000000000000000000000000000000000000;hb=60f056880ec6929c5f23af4d66aea0f0cb7b0245;hpb=4412b96c81ca09dcce6305579dd86d4bf3b808da;ds=sidebyside diff --git a/mt/bp_matmul/bp_matmul.c b/mt/bp_matmul/bp_matmul.c new file mode 100755 index 0000000..de964db --- /dev/null +++ b/mt/bp_matmul/bp_matmul.c @@ -0,0 +1,341 @@ +//************************************************************************** +// 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_MI_transpose(const int lda, const data_t A[], const data_t B[], data_t C[] ) +{ + int i, j, k; + data_t B_trans[32*32]; + data_t acc_temp0, acc_temp1; + data_t *A_j, *B_i; + data_t *A_j_k, *B_i_k; + int z; + + //for (i = 0; i < 32; i++) { + // for (j = 0; j < 32; j++) { + // B_trans[i*lda+j] = B[i+j*lda]; + // } + //} + + if (coreid == 0) { + for (i = 0; i < 32; i++) { + B_i = B_trans+i*32; + for (z = 0; z < 32; z++) { + *(B_i+z) = B[i+z*32]; + } + for (j = 0; j < 16; j+=2) { + A_j = A+j*lda; + acc_temp0 = 0; + for (k = 0; k < 32; k+=8) { + A_j_k = A_j+k; + B_i_k = B_i+k; + acc_temp0 += *(A_j_k) * *(B_i_k); + acc_temp0 += *(A_j_k + 1) * *(B_i_k + 1); + acc_temp0 += *(A_j_k + 2) * *(B_i_k + 2); + acc_temp0 += *(A_j_k + 3) * *(B_i_k + 3); + acc_temp0 += *(A_j_k + 4) * *(B_i_k + 4); + acc_temp0 += *(A_j_k + 5) * *(B_i_k + 5); + acc_temp0 += *(A_j_k + 6) * *(B_i_k + 6); + acc_temp0 += *(A_j_k + 7) * *(B_i_k + 7); + } + A_j += 32; + + acc_temp1 = 0; + for (k = 0; k < 32; k+=8) { + acc_temp1 += *(A_j+k) * *(B_i+k); + acc_temp1 += *(A_j+k + 1) * *(B_i+k + 1); + acc_temp1 += *(A_j+k + 2) * *(B_i+k + 2); + acc_temp1 += *(A_j+k + 3) * *(B_i+k + 3); + acc_temp1 += *(A_j+k + 4) * *(B_i+k + 4); + acc_temp1 += *(A_j+k + 5) * *(B_i+k + 5); + acc_temp1 += *(A_j+k + 6) * *(B_i+k + 6); + acc_temp1 += *(A_j+k + 7) * *(B_i+k + 7); + } + + C[i + j*lda] = acc_temp0; + C[i + (j+1)*lda] = acc_temp1; + } + } + } else if (coreid == 1) { + for (i = 0; i < 32; i++) { + B_i = B_trans+i*32; + for (z = 0; z < 32; z++) { + *(B_i+z) = B[i+z*32]; + } + for (j = 16; j < 32; j+=2) { + A_j = A+j*lda; + acc_temp0 = 0; + for (k = 0; k < 32; k+=8) { + acc_temp0 += *(A_j+k) * *(B_i+k); + acc_temp0 += *(A_j+k + 1) * *(B_i+k + 1); + acc_temp0 += *(A_j+k + 2) * *(B_i+k + 2); + acc_temp0 += *(A_j+k + 3) * *(B_i+k + 3); + acc_temp0 += *(A_j+k + 4) * *(B_i+k + 4); + acc_temp0 += *(A_j+k + 5) * *(B_i+k + 5); + acc_temp0 += *(A_j+k + 6) * *(B_i+k + 6); + acc_temp0 += *(A_j+k + 7) * *(B_i+k + 7); + } + A_j += 32; + + acc_temp1 = 0; + for (k = 0; k < 32; k+=8) { + acc_temp1 += *(A_j+k) * *(B_i+k); + acc_temp1 += *(A_j+k + 1) * *(B_i+k + 1); + acc_temp1 += *(A_j+k + 2) * *(B_i+k + 2); + acc_temp1 += *(A_j+k + 3) * *(B_i+k + 3); + acc_temp1 += *(A_j+k + 4) * *(B_i+k + 4); + acc_temp1 += *(A_j+k + 5) * *(B_i+k + 5); + acc_temp1 += *(A_j+k + 6) * *(B_i+k + 6); + acc_temp1 += *(A_j+k + 7) * *(B_i+k + 7); + } + C[i + j*lda] = acc_temp0; + C[i + (j+1)*lda] = acc_temp1; + } + } + } +} + +void __attribute__((noinline)) matmul_MI(const int lda, const data_t A[], const data_t B[], data_t C[] ) +{ + int i, j, k; + data_t acc_temp; + data_t *A_j, *B_i; + int j_start = coreid*16; + int j_end = (coreid*16)+16; + if (coreid == 0) { + for ( i = 0; i < 32; i++ ) { + B_i = B + i; + for ( j = j_start; j < j_end; j++ ) + { + acc_temp = 0; + A_j = A + j*32; + for ( k = 0; k < 32; k++ ) + { + acc_temp += *(A_j + k) * *(B_i + k*32); + } + C[i + j*32] = acc_temp; + } + } + } else if (coreid == 1) { + for ( i = 16; i < 32; i++ ) { + B_i = B + i; + for ( j = j_start; j < j_end; j++ ) + { + acc_temp = 0; + A_j = A + j*32; + for ( k = 0; k < 32; k+=4 ) + { + acc_temp += *(A_j + k) * *(B_i + k*32); + acc_temp += *(A_j + k + 1) * *(B_i + (k+1)*32); + acc_temp += *(A_j + k + 2) * *(B_i + (k+2)*32); + acc_temp += *(A_j + k + 3) * *(B_i + (k+3)*32); + } + C[i + j*32] = acc_temp; + } + } + for ( i = 0; i < 16; i++ ) { + B_i = B + i; + for ( j = j_start; j < j_end; j++ ) + { + acc_temp = 0; + A_j = A + j*32; + for ( k = 0; k < 32; k+=4 ) + { + acc_temp += *(A_j + k) * *(B_i + k*32); + acc_temp += *(A_j + k + 1) * *(B_i + (k+1)*32); + acc_temp += *(A_j + k + 2) * *(B_i + (k+2)*32); + acc_temp += *(A_j + k + 3) * *(B_i + (k+3)*32); + } + C[i + j*32] = acc_temp; + } + } + + } +} + +void __attribute__((noinline)) matmul_MSI(const int lda, const data_t A[], const data_t B[], data_t C[] ) +{ + int i, j, k; + data_t acc_temp; + data_t *A_j, *B_i; + int j_start = coreid*16; + int j_end = (coreid*16)+16; + for ( i = 0; i < 32; i++ ) { + B_i = B + i; + for ( j = j_start; j < j_end; j++ ) + { + acc_temp = 0; + A_j = A + j*32; + for ( k = 0; k < 32; k++ ) + { + acc_temp += *(A_j + k) * *(B_i + k*32); + } + C[i + j*32] = acc_temp; + } + } +} + +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. + // ENABLE_SHARING = false is MI + // ENABLE_SHARING = true is MSI + matmul_MI_transpose(lda, A, B, C); + //matmul_MSI(lda, A, B, C); +} + +//-------------------------------------------------------------------------- +// 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); +} +