//************************************************************************** // 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); }