2022-09-28 11:20:37 +00:00
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#include <string.h>
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2022-09-28 16:20:36 +00:00
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2022-09-27 06:35:59 +00:00
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/*
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Please include compiler name below (you may also include any other modules you would like to be loaded)
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COMPILER= gnu
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Please include All compiler flags and libraries as you want them run. You can simply copy this over from the Makefile's first few lines
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CC = cc
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OPT = -O3
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CFLAGS = -Wall -std=gnu99 $(OPT)
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MKLROOT = /opt/intel/composer_xe_2013.1.117/mkl
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LDLIBS = -lrt -Wl,--start-group $(MKLROOT)/lib/intel64/libmkl_intel_lp64.a $(MKLROOT)/lib/intel64/libmkl_sequential.a $(MKLROOT)/lib/intel64/libmkl_core.a -Wl,--end-group -lpthread -lm
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*/
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2022-09-28 11:20:37 +00:00
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const char* dgemm_desc = "Block-based dgemm.";
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2022-10-03 19:02:08 +00:00
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const int block_size = 27;
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2022-09-28 11:20:37 +00:00
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inline int min(int a, int b) {
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return a < b ? a : b;
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}
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2022-10-03 19:02:08 +00:00
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inline void naivemm(int r_min, int r_max, int k_min, int k_max, int c_min, int c_max, int n, double* A_row, double* B, double* C_temp) {
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for (int i = r_min, ii = 0; i < r_max; ++i, ++ii) {
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for (int j = c_min, jj = 0; j < c_max; ++j, ++jj) {
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for (int k = k_min; k < k_max; k++) {
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C_temp[ii + jj * block_size] += A_row[i * n + k] * B[k + j * n];
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2022-09-28 11:20:37 +00:00
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}
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}
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}
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}
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2022-09-28 16:20:36 +00:00
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inline void store_c(double* C, double* C_temp, int r_min, int r_max, int c_min, int c_max, int n) {
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for (int j = c_min, jj = 0; j < c_max; ++j, ++jj) {
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memcpy(C + j * n + r_min, C_temp + jj * block_size, (r_max - r_min) * sizeof(double));
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}
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}
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2022-09-27 06:35:59 +00:00
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/* This routine performs a dgemm operation
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* C := C + A * B
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* where A, B, and C are lda-by-lda matrices stored in column-major format.
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* On exit, A and B maintain their input values. */
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void square_dgemm(int n, double* A, double* B, double* C) {
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double A_row[n * n];
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double C_temp[block_size * block_size];
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2022-10-03 19:02:08 +00:00
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for (int m = 0; m < n; ++m) {
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double row_tmp[n];
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memcpy(row_tmp, A + m * n, n * sizeof(double));
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for (int l = 0; l < n; ++l) {
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A_row[l * n + m] = row_tmp[l];
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}
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}
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2022-09-28 11:20:37 +00:00
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for (int i = 0; i < n; i += block_size) {
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int i_next = min(i + block_size, n);
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for (int j = 0; j < n; j += block_size) {
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int j_next = min(j + block_size, n);
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2022-09-28 16:20:36 +00:00
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memset(C_temp, 0, block_size * block_size * sizeof(double));
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2022-09-28 11:20:37 +00:00
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for (int k = 0; k < n; k += block_size) {
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int k_next = min(k + block_size, n);
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2022-10-03 19:02:08 +00:00
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naivemm(i, i_next, k, k_next, j, j_next, n, A_row, B, C_temp);
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}
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store_c(C, C_temp, i, i_next, j, j_next, n);
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2022-09-28 11:20:37 +00:00
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}
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2022-09-27 06:35:59 +00:00
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}
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}
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