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182 | /*************************************************************************
*
* Project
* _____ _____ __ __ _____
* / ____| __ \| \/ | __ \
* ___ _ __ ___ _ __ | | __| |__) | \ / | |__) |
* / _ \| '_ \ / _ \ '_ \| | |_ | ___/| |\/| | ___/
*| (_) | |_) | __/ | | | |__| | | | | | | |
* \___/| .__/ \___|_| |_|\_____|_| |_| |_|_|
* | |
* |_|
*
* Copyright (C) Akiel Aries, <akiel@akiel.org>, et al.
*
* This software is licensed as described in the file LICENSE, which
* you should have received as part of this distribution. The terms
* among other details are referenced in the official documentation
* seen here : https://akielaries.github.io/openGPMP/ along with
* important files seen in this project.
*
* You may opt to use, copy, modify, merge, publish, distribute
* and/or sell copies of the Software, and permit persons to whom
* the Software is furnished to do so, under the terms of the
* LICENSE file. As this is an Open Source effort, all implementations
* must be of the same methodology.
*
*
*
* This software is distributed on an AS IS basis, WITHOUT
* WARRANTY OF ANY KIND, either express or implied.
*
************************************************************************/
#include <cassert>
#include <cstddef>
#include <cstdint>
#include <iostream>
#include <openGPMP/linalg/mtx.hpp>
#include <vector>
#if defined(__x86_64__) || defined(__amd64__) || defined(__amd64)
/************************************************************************
*
* Matrix Operations for SSE ISA
*
************************************************************************/
#elif defined(__SSE2__)
// SSE2
#include <emmintrin.h>
#include <smmintrin.h>
/************************************************************************
*
* Matrix Operations on vector<vector>
*
************************************************************************/
void gpmp::linalg::Mtx::mtx_add(const std::vector<std::vector<double>> &A,
const std::vector<std::vector<double>> &B,
std::vector<std::vector<double>> &C) {
const int rows = A.size();
const int cols = A[0].size();
for (int i = 0; i < rows; ++i) {
int j = 0;
// requires at least size 2x2 matrices for SSE2
for (; j < cols - 1; j += 2) {
// load 2 elements from A, B, and C matrices using SSE2
__m128d a = _mm_loadu_pd(&A[i][j]);
__m128d b = _mm_loadu_pd(&B[i][j]);
__m128d c = _mm_loadu_pd(&C[i][j]);
// perform vectorized addition
c = _mm_add_pd(a, b);
// store the result back to the C matrix
_mm_storeu_pd(&C[i][j], c);
}
// handle the remaining elements that are not multiples of 2
for (; j < cols; ++j) {
C[i][j] = A[i][j] + B[i][j];
}
}
}
// matrix subtraction using Intel intrinsics, accepts double types
void gpmp::linalg::Mtx::mtx_sub(const std::vector<std::vector<double>> &A,
const std::vector<std::vector<double>> &B,
std::vector<std::vector<double>> &C) {
const int rows = A.size();
const int cols = A[0].size();
for (int i = 0; i < rows; ++i) {
int j = 0;
// requires at least size 2x2 matrices for SSE2
for (; j < cols - 1; j += 2) {
// load 2 elements from A, B, and C matrices using SSE2
__m128d a = _mm_loadu_pd(&A[i][j]);
__m128d b = _mm_loadu_pd(&B[i][j]);
__m128d c = _mm_loadu_pd(&C[i][j]);
// perform vectorized subtraction
c = _mm_sub_pd(a, b);
// store the result back to the C matrix
_mm_storeu_pd(&C[i][j], c);
}
// handle the remaining elements that are not multiples of 2
for (; j < cols; ++j) {
C[i][j] = A[i][j] - B[i][j];
}
}
}
// matrix multiplication using Intel intrinsics, accepts double types
void gpmp::linalg::Mtx::mtx_mult(const std::vector<std::vector<double>> &A,
const std::vector<std::vector<double>> &B,
std::vector<std::vector<double>> &C) {
const int rows_a = A.size();
const int cols_a = A[0].size();
const int cols_b = B[0].size();
for (int i = 0; i < rows_a; ++i) {
for (int j = 0; j < cols_b; j += 2) {
// initialize a vector of zeros for the result
__m128d c = _mm_setzero_pd();
for (int k = 0; k < cols_a; ++k) {
// load 2 elements from matrices A and B using SSE2
__m128d a = _mm_set1_pd(A[i][k]);
__m128d b = _mm_loadu_pd(&B[k][j]);
// perform vectorized multiplication
__m128d prod = _mm_mul_pd(a, b);
// perform vectorized addition
c = _mm_add_pd(c, prod);
}
// store the result back to the C matrix
_mm_storeu_pd(&C[i][j], c);
}
// handle the remaining elements that are not multiples of 2
for (int j = cols_b - cols_b % 2; j < cols_b; ++j) {
double sum = 0.0;
for (int k = 0; k < cols_a; ++k) {
sum += A[i][k] * B[k][j];
}
C[i][j] = sum;
}
}
}
// transpose matrices of type double using Intel intrinsics
void gpmp::linalg::Mtx::mtx_tpose(std::vector<std::vector<double>> &matrix) {
const int rows = matrix.size();
const int cols = matrix[0].size();
for (int i = 0; i < rows; i += 2) {
for (int j = i; j < cols; j += 2) {
__m128d row1 = _mm_loadu_pd(&matrix[i][j]);
__m128d row2 = _mm_loadu_pd(&matrix[i + 1][j]);
// transpose 2x2 submatrix
__m128d tmp1 = _mm_unpacklo_pd(row1, row2);
__m128d tmp2 = _mm_unpackhi_pd(row1, row2);
// store the transposed 2x2 submatrix back to the matrix
_mm_storeu_pd(&matrix[i][j], tmp1);
_mm_storeu_pd(&matrix[i + 1][j], tmp2);
}
}
}
#endif
#endif<--- #endif without #if
|