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198 | /*************************************************************************
*
* 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 AVX ISA
*
************************************************************************/
#if defined(__AVX2__)
// AVX family intrinsics
#include <immintrin.h>
/************************************************************************
*
* Matrix Operations on vector<vector>
*
************************************************************************/
// matrix addition using Intel intrinsics, accepts type double
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();
if (rows > 8) {
for (int i = 0; i < rows; ++i) {
int j = 0;
// requires at least size 4x4 matrices
for (; j < cols - 3; j += 4) {
// load 4 elements from A, B, and C matrices using SIMD
__m256d a = _mm256_loadu_pd(&A[i][j]);
__m256d b = _mm256_loadu_pd(&B[i][j]);
__m256d c = _mm256_loadu_pd(&C[i][j]);<--- c is initialized
// perform vectorized addition
c = _mm256_add_pd(a, b);<--- c is overwritten
// store the result back to the C matrix
_mm256_storeu_pd(&C[i][j], c);
}
// handle the remaining elements that are not multiples of 4
for (; j < cols; ++j) {
C[i][j] = A[i][j] + B[i][j];
}
}
} else {
std_mtx_add(A, B, C);
}
}
// 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 4x4 matrices
for (; j < cols - 3; j += 4) {
// load 4 elements from A, B, and C matrices using SIMD
__m256d a = _mm256_loadu_pd(&A[i][j]);
__m256d b = _mm256_loadu_pd(&B[i][j]);
__m256d c = _mm256_loadu_pd(&C[i][j]);<--- c is initialized
// perform vectorized subtraction
c = _mm256_sub_pd(a, b);<--- c is overwritten
// store the result back to the C matrix
_mm256_storeu_pd(&C[i][j], c);
}
// handle the remaining elements that are not multiples of 4
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 += 4) {
// initialize a vector of zeros for the result
__m256d c = _mm256_setzero_pd();
for (int k = 0; k < cols_a; ++k) {
// load 4 elements from matrices A and B using SIMD
__m256d a = _mm256_set1_pd(A[i][k]);
__m256d b = _mm256_loadu_pd(&B[k][j]);
// perform vectorized multiplication
__m256d prod = _mm256_mul_pd(a, b);
// perform vectorized addition
c = _mm256_add_pd(c, prod);
}
// store the result back to the C matrix
_mm256_storeu_pd(&C[i][j], c);
}
// handle the remaining elements that are not multiples of 4
for (int j = cols_b - cols_b % 4; 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;
}
}
}
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 += 4) {
for (int j = i; j < cols; j += 4) {
__m256d row1 = _mm256_loadu_pd(&matrix[i][j]);
__m256d row2 = _mm256_loadu_pd(&matrix[i + 1][j]);
__m256d row3 = _mm256_loadu_pd(&matrix[i + 2][j]);
__m256d row4 = _mm256_loadu_pd(&matrix[i + 3][j]);
__m256d tmp1, tmp2, tmp3, tmp4;
// Transpose 4x4 submatrix
tmp1 = _mm256_unpacklo_pd(row1, row2);
tmp2 = _mm256_unpackhi_pd(row1, row2);
tmp3 = _mm256_unpacklo_pd(row3, row4);
tmp4 = _mm256_unpackhi_pd(row3, row4);
row1 = _mm256_permute2f128_pd(tmp1, tmp3, 0x20);
row2 = _mm256_permute2f128_pd(tmp2, tmp4, 0x20);
row3 = _mm256_permute2f128_pd(tmp1, tmp3, 0x31);
row4 = _mm256_permute2f128_pd(tmp2, tmp4, 0x31);
// Store the transposed 4x4 submatrix back to the matrix
_mm256_storeu_pd(&matrix[i][j], row1);
_mm256_storeu_pd(&matrix[i + 1][j], row2);
_mm256_storeu_pd(&matrix[i + 2][j], row3);
_mm256_storeu_pd(&matrix[i + 3][j], row4);
}
}
}
#endif
#endif
|