_mm512_loadu_si512

_mm512_loadu_si512是一个用于从内存中加载未对齐数据的指令。它可以加载一个512位的数据块到一个__m512i寄存器中。这个指令相对于_mm512_load_si512来说稍微慢一些，因为它需要处理未对齐的数据。\[2\]
#### 引用[.reference_title]
- *1* *2* *3* [AVX512笔记](https://blog.csdn.net/longxiawei/article/details/129713310)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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相关问题

使用getRotationMatrix2D创建变换矩阵通过SIMD指令加速warpAffine带参数WARP_INVERSE_MAP效果例程C++

下面是一个使用SIMD指令加速warpAffine函数的例程，该例程使用getRotationMatrix2D函数创建变换矩阵，并使用WARP_INVERSE_MAP参数来反向映射。这个例程使用OpenCV库实现，需要确保您已经安装了OpenCV库并正确配置了编译环境。

#include <opencv2/opencv.hpp>
#include <opencv2/core/hal/intrin.hpp>
#include <iostream>

using namespace std;
using namespace cv;

int main()
{
    Mat src = imread("test.jpg");
    if (src.empty())
    {
        cout << "Could not open or find the image!\n" << endl;
        return -1;
    }

    double angle = 45.0;
    double scale = 1.0;
    Point2f center(src.cols / 2.0, src.rows / 2.0);
    Mat rot_mat = getRotationMatrix2D(center, angle, scale);

    const int N = 4;
    int rows = src.rows, cols = src.cols;
    int x[N] = { 0, cols, 0, cols };
    int y[N] = { 0, 0, rows, rows };
    float fx[N], fy[N];
    __m128i zero = _mm_set1_epi32(0);
    __m128i one = _mm_set1_epi32(1);
    __m128i cols_epi32 = _mm_set1_epi32(cols);
    __m128i rows_epi32 = _mm_set1_epi32(rows);
    __m128 fx_0, fx_1, fx_2, fx_3;
    __m128 fy_0, fy_1, fy_2, fy_3;
    __m128i x_epi32 = _mm_loadu_si128((__m128i*)x);
    __m128i y_epi32 = _mm_loadu_si128((__m128i*)y);
    __m128i x_0 = _mm_unpacklo_epi32(x_epi32, zero);
    __m128i x_1 = _mm_unpackhi_epi32(x_epi32, zero);
    __m128i y_0 = _mm_unpacklo_epi32(y_epi32, zero);
    __m128i y_1 = _mm_unpackhi_epi32(y_epi32, zero);
    __m128 x_0f = _mm_cvtepi32_ps(x_0);
    __m128 x_1f = _mm_cvtepi32_ps(x_1);
    __m128 y_0f = _mm_cvtepi32_ps(y_0);
    __m128 y_1f = _mm_cvtepi32_ps(y_1);
    __m128 center_x = _mm_set1_ps(center.x);
    __m128 center_y = _mm_set1_ps(center.y);
    __m128 scale_f = _mm_set1_ps(scale);
    __m128i add_mask = _mm_set1_epi32(0xffffffff);
    __m128i sub_mask = _mm_set1_epi32(0x80000000);

    __m128 m00 = _mm_set1_ps(rot_mat.at<double>(0, 0));
    __m128 m01 = _mm_set1_ps(rot_mat.at<double>(0, 1));
    __m128 m02 = _mm_set1_ps(rot_mat.at<double>(0, 2));
    __m128 m10 = _mm_set1_ps(rot_mat.at<double>(1, 0));
    __m128 m11 = _mm_set1_ps(rot_mat.at<double>(1, 1));
    __m128 m12 = _mm_set1_ps(rot_mat.at<double>(1, 2));

    fx_0 = _mm_mul_ps(_mm_sub_ps(x_0f, center_x), m00);
    fy_0 = _mm_mul_ps(_mm_sub_ps(x_0f, center_x), m10);
    fx_0 = _mm_add_ps(fx_0, _mm_mul_ps(_mm_sub_ps(y_0f, center_y), m01));
    fy_0 = _mm_add_ps(fy_0, _mm_mul_ps(_mm_sub_ps(y_0f, center_y), m11));
    fx_0 = _mm_add_ps(fx_0, center_x);
    fy_0 = _mm_add_ps(fy_0, center_y);
    fx_0 = _mm_mul_ps(fx_0, scale_f);
    fy_0 = _mm_mul_ps(fy_0, scale_f);
    fx_0 = _mm_add_ps(fx_0, center_x);
    fy_0 = _mm_add_ps(fy_0, center_y);
    fx_0 = _mm_round_ps(fx_0, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    fy_0 = _mm_round_ps(fy_0, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    __m128i fx_0i = _mm_cvtps_epi32(fx_0);
    __m128i fy_0i = _mm_cvtps_epi32(fy_0);

    fx_1 = _mm_mul_ps(_mm_sub_ps(x_1f, center_x), m00);
    fy_1 = _mm_mul_ps(_mm_sub_ps(x_1f, center_x), m10);
    fx_1 = _mm_add_ps(fx_1, _mm_mul_ps(_mm_sub_ps(y_0f, center_y), m01));
    fy_1 = _mm_add_ps(fy_1, _mm_mul_ps(_mm_sub_ps(y_0f, center_y), m11));
    fx_1 = _mm_add_ps(fx_1, center_x);
    fy_1 = _mm_add_ps(fy_1, center_y);
    fx_1 = _mm_mul_ps(fx_1, scale_f);
    fy_1 = _mm_mul_ps(fy_1, scale_f);
    fx_1 = _mm_add_ps(fx_1, center_x);
    fy_1 = _mm_add_ps(fy_1, center_y);
    fx_1 = _mm_round_ps(fx_1, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    fy_1 = _mm_round_ps(fy_1, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    __m128i fx_1i = _mm_cvtps_epi32(fx_1);
    __m128i fy_1i = _mm_cvtps_epi32(fy_1);

    fx_2 = _mm_mul_ps(_mm_sub_ps(x_0f, center_x), m00);
    fy_2 = _mm_mul_ps(_mm_sub_ps(x_0f, center_x), m10);
    fx_2 = _mm_add_ps(fx_2, _mm_mul_ps(_mm_sub_ps(y_1f, center_y), m01));
    fy_2 = _mm_add_ps(fy_2, _mm_mul_ps(_mm_sub_ps(y_1f, center_y), m11));
    fx_2 = _mm_add_ps(fx_2, center_x);
    fy_2 = _mm_add_ps(fy_2, center_y);
    fx_2 = _mm_mul_ps(fx_2, scale_f);
    fy_2 = _mm_mul_ps(fy_2, scale_f);
    fx_2 = _mm_add_ps(fx_2, center_x);
    fy_2 = _mm_add_ps(fy_2, center_y);
    fx_2 = _mm_round_ps(fx_2, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    fy_2 = _mm_round_ps(fy_2, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    __m128i fx_2i = _mm_cvtps_epi32(fx_2);
    __m128i fy_2i = _mm_cvtps_epi32(fy_2);

    fx_3 = _mm_mul_ps(_mm_sub_ps(x_1f, center_x), m00);
    fy_3 = _mm_mul_ps(_mm_sub_ps(x_1f, center_x), m10);
    fx_3 = _mm_add_ps(fx_3, _mm_mul_ps(_mm_sub_ps(y_1f, center_y), m01));
    fy_3 = _mm_add_ps(fy_3, _mm_mul_ps(_mm_sub_ps(y_1f, center_y), m11));
    fx_3 = _mm_add_ps(fx_3, center_x);
    fy_3 = _mm_add_ps(fy_3, center_y);
    fx_3 = _mm_mul_ps(fx_3, scale_f);
    fy_3 = _mm_mul_ps(fy_3, scale_f);
    fx_3 = _mm_add_ps(fx_3, center_x);
    fy_3 = _mm_add_ps(fy_3, center_y);
    fx_3 = _mm_round_ps(fx_3, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    fy_3 = _mm_round_ps(fy_3, _MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC);
    __m128i fx_3i = _mm_cvtps_epi32(fx_3);
    __m128i fy_3i = _mm_cvtps_epi32(fy_3);

    int fx[N], fy[N];
    _mm_storeu_si128((__m128i*)fx, fx_0i);
    _mm_storeu_si128((__m128i*)(fx + 2), fx_1i);
    _mm_storeu_si128((__m128i*)fy, fy_0i);
    _mm_storeu_si128((__m128i*)(fy + 2), fy_1i);
    fx[1] = fx[2];
    fy[1] = fy[2];
    _mm_storeu_si128((__m128i*)(fx + 4), fx_2i);
    _mm_storeu_si128((__m128i*)(fy + 4), fy_2i);
    fx[3] = fx[4];
    fy[3] = fy[4];
    _mm_storeu_si128((__m128i*)(fx + 6), fx_3i);
    _mm_storeu_si128((__m128i*)(fy + 6), fy_3i);

    Mat dst(rows, cols, src.type());
    for (int r = 0; r < rows; r++)
    {
        for (int c = 0; c < cols; c += 8)
        {
            __m256i c_epi32 = _mm256_set_epi32(c + 7, c + 6, c + 5, c + 4, c + 3, c + 2, c + 1, c);
            __m256i r_epi32 = _mm256_set1_epi32(r);
            __m256i index = _mm256_add_epi32(_mm256_mullo_epi32(r_epi32, cols_epi32), c_epi32);
            __m256i index_0 = _mm256_cvtepi32_epi64(_mm256_castsi256_si128(index));
            __m256i index_1 = _mm256_cvtepi32_epi64(_mm256_extracti128_si256(index, 1));
            __m256i index_0_4 = _mm256_mullo_epi32(_mm256_srli_si256(index_0, 2), _mm256_set1_epi32(4));
            __m256i index_1_4 = _mm256_mullo_epi32(_mm256_srli_si256(index_1, 2), _mm256_set1_epi32(4));
            __m256i index_0_0 = _mm256_add_epi32(index_0, _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0));
            __m256i index_1_0 = _mm256_add_epi32(index_1, _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0));
            __m256i index_0_1 = _mm256_add_epi32(index_0, _mm256_set_epi32(8, 8, 8, 8, 4, 4, 4, 4));
            __m256i index_1_1 = _mm256_add_epi32(index_1, _mm256_set_epi32(8, 8, 8, 8, 4, 4, 4, 4));
            __m256i index_0_2 = _mm256_add_epi32(index_0, _mm256_set_epi32(16, 16, 16, 16, 12, 12, 12, 12));
            __m256i index_1_2 = _mm256_add_epi32(index_1, _mm256_set_epi32(16, 16, 16, 16, 12, 12, 12, 12));
            __m256i index_0_3 = _mm256_add_epi32(index_0, _mm256_set_epi32(24, 24, 24, 24, 20, 20, 20, 20));
            __m256i index_1_3 = _mm256_add_epi32(index_1, _mm256_set_epi32(24, 24, 24, 24, 20, 20, 20, 20));
            __m256i fx_epi32 = _mm256_set_epi32(fx[index_1_3.m256i_i32[7]], fx[index_1_3.m256i_i32[6]],
                fx[index_1_3.m256i_i32[5]], fx[index_1_3.m256i_i32[4]], fx[index_1_1.m256i_i32[3]],
                fx[index_1_1.m256i_i32[2]], fx[index_1_1.m256i_i32[1]], fx[index_1_1.m256i_i32[0]]);
            __m256i fy_epi32 = _mm256_set_epi32(fy[index_1_3.m256i_i32[7]], fy[index_1_3.m256i_i32[6]],
                fy[index_1_3.m256i_i32[5]], fy[index_1_3.m256i_i32[4]], fy[index_1_1.m256i_i32[3]],
                fy[index_1_1.m256i_i32[2]], fy[index_1_1.m256i_i32[1]], fy[index_1_1.m256i_i32[0]]);
            __m256i diff_x_0 = _mm256_sub_epi32(_mm256_castsi128_si256(_mm256_extracti128_si256(index_0, 0)), index_0_0);
            __m256i diff_x_1 = _mm256_sub_epi32(_mm256_castsi128_si256(_mm256_extracti128_si256(index_0, 1)), index_0_1);
            __m256i diff_x_2 = _mm256_sub_epi32(_mm256_castsi128_si256(_mm256_extracti128_si256(index_1, 0)), index_1_0);
            __m256i diff_x_3 = _mm256_sub_epi32(_mm256_castsi128_si256(_mm256_extracti128_si256(index_1, 1)), index_1_1);
            __m256i diff_y_0 = _mm256_mullo_epi32(diff_x_0, _mm256_set1_epi32(cols));
            __m256i diff_y_1 = _mm256_mullo_epi32(diff_x_1, _mm256_set1_epi32(cols));
            __m256i diff_y_2 = _mm256_mullo_epi32(diff_x_2, _mm256_set1_epi32(cols));
            __m256i diff_y_3 = _mm256_mullo_epi32(diff_x_3, _mm256_set1_epi32(cols));
            __m256i index_0_0_0 = _mm256_add_epi32(index_0_0, diff_y_0);
            __m256i index_0_0_1 = _mm256_add_epi32(index_0_0, diff_y_1);
            __m256i index_1_0_0 = _mm256_add_epi32(index_1_0, diff_y_2);
            __m256i index_1_0_1 = _mm256_add_epi32(index_1_0, diff_y_3);
            __m256i index_0_1_0 = _mm256_add_epi32(index_0_1, diff_y_0);
            __m256i index_0_1_1 = _mm256_add_epi32(index_0_1, diff_y_1);
            __m256i index_1_1_0 = _mm256_add_epi32(index_1_1, diff_y_2);
            __m256i index_1_1_1 = _mm256_add_epi32(index_1_1, diff_y_3);
            __m256i index_0_2_0 = _mm256_add_epi32(index_0_2, diff_y_0);
            __m256i index_0_2_1 = _mm256_add_epi32(index_0_2, diff_y_1);
            __m256i index_1_2_0 = _mm256_add_epi32(index_1_2, diff_y_2);
            __m256i index_1_2_1 = _mm256_add_epi32(index_1_2, diff_y_3);
            __m256i index_0_3_0 = _mm256_add_epi32(index_0_3, diff_y_0);
            __m256i index_0_3_1 = _mm256_add_epi32(index_0_3, diff_y_1);
            __m256i

如何使用AVX2指令集加速cv::bitwise_and函数

AVX2指令集是一组针对Intel处理器的指令集，可以在处理器上并行执行多个操作，可以用来加速cv::bitwise_and函数。

以下是使用AVX2指令集加速cv::bitwise_and函数的示范代码：

#include <immintrin.h> // 包含AVX2指令集的头文件

void bitwise_and_avx2(const cv::Mat& src1, const cv::Mat& src2, cv::Mat& dst)
{
    CV_Assert(src1.type() == CV_8UC1 && src2.type() == CV_8UC1); // 确保输入图像类型为8位无符号整数类型

    dst.create(src1.size(), src1.type()); // 创建与输入图像相同大小和类型的输出图像

    int rows = src1.rows;
    int cols = src1.cols;

    const uchar* src1_ptr = src1.ptr<uchar>();
    const uchar* src2_ptr = src2.ptr<uchar>();
    uchar* dst_ptr = dst.ptr<uchar>();

    const int step = dst.step; // 图像步长

    __m256i v_mask = _mm256_set1_epi8(0xff); // 创建掩码向量

    for (int i = 0; i < rows; i++)
    {
        int j = 0;

        // 循环处理8个像素点
        for (; j <= cols - 8; j += 8)
        {
            __m256i v_src1 = _mm256_loadu_si256((__m256i*)(src1_ptr + i * step + j)); // 读取src1中的8个像素点
            __m256i v_src2 = _mm256_loadu_si256((__m256i*)(src2_ptr + i * step + j)); // 读取src2中的8个像素点

            __m256i v_dst = _mm256_and_si256(v_src1, v_src2); // 对8个像素点进行按位与运算

            _mm256_storeu_si256((__m256i*)(dst_ptr + i * step + j), v_dst); // 将8个像素点写入输出图像
        }

        // 处理剩余的像素点
        for (; j < cols; j++)
        {
            dst_ptr[i * step + j] = src1_ptr[i * step + j] & src2_ptr[i * step + j]; // 对单个像素点进行按位与运算
        }
    }
}

上述代码使用AVX2指令集对8个像素点进行按位与运算，从而提高了计算速度。但是，由于AVX2指令集只能在支持AVX2的CPU上运行，因此需要在运行代码前检查CPU是否支持AVX2指令集。