高性能计算实践-OpenCV图像矩阵转置 transpose SIMD加速(ippicv)复现

觞刈 · 2025-11-19 03:05:03

说明

矩阵转置是高性能计算中的经典问题。OpenCV 的 transpose 函数内部依赖 ippicv 库中的 ippiTranspose_8u_C1R 实现。本文将对该优化算法进行复现与分析。
与上一篇基于 cv::flip / ippiMirror 的图像翻转不同，矩阵转置不再是简单的行内倒序，而是将整幅图像在行列维度上重新映射。我们可以用块划分（tiling）的遍历方式来解决，同时加上各种优化技巧。
复现

#ifdef _MSC_VER
#define FORCE_INLINE __forceinline
#elif defined(__GNUC__)
#define FORCE_INLINE __attribute__((always_inline)) inline
#else
#define FORCE_INLINE inline
#endif
/**
* 8x8 SSE 转置微核
* 读取 8 行源数据 -> 转置 -> 连续写入 64 字节到 buffer
*/
FORCE_INLINE void transpose_8x8_store_contiguous(const uint8_t* src0,
const uint8_t* src1,
const uint8_t* src2,
const uint8_t* src3,
const uint8_t* src4,
const uint8_t* src5,
const uint8_t* src6,
const uint8_t* src7,
uint8_t* pDst) {
__m128i r0 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src0));
__m128i r1 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src1));
__m128i r2 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src2));
__m128i r3 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src3));
__m128i r4 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src4));
__m128i r5 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src5));
__m128i r6 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src6));
__m128i r7 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src7));
__m128i t0 = _mm_unpacklo_epi8(r0, r1);
__m128i t1 = _mm_unpacklo_epi8(r2, r3);
__m128i t2 = _mm_unpacklo_epi8(r4, r5);
__m128i t3 = _mm_unpacklo_epi8(r6, r7);
__m128i t4 = _mm_unpacklo_epi16(t0, t1);
__m128i t5 = _mm_unpacklo_epi16(t2, t3);
__m128i t6 = _mm_unpackhi_epi16(t0, t1);
__m128i t7 = _mm_unpackhi_epi16(t2, t3);
__m128i c0 = _mm_unpacklo_epi32(t4, t5);
__m128i c1 = _mm_unpackhi_epi32(t4, t5);
__m128i c2 = _mm_unpacklo_epi32(t6, t7);
__m128i c3 = _mm_unpackhi_epi32(t6, t7);
// 将转置后的 8x8 块 (64字节) 连续写入 buffer
// buffer 是 alignas(64) 的，始终对齐
_mm_store_si128(reinterpret_cast<__m128i*>(pDst + 0), c0);
_mm_store_si128(reinterpret_cast<__m128i*>(pDst + 16), c1);
_mm_store_si128(reinterpret_cast<__m128i*>(pDst + 32), c2);
_mm_store_si128(reinterpret_cast<__m128i*>(pDst + 48), c3);
}
/**
* 64x64 Tile 转置核心优化
* 使用 64x64 栈上缓存
* 1. 读 Src (8x8 块)，转置后线性写入 Tmp (Row-Major Block)
* 2. 读 Tmp (Strided)，合并后流式写入 Dst (Contiguous Rows)
*/
template <bool UseStream>
FORCE_INLINE void
transpose_64x64_tile_impl(const uint8_t* pSrc, unsigned int srcStep, uint8_t* pDst, unsigned int dstStep) {
// 64x64 临时 Buffer
alignas(64) uint8_t tmp[64 * 64];
uint8_t* tmpPtr = tmp;
// 1. 读取源并填充 Buffer
// 策略：保持源图像的线性访问 (Y then X)，这对性能至关重要
// 结果：tmp 中的块是按 "Row-Major Block" 顺序排列的
// 即：[B(0,0)] [B(0,1)] ... [B(0,7)] [B(1,0)] ...
// 预计算步长指针，减少循环内乘法
size_t srcStep8 = (size_t)srcStep * 8;
const uint8_t* s0 = pSrc;
for (int y = 0; y < 64; y += 8) {
const uint8_t* r0 = s0;
const uint8_t* r1 = s0 + srcStep;
const uint8_t* r2 = s0 + srcStep * 2;
const uint8_t* r3 = s0 + srcStep * 3;
const uint8_t* r4 = s0 + srcStep * 4;
const uint8_t* r5 = s0 + srcStep * 5;
const uint8_t* r6 = s0 + srcStep * 6;
const uint8_t* r7 = s0 + srcStep * 7;
for (int x = 0; x < 64; x += 8) {
transpose_8x8_store_contiguous(r0 + x, r1 + x, r2 + x, r3 + x, r4 + x, r5 + x, r6 + x, r7 + x, tmpPtr);
tmpPtr += 64; // buffer 线性写入
}
s0 += srcStep8;
}
// 2. 从 Buffer 读取并流式写入 Dst
// 目标：写入 Dst 的行
// Dst 的第 i 个条带 (由8行组成) 对应 Source 的第 i 个块列
// Source 的块列 i 包含块：B(0,i), B(1,i), ... B(7,i)
// 在 Row-Major 的 tmp 中，这些块的内存地址不是连续的，而是相隔 8个块 (8*64 = 512字节)
// 外层循环：遍历 8 个垂直条带 (Strip)，对应 tmp 中的 Block Column 0..7
for (int colBlock = 0; colBlock < 8; ++colBlock) {
// 当前条带中 B(0, colBlock) 的起始地址
// 在 tmp 中，Block(row, col) 的索引是 row*8 + col
// Block(0, colBlock) 的偏移是 colBlock * 64
const uint8_t* bBase = tmp + colBlock * 64;
// 处理条带内的 8 行
for (int r = 0; r < 8; ++r) {
// 我们需要从 8 个垂直堆叠的块中，分别取出第 r 行
// Block stride = 512 bytes.
// Row offset inside block = r * 8 bytes.
int laneOffset = r * 8;
// 从 tmp 中以 512 字节 stride 读取
__m128i b0 = _mm_loadl_epi64((const __m128i*)(bBase + 0 * 512 + laneOffset));
__m128i b1 = _mm_loadl_epi64((const __m128i*)(bBase + 1 * 512 + laneOffset));
__m128i b2 = _mm_loadl_epi64((const __m128i*)(bBase + 2 * 512 + laneOffset));
__m128i b3 = _mm_loadl_epi64((const __m128i*)(bBase + 3 * 512 + laneOffset));
__m128i b4 = _mm_loadl_epi64((const __m128i*)(bBase + 4 * 512 + laneOffset));
__m128i b5 = _mm_loadl_epi64((const __m128i*)(bBase + 5 * 512 + laneOffset));
__m128i b6 = _mm_loadl_epi64((const __m128i*)(bBase + 6 * 512 + laneOffset));
__m128i b7 = _mm_loadl_epi64((const __m128i*)(bBase + 7 * 512 + laneOffset));
__m128i v0 = _mm_unpacklo_epi64(b0, b1);
__m128i v1 = _mm_unpacklo_epi64(b2, b3);
__m128i v2 = _mm_unpacklo_epi64(b4, b5);
__m128i v3 = _mm_unpacklo_epi64(b6, b7);
// 计算目标地址：
// 当前是第 colBlock 个条带，第 r 行 -> 全局行 colBlock*8 + r
uint8_t* dstRowPtr = pDst + (colBlock * 8 + r) * dstStep;
if (UseStream) { // 编译期优化，生成无分支代码
// Stream 路径：要求 dstRowPtr 必须 16 字节对齐
// 适用于 dstStep % 16 == 0 且 pDst 对齐的情况
_mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 0), v0);
_mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 16), v1);
_mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 32), v2);
_mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 48), v3);
} else {
// StoreU 路径：安全处理任意对齐，且依然是 SIMD 向量化
// 适用于 dstStep % 16 != 0 的情况
_mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 0), v0);
_mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 16), v1);
_mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 32), v2);
_mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 48), v3);
}
}
}
}
/**
* 处理边缘的小块 (8x8 fallback)
* 将 8x8 源块 (srcStep) 转置写入 8x8 目标块 (dstStep)
*/
FORCE_INLINE void
transpose_8x8_u8_to_strided(const uint8_t* pSrc, unsigned int srcStep, uint8_t* pDst, unsigned int dstStep) {
__m128i r0 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 0 * srcStep));
__m128i r1 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 1 * srcStep));
__m128i r2 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 2 * srcStep));
__m128i r3 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 3 * srcStep));
__m128i r4 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 4 * srcStep));
__m128i r5 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 5 * srcStep));
__m128i r6 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 6 * srcStep));
__m128i r7 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 7 * srcStep));
__m128i t0 = _mm_unpacklo_epi8(r0, r1);
__m128i t1 = _mm_unpacklo_epi8(r2, r3);
__m128i t2 = _mm_unpacklo_epi8(r4, r5);
__m128i t3 = _mm_unpacklo_epi8(r6, r7);
__m128i t4 = _mm_unpacklo_epi16(t0, t1);
__m128i t5 = _mm_unpacklo_epi16(t2, t3);
__m128i t6 = _mm_unpackhi_epi16(t0, t1);
__m128i t7 = _mm_unpackhi_epi16(t2, t3);
__m128i c0 = _mm_unpacklo_epi32(t4, t5);
__m128i c1 = _mm_unpackhi_epi32(t4, t5);
__m128i c2 = _mm_unpacklo_epi32(t6, t7);
__m128i c3 = _mm_unpackhi_epi32(t6, t7);
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 0 * dstStep), c0);
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 1 * dstStep), _mm_srli_si128(c0, 8));
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 2 * dstStep), c1);
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 3 * dstStep), _mm_srli_si128(c1, 8));
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 4 * dstStep), c2);
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 5 * dstStep), _mm_srli_si128(c2, 8));
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 6 * dstStep), c3);
_mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 7 * dstStep), _mm_srli_si128(c3, 8));
}
/**
* 核心转置内核，处理任意 WxH 块
* 内部使用 64x64 Tile 优化，并处理 8x8 和 1x1 边缘
*/
template <bool UseStream>
int64_t icv_y8_owniTransposeWxH_8uC1_impl(const uint8_t* pSrc,
unsigned int srcStep,
uint8_t* pDst,
unsigned int dstStep,
int width,
int height) {
if (width <= 0 || height <= 0)
return 0;
constexpr int TILE = 64;
constexpr int MICRO = 8;
const int wMain = width & ~(TILE - 1); // 64x 块主区域
const int hMain = height & ~(TILE - 1); // 64x 块主区域
// 1. 主循环 64x64 Tile (使用模板参数选择优化策略)
for (int y = 0; y < hMain; y += TILE) {
for (int x = 0; x < wMain; x += TILE) {
// Source Tile (x, y) 转置后写入 Dst Tile (y, x)
const uint8_t* srcTile = pSrc + y * srcStep + x;
uint8_t* dstTile = pDst + x * dstStep + y;
transpose_64x64_tile_impl<UseStream>(srcTile, srcStep, dstTile, dstStep);
}
}
// 2. 边缘处理 (通用代码，不依赖 UseStream，因为 storel 总是安全的)
// 高度为 hMain，宽度为 wTail
const int wTail = width - wMain;
if (wTail > 0) {
int wTailMain = wTail & ~(MICRO - 1); // 8x 块区域
int wTailTail = wTail - wTailMain; // 1x 标量区域
for (int y = 0; y < hMain; y += MICRO) {
const uint8_t* srcRow = pSrc + y * srcStep + wMain;
uint8_t* dstCol = pDst + wMain * dstStep + y;
int xOff = 0;
// 8x8 块
for (; xOff < wTailMain; xOff += MICRO) {
transpose_8x8_u8_to_strided(srcRow + xOff, srcStep, dstCol + xOff * dstStep, dstStep);
}
// 标量补齐
// (y, xOff) -> (xOff, y)
for (int k = 0; k < MICRO; ++k) { // 遍历 8 行
for (int x = 0; x < wTailTail; ++x) {
dstCol[(xOff + x) * dstStep + k] = srcRow[k * srcStep + (xOff + x)];
}
}
}
}
// 3. 处理底部边缘 (Height non-64, 左侧部分)
// 高度为 hBottomTail，宽度为 wMain
const int hBottomTail = height - hMain;
if (hBottomTail > 0) {
int hBottomMain = hBottomTail & ~(MICRO - 1); // 8x 块区域
int hBottomTailTail = hBottomTail - hBottomMain; // 1x 标量区域
for (int x = 0; x < wMain; x += MICRO) {
const uint8_t* srcCol = pSrc + hMain * srcStep + x;
uint8_t* dstRow = pDst + x * dstStep + hMain;
int yOff = 0;
// 8x8 块
for (; yOff < hBottomMain; yOff += MICRO) {
transpose_8x8_u8_to_strided(srcCol + yOff * srcStep, srcStep, dstRow + yOff, dstStep);
}
// 标量补齐
// (yOff, k) -> (k, yOff)
for (int k = 0; k < MICRO; ++k) { // 遍历 8 列
for (int y = 0; y < hBottomTailTail; ++y) {
dstRow[k * dstStep + (yOff + y)] = srcCol[(yOff + y) * srcStep + k];
}
}
}
}
// 4. 处理右下角 (wTail x hBottomTail)
if (wTail > 0 && hBottomTail > 0) {
// C++ 标量实现
const uint8_t* srcCorner = pSrc + hMain * srcStep + wMain;
uint8_t* dstCorner = pDst + wMain * dstStep + hMain;
for (int y = 0; y < hBottomTail; ++y) {
for (int x = 0; x < wTail; ++x) {
dstCorner[x * dstStep + y] = srcCorner[y * srcStep + x];
}
}
}
// 如果使用了 Stream (NT Store)，需要 sfence 确保数据可见性
if (UseStream) {
_mm_sfence();
}
return 0;
}
/**
* 核心转置内核 Dispatcher
* 根据 dstStep 和 pDst 的对齐情况，分发到 Stream 版或 StoreU 版
*/
int64_t icv_y8_owniTransposeWxH_8uC1(const uint8_t* pSrc,
unsigned int srcStep,
uint8_t* pDst,
unsigned int dstStep,
int width,
int height) {
// 检查对齐
// 1. pDst 地址必须 16 字节对齐
// 2. dstStep 必须是 16 的倍数
// 只有同时满足，才能在 64x64 块内部安全使用 stream 指令
bool isAligned = (((uintptr_t)pDst | (uintptr_t)dstStep) & 0xF) == 0;
if (isAligned) {
return icv_y8_owniTransposeWxH_8uC1_impl<true>(pSrc, srcStep, pDst, dstStep, width, height);
} else {
return icv_y8_owniTransposeWxH_8uC1_impl<false>(pSrc, srcStep, pDst, dstStep, width, height);
}
}
/**
* 顶层转置函数：将整幅图像按 512x512 分块，调度到 icv_y8_owniTransposeWxH_8uC1
*/
int64_t icv_transpose_8u_C1(const uint8_t* pSrc,
unsigned int srcStep,
uint8_t* pDst,
unsigned int dstStep,
int width,
int height) {
constexpr int TILE = 512;
if (width <= 0 || height <= 0) {
return 0;
}
const int h_main = height & ~(TILE - 1); // height - height % 512
const int w_main = width & ~(TILE - 1); // width - width % 512
const int h_tail = height - h_main; // height % 512
const int w_tail = width - w_main; // width % 512
int64_t last_ret = 0; // 保存最后一次调用内核的返回值
// 1. 主 512x512 网格区域：0..h_main-1, 0..w_main-1
// 外层循环 Width (bj)，内层展开 Height (bi)
// 这使得 Source 每次读取跳跃 512 行 (垂直)，
// 而 Destination 每次写入跳跃 512 列 (水平，即连续内存)，
// 这对写合并缓冲 (Write Combining) 非常友好
if (h_main > 0 && w_main > 0) {
const int blocksH = h_main / TILE; // 垂直方向块数
const int blocksW = w_main / TILE; // 水平方向块数
const int GROUP = 8; // 8 个 512x512 块一组
// 外层遍历 Destination 的行 (即 Source 的列)
for (int bj = 0; bj < blocksW; ++bj) {
const int srcColOffset = bj * TILE;
const int dstRowOffset = bj * TILE * static_cast<int>(dstStep);
int bi = 0;
// 1a. 内层展开：处理 Source 的 8 个垂直块 (Vertical Blocks)
// 这会生成 Destination 的 8 个水平块 (Horizontal Blocks -> 连续写入)
for (; bi + GROUP - 1 < blocksH; bi += GROUP) {
const int srcRowOffset = bi * TILE * static_cast<int>(srcStep);
const int dstColOffset = bi * TILE;
const uint8_t* srcBase = pSrc + srcRowOffset + srcColOffset;
uint8_t* dstBase = pDst + dstRowOffset + dstColOffset;
// Source 指针每次加 srcStep * TILE (垂直移动)
// Dest 指针每次加 TILE (水平移动)
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 0 * TILE * srcStep, srcStep,
dstBase + 0 * TILE, dstStep, TILE, TILE);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 1 * TILE * srcStep, srcStep,
dstBase + 1 * TILE, dstStep, TILE, TILE);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 2 * TILE * srcStep, srcStep,
dstBase + 2 * TILE, dstStep, TILE, TILE);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 3 * TILE * srcStep, srcStep,
dstBase + 3 * TILE, dstStep, TILE, TILE);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 4 * TILE * srcStep, srcStep,
dstBase + 4 * TILE, dstStep, TILE, TILE);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 5 * TILE * srcStep, srcStep,
dstBase + 5 * TILE, dstStep, TILE, TILE);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 6 * TILE * srcStep, srcStep,
dstBase + 6 * TILE, dstStep, TILE, TILE);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 7 * TILE * srcStep, srcStep,
dstBase + 7 * TILE, dstStep, TILE, TILE);
}
// 1b. 本行(列)剩余的 512x512 块（不足 8 个的一段）
for (; bi < blocksH; ++bi) {
const int srcRowOffset = bi * TILE * static_cast<int>(srcStep);
const int dstColOffset = bi * TILE;
const uint8_t* srcBlock = pSrc + srcRowOffset + srcColOffset;
uint8_t* dstBlock = pDst + dstRowOffset + dstColOffset;
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBlock, srcStep, dstBlock, dstStep, TILE, TILE);
}
}
}
// 2. 右侧边缘：宽度剩余 w_tail x 高度 h_main
// 这个区域的块尺寸是 w_tail x 512
if (w_tail > 0 && h_main > 0) {
const int blocksH = h_main / TILE;
for (int bi = 0; bi < blocksH; ++bi) {
const int srcRowOffset = bi * TILE * static_cast<int>(srcStep);
const int dstColOffset = bi * TILE;
const uint8_t* srcBlock = pSrc + srcRowOffset + w_main;
uint8_t* dstBlock = pDst + w_main * static_cast<int>(dstStep) + dstColOffset;
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBlock, srcStep, dstBlock, dstStep, w_tail, TILE);
}
}
// 3. 底部边缘：宽度 w_main x 高度剩余 h_tail
// 区域被拆成若干 512x h_tail 的块，同样按宽度做 8x 展开
// (这部分保持不变，因为高度 < 512，无法进行垂直展开)
if (h_tail > 0 && w_main > 0) {
const int blocksW = w_main / TILE;
const int GROUP = 8;
const int srcRowOffsetBase = h_main * static_cast<int>(srcStep);
const int dstColOffsetBase = h_main;
int bj = 0;
// 3a. 每次处理 8 个 512x h_tail 的块
for (; bj + GROUP - 1 < blocksW; bj += GROUP) {
const int srcColOffset = bj * TILE;
const int dstRowOffset = bj * TILE * static_cast<int>(dstStep);
const uint8_t* srcBase = pSrc + srcRowOffsetBase + srcColOffset;
uint8_t* dstBase = pDst + dstRowOffset + dstColOffsetBase;
// 水平展开
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 0 * TILE,
srcStep,
dstBase + 0 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 1 * TILE,
srcStep,
dstBase + 1 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 2 * TILE,
srcStep,
dstBase + 2 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 3 * TILE,
srcStep,
dstBase + 3 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 4 * TILE,
srcStep,
dstBase + 4 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 5 * TILE,
srcStep,
dstBase + 5 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 6 * TILE,
srcStep,
dstBase + 6 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBase + 7 * TILE,
srcStep,
dstBase + 7 * TILE * static_cast<int>(dstStep),
dstStep,
TILE,
h_tail);
}
// 3b. 本行剩余的 512x h_tail 块
for (; bj < blocksW; ++bj) {
const uint8_t* srcBlock = pSrc + srcRowOffsetBase + bj * TILE;
uint8_t* dstBlock = pDst + bj * TILE * static_cast<int>(dstStep) + dstColOffsetBase;
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBlock, srcStep, dstBlock, dstStep, TILE, h_tail);
}
}
// 4. 右下角小块：w_tail x h_tail
if (h_tail > 0 && w_tail > 0) {
const uint8_t* srcBlock = pSrc + h_main * static_cast<int>(srcStep) + w_main;
uint8_t* dstBlock = pDst + w_main * static_cast<int>(dstStep) + h_main;
last_ret = icv_y8_owniTransposeWxH_8uC1(srcBlock, srcStep, dstBlock, dstStep, w_tail, h_tail);
}
return last_ret;
}

复制代码

性能测试

普通c++基准代码如下，一般可以得到6倍加速

template <typename T>
void simple_transpose(
const T* pSrc, unsigned int srcStep,
T* pDst, unsigned int dstStep,
int width, int height)
{
// 块大小根据 CPU L1 Cache 大小调整
// 对于 uint8_t，64x64 = 4KB，通常适合 L1 Cache
// 对于 float，可能需要减小到 32 或 16
constexpr int BLOCK_SIZE = 64;
// 以块为单位遍历 (i, j 指向块的左上角)
for (int i = 0; i < height; i += BLOCK_SIZE)
{
for (int j = 0; j < width; j += BLOCK_SIZE)
{
// 如果 i + BLOCK_SIZE 超过了 height，则只处理到 height 为止
// 解决了矩阵尺寸不能被 BLOCK_SIZE 整除的情况
const int i_max = std::min(i + BLOCK_SIZE, height);
const int j_max = std::min(j + BLOCK_SIZE, width);
for (int ii = i; ii < i_max; ++ii)
{
for (int jj = j; jj < j_max; ++jj)
{
// Dst(row, col) = Src(col, row)
pDst[jj * dstStep + ii] = pSrc[ii * srcStep + jj];
}
}
}
}
}

复制代码

为了节约篇幅就不展示功能测试了。
性能测试代码如下

class TransposeFixture : public benchmark::Fixture
{
public:
void SetUp(const ::benchmark::State& state) override
{
width = state.range(0);
height = state.range(1);
srcStep = width;
src = std::make_unique<uint8_t[]>(static_cast<size_t>(height) * srcStep);
dst = std::make_unique<uint8_t[]>(static_cast<size_t>(width) * height);
for (int i = 0; i < height; ++i) {
for (int j = 0; j < width; ++j) {
src[i * srcStep + j] = static_cast<uint8_t>((i + j) % 256);
}
}
srcMat = cv::Mat(height, width, CV_8UC1, src.get(), srcStep);
dstMat = cv::Mat(width, height, CV_8UC1, dst.get(), height);
bytes_per_iteration = static_cast<int64_t>(width) * height * 2;
}
void TearDown(const ::benchmark::State&) override
{
src.reset();
dst.reset();
}
protected:
int width{};
int height{};
int srcStep{};
int64_t bytes_per_iteration{};
std::unique_ptr<uint8_t[]> src;
std::unique_ptr<uint8_t[]> dst;
cv::Mat srcMat; // 预创建的cv::Mat对象
cv::Mat dstMat; // 预创建的cv::Mat对象
};
BENCHMARK_DEFINE_F(TransposeFixture, Optimized)(benchmark::State& state)
{
for (auto _ : state) {
icv_transpose_8u_C1(
src.get(), srcStep,
dst.get(), height,
width, height
);
benchmark::DoNotOptimize(dst.get());
benchmark::ClobberMemory();
}
state.SetBytesProcessed(state.iterations() * bytes_per_iteration);
}
BENCHMARK_DEFINE_F(TransposeFixture, OpenCV)(benchmark::State& state)
{
for (auto _ : state) {
cv::transpose(srcMat, dstMat);
benchmark::DoNotOptimize(dstMat.data);
benchmark::ClobberMemory();
}
state.SetBytesProcessed(state.iterations() * bytes_per_iteration);
}

复制代码

性能测试结果如下

TransposeFixture/Optimized/4096/4096 1538876 ns 1537400 ns 498 bytes_per_second=20.3265Gi/s
TransposeFixture/OpenCV/4096/4096 6065054 ns 5998884 ns 112 bytes_per_second=5.2093Gi/s
TransposeFixture/Optimized/2050/1920 285198 ns 288771 ns 2489 bytes_per_second=25.3882Gi/s
TransposeFixture/OpenCV/2050/1920 279878 ns 284630 ns 2635 bytes_per_second=25.7576Gi/s

复制代码

可以看到在大图上能够完全超越，在普通图像上性能接近。

来源：程序园用户自行投稿发布，如果侵权，请联系站长删除
免责声明：如果侵犯了您的权益，请联系站长，我们会及时删除侵权内容，谢谢合作！

衣旱 · 2025-11-27 06:33:53

新版吗？好像是停更了吧。

账号		自动登录	找回密码
密码			立即注册

高性能计算实践-OpenCV图像矩阵转置 transpose SIMD加速(ippicv)复现

相关帖子

回复

浏览过的版块

签约作者

高性能计算实践-OpenCV图像矩阵转置 transpose SIMD加速(ippicv)复现

相关帖子

相关推荐

回复

浏览过的版块

签约作者