矩阵转置的SSE汇编优化艺术

//    http://blog.csdn.net/feixiang_john/article/details/8438658

 

平时我们做图像处理或者视频处理, 很多地方会用到矩阵转置:

比如: DCT变换, 图像旋转, 图像滤波, 以及一些数据的内存行和列的交换等, 会大量使用转置这个动作.

然而由于数据量很大,处理速度很慢!如何来提高处理速度呢?

下面看看分析: 

HEVC中有个地方是如下这样实现(直接行和列对应的位置交换):

      Pel  tmp;      for (k=0;k<blkSize-1;k++)      {        for (l=k+1;l<blkSize;l++)        {          tmp                 = pDst[k*dstStride+l];          pDst[k*dstStride+l] = pDst[l*dstStride+k];          pDst[l*dstStride+k] = tmp;        }      }

如何用汇编来实现呢?

我们先用SSE汇编来实现一个8X8的矩阵转置吧: 这里输入地址pSrc_128[i] 和输出地址pDst_128[i]可以相同也可以不同:

相同的话就是原地转置, 不同的话就是非原地转置.

  __m128i* m_pSrc_tmp = pSrc_128[i];  __m128i* m_pDst_tmp = pDst_128[i];  __m128i Org_8_0,Org_8_1, Org_8_2, Org_8_3;  __m128i tttt1,tttt2,tttt3,tttt4,tttt33,tttt44;  __m128i tttt5,tttt6, tttt7, tttt8;  int stride_ii = dstStride>>3;//one  Org_8_0 = _mm_load_si128(m_pSrc_tmp);  m_pSrc_tmp+=8;  Org_8_1 = _mm_load_si128(m_pSrc_tmp);  m_pSrc_tmp+=8;  Org_8_2 = _mm_load_si128(m_pSrc_tmp);  m_pSrc_tmp+=8;  Org_8_3 = _mm_load_si128(m_pSrc_tmp);  m_pSrc_tmp+=8;  tttt1 = _mm_unpacklo_epi16(Org_8_0, Org_8_1);  tttt2 = _mm_unpacklo_epi16(Org_8_2, Org_8_3);  tttt3 = _mm_unpackhi_epi16(Org_8_0, Org_8_1);  tttt4 = _mm_unpackhi_epi16(Org_8_2, Org_8_3);  tttt5 = _mm_unpacklo_epi32(tttt1, tttt2);  tttt6 = _mm_unpackhi_epi32(tttt1, tttt2);  Org_8_0 = _mm_load_si128(m_pSrc_tmp);  m_pSrc_tmp+=8;;   Org_8_1 = _mm_load_si128(m_pSrc_tmp);  m_pSrc_tmp+=8;  Org_8_2 = _mm_load_si128(m_pSrc_tmp);  m_pSrc_tmp+=8;   Org_8_3 = _mm_load_si128(m_pSrc_tmp);  //m_pSrc_tmp+=8;  tttt1 = _mm_unpacklo_epi16(Org_8_0, Org_8_1);  tttt2 = _mm_unpacklo_epi16(Org_8_2, Org_8_3);  tttt33 = _mm_unpackhi_epi16(Org_8_0, Org_8_1);  tttt44 = _mm_unpackhi_epi16(Org_8_2, Org_8_3);  tttt7 = _mm_unpacklo_epi32(tttt1, tttt2);  tttt8 = _mm_unpackhi_epi32(tttt1, tttt2);  tttt1 = _mm_unpacklo_epi64(tttt5, tttt7);  tttt2 = _mm_unpackhi_epi64(tttt5, tttt7);  _mm_storeu_si128(m_pDst_tmp, tttt1);  m_pDst_tmp+=stride_ii;  _mm_storeu_si128(m_pDst_tmp, tttt2);  m_pDst_tmp+=stride_ii;  tttt5 = _mm_unpacklo_epi64(tttt6, tttt8);  tttt7 = _mm_unpackhi_epi64(tttt6, tttt8);  _mm_storeu_si128(m_pDst_tmp, tttt5);  m_pDst_tmp+=stride_ii;  _mm_storeu_si128(m_pDst_tmp, tttt7);  m_pDst_tmp+=stride_ii;//tow  tttt5 = _mm_unpacklo_epi32(tttt3, tttt4);  tttt6 = _mm_unpackhi_epi32(tttt3, tttt4);  tttt7 = _mm_unpacklo_epi32(tttt33, tttt44);  tttt8 = _mm_unpackhi_epi32(tttt33, tttt44);  tttt1 = _mm_unpacklo_epi64(tttt5, tttt7);  tttt2 = _mm_unpackhi_epi64(tttt5, tttt7);  _mm_storeu_si128(m_pDst_tmp, tttt1);  m_pDst_tmp+=stride_ii;  _mm_storeu_si128(m_pDst_tmp, tttt2);  m_pDst_tmp+=stride_ii;  tttt5 = _mm_unpacklo_epi64(tttt6, tttt8);  tttt7 = _mm_unpackhi_epi64(tttt6, tttt8);  _mm_storeu_si128(m_pDst_tmp, tttt5);  m_pDst_tmp+=stride_ii;  _mm_storeu_si128(m_pDst_tmp, tttt7);

要实现的是NXN的转置,如何实现呢:

基于8X8来实现NXN的块或者图像的转置:

这里先把NXN划分为size_case 个8X8, 然后循环调用8X8的转置!

  __m128i* pDst_128[64];  __m128i* pSrc_128[64];  int size_case = (blkSize>>3);  dstStride = dstStride_tmp;  for(int y = 0; y<size_case; y++)//对所有8x8的块进行地址映射   for(int x = 0; x<size_case; x++)   {    pSrc_128[y*size_case + x] = (__m128i*)(pDst + 8*x + y*8*64);    pDst_128[y*size_case + x] = (__m128i*)(rpDst + 8*y + x*8*dstStride);   }  size_case = size_case*size_case;  for(int  i = 0;i <size_case; i++)//开始转置  {     8x8转置的代码: }

 

通过比较, 用SSE汇编优化实现转置比用纯 C代码实现的转置速度快5倍左右!