interface OpenCV's Mat containers with blas for matrix multiplication - opencv

Question

I am processing UHD (2160 x 3840) images.
One of the processing I do consist to process a Sobel filtering on X and Y axis then I have to multiply every output matrix by it's transpose and then I process the gradient image as the square root of the sum of the gradient.
So : S = sqrt( S_x * S_x^t + S_y * S_y^t).
Due to dimension of the image OpenCV take up to twenty seconds to process that without multithreading and ten with multithreading.
I know there OpenCV call OpenCL in order to speed up the filtering operations so I think it can take a long time in order to try to gain performance from the filtering step.
For the matrix multiplication I experience a kind of unstability from the OpenCV's OpenCL gemm kernel implementation.
So I would like to try to use OpenBLAS insted.
My questions are :
1.)
I wrote the following code but I face some issue for interface OpenCV's Mat objects :
template<class _Ty>
void mm(cv::Mat& A,cv::Mat& B,cv::Mat& C)
{
static_assert(true,"support matrix_multiply is only defined for floating precision numbers.");
}
template<>
inline void mm<float>(cv::Mat& A,cv::Mat& B,cv::Mat& C)
{
const int M = A.rows;
const int N = B.cols;
const int K = A.cols;
cblas_sgemm( CblasRowMajor ,// 1
CblasNoTrans, // 2 TRANSA
CblasNoTrans, // 3 TRANSB
M, // 4 M
N, // 5 N
K, // 6 K
1., // 7 ALPHA
A.ptr<float>(),//8 A
A.rows, //9 LDA
B.ptr<float>(),//10 B
B.rows, //11 LDB
0., //12 BETA
C.ptr<float>(),//13 C
C.rows); //14 LDC
}
template<>
inline void mm<double>(cv::Mat& A,cv::Mat& B,cv::Mat& C)
{
cblas_dgemm(CblasRowMajor,CblasNoTrans,CblasNoTrans,A.rows,B.cols,A.cols,1.,A.ptr<double>(),A.rows,B.ptr<double>(),B.cols,0.,C.ptr<double>(),C.rows);
}
void matrix_multiply(cv::InputArray _src1, cv::InputArray _src2, cv::OutputArray _dst)
{
CV_DbgAssert( (_src1.isMat() || _src1.isUMat()) && (_src1.kind() == _src2.kind()) &&
(_src1.depth() == _src2.depth()) && (_src1.depth() == CV_32F) && (_src1.depth() == _src1.type()) &&
(_src1.rows() == _src2.cols())
);
cv::Mat src1 = _src1.getMat();
cv::Mat src2 = _src2.getMat();
cv::Mat dst;
bool cpy(false);
if(_dst.rows() == _src1.rows() && _dst.cols() == _src2.cols() && _dst.type() == _src1.type())
dst = _dst.getMat();
else
{
dst = cv::Mat::zeros(src1.rows,src2.cols,src1.type());
cpy = true;
}
if(cpy)
dst.copyTo(_dst);
}
I tried to organize the datas as specified here :
http://www.netlib.org/lapack/explore-html/db/dc9/group__single__blas__level3.html#gafe51bacb54592ff5de056acabd83c260
without succes.
This is my main issue
2.)
I was thinking in order to try to speed up a little my implementation to apply the divide and conquer approach illustrated here :
https://en.wikipedia.org/wiki/Matrix_multiplication_algorithm
But for only four submatrix.
Does any one tried some similar approach or got a better way to gain performance in matrix multiplication (without using GPU) ?
Thank you in advance for any help.

Answer 1

I found a solution to the question 1).
I based my first implementation on the documentation of the BLAS library.
BLAS has been written in Fortran language, in this language the index start at 1 and not at 0 like in C or C++.
Another thing is many libraries wrote in Fortran language organize their memory in column order (e.g. BLAS,LAPACK) rather than most of the C or C++ library (e.g. OpenCV) organize the memory in row order.
After taking these two properties in count I modified my code to :
template<class _Ty>
void mm(cv::Mat& A,cv::Mat& B,cv::Mat& C)
{
static_assert(true,"The function gemm is only defined for floating precision numbers.");
}
template<>
void mm<float>(cv::Mat& A,cv::Mat& B,cv::Mat& C)
{
const int M = A.cols+1;
const int N = B.rows;
const int K = A.cols;
cblas_sgemm( CblasRowMajor ,// 1
CblasNoTrans, // 2 TRANSA
CblasNoTrans, // 3 TRANSB
M, // 4 M
N, // 5 N
K, // 6 K
1., // 7 ALPHA
A.ptr<float>(),//8 A
A.step1(), //9 LDA
B.ptr<float>(),//10 B
B.step1(), //11 LDB
0., //12 BETA
C.ptr<float>(),//13 C
C.step1()); //14 LDC
}
template<>
void mm<double>(cv::Mat& A,cv::Mat& B,cv::Mat& C)
{
const int M = A.cols+1;
const int N = B.rows;
const int K = A.cols;
cblas_dgemm( CblasRowMajor ,// 1
CblasNoTrans, // 2 TRANSA
CblasNoTrans, // 3 TRANSB
M, // 4 M
N, // 5 N
K, // 6 K
1., // 7 ALPHA
A.ptr<double>(),//8 A
A.step1(), //9 LDA
B.ptr<double>(),//10 B
B.step1(), //11 LDB
0., //12 BETA
C.ptr<double>(),//13 C
C.step1()); //14 LDC
}
And every thing work well.
Without additional multithreading or divide and conquer approach I was able to reduce the processing time of one step of my code from 150 ms to 500 us.
So it fix every thing for me :).