dgemm | Writing R and Python Packages | GPU library

I am under windows. The fortran code in mkl_example.f :

Related question Fortran: Which method is faster to change the rank of arrays? (Reshape vs. Pointer)

If I have a tensor contraction A[a,b] * B[b,c,d] = C[a,c,d] If I use BLAS, I think I need DGEMM (assume real values), then I can

1. first reshape tensor B[b,c,d] as D[b,e] where e = c*d,
2. DGEMM, A[a,b] * D[b,e] = E[a,e]
3. reshape E[a,e] into C[a,c,d]

The problem is, reshape is not that fast :( I saw discussions in Fortran: Which method is faster to change the rank of arrays? (Reshape vs. Pointer) , in the above link, the author met some error messages, except reshape itself.

Thus, I am asking if there is a convenient solution.

I have two dense matrices with the sizes (2500, 208) and (208, 2500). I want to calculate their product. It works fine and fast when it is a single process but when it is in a multiprocessing block, the processes stuck in there for hours. I do sparse matrices multiplication with even larger sizes but I have no problem. My code looks like this:

I have the following Fortran code from https://software.intel.com/content/www/us/en/develop/documentation/mkl-tutorial-fortran/top/multiplying-matrices-using-dgemm.html

I am trying to use gfortran complile it (named as dgemm.f90)

Related question Multiplying real matrix with a complex vector using BLAS

Suppose I aim at C = A*B, where A, B, C are real, complex, and complex matrices, respectively. A[i,j] * B[j,k] := (A[i,j] Re(B[j,k]), A[i,j] Im(B[j,k])). Is there any available subroutine in BLAS?

I can think about split B into two real matrices for the real and imaginary part, do dgemm then combine, (combine should be faster than matrix multiplication, even directly using nested loops(?)) as suggested by Multiplying real matrix with a complex vector using BLAS

I don't know if there is a direct option in BLAS.

In my fortran code, matrix multiplication is handled with 'dgemm' in openblas library. The size of matrix is quite big, 7000 X 7000, so I want to reduce the computational cost in the matrix manipulation.

I tried to call 'dgemm' using multi-threads, but it seems not working (working as single thread only). The 'time' command is used to record the required time to calculate. Regardless I use -lpthreads flag or not, my calculation time is the same. It seems to me that the multi-threading is not working.

The below is my test.f and compile command. Can you recommend the way that I can use multi-threads in my matrix manipulation? Sorry about the duplication of questions and too simple and fundamental things, but the existing Q&As are not working for me. Thank you for any comments!

• In bashrc :

export OPENBLAS_LIB=/mypath/lib

export OPENBLAS_INC=/mypath/include

export OMP_NUM_THREADS=4

export GOTO_NUM_THREADS=4

export OPENBLAS_NUM_THREADS=4

• command for source :

gfortran test.f -o test.x -lopenblas -lpthread

• sample source

  ...

I'm trying to speed up an optimization routine using MKL's blas implementation in fortran. I need to have the result in a shared library so that it is accessible from a larger script. I can compile and link my code without any warnings, but the resulting .so file has an undefined reference to the blas routine I'm trying to call, namely dgemm.

relevant section of the fortran code:

I have an app which uses the armadillo library to do some matrix calculations. It compiles fine against the Accelerate.framework, but is rejected at the app store:

ITMS-90338: Non-public API usage - The app references non-public symbols...ddot, dgemm, dgemv, dsyrk.

These symbols are from the BLAS library and are included in Accelerate, but are apparently not public. Is there a way to use armadillo without getting this error?

I have been trying for some time to compile and link the OpenBLAS lib to multiply to matrices together using dgemm. I finally was able to compile the OpenBLAS after installing MSYS2 and the mingw64 packages from MSYS2 using pacman. My intention is to use the OpenBLAS as a static lib. So I wrote a simple fortran code to multiply the matrices together using dgemm. Here is the fortran code: