*mkl_dcsrmm* multiplies a CSR sparse matrix by a dense matrix, storing the result in a dense matrix.

From what I can tell, this function expects the B and C dense matrices to be in row-major order, as opposed to the column-major order used in most other BLAS routines. I've attached a test case which demonstrates that, given the same B and C inputs in the same format, *mkl_dcsrmm* produces completely different results than *dgemm. dgemm* calculates C = alpha*A*B + beta*C (as expected), but *mkl_dcsrmm* appears to calculate C' = alpha*A*B' + beta*C' instead.

This seems to make it very difficult to mix sparse functions with normal BLAS function. Must I transpose my matrices before and after using the sparse functions?

I tried using *mkl_dcs cmm* instead, but it seems to do the same thing. In fact, I think that

*mkl_dcsrmm*and

*mkl_dcscmm*are identical except that they invert the meaning of the

*transa*parameter.

Do all of the sparse matrix-matrix multiply functions expect the dense parts to be row-major? If so, what is the best practise for using these beside traditional column-major BLAS functions?

(I've found that this question has been asked before, but not answered.)