parallel processing - Could a CUDA kernel call a cublas function?

Question

I know it sound weird, but here is my scenario:

I need to do a matrix-matrix multiplication (A(n*k)*B(k*n)), but I only needs the diagonal elements to be evaluated for the output matrix. I searched cublas library and didn't find any level 2 or 3 functions that can do that. So, I decided to distribute each row of A and each column of B into CUDA threads. For each thread (idx), I need to calculate the dot product "A[idx,:]*B[:,idx]" and save it as the corresponding diagonal output. Now since this dot product also takes some time, and I wonder whether I could somehow call cublas function here (say cublasSdot) to achieve it.

If I missed some cublas function that can achieve my goal directly (only calculate the diagonal elements for a matrix-matrix multiplication), this question could be discarded.

Answer 1

Yes, it can (until (and excluding) version CUDA 10).

"The language interface and Device Runtime API available in CUDA C/C++ is a subset of the CUDA Runtime API available on the Host. The syntax and semantics of the CUDA Runtime API have been retained on the device in order to facilitate ease of code reuse for API routines that may run in either the host or device environments. A kernel can also call GPU libraries such as CUBLAS directly without needing to return to the CPU." Source

Here you can see and Matrix-Vector Multiplication using cuda and CUBLAS library function cublasSgemv.

Bear in mind, however that there is no longer a device CUBLAS capability in CUDA 10.. From Robert_Crovella one can cite:

The current recommendation would be to see if CUTLASS 2 will help (it is mostly focused on GEMM related activities). If not, write your own code to perform the function, or call cublas from host code.

Nonetheless, currently there are several implementation online of Matrix-Vector Multiplication, for instance 1, 2, among others.