Hi, I'm trying to accelerate a shift-invert eigensolve with GPU, but the computation seems to be spending a lot of its time in the CPU. Looking at the output with "-log_view -log_view_gpu_time" I see that MatLUFactorNum is not using the GPU (GPU Mflops/s is 0), and is taking the majority of the computation time. Is LU factorization on the GPU supported? I am currently applying the command line options "-vec_type cuda -mat_type aijcusparse", please let me know if there are other options I can apply to accelerate the LU factorization as well. I tried digging through the documentation but couldn't find a clear answer.
Thanks in advance! Kind regards, Greg KM -- *Gregory D. Kahanamoku-Meyer* PhD Candidate quantum computing | cryptography | high-performance computing Department of Physics University of California at Berkeley personal website <https://gregdmeyer.github.io/>
