* This commit has CUDA based smoothers for AMG based on the triangular parts of sparse matrices. This includes an Gauss-Seidel (relax_type==3), which uses CUSPARSE triangular solvers to invert L. Symmetric Gauss Seidel is implemented in relax_type==6 also via CUSPARSE. Finally, 2 new smoothers are added. THe first is a 2 stage approximation to Gauss Seidel using a parallel MatVec and L (relax_type==11). The second (relax_type==12) is a less effective version of 11. It uses A_diag instead of L for the smoothing. CPU implementations of these new smoothers are also provided. For the two stage algorithms, L and U are NOT explicitly created. This seems faster and saves memory. In the two stage preconditioner, multiply by invdiag rather than divide by diagonal reduces register pressure and yields full occupancy.
Co-authored-by: Paul Mullowney <pmullown@nrel.gov>
Co-authored-by: PaulMullowney <60452402+PaulMullowney@users.noreply.github.com>
