This is a small problem for which direct Householder QR may be fast enough (depending on the rest of your application). For multi-node, you can use TSQR (backward stable like Householder) or Cholesky (unstable).
julia> A = rand(200000, 200); julia> @time Q, R = qr(A); 0.866989 seconds (14 allocations: 305.591 MiB) julia> @time begin C = A' * A; cholesky(C); end; 0.300977 seconds (8 allocations: 625.250 KiB) Lucas Banting <[email protected]> writes: > Hello, > > I have an MPIDENSE matrix of size about 200,000 x 200, using KSPLSQR on my > machine a solution takes about 15 s. I typically run with six to eight > processors. > I have to solve the system several times, typically 4-30, and was looking for > recommendations on reusable preconditioners to use with KSPLSQR to increase > speed. > > Would it make the most sense to use PCCHOLESKY on the smaller system A^T * A? > > Thanks, > Lucas
