I currently use JuMP and Ipopt to solve Linear Constraints Optimisation problems, primarily with linear equality and inequality constraints. I would like to either solve many of these problems in parallel, or sequentially but with GPU acceleration. Are there any packages that would allow this kind of computation?
Not a specialist of mathematical optimization but I would guess that most of underlying algorithms are strongly divergent (branching all the time) which is not a good fit for GPUs.
SCS has GPU support: GitHub - jump-dev/SCS.jl: Julia Wrapper for SCS (https://github.com/cvxgrp/scs)
If you’re using Ipopt, you should try a different linear solver: https://github.com/jump-dev/Ipopt.jl#linear-solvers
Check https://github.com/sshin23/MadNLP.jl with cuSOLVER.
Thank you very much for the suggestion. I was wrong, I was actually using Tulip, which is reasonably fast for solving one problem at a time, but not at scale.
Thank you! this is what I will probably try first, since it interfaces with JuMP . There does not appear to be a lot of documentation as to how to get the cuSolver extension however?