I wanted to share a recent test project I’ve been working on. With the assistance of ChatGPT, I integrated the Lis (Library of Iterative Solvers for Linear Systems) solver into CalculiX (ccx) to test its performance on large-scale problems.
I am uploading the binary for anyone interested in testing and benchmarking it on their own models (Windows):
In my initial tests, the results have been very promising compared to PARDISO:
Execution Time: Dropped significantly from 3,600s down to 980s.
RAM Usage: Decreased dramatically from around 5 GB down to 480 MB.
Simple 3D box (nonlinear material)
I would appreciate it if others could give it a try and share their feedback, benchmarks, or any issues encountered across different model types.
Hi @rafal.brzegowy, thanks for testing! Could you explain a little more which type of iterative solution strategy you’re using? In particular, which preconditioner & solver? Our experience with iterative solvers in CalculiX is “mixed” for the general case, which is why we have a single precision direct solve followed by a double precision iterative solve strategy using PaStiX. I’d be interested in any better experiences with iterative solvers (like LIS).
i hope these limitations can be improved since LIS solver given promise in optimum and effectiveness of memeory usage and computational times, thank you.
maybe something wrong here on my computer, after zip file extracted it’s detect as treat by Windows security. Even for simpel case of solid with static linear analysis still not works with LIS solver, is there something blocked by the system probably.
