"optimal" configuration file
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- Cuda
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Description
Dear all, i have a laplacian matrix written as a matrix market file.
the problem is fairly well conditioned (16 iterations using pyamg's smoothed-aggregation as in the attached file)
i would like to test amgx, ideally through its python interface (although if that it is impossible i can write my own c++ wrapper). could you propose a working configuration file?
i tried to post the same issue in the pyamgx project, but no one is answering there and I am repositing here in the hope that someone could eventually propose a working configuration file here
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Review test_pyamg.zip and the Matrix Market input, then compare the pyamg smoothed-aggregation setup with AMGX's Python interface and available configuration examples. The requested outcome is a working AMGX configuration for this problem, or a confirmed path through a C++ wrapper if the Python interface cannot support it.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- cpp, python
- Domain
- hpc
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100