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2021.05.27 Meeting Notes
Jonas Lippuner edited this page May 27, 2021
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5 revisions
- Individual/group updates
- Paper
- Review non-WIP PRs
- Fixed some CI issues
- Fixed CI issue that wouldn't run after a branch was rebased
- Working on open code coverage PR
- Looking into performance impact from HDF5 compression: https://github.com/lanl/parthenon/issues/470
- Setting up training with HDF5 Group
- GitHub application backend for performance regression is available standalone now for other projects, working on adding documentation
- Looking for suggestions for additional performance regression tests. Only running MPI advection right now
- Working on sparse, added
SparsePool
and madeStateDescriptor
aware of sparse fields/pools - Question: Is it ok to make a first sparse PR that introduces new interface but not sparse functionality in the sense of allocating/deallocating sparse fields on the fly (all sparse fields would have to be allocated on all blocks, like now). Consensus: this is a good plan to keep PR size manageable
- Will be looking into some MPI communication patterns
- Managed to build Parthenon with external Kokkos library
- Working on downstream astro codes
- Doing some tasks on particle PR. One down one two to go
- Unified restriction PR finally ready to go in, fixed two issues:
- Slower than before change, caused by old machinery was still called, removing that resulted in 10x speed up
- Test failed, solution was to add restriction in some places that were missed (Mesh initialize and restrict before prolongation), all tests pass now
- Busy writing proposal => probably/hopefully more folks working on Parthenon
- Mentored GPU Hackathon team in France working on astro code with Kokkos
- Looked into shared memory on GPU, team policy with shared memory might not work very well, or needs some extra care to make work well
- Getting performance metrics for unified restriction
- Reviewing particle PRs
- Submitted PR to add workaround for GCC 7.3 bug
- Submitted PR to fix component name order
- Also working on PR to make Python tools more efficient
- Once Hackathon improvements are merged in, we can start writing paper showing good CPU/GPU performance
- Philipp will start outlining paper and assign tasks, will open an Overleaf project
- Philipp to submit PR with additional performance regression tests (the ones we used during Hackathon)