Example of running PyDDA in HPC ？
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- Dominant language
- Jupyter Notebook
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Description
The example of nested wind retrieval in the doc is based on LocalCluster. Is PyDDA designed to be run on HPC such as Summit or TianHe-2? If yes , i think it would be really helpful if there is a example about the best strategy of spliting the grid and distributing the computations to workers under Dask in consideration of maximizing CPU usage and balancing the time of IO, including the setting of the number of jobs/n_workers/processess etc .
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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
Start with the nested wind retrieval documentation example that uses LocalCluster and review the issue discussion about Summit and TianHe-2. Determine whether PyDDA supports HPC execution, then document the relevant Dask grid-splitting, worker, process, job, CPU, and I/O settings if the project can specify them.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation, hpc
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100