SpikeInterface / SpikeInterface/spikeinterface
Total memory limitations for PCA
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- Python
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Description
As request, moving this over from discussions.
This was discussed a bit more in #3722 : computing PCA metrics on analyzers with a lot of units and a lof of channels, using a high n_jobs can sometime pre-allocate a very large array that does not fit in memory.
The current job_kwargs that apply to memory limitation are, as far as I understand, only relevant when dealing with recordings.
The current fix in #3721 is to automatically throttle n_jobs to stay within memory constraints. Something else that was mentionned would be to allocate a single array to be used by multiple workers, but I believe there were concerns about concurrent access.
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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 by reading discussion #3722 and the current fix in #3721 to understand how PCA metrics allocate memory and how recording-related job_kwargs currently behave. Clarify whether the intended result is automatic n_jobs throttling, shared-array allocation, or another approach, then verify that high-unit, high-channel analyses stay within memory constraints without unsafe concurrent access.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100