dfm / dfm/tinygp

GPU Usage for Quasisep Kernels

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Dominant language
Python
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349
Forks
35
Avg merge
2d 2h
Merged PRs (30d)
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Description

Hi,

I was interested in PR #210 as I'm working on a multiwavelength transit fitting routine that involves simultaneously detrending each light curve. For reference, with just linear detrending on a GPU, this routine takes a remarkably short time (~20 mins for 2000 LC's on an A100), but implementing a GP detrending such as with the quasisep module encounters the issue where a CPU is required or else its hundreds of times slower.

I know the PR is not actively being worked on but my question is if the current changes in PR #210 could be implemented to boost GPU performance or if a lot of tweaking would be required.

Thanks!

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Research direction

Start by reading PR #210 and the quasisep module to understand the proposed changes and where CPU-only behavior occurs. Benchmark the current quasisep workflow on CPU and GPU, then determine the scope needed for GPU support. Done means the required changes are defined and GPU performance is demonstrated for the reported workload.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning, performance
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
25/100

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