google-deepmind / google-deepmind/torax

Speed improvement: pre-interpolate geometries when using `fixed_time_step`

Open
#2,315 1 comment 1 reaction 0 assignees View on GitHub
Dominant language
Python
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Forks
145
Avg merge
2d 14h
Merged PRs (30d)
49

Description

`fixed_time_step` loop calls the geometry provider's time-interpolation once per step. As a result, loading multiple geometry time slices pays the interpolation cost on every step rather than once at the geometry initialisation, inflating both compile time (~2-5x) and run time (scaling with step count).

However, with a fixed dt, every future time point is knowable in advance, so we could in theory make `fixed_time_step` pre-interpolate all of the geometries.

Contributor guide

Open the contributing guide

Research direction

Locate the fixed_time_step loop and the geometry provider's time-interpolation call mentioned in the issue. First measure the current compile and run-time costs for multiple geometry time slices, then trace geometry initialization and fixed-step time points. Done means interpolation is performed once where appropriate while simulation results remain unchanged and the reported costs improve.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
backend, performance
Issue type
Refactor
Difficulty
4/5
Estimated time
3-5 days
Activity status
Active
Clarity
Mostly clear
Newbie friendliness
55/100

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