Incremental Smiles Parsing + Sanitization improvements
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
- Issue type
- Refactor
- Clarity
- Needs clarification
- Activity status
- Active
- Tech stack
- cpp
- Domain
- performance
Research direction
Start with the benchmark code on the smiles-pipeline-benchmark branch and the supplied benchmark_cxsmiles.zip; the issue names no source files or tests. Compare the benchmark results with the rdkit-opt-everything branch and define one independently measurable hotspot improvement before contributing.
Written by the indexing model from the issue text.
Description
Smiles Parsing + Sanitization doesn't have a single giant bottleneck, but has lots of individually contributing functions. Below is a flame graph of the non-legacy workflow measured on >100k Enamine REAL molecules.
Based on the flame graph hotspots, I had models look at anything that had > 1% total runtime for optimization opportunities and had them do a build/test/measure cycle. Got quite solid results, 13 valid changes (I've personally checked most of them) leading to a cumulative speedup of almost 20% e2e.
This is a tracking bug for a bunch of small PRs I plan to put up to make small improvements to the pipeline performance.
Reproducing Benchmarks:
Sampling of molecules used in the benchmark:
benchmark_cxsmiles.zip
Branches with just the benchmark code, and with all of the commits stacked
- Dominant language
- HTML
- Stars
- 3.6k
- Forks
- 1.1k
- Avg merge
- 2d 9h
- Merged PRs (30d)
- 42
Contributor guide
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.
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