Comments (3)
What is the profiling data are we trying to generate?
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I think we start with simple runtime for the core pieces of fastq.pyx
without any validators running to see
- How does the speed compare to other tools?
- Can we make sure that we don't regress in speed (relatively speaking)?
From there, I think getting profiling on different pieces relative to each of the validators might be a good use of time. Especially in the case where there might be problem areas, like memory for the duplicate read names validator.
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@davisem Any update on this?
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Related Issues (19)
- ImportError
- Use string_view instead of char* HOT 2
- Avoid nuking the root directory when running `make clean` HOT 1
- ModuleNotFoundError HOT 4
- Version reporting not working HOT 5
- Install on mac not working with 2021 editioning HOT 4
- Subsample to a fixed number of reads HOT 9
- Subsampling paired, compressed reads seems problematic HOT 1
- FASTQ to (unaligned) BAM/SAM/CRAM HOT 3
- Feature request: Filtering based on reads HOT 1
- Docs typo HOT 1
- Add GZIP Support
- commands/filter: Apply filter rule to multiple segments HOT 1
- Implement BloomFilter duplicate read name validator. HOT 3
- Enhance Cython performance by removing PyObject marshalling. HOT 1
- Use abc for abstract classes HOT 1
- Read name should exclude or ignore "the member of a pair" HOT 3
- __exit__ takes 4 arguments HOT 2
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