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When initially implementing long read support, I was unable to demonstrate significantly reduced execution time versus recreating the index from scratch every time hostile clean is called. Using a prebuilt index was only marginally quicker and not worth the complexity of managing indexes. However, recently I tested whether this is still the case and observed that running hostile clean on a small long read fastq drops from taking ~45s to ~7s through use of a precomputed index.
This behaviour should first be characterised / verified on Linux and MacOS. Assuming the performance benefits are replicated on both OSs, adding invisible (but suitably logged) index caching and reuse should be done unless a good reason not to do so becomes apparent.
This will dramatically reduce execution time for processing many long read samples where this redundant indexing overhead is a nuisance.
The text was updated successfully, but these errors were encountered:
When initially implementing long read support, I was unable to demonstrate significantly reduced execution time versus recreating the index from scratch every time
hostile clean
is called. Using a prebuilt index was only marginally quicker and not worth the complexity of managing indexes. However, recently I tested whether this is still the case and observed that runninghostile clean
on a small long read fastq drops from taking ~45s to ~7s through use of a precomputed index.This behaviour should first be characterised / verified on Linux and MacOS. Assuming the performance benefits are replicated on both OSs, adding invisible (but suitably logged) index caching and reuse should be done unless a good reason not to do so becomes apparent.
This will dramatically reduce execution time for processing many long read samples where this redundant indexing overhead is a nuisance.
The text was updated successfully, but these errors were encountered: