Comments (1)
The specified number of cores is passed to RAPsearch2 during the alignment step. I've found that RAPsearch2 does go faster with more cores, but the increase in speed is not linear - you get a 60% increase going from 1 to 2 cores, but only a 10% increase going from 4 to 8 cores. So this is really an issue with RAPsearch2 that cannot be solved in this package.
The best way to increase speed is to use only a subset of reads for AGS estimation. The default is 1 million reads, which should work well for most communities. Even using as many as 5 million reads will still be a big increase in speed relative to the entire dataset.
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Related Issues (20)
- MC should use the Unix exit code convention HOT 1
- temp files when program exits of error HOT 1
- installation on a cluster HOT 2
- Doc request: TMPDIR HOT 1
- training.py: Unused test set? HOT 3
- Pip install error from official 1.1.0 tarball HOT 4
- Installation on a cluster redux HOT 6
- could not convert string to float: FIRM56_P641593085 HOT 4
- Test failed HOT 3
- Python2 dependency in conda HOT 1
- problem reading multiple files HOT 9
- Gene Sequences in Fasta HOT 2
- How the performance would be for metatranscriptomes? HOT 2
- making sense of results... HOT 3
- question: calculation of RPKG HOT 1
- MicrobeCensus is not using all the reads when set to a large number HOT 3
- TypeError: 'NoneType' object is not iterable HOT 5
- "Could not import module 'numpy'" HOT 3
- Something wrong when put 'python setup.py install' HOT 1
- differences with other normalization methods HOT 2
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