Comments (1)
Yes, that's reasonable although you may see limitations in performance with that many cores, but as you say depends on i/o rate. ~25 cores is the most I've tested on, seems to work just fine. Depending on the quality of the library, svaba can be very heavy i/o because it's looking up mate-regions if 3 or more reads point to another region. For low-quality libraries with a lot of spurious mapping, this can be expensive.
HP flag just keeps more in memory before dumping it to disk, which prevents threads from having to pause when one of them is writing to a common file. Given your memory, this should be a great flag.
There are various other parameters that could make it run faster, but they also potentially alter sensitivity (e.g. the -L flag which is described above).
BTW, I just merged in a fix to another issue that can come up with massively parallel runs, so recommend that you pull and merge the latest version in main
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Related Issues (20)
- No SV output with metagenomics data HOT 1
- Request to use Python instead of C++ and Java
- option --max-reads-mate-region (-M) doesn't work in v1.2
- "Caught stoi/stod/stof error" in breakpoints file
- Installation issue HOT 10
- [E::bwa_idx_load_from_disk] fail to locate the index files HOT 1
- Number of sequences in BAM header mismatches reference HOT 5
- The target SV in *.svaba.sv.vcf is not found in *.alignments.txt.gz HOT 4
- recommended parameters when one chr has higher coverage than the others HOT 1
- Compiling error for missing header files HOT 4
- multiple wgs cohort
- Genotype quality HOT 2
- std::regex_error HOT 3
- True deletion discarded in final vcf
- Unexpected SV with a zero coordinate HOT 1
- svaba segmentation fault (core dumped) HOT 12
- version number not updated?
- Fastest mode to run svaba
- Error: Found chromosome in region file not in reference genome. Skipping HOT 5
- missing make in Dockerfile
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