Valerie, Apologies for this taking much longer than it should have. The changes in Bioc-devel have wreaked havoc on the code we use to to generate and process the data we need to write out, but the fault is mine for not getting on top of it sooner.
I'm not seeing the speed you mentioned above in the latest devel version (1.11.35). It took ~1.5hrs to write the an expanded vcf with 56M rows (print output and sessionInfo() follow). I'll try reading in the illumina platinum and writing it back out to see if it is something about our specific vcf object (could ExpandedVCF vs VCF be an issue?). > vcfgeno *class: ExpandedVCF * *dim: 50307989 1 * rowData(vcf): GRanges with 4 metadata columns: REF, ALT, QUAL, FILTER info(vcf): DataFrame with 1 column: END Fields with no header: END geno(vcf): SimpleList of length 7: AD, DP, FT, GT, GQ, PL, MIN_DP geno(header(vcf)): Number Type Description AD 2 Integer Allelic depths (number of reads in each observed al... DP 1 Integer Total read depth FT 1 String Variant filters GT 1 String Genotype GQ 1 Integer Genotype quality PL 3 Integer Normalized, Phred-scaled likelihoods for genotypes MIN_DP 1 Integer Minimum DP observed within the GVCF block > sessionInfo() R version 3.1.1 (2014-07-10) Platform: x86_64-unknown-linux-gnu (64-bit) locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=en_US.UTF-8 LC_NAME=C [9] LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages: [1] stats4 parallel stats graphics grDevices utils datasets [8] methods base other attached packages: [1] VariantCallingPaper_0.0.3 GenomicFeatures_1.17.17 [3] AnnotationDbi_1.27.16 Biobase_2.25.0 [5] gmapR_1.7.8 VTGenotyping_0.0.1 [7] BiocParallel_0.99.22 futile.logger_1.3.7 [9] VariantTools_1.7.5 *VariantAnnotation_1.11.35* [11] Rsamtools_1.17.34 Biostrings_2.33.14 [13] XVector_0.5.8 rtracklayer_1.25.16 [15] GenomicRanges_1.17.42 GenomeInfoDb_1.1.23 [17] IRanges_1.99.28 S4Vectors_0.2.4 [19] BiocGenerics_0.11.5 switchr_0.2.1 loaded via a namespace (and not attached): [1] annotate_1.43.5 base64enc_0.1-2 [3] BatchJobs_1.4 BBmisc_1.7 [5] biomaRt_2.21.1 bitops_1.0-6 [7] brew_1.0-6 BSgenome_1.33.9 [9] CGPtools_2.2.0 checkmate_1.4 [11] codetools_0.2-9 DBI_0.3.1 [13] DESeq_1.17.0 digest_0.6.4 [15] fail_1.2 foreach_1.4.2 [17] futile.options_1.0.0 genefilter_1.47.6 [19] geneplotter_1.43.0 GenomicAlignments_1.1.29 [21] genoset_1.19.32 gneDB_0.4.18 [23] grid_3.1.1 iterators_1.0.7 [25] lambda.r_1.1.6 lattice_0.20-29 [27] Matrix_1.1-4 RColorBrewer_1.0-5 [29] RCurl_1.95-4.3 rjson_0.2.14 [31] RSQLite_0.11.4 sendmailR_1.2-1 [33] splines_3.1.1 stringr_0.6.2 [35] survival_2.37-7 tools_3.1.1 [37] TxDb.Hsapiens.BioMart.igis_2.3 XML_3.98-1.1 [39] xtable_1.7-4 zlibbioc_1.11.1 On Wed, Sep 17, 2014 at 2:08 PM, Valerie Obenchain <voben...@fhcrc.org> wrote: > Hi Gabe, > > Have you had a chance to test writeVcf? The changes made over the past > week have shaved off more time. It now takes ~ 9 minutes to write the > NA12877 example. > > dim(vcf) >>> >> [1] 51612762 1 >> >>> gc() >>> >> used (Mb) gc trigger (Mb) max used (Mb) >> Ncells 157818565 8428.5 298615851 15947.9 261235336 13951.5 >> Vcells 1109849222 8467.5 1778386307 13568.1 1693553890 12920.8 >> >>> print(system.time(writeVcf(vcf, tempfile()))) >>> >> user system elapsed >> 555.282 6.700 565.700 >> >>> gc() >>> >> used (Mb) gc trigger (Mb) max used (Mb) >> Ncells 157821990 8428.7 329305975 17586.9 261482807 13964.7 >> Vcells 1176960717 8979.5 2183277445 16657.1 2171401955 16566.5 >> > > > In the most recent version (1.11.35) I've added chunking for files with > > 1e5 records. Right now the choice of # records per chunk is simple, based > on total records only. We are still experimenting with this. You can > override default chunking with 'nchunk'. Examples on the man page. > > Valerie > > > On 09/08/14 08:43, Gabe Becker wrote: > >> Val, >> >> That is great. I'll check this out and test it on our end. >> >> ~G >> >> On Mon, Sep 8, 2014 at 8:38 AM, Valerie Obenchain <voben...@fhcrc.org >> <mailto:voben...@fhcrc.org>> wrote: >> >> The new writeVcf code is in 1.11.28. >> >> Using the illumina file you suggested, geno fields only, writing now >> takes about 17 minutes. >> >> > hdr >> class: VCFHeader >> samples(1): NA12877 >> meta(6): fileformat ApplyRecalibration ... reference source >> fixed(1): FILTER >> info(22): AC AF ... culprit set >> geno(8): GT GQX ... PL VF >> >> > param = ScanVcfParam(info=NA) >> > vcf = readVcf(fl, "", param=param) >> > dim(vcf) >> [1] 51612762 1 >> >> > system.time(writeVcf(vcf, "out.vcf")) >> user system elapsed >> 971.032 6.568 1004.593 >> >> In 1.11.28, parsing of geno data was moved to C. If this didn't >> speed things up enough we were planning to implement 'chunking' >> through the VCF and/or move the parsing of info to C, however, it >> looks like geno was the bottleneck. >> >> I've tested a number of samples/fields combinations in files with >= >> .5 million rows and the improvement over writeVcf() in release is ~ >> 90%. >> >> Valerie >> >> >> >> >> On 09/04/14 15:28, Valerie Obenchain wrote: >> >> Thanks Gabe. I should have something for you on Monday. >> >> Val >> >> >> On 09/04/2014 01:56 PM, Gabe Becker wrote: >> >> Val and Martin, >> >> Apologies for the delay. >> >> We realized that the Illumina platinum genome vcf files make >> a good test >> case, assuming you strip out all the info (info=NA when >> reading it into >> R) stuff. >> >> ftp://platgene:G3n3s4me@ussd-__ftp.illumina.com/NA12877_S1._ >> _genome.vcf.gz >> <ftp://platgene:g3n3s...@ussd-ftp.illumina.com/NA12877_S1. >> genome.vcf.gz> >> took about ~4.2 hrs to write out, and is about 1.5x the size >> of the >> files we are actually dealing with (~50M ranges vs our ~30M). >> >> Looking forward a new vastly improved writeVcf :). >> >> ~G >> >> >> On Tue, Sep 2, 2014 at 1:53 PM, Michael Lawrence >> <lawrence.mich...@gene.com >> <mailto:lawrence.mich...@gene.com> >> <mailto:lawrence.michael@gene.__com >> <mailto:lawrence.mich...@gene.com>>> wrote: >> >> Yes, it's very clear that the scaling is non-linear, >> and Gabe has >> been experimenting with a chunk-wise + parallel >> algorithm. >> Unfortunately there is some frustrating overhead with the >> parallelism. But I'm glad Val is arriving at something >> quicker. >> >> Michael >> >> >> On Tue, Sep 2, 2014 at 1:33 PM, Martin Morgan >> <mtmor...@fhcrc.org <mailto:mtmor...@fhcrc.org> >> <mailto:mtmor...@fhcrc.org >> <mailto:mtmor...@fhcrc.org>>> wrote: >> >> On 08/27/2014 11:56 AM, Gabe Becker wrote: >> >> The profiling I attached in my previous email >> is for 24 geno >> fields, as I said, >> but our typical usecase involves only ~4-6 >> fields, and is >> faster but still on >> the order of dozens of minutes. >> >> >> I think Val is arriving at a (much) more efficient >> implementation, but... >> >> I wanted to share my guess that the poor _scaling_ >> is because >> the garbage collector runs multiple times as the >> different >> strings are pasted together, and has to traverse, >> in linear >> time, increasing numbers of allocated SEXPs. So >> times scale >> approximately quadratically with the number of rows >> in the VCF >> >> An efficiency is to reduce the number of SEXPs in >> play by >> writing out in chunks -- as each chunk is written, >> the SEXPs >> become available for collection and are re-used. >> Here's my toy >> example >> >> time.R >> ====== >> splitIndices <- function (nx, ncl) >> { >> i <- seq_len(nx) >> if (ncl == 0L) >> list() >> else if (ncl == 1L || nx == 1L) >> list(i) >> else { >> fuzz <- min((nx - 1L)/1000, 0.4 * nx/ncl) >> breaks <- seq(1 - fuzz, nx + fuzz, length >> = ncl + 1L) >> structure(split(i, cut(i, breaks, >> labels=FALSE)), names >> = NULL) >> } >> } >> >> x = as.character(seq_len(1e7)); y = sample(x) >> if (!is.na <http://is.na> >> <http://is.na>(Sys.getenv("__SPLIT", NA))) { >> >> idx <- splitIndices(length(x), 20) >> system.time(for (i in idx) paste(x[i], y[i], >> sep=":")) >> } else { >> system.time(paste(x, y, sep=":")) >> } >> >> >> running under R-devel with $ SPLIT=TRUE R --no-save >> --quiet -f >> time.R the relevant time is >> >> user system elapsed >> 15.320 0.064 15.381 >> >> versus with $ R --no-save --quiet -f time.R it is >> >> user system elapsed >> 95.360 0.164 95.511 >> >> I think this is likely an overall strategy when >> dealing with >> character data -- processing in independent chunks >> of moderate >> (1M?) size (enabling as a consequence parallel >> evaluation in >> modest memory) that are sufficient to benefit from >> vectorization, but that do not entail allocation of >> large >> numbers of in-use SEXPs. >> >> Martin >> >> >> Sorry for the confusion. >> ~G >> >> >> On Wed, Aug 27, 2014 at 11:45 AM, Gabe Becker >> <becke...@gene.com <mailto:becke...@gene.com> >> <mailto:becke...@gene.com <mailto:becke...@gene.com>> >> <mailto:becke...@gene.com >> <mailto:becke...@gene.com> <mailto:becke...@gene.com >> <mailto:becke...@gene.com>>>> wrote: >> >> Martin and Val. >> >> I re-ran writeVcf on our (G)VCF data >> (34790518 ranges, >> 24 geno fields) with >> profiling enabled. The results of >> summaryRprof for that >> run are attached, >> though for a variety of reasons they are >> pretty >> misleading. >> >> It took over an hour to write >> (3700+seconds), so it's >> definitely a >> bottleneck when the data get very large, >> even if it >> isn't for smaller data. >> >> Michael and I both think the culprit is >> all the pasting >> and cbinding that is >> going on, and more to the point, that >> memory for an >> internal representation >> to be written out is allocated at all. >> Streaming >> across the object, looping >> by rows and writing directly to file (e.g. >> from C) >> should be blisteringly >> fast in comparison. >> >> ~G >> >> >> On Tue, Aug 26, 2014 at 11:57 AM, Michael >> Lawrence >> <micha...@gene.com <mailto:micha...@gene.com> >> <mailto:micha...@gene.com <mailto:micha...@gene.com>> >> <mailto:micha...@gene.com >> <mailto:micha...@gene.com> <mailto:micha...@gene.com >> <mailto:micha...@gene.com>>>> >> wrote: >> >> Gabe is still testing/profiling, but >> we'll send >> something randomized >> along eventually. >> >> >> On Tue, Aug 26, 2014 at 11:15 AM, >> Martin Morgan >> <mtmor...@fhcrc.org <mailto:mtmor...@fhcrc.org> >> <mailto:mtmor...@fhcrc.org <mailto:mtmor...@fhcrc.org>> >> <mailto:mtmor...@fhcrc.org >> <mailto:mtmor...@fhcrc.org> >> <mailto:mtmor...@fhcrc.org >> <mailto:mtmor...@fhcrc.org>>>> wrote: >> >> I didn't see in the original thread >> a >> reproducible (simulated, I >> guess) example, to be explicit >> about what the >> problem is?? >> >> Martin >> >> >> On 08/26/2014 10:47 AM, Michael >> Lawrence wrote: >> >> My understanding is that the >> heap >> optimization provided marginal >> gains, and >> that we need to think harder >> about how to >> optimize the all of >> the string >> manipulation in writeVcf. We >> either need to >> reduce it or reduce its >> overhead (i.e., the CHARSXP >> allocation). >> Gabe is doing more tests. >> >> >> On Tue, Aug 26, 2014 at 9:43 >> AM, Valerie >> Obenchain >> <voben...@fhcrc.org >> <mailto:voben...@fhcrc.org> >> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org>> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org> >> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org>>>> >> >> wrote: >> >> Hi Gabe, >> >> Martin responded, and so >> did Michael, >> >> >> https://stat.ethz.ch/______pipermail/bioc-devel/2014-____ >> __August/006082.html >> <https://stat.ethz.ch/____pipermail/bioc-devel/2014-____ >> August/006082.html> >> >> >> <https://stat.ethz.ch/____pipermail/bioc-devel/2014-____ >> August/006082.html >> <https://stat.ethz.ch/__pipermail/bioc-devel/2014-__ >> August/006082.html>> >> >> >> >> <https://stat.ethz.ch/____pipermail/bioc-devel/2014-____ >> August/006082.html >> <https://stat.ethz.ch/__pipermail/bioc-devel/2014-__ >> August/006082.html> >> >> <https://stat.ethz.ch/__pipermail/bioc-devel/2014-__ >> August/006082.html >> <https://stat.ethz.ch/pipermail/bioc-devel/2014- >> August/006082.html>>> >> >> It sounded like Michael >> was ok with >> working with/around heap >> initialization. >> >> Michael, is that right or >> should we >> still consider this on >> the table? >> >> >> Val >> >> >> On 08/26/2014 09:34 AM, >> Gabe Becker >> wrote: >> >> Val, >> >> Has there been any >> movement on >> this? This remains a >> substantial >> bottleneck for us when >> writing very >> large VCF files (e.g. >> variants+genotypes for >> whole genome >> NGS samples). >> >> I was able to see a >> ~25% speedup >> with 4 cores and an >> "optimal" speedup >> of ~2x with 10-12 >> cores for a VCF >> with 500k rows using >> a very naive >> parallelization >> strategy and no >> other changes. I suspect >> this could be >> improved on quite a >> bit, or >> possibly made irrelevant >> with judicious use >> of serial C code. >> >> Did you and Martin >> make any plans >> regarding optimizing >> writeVcf? >> >> Best >> ~G >> >> >> On Tue, Aug 5, 2014 at >> 2:33 PM, >> Valerie Obenchain >> <voben...@fhcrc.org >> <mailto:voben...@fhcrc.org> >> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org>> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org> >> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org>>> >> >> <mailto:voben...@fhcrc.org <mailto:voben...@fhcrc.org> >> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org>> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org> >> <mailto:voben...@fhcrc.org >> <mailto:voben...@fhcrc.org>>>>> >> >> wrote: >> >> Hi Michael, >> >> I'm interested >> in working on >> this. I'll discuss >> with Martin next >> week when we're >> both back in >> the office. >> >> Val >> >> >> >> >> >> On 08/05/14 >> 07:46, Michael >> Lawrence wrote: >> >> Hi guys >> (Val, Martin, >> Herve): >> >> Anyone have >> an itch for >> optimization? The >> writeVcf function is >> currently a >> bottleneck >> in our WGS >> genotyping pipeline. For >> a typical 50 >> million row >> gVCF, it was >> taking 2.25 >> hours prior to >> yesterday's improvements >> >> (pasteCollapseRows) that >> brought it down to >> about 1 hour, which >> is still >> too long by >> my standards >> (> 0). Only takes 3 >> minutes to call the >> genotypes >> (and >> associated >> likelihoods etc) from the >> variant calls (using >> 80 cores and >> 450 GB RAM >> on one node), >> so the output is an >> issue. Profiling >> suggests that >> the running >> time scales >> non-linearly in the >> number of rows. >> >> Digging a >> little deeper, >> it seems to be >> something with R's >> string/memory >> allocation. >> Below, >> pasting 1 million strings >> takes 6 seconds, but >> 10 >> million >> strings takes >> over 2 minutes. It gets >> way worse with 50 >> million. I >> suspect it >> has something >> to do with R's string >> hash table. >> >> set.seed(1000) >> end <- >> sample(1e8, 1e6) >> >> system.time(paste0("END", >> "=", end)) >> user >> system elapsed >> 6.396 >> 0.028 6.420 >> >> end <- >> sample(1e8, 1e7) >> >> system.time(paste0("END", >> "=", end)) >> user >> system elapsed >> 134.714 >> 0.352 134.978 >> >> Indeed, even >> this takes a >> long time (in a >> fresh session): >> >> set.seed(1000) >> end <- >> sample(1e8, 1e6) >> end <- >> sample(1e8, 1e7) >> >> system.time(as.character(end)) >> user >> system elapsed >> 57.224 >> 0.156 57.366 >> >> But running >> it a second >> time is faster (about >> what one would >> expect?): >> >> >> system.time(levels <- >> as.character(end)) >> user >> system elapsed >> 23.582 >> 0.021 23.589 >> >> I did some >> simple >> profiling of R to find that >> the resizing of >> the string >> hash table >> is not a >> significant component of >> the time. So maybe >> something >> to do with >> the R heap/gc? >> No time right now to >> go deeper. But I >> know Martin >> likes this >> sort of >> thing ;) >> >> Michael >> >> >> [[alternative >> HTML version deleted]] >> >> >> >> >> _______________________________________________________ >> Bioc-devel@r-project.org <mailto:Bioc-devel@r-project.org> >> <mailto:Bioc-devel@r-project.__org >> <mailto:Bioc-devel@r-project.org>> >> >> <mailto:Bioc-devel@r-project. >> <mailto:Bioc-devel@r-project.>____org >> <mailto:Bioc-devel@r-project.__org >> <mailto:Bioc-devel@r-project.org>>> >> >> <mailto:Bioc-devel@r-project <mailto:Bioc-devel@r-project>. >> <mailto:Bioc-devel@r-project >> <mailto:Bioc-devel@r-project>.>______org >> >> <mailto:Bioc-devel@r-project. >> <mailto:Bioc-devel@r-project.>____org >> <mailto:Bioc-devel@r-project.__org >> <mailto:Bioc-devel@r-project.org>>>> >> mailing list >> >> https://stat.ethz.ch/mailman/________listinfo/bioc-devel >> <https://stat.ethz.ch/mailman/______listinfo/bioc-devel> >> >> <https://stat.ethz.ch/mailman/______listinfo/bioc-devel >> 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