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>
wrote:
Hi Gabe,
Martin responded, and so did Michael,
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>> 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
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