[Bioc-devel] Using SerialParam() as the registered back-end for all platforms

Aaron Lun infinite@monkey@@with@keybo@rd@ @ending from gm@il@com
Mon Jan 7 16:44:14 CET 2019


The main problem I’ve described refers to changes in the random seed due to the MulticoreParam() constructor, prior to dispatch to workers. For the related-but-separate problem of obtaining consistent random results within each worker, we’ve been discussing the possible solutions on another Bioc-devel thread (https://stat.ethz.ch/pipermail/bioc-devel/2019-January/014498.html <https://stat.ethz.ch/pipermail/bioc-devel/2019-January/014498.html>). 

-A

> On 7 Jan 2019, at 15:03, Ryan Thompson <rct using thompsonclan.org> wrote:
> 
> I don't know if this is helpful for BiocParallel, but there's an extension for the foreach package that ensures reproducible RNG behavior for all parallel backends: https://cran.r-project.org/web/packages/doRNG/index.html <https://cran.r-project.org/web/packages/doRNG/index.html>
> 
> Perhaps some of the principles from that package can be re-used?
> 
> On Mon, Jan 7, 2019 at 9:37 AM Aaron Lun <infinite.monkeys.with.keyboards using gmail.com <mailto:infinite.monkeys.with.keyboards using gmail.com>> wrote:
> > I hope for 1. to have a 'local socket' (i.e., not using ports) implementation shortly.
> 
> Yes, that would be helpful.
> 
> > I committed a patch in 1.17.6 for the wrong-seeming behavior of 2. We now have
> > 
> >> library(BiocParallel)
> >> set.seed(1); p = bpparam(); rnorm(1)
> > [1] -0.6264538
> >> set.seed(1); p = bpparam(); rnorm(1)
> > [1] -0.6264538
> > 
> > at the expensive of using the generator when the package is loaded.
> > 
> >> set.seed(1); rnorm(1)
> > [1] -0.6264538
> >> set.seed(1); library(BiocParallel); rnorm(1)
> > [1] 0.1836433
> > 
> > Is that bad? It will be consistent across platforms.
> 
> Hm. I guess the changed behaviour is… better, in the sense that the second scenario (setting the seed before loading the package) is less likely in real analysis code.
> 
> Even so, there are probably some edge cases where this could cause issues, e.g., when:
> 
> set.seed(1)
> MyPackage::SomeFun()
> 
> … where MyPackage causes BiocParallel to be attached, which presumably changes the seed.
> 
> Having thought about it for a while, the fact that bpparam() changes the random seed is only a secondary issue. The main issue is that it doesn’t change the seed reproducibly. So I wouldn’t mind *as much* if repeated calls of:
> 
> set.seed(1)
> bpparam()
> rnorm(1)
> 
> … gave the same result, even if it were different from just running “set.seed(1); rnorm(1)”. (Mind you, I’d still mind a little, but it wouldn’t be so bad.) The biggest problem with the current state of affairs is that the first call gives different results to all subsequent calls, which really interferes with debugging attempts.
> 
> > This behavior
> > 
> >> set.seed(1); unlist(bplapply(1:2, function(i) rnorm(1)))
> > [1] 0.9624337 0.8925947
> >> set.seed(1); unlist(bplapply(1:2, function(i) rnorm(1)))
> > [1] -0.5703597  0.1102093
> > 
> > seems wrong, but is consistent with mclapply
> > 
> >> set.seed(1); unlist(mclapply(1:2, function(i) rnorm(1)))
> > [1] -0.02704527  0.40721777
> >> set.seed(1); unlist(mclapply(1:2, function(i) rnorm(1)))
> > [1] -0.8239765  1.2957928
> > 
> > The documented behavior is to us the RNGseed= argument to *Param, but I think it could be made consistent (by default, obey the global random number seed on workers) at least on a single machine (where the default number of cores is constant).
> 
> I’m less concerned with that behaviour, given it’s inherently hard to take randomization code written for serial execution and make it give the same results on multiple cores (as we discussed elsewhere).
> 
> > I have not (yet?) changed the default behavior to SerialParam. I guess the cost of SerialParam is from the dependent packages that need to be loaded
> > 
> >> system.time(suppressPackageStartupMessages(library(DelayedArray)))
> >   user  system elapsed
> >  3.068   0.082   3.150
> 
> Does calling "SerialParam()” cause DelayedArray to be attached? That seems odd.
> 
> > If fastMNN() makes several calls to bplapply(), it might make sense to start the default cluster at the top of the function once
> > 
> >    if (!isup(bpparam())) {
> >        bpstart(bpparam())
> >        on.exit(bpstop(bpparam()))
> >    }
> 
> This is probably a good idea to do in general to all of my parallelized functions, though I don’t know how much this will solve the time problem. Perhaps I should just do it and see.
> 
> -A
> 
> > On 1/6/19, 11:16 PM, "Bioc-devel on behalf of Aaron Lun" <bioc-devel-bounces using r-project.org <mailto:bioc-devel-bounces using r-project.org> on behalf of infinite.monkeys.with.keyboards using gmail.com <mailto:infinite.monkeys.with.keyboards using gmail.com>> wrote:
> > 
> >    As we know, the default BiocParallel backends are currently set to MulticoreParam (Linux/Mac) or SnowParam (Windows). I can understand this to some extent because a new user running, say, bplapply() without additional arguments or set-up would expect some kind of parallelization. However, from a developer’s perspective, I would argue that it makes more sense to use SerialParam() by default. 
> > 
> >    1. It avoids problems with MulticoreParam stalling (especially on Macs) when the randomly chosen port is in already use. This used to be a major problem, to the point that all my BiocParallel-using functions in scran passed BPPARAM=SerialParam() by default. Setting SerialParam() as package default would ensure BiocParallel functions run properly in the first place; if the code stalls due to switching to MulticoreParam, then it’s obvious where the problem lies (and how to fix it).
> > 
> >    2. It avoids the alteration of the random seed when the MulticoreParam instance is constructed for the first time. 
> > 
> >    library(BiocParallel) # new R session
> >    set.seed(100)
> >    invisible(bplapply(1:5, identity))
> >    rnorm(1) # 0.1315312
> >    set.seed(100)
> >    invisible(bplapply(1:5, identity))
> >    rnorm(1) # -0.5021924
> > 
> >    This is because the first bplapply() call calls bpparam(), which constructs a MulticoreParam() for the first time; this calls the PRNG to choose a random port number. Ensuing random numbers are altered, as seen above. To avoid this, I need to define the MulticoreParam() object prior to set.seed(), which undermines the utility of a default-defined bpparam().
> > 
> >    3. Job dispatch via SnowParam() is quite slow, which potentially makes Windows package builds run slower by default. A particularly bad example is that of scran::fastMNN(), which has a few matrix multiplications that use DelayedArray:%*%. The %*% is parallelized with the default bpparam(), resulting in SNOW parallelization on Windows. This slowed down fastMNN()’s examples from 4 seconds (unix) to ~100 seconds (windows). Clearly, serial execution is the faster option here. A related problem is MulticoreParam()’s tendency to copy the environment, which may result in problems from inflated memory consumption.
> > 
> >    So, can we default to SerialParam() on all platforms? And by this I mean the BiocParallel in-built default - I don’t want to have to instruct all my users to put a “register(SerialParam())” at the start of their analysis scripts. I feel that BiocParallel’s job is to provide downstream code with the potential for parallelization. If end-users want actual parallelization, they had better be prepared to specify an appropriate scheme via *Param() objects. 
> > 
> >    -A
> > 
> > 
> > 
> > 
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> > 
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> > 
> 
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