Do NOT use mcparallel() in packages except as a non-default option that user
can set for the reasons Henrik explained. Multicore is intended for HPC
applications that need to use many cores for computing-heavy jobs, but it does
not play well with RStudio and more importantly you don't know the resource
available so only the user can tell you when it's safe to use. Multi-core
machines are often shared so using all detected cores is a very bad idea. The
user should be able to explicitly enable it, but it should not be enabled by
default.
As for parallelism, it depends heavily on your use-case. Native parallelism is
preferred (threads, OpenMP, ...) and I assume you're not talking about that as
that is always the first option. Multicore works well in cases where there is
no easy native solution and you need to share a lot of data for small results.
If the data is small, or you need to read it first, then other methods like
PSOCK may be preferable. In any case, parallelization only makes sense for code
that you know will take a long time to run.
Cheers,
Simon
> On 29/04/2020, at 11:54 AM, Shian Su <s...@wehi.edu.au> wrote:
>
> Thanks Henrik,
>
> That clears things up significantly. I did see the warning but failed to
> include it my initial email. It sounds like an RStudio issue, and it seems
> like that it’s quite intrinsic to how forks interact with RStudio. Given this
> code is eventually going to be a part of a package, should I expect it to
> fail mysteriously in RStudio for my users? Is the best solution here to
> migrate all my parallelism to PSOCK for the foreseeable future?
>
> Thanks,
> Shian
>
>> On 29 Apr 2020, at 2:08 am, Henrik Bengtsson <henrik.bengts...@gmail.com>
>> wrote:
>>
>> Hi, a few comments below.
>>
>> First, from my experience and troubleshooting similar reports from
>> others, a returned NULL from parallel::mclapply() is often because the
>> corresponding child process crashed/died. However, when this happens
>> you should see a warning, e.g.
>>
>>> y <- parallel::mclapply(1:2, FUN = function(x) if (x == 2) quit("no") else
>>> x)
>> Warning message:
>> In parallel::mclapply(1:2, FUN = function(x) if (x == 2) quit("no") else x) :
>> scheduled core 2 did not deliver a result, all values of the job
>> will be affected
>>> str(y)
>> List of 2
>> $ : int 1
>> $ : NULL
>>
>> This warning is produces on R 4.0.0 and R 3.6.2 in Linux, but I would
>> assume that warning is also produced on macOS. It's not clear from
>> you message whether you also got that warning or not.
>>
>> Second, forked processing, as used by parallel::mclapply(), is advised
>> against when using the RStudio Console [0]. Unfortunately, there's no
>> way to disable forked processing in R [1]. You could add the
>> following to your ~/.Rprofile startup file:
>>
>> ## Warn when forked processing is used in the RStudio Console
>> if (Sys.getenv("RSTUDIO") == "1" && !nzchar(Sys.getenv("RSTUDIO_TERM"))) {
>> invisible(trace(parallel:::mcfork, tracer =
>> quote(warning("parallel::mcfork() was used. Note that forked
>> processes, e.g. parallel::mclapply(), may be unstable when used from
>> the RStudio Console
>> [https://github.com/rstudio/rstudio/issues/2597#issuecomment-482187011]";,
>> call.=FALSE))))
>> }
>>
>> to detect when forked processed is used in the RStudio Console -
>> either by you or by some package code that you use directly or
>> indirectly. You could even use stop() here if you wanna be
>> conservative.
>>
>> [0] https://github.com/rstudio/rstudio/issues/2597#issuecomment-482187011
>> [1] https://stat.ethz.ch/pipermail/r-devel/2020-January/078896.html
>>
>> /Henrik
>>
>> On Tue, Apr 28, 2020 at 2:39 AM Shian Su <s...@wehi.edu.au> wrote:
>>>
>>> Yes I am running on Rstudio 1.2.5033. I was also running this code without
>>> error on Ubuntu in Rstudio. Checking again on the terminal and it does
>>> indeed work fine even with large data.frames.
>>>
>>> Any idea as to what interaction between Rstudio and mclapply causes this?
>>>
>>> Thanks,
>>> Shian
>>>
>>> On 28 Apr 2020, at 7:29 pm, Simon Urbanek
>>> <simon.urba...@r-project.org<mailto:simon.urba...@r-project.org>> wrote:
>>>
>>> Sorry, the code works perfectly fine for me in R even for 1e6 observations
>>> (but I was testing with R 4.0.0). Are you using some kind of GUI?
>>>
>>> Cheers,
>>> Simon
>>>
>>>
>>> On 28/04/2020, at 8:11 PM, Shian Su
>>> <s...@wehi.edu.au<mailto:s...@wehi.edu.au>> wrote:
>>>
>>> Dear R-devel,
>>>
>>> I am experiencing issues with running GAM models using mclapply, it fails
>>> to return any values if the data input becomes large. For example here the
>>> code runs fine with a df of 100 rows, but fails at 1000.
>>>
>>> library(mgcv)
>>> library(parallel)
>>>
>>> df <- data.frame(
>>> + x = 1:100,
>>> + y = 1:100
>>> + )
>>>
>>> mclapply(1:2, function(i, df) {
>>> + fit <- gam(y ~ s(x, bs = "cs"), data = df)
>>> + },
>>> + df = df,
>>> + mc.cores = 2L
>>> + )
>>> [[1]]
>>>
>>> Family: gaussian
>>> Link function: identity
>>>
>>> Formula:
>>> y ~ s(x, bs = "cs")
>>>
>>> Estimated degrees of freedom:
>>> 9 total = 10
>>>
>>> GCV score: 0
>>>
>>> [[2]]
>>>
>>> Family: gaussian
>>> Link function: identity
>>>
>>> Formula:
>>> y ~ s(x, bs = "cs")
>>>
>>> Estimated degrees of freedom:
>>> 9 total = 10
>>>
>>> GCV score: 0
>>>
>>>
>>>
>>> df <- data.frame(
>>> + x = 1:1000,
>>> + y = 1:1000
>>> + )
>>>
>>> mclapply(1:2, function(i, df) {
>>> + fit <- gam(y ~ s(x, bs = "cs"), data = df)
>>> + },
>>> + df = df,
>>> + mc.cores = 2L
>>> + )
>>> [[1]]
>>> NULL
>>>
>>> [[2]]
>>> NULL
>>>
>>> There is no error message returned, and the code runs perfectly fine in
>>> lapply.
>>>
>>> I am on a MacBook 15 (2016) running MacOS 10.14.6 (Mojave) and R version
>>> 3.6.2. This bug could not be reproduced on my Ubuntu 19.10 running R 3.6.1.
>>>
>>> Kind regards,
>>> Shian Su
>>> ----
>>> Shian Su
>>> PhD Student, Ritchie Lab 6W, Epigenetics and Development
>>> Walter & Eliza Hall Institute of Medical Research
>>> 1G Royal Parade, Parkville VIC 3052, Australia
>>>
>>>
>>> _______________________________________________
>>>
>>> The information in this email is confidential and intend...{{dropped:26}}
>>>
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>>> R-devel@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-devel
>
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