[Bioc-devel] Memory issues with BiocParallel::SnowParam()
Hi, I ran my example code with SerialParam() which had a negligible 4% memory increase between R 3.2.x and 3.1.x This 4% could very well fluctuate a little bit and might be non significantly different from 0 if I run the test more times. I also added a second example using code based on my analysis script. With SerialParam(), the memory change is 13%, but with SnowParam() it's 82% between the R versions mentioned already using 10 cores. It's still far from the > 150% increase (2.5 fold change) I'm seeing with the real data. I initially thought that these observations ruled out everything else except SnowParam(). However, maybe the initial 13% memory increase multiplied by 10 (well, less then linear) is what I'm seeing with 10 cores (82% increase). The updated information is available at http://lcolladotor.github.io/SnowParam-memory/ As for what Vincent suggested of an AMI and EC2, I don't have experience with them. I'm not sure I'll be able to look into them and create a reproducible environment. Cheers, Leo On Fri, Jul 10, 2015 at 7:12 AM, Vincent Carey
<stvjc at channing.harvard.edu> wrote:
I have had (potentially transient and environment-related) problems with bplapply in gQTLstats. I substituted the foreach abstractions and the code worked. I still have difficulty seeing how to diagnose the trouble I ran into. I'd suggest that you code so that you can easily substitute parallel- or foreach- or BatchJobs-based cluster control. This can help crudely isolate the source of trouble. It would be very nice to have a way of measuring resource usage in cluster settings, both for diagnosis and strategy selection. For jobs that succeed, BatchJobs records memory used in its registry database, based on gc(). I would hope that there are tools that could be used to help one figure out how to factor a task so that it is feasible given some view of environment constraints. It might be useful for you to build an AMI and then a cluster that allows replication of the condition you are seeing on EC2. This could help with diagnosis and might be a basis for defining better instrumentation tools for both diagnosis and planning. On Fri, Jul 10, 2015 at 12:23 AM, Leonardo Collado Torres <lcollado at jhu.edu> wrote:
Hi, I have a script that at some point generates a list of DataFrame objects which are rather large matrices. I then feed this list to BiocParallel::bplapply() and process them. Previously, I noticed that in our SGE managed cluster using MulticoreParam() lead to 5 to 8 times higher memory usage as I posted in https://support.bioconductor.org/p/62551/#62877. Martin posted in https://support.bioconductor.org/p/62551/#62880 that "Probably the tools used to assess memory usage are misleading you." This could be true, but they are the tools that determine memory usage for all jobs in the cluster. Meaning that if my memory usage blows up according to these tools, my jobs get killed. That was with R 3.1.x and in particular running https://github.com/leekgroup/derSoftware/blob/gh-pages/step1-fullCoverage.sh with $ sh step1-fullCoverage.sh brainspan which at the time (Nov 4th, 2014) used 173.5 GB of RAM with 10 cores. I recently tried to reproduce this (to check changes in run time given rtracklayer's improvements with BigWig files) using R 3.2.x and the memory went up to 450 GB before the job got killed given the maximum memory I specified for the job. The same is true using R 3.2.0. Between R 3.1.x and 3.2.0, `derfinder` is nearly identical (just one bug fix is different, for other code not used in this script). I know that BiocParallel changed quite a bit between those versions, and in particular SnowParam(). So that's why my prime suspect is BiocParallel. I made a smaller reproducible example which you can view at http://lcolladotor.github.io/SnowParam-memory/. This example uses a list of data frames with random data, and also uses 10 cores. You can see there that in R versions 3.1.x, 3.2.0 and 3.2.x, MulticoreParam() does use more memory than SnowParam(), as reported by SGE. Beyond the actual session info differences due to changes in BiocParalell's implementation, I noticed that the cluster type changed from PSOCK to SOCK. I ignore if this could explain the memory increase. The example doesn't generate the huge fold change between R 3.1.x and the other two versions (still 1.27x > 1x) that I see with my analysis script, so in that sense it's not the best example for the problem I'm observing. My tests with https://github.com/leekgroup/derSoftware/blob/gh-pages/step1-fullCoverage.sh were between June 23rd and 28th, so maybe some recent changes in BiocParallel addressed this issue. I'm not sure how to proceed now. One idea is to make another example with the same type of objects and operations I use in my analysis script. A second one is to run my analysis script with SerialParam() on the different R versions to check if they use different amounts of memory which would suggest that the memory issue is not caused by SnowParam(). For example, maybe changes in rtracklayer are the ones driving the huge memory changes I'm seeing in my analysis scripts. However, I don't really suspect rtracklayer given the memory load reported that I checked manually a couple of times with "qmem". I believe that the memory blows up at https://github.com/leekgroup/derSoftware/blob/gh-pages/step1-fullCoverage.R#L124 which in turn uses derfinder::filterData(). This function imports: '[', '[<-', '[[', colnames, 'colnames<-', lapply methods from IRanges Rle, DataFrame from S4Vectors Reduce method from S4Vectors https://github.com/lcolladotor/derfinder/blob/master/R/filterData.R#L49-L51 Best, Leo History of analysis scripts doesn't reveal any other leads https://github.com/leekgroup/derSoftware/commits/gh-pages/step1-fullCoverage.sh https://github.com/leekgroup/derSoftware/commits/gh-pages/step1-fullCoverage.R
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