From jgbradley1 at gmail.com  Fri Jul 24 22:21:03 2015
From: jgbradley1 at gmail.com (Joshua Bradley)
Date: Fri, 24 Jul 2015 16:21:03 -0400
Subject: [Rd] Memory limitations for parallel::mclapply
Message-ID: <CAKShX4AX88HfBxxVDQNZa5S_YVMN3z3FgzZTP0Ww=yneAMNkSA@mail.gmail.com>

Hello,

I have been having issues using parallel::mclapply in a memory-efficient
way and would like some guidance. I am using a 40 core machine with 96 GB
of RAM. I've tried to run mclapply with 20, 30, and 40 mc.cores and it has
practically brought the machine to a standstill each time to the point
where I do a hard reset.

When running mclapply with 10 mc.cores, I can see that each process takes
7.4% (~7 GB) of memory. My use-case for mclapply is the following: run
mclapply over a list of 150000 names, for each process I refer to a larger
pre-computed data.table to compute some stats with the name, and return
those stats . Ideally I want to use the large data.table as shared-memory
but the number of mc.cores I can use are being limited because each one
requires 7 GB. Someone posted this exact same issue
<http://stackoverflow.com/questions/13942202/r-and-shared-memory-for-parallelmclapply>
on
stackoverflow a couple years ago but it never got answered.

Do I have to manually tell mclapply to use shared memory (if so, how?)? Is
this type of job better with the doParallel package and foreach approach?

Josh Bradley

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[Rd] Memory limitations for parallel::mclapply
Joshua Bradley