Jan 262014
 

R Package Parallel: How Not to Solve a Problem That Does Not Exist

Somewhat foolishly, my university has granted me access to Mogon: not the god, not the death metal band but rather their supercomputer, which currently holds the 182th spot in the top 500 list of the fastest computers on the planet. It has some 34,000+ cores and more than 80 TB of RAM, but basically it’s just a very large bunch of Linux boxes. That means that I have a rough idea how to handle it, and that it happily runs my native Linux Stata and MPlus (and hopefully Jags) binaries for me. It also has R installed, and this is where my misery began.

I have a lengthy R job that deals with census data. Basically, it looks up the absolute number of minority residents in some 25,000 output areas and their immediate neighbours and calculates a series of percentages from these figures. I think this could in principle be done in Stata, but R provides convenient libraries for dealing with geo-coded data (sp and friends), non-rectangular data structures and all the trappings of a full-featured programming language, so it would be stupid not to make use of it. The only problem is that R is relatively slow and single-threaded, and that my script is what they call embarrassingly parallel: The same trivial function is applied to 33 vectors with 25,000 elements each. Each calculation on a vector takes about eight seconds to complete, which amounts to roughly five minutes in total. Add the time it takes to read in the data and some fairly large lookup-tables (it would be very time-consuming to repeatedly calculate which output area is close enough to each other output area to be considered a neighbour), and we are looking at eight to ten minutes for one run.

Mogon

Mogon. Image Credit: ZDV JGU Mainz

While I do not plan to run this script very often – once the calculations are done and saved, the results can be used in the analysis proper over and over again – I fully expect that I might change some operationalisations, include different variables etc., and so I began toying with the parallel package for R to make use of the many cores suddenly at my disposal.

Twelve hours later, I had learned the basics of the scheduling system (LSF), solved the problem of synching my data between home, office, central, and super-computer, gained some understanding of the way parallel works and otherwise achieved basically nothing: Even the best attempt at running a parallelised version of the script on the supercomputer was a little slower than the serialised version on my very capable office machine (and that is without the time (between 15 and 90 seconds) the scripts spends waiting to be transferred to a suitable node of the cluster). I tried different things: replacing lapply with mclapply, which was slower, regardless of the number of cores; using clusterApply instead of lapply (same result), and forking the 33 serial jobs into the background, which was even worse, presumably because storing the returned values resulted in changes to rather large data structures that were propagated to all cores involved.

Lessons Learned?

So yes, to save a few minutes in a script that I will presumably run not more than four or five times over the next couple of weeks, I spent 12 hours, with zilch results. But at least I learned a few things (apart from the obvious re-iteration of the old ‘never change a half-way running system’ mantra). First, even if it takes eight seconds to do the sums, a vector of 25,000 elements is probably to short to really benefit from shifting the calculations to more cores. While forking should be cheap, the overhead of setting up the additional threads dominates any savings. Second, running jobs in parallel without really understanding what overhead this creates is a stupid idea, and knowing what overhead this creates and how to avoid this is probably not worth the candle (see the above). Third, I can always re-use the infrastructure I’ve created (for more pointless experiments). Forth, my next go at Mogon shall avoid half-baked middle-level parallelisation altogether. Instead I shall combine fine-grained implicit parallelism (built into Stata and Mplus) and very coarse explicit parallelism (by breaking up lengthy scripts into small chunks that can be run independently). Further research is definitively needed.

  One Response to “Embarrassing Parallelism: I Got 99 Problems, but a Core ain’t One”

  1. Embarrassing #Parallelism: I Got 99 Problems, but a Core ain’t One » kai arzheimer http://t.co/ZkWhFyjlZ0 #R #Stata

Agree? Disagree? Leave a reply (also works with Facebook, G+, Twitter...)

%d bloggers like this: