Question

I have a fairly large dataset (~1.4m rows) that I’m doing some splitting and summarizing on. The whole thing takes a while to run, and my final application depends on frequent running, so my thought was to use doMC and the .parallel=TRUE flag with plyr like so (simplified a bit):

library(plyr)
require(doMC)
registerDoMC()

df <- ddply(df, c("cat1", "cat2"), summarize, count=length(cat2), .parallel = TRUE)

If I set the number of cores explicitly to two (using registerDoMC(cores=2)) my 8 GB of RAM see me through, and it shaves a decent amount of time. However, if I let it use all 8 cores, I quickly run out of memory due to the fact that each of the forked processes appears to clone the entire dataset in memory.

My question is whether or not it is possible to use plyr’s parallel execution facilities in a more memory-thrifty way? I tried converting my dataframe to a big.matrix, but this simply seemed to force the whole thing back to using a single core:

library(plyr)
library(doMC)
registerDoMC()
library(bigmemory)

bm <- as.big.matrix(df)
df <- mdply(bm, c("cat1", "cat2"), summarize, count=length(cat2), .parallel = TRUE)

This is my first foray into multicore R computing, so if there is a better way of thinking about this, I’m open to suggestion.

UPDATE: As with many things in life, it turns out I was doing Other Stupid Things elsewhere in my code, and that the whole issue of multi-processing becomes a moot point in this particular instance. However, for big data folding tasks, I’ll keep data.table in mind. I was able to replicate my folding task in a straightforward way.

Answer 1

I do not think that plyr makes copies of the entire dataset. However, when processing a chunk of data, that subset is copied to the worker. Therefore, when using more workers, more subsets are in memory simultaneously (i.e. 8 instead of 2).

I can think of a few tips you could try:

• Put your data in to an array structure in stead of a data.frame and use adply to do the summarizing. arrays are much more efficient in terms of memory use and speed. I mean using normal matrices, not big.matrix.
• Give data.table a try, in some cases this can lead to a speed increase of several orders of magnitude. I’m not sure if data.table supports parallel processing, but even without parallelization, data.table might be hunderds of times faster. See a blog post of mine comparing ave, ddply and data.table for processing chunks of data.