[R] Parallel Scan of Large File
jholtman at gmail.com
Wed Dec 8 14:34:40 CET 2010
You might be better off partitioning the file before processing with
R. If you are planning on using "skip = n" to skip over records
before processing, then the last thread you would start would have to
read through 7/8 of the file before starting. The actual I/O, plus
the looking for the line feed to determine the end of the line might
take as much time as just processing the file in a single thread
depending on the complexity of the analysis. If you expect to be
making multiple passes of the file (especially while debugging the
code), then I would recommend that you split the file into the sizes
you want to process with each thread one time, and then use the
partitions in your parallel processing so that you don't waste a lot
of I/O just reading to fine a line feed. Also if your system has a
number of I/O devices, you would put each partition on a separate
spindle and potentially reduce the bottleneck of trying to have eight
threads read from the same disk causing a lot of head seeking
On Wed, Dec 8, 2010 at 8:24 AM, Mike Marchywka <marchywka at hotmail.com> wrote:
>> Date: Tue, 7 Dec 2010 17:22:57 -0800
>> From: ryan.steven.garner at gmail.com
>> To: r-help at r-project.org
>> Subject: [R] Parallel Scan of Large File
>> Is it possible to parallel scan a large file into a character vector in 1M
>> chunks using scan() with the "doMC" package? Furthermore, can I specify the
>> tasks for each child?
>> i.e. I'm working on a Linux box with 8 cores and would like to scan in 8M
>> records at time (all 8 cores scan 1M records at a time) from a file with 40M
>> records total.
> I can't comment on R approaches but if your rational here is speed
> and you hope to scale this up to bigger files I would suggest more
> analysis or measurement. In the case you outline, disk IO is probably
> going to be the rate limiting step. It usually helps if you can make
> thing predictable so the disk and memory caches can be used efficiently.
> If you split up disk IO among different threads there is no reasonable
> way the hardware can figure out what access is likely to be next.
> Further, often times things like "skip()" are implemented as dummy reads
> on sequential file access calls.
> If you pursue this, I'd be curious to see what kind of results you get as
> you go from 1 to 8 core with larger files.
> You would probably be better off if you could find a way to pipeline this
> work rather than split it up as you suggest. The idea sounds good of course,
> you end up with 8 cores looking at your text, but you could easily be limited
> by some other resource like bus bandwidths to disk or memory. As each core
> gets a bigger junk, eventually you run out of physical memory and then of
> course you are just doing disk IO for VM.
>> file <- file("data.txt","r")
>> child <- foreach(i = icount(40)) %dopar%
>> scan(file,what = "character",sep = "\n",skip = 0,nlines = 1e6)
>> Thus, each child would have a different skip argument. child[]: skip = 0,
>> child[]: skip = 1e6 + 1, child[]: skip = 2e6 + 1, ... ,child[]:
>> skip = 39e6 + 1. I would then end up with a list of 40 vectors with
>> child[] containing records 1 to 1000000, child[] containing records
>> 1000001 to 2000000, ... ,child[] containing records 39000001 to
>> Also, would one file connection suffice or does their need to be a file
>> connection that opens and closes for each child?
>> View this message in context: http://r.789695.n4.nabble.com/Parallel-Scan-of-Large-File-tp3077545p3077545.html
>> Sent from the R help mailing list archive at Nabble.com.
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> R-help at r-project.org mailing list
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Data Munger Guru
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