Michael Benjamin wrote:
Hi, all--
I wanted to start a (new) thread on R speed/benchmarking. There is a
nice R benchmarking overview at
http://www.sciviews.org/other/benchmark.htm, along with a free script so
you can see how your machine stacks up.
Looks like R is substantially faster than S-plus.
My problem is this: with 512Mb and an overclocked AMD Athlon XP 1800+,
running at 588 SPEC-FP 2000, it still takes me 30 minutes to analyze 4Mb
.cel files x 120 files using affy (expresso). Running svm takes a
mighty long time with more than 500 genes, 150 samples.
Questions:
1) Would adding RAM or processing speed improve performance the most?
I usually find adding RAM makes a big difference, especially for Windows boxes.
2) Is it possible to run R on a cluster without rewriting my high-level code? In other words,
I think the answer is most likely "no". The `snow' package of Tierney/Rossini/Li on CRAN has gone a long way in making parallel computing in R much easier.
3) What are we going to do when we start collecting terabytes of array data to analyze? There will come a "breaking point" at which desktop systems can't perform these analyses fast enough for large quantities of data. What then?
Hasn't that "breaking point" always existed in some form or another? If large datasets can be broken up then clusters can be useful because smaller chunks can be parceled out to the cluster nodes and processed. Another thing to think about is that as R moves into the world of 64 bit processors, we will be able to load much larger datasets into RAM. I didn't think it was possible, but I recently loaded an 8GB dataset into R running on a Solaris/Sparc box!
-roger
Michael Benjamin, MD
Winship Cancer Institute
Emory University,
Atlanta, GA
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