I am using R with Bioconductor to perform analyses on large datasets
using bootstrap methods. In an attempt to speed up my work, I have
inquired about using our local supercomputer and asked the administrator
if he thought R would run faster on our parallel network. I received the
following reply:
"The second benefit is that the processors have large caches.
Briefly, everything is loaded into cache before going into the
processor. With large caches, there is less movement of data between
memory and cache, and this can save quite a bit of time. Indeed, when
programmers optimize code they usually think about how to do things to
keep data in cache as long as possible.
Whether you would receive any benefit from larger cache depends on how
R is written. If it's written such that data remain in cache, the
speed-up could be considerable, but I have no way to predict it."
My question is, "is R written such that data remain in cache?"
Thanks,
Mark W. Kimpel MD
Indiana University School of Medicine
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