smp in Linux

On the other hand, most of the problems I have where speed is an issue are
simulations or bootstrapping problems that scale almost perfectly by
running 1/n as many on each of n processors.

This has been my experience too, and I have lots of problems like this. It would
sure be nice to have some possibility like:

parallelfor (i in 1:N, listofmachines )  x[[i]] <- simulate(model[[i]],
data[[i]])

This may be a case for implementing .For().  We currently say it is not
necessary and thus not implemented, which is true for systems with more
users than processors, but it would be useful for multiprocessor systems.

	-thomas


Thomas Lumley
Assistant Professor, Biostatistics
University of Washington, Seattle

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This has been my experience too, and I have lots of problems like this.
It would sure be nice to have some possibility like: 

parallelfor (i in 1:N, listofmachines )  x[[i]] <- simulate(model[[i]],
data[[i]]) 

Wouldn't it be better to do these sort of coase grain parallisation at a
higher level?  ie with a little bit of perl code, and/or some other load
distribution package from outside R. 

Nicholas


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