**Revolutions**, and kindly contributed to R-bloggers)

Over at Cerebral Mastication, JD Long tells a characteristically entertaining and informative story about how he uses R to run stochastic simulations of insurance portfolios and reinsurance treaties. A typical job involves 10,000 simulations, and when each estimate takes over 20 seconds you're talking some serious time to get the job done.

Fortunately, this is the kind of problem that lends itself to parallelization: with a multiprocessor machine running R, you can run as many simulations as you have processors at the same time, and cut the total computation time by the same factor. JD describes how he boots up an 8-core instance running R on Amazon EC2, and then uses the multicore package on Linux to reduce the computation time by a factor of 8.

Sadly for Windows users, multicore doesn't run on Windows. But as Boris Shor points out in a comment to JD's post, REvolution R Enterprise allows you to do parallel programming like this (using the foreach operator) on a multiprocessor Windows machine (or even a cluster of many machines).

Cerebral Mastication: Using the R multicore package in Linux with wild an passionate abandon

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**Revolutions**.

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