R is not inherently a multi-threaded application, so in normal circumstances it only uses one processor at a time.
There are several options for parallel programming that provide value:
1. Revolution R Enterprise will use all available processors for some common math operations, like matrix multiplication. (It is linked with multi-threaded math libraries which improve performance on multi-core Intel processors.)
2. The RevoScaleR package, shipped with Revolution R Enterprise, offers parallel external memory algorithms and a very efficient data file format (.xdf).
3. You can also write explicit parallel code using ParallelR. See CRAN Task View: High-Performance and Parallel Computing with R for a list of options:
4. The RevoScaleR package included with Revolution R Enterprise provides new tools for parallel and distributed computing with R that can scale out.