ParallelR Important Features

Easy to Use

• Users can easily start up a team or "sleigh" of worker R processes with a shared workspace from within their R sessions and scripts. The workers can be started on the local machine or across networks through a wide variety of invocation mechanisms including enterprise
  schedulers, again all from within the R environment.
• Parallel R 2.0 includes several new features that simplify
  parallel and distributed programming with R. The new
  foreach command provides a looping construct that can be
  viewed as a hybrid of the standard for loop and lapply function.
• A key innovation of foreach is migration of the portions of the R environment that are required by the loop statement to worker R processes. The practical implication is that R programmers and users don't need to explicitly define functions and variables that may be required by the worker processes. The result is that parallel programs look and run similarly to their sequential counterparts, making it easier to migrate from sequential to parallel without extensive re-coding.

Simple to Set up and Administer

• Parallel and distributed programming systems are not generally known for their ease of installation and configuration. Parallel R 2.0 consists simply of a set of R packages with minimal external system dependencies (Python). No special server configuration or root/administrator access is required. The Parallel R 2.0 framework is dynamically initiated from within R sessions that load the software stack and don't require special external commands to start or stop the worker processes.
• Thanks to its lightweight nature, Parallel R 2.0 is cross-platform and operates uniformly across Windows, Mac OS X, Linux and other operating systems. That means that users can experiment with ideas on a multicore Windows workstation, for example, and then scale them out across a big Linux cluster with exactly the same R code. It is even possible to use Parallel R across heterogeneous mixtures of operating systems.
  

Reliable Fault-tolerance

• Many parallel and distributed programming systems do not deal with failures or other faults very well in practice. Parallel R 2.0 includes new features that can detect and recover from worker failures automatically. That means that long-running analyses and simulations running across large clusters are largely immune to hardware faults and other problems.

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