New software just released from the guys in California (@RevolutionR) so if you are a Linux user and have academic credentials you can download it for free (@Cmastication doesnt), you can test it to see what the big fuss is all about (also see http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php) –

Revolution Analytics has just released Revolution R Enterprise 4.0.1 for Red Hat Enterprise Linux, a significant step forward in enterprise data analytics. Revolution R Enterprise 4.0.1 is built on R 2.11.1, the latest release of the open-source environment for data analysis and graphics. Also available is the initial release of our deployment server solution, RevoDeployR 1.0, designed to help you deliver R analytics via the Web. And coming soon to Linux: RevoScaleR, a new package for fast and efficient multi-core processing of large data sets.

As a registered user of the Academic version of Revolution R Enterprise for Linux, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.0.1 today. You can install Revolution R Enterprise 4.0.1 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.

## Download Information

The following information is all you will need to download and install the Academic Edition.

## Supported Platforms:

Revolution R Enterprise Academic edition and RevoDeployR are supported on Red Hat® Enterprise Linux® 5.4 or greater (64-bit processors).

Approximately 300MB free disk space is required for a full install of Revolution R Enterprise. We recommend at least 1GB of RAM to use Revolution R Enterprise.

For the full list of system requirements for RevoDeployR, refer to the RevoDeployR™ Installation Guide for Red Hat® Enterprise Linux®.

## Download Links:

You will first need to download the Revolution R Enterprise installer.

## Installation Instructions for Revolution R Enterprise Academic Edition

After downloading the installer, do the following to install the software:

- Log in as root if you have not already.
- Change directory to the directory containing the downloaded installer.
- Unpack the installer using the following command:

tar -xzf Revo-Ent-4.0.1-RHEL5-desktop.tar.gz- Change directory to the RevolutionR_4.0.1 directory created.
- Run the installer by typing ./install.py and following the on-screen prompts.
## Getting Started with the Revolution R Enterprise

After you have installed the software, launch Revolution R Enterprise by typing

Revo64at the shell prompt.Documentation is available in the form of PDF documents installed as part of the Revolution R Enterprise distribution. Type

Revo.home(“doc”)at the R prompt to locate the directory containing the manualsGetting Started with Revolution R(RevoMan.pdf) and theParallelR User’s Guide(parRman.pdf).## Installation Instructions for RevoDeployR (and RServe)

After downloading the RevoDeployR distribution, use the following steps to install the software:

Note: These instructions are for an automatic install. For more details or for manual install instructions, refer to RevoDeployR_Installation_Instructions_for_RedHat.pdf.

- Log into the operating system as root.

su -- Change directory to the directory containing the downloaded distribution for RevoDeployR and RServe.
- Unzip the contents of the RevoDeployR tar file. At prompt, type:

tar -xzf deployrRedHat.tar.gz- Change directories. At the prompt, type:

cd installFiles- Launch the automated installation script and follow the on-screen prompts. At the prompt, type:

./installRedHat.sh

Note:Red Hat installs MySQL without a password.## Getting Started with RevoDeployR

After installing RevoDeployR, you will be directed to the RevoDeployR landing page. The landing page has links to documentation, the RevoDeployR management console, the API Explorer development tool, and sample code.

## Support

For help installing this Academic Edition, please email support@revolutionanalytics.com

Also interestingly some benchmarks on Revolution R vs R.

http://www.revolutionanalytics.com/why-revolution-r/benchmarks.php

## R-25 Benchmarks

The simple R-benchmark-25.R test script is a quick-running survey of general R performance. The Community-developed test consists of three sets of small benchmarks, referred to in the script as Matrix Calculation, Matrix Functions, and Program Control.

R-25 Benchmarks |
Base R 2.9.2 |
Revolution R (1-core) |
Revolution R (4-core) |
Speedup (4 core) |

Matrix Calculation | 34 sec | 6.6 sec | 4.4 sec | 7.7x |

Matrix Functions | 20 sec | 4.4 sec | 2.1 sec | 9.5x |

Program Control | 4.7 sec | 4 sec | 4.2 sec | Not Appreciable |

Speedup = Slower time / Faster Time – 1 Test descriptions available at http://r.research.att.com/benchmarks

## Additional Benchmarks

Revolution Analytics has created its own tests to simulate common real-world computations. Their descriptions are explained below.

Linear Algebra Computation |
Base R 2.9.2 |
Revolution R (1-core) |
Revolution R (4-core) |
Speedup (4 core) |

Matrix Multiply | 243 sec | 22 sec | 5.9 sec | 41x |

Cholesky Factorization | 23 sec | 3.8 sec | 1.1 sec | 21x |

Singular Value Decomposition | 62 sec | 13 sec | 4.9 sec | 12.6x |

Principal Components Analysis | 237 sec | 41 sec | 15.6 sec | 15.2x |

Linear Discriminant Analysis | 142 sec | 49 sec | 32.0 sec | 4.4x |

Speedup = Slower time / Faster Time – 1

**Matrix Multiply**

This routine creates a random uniform 10,000 x 5,000 matrix A, and then times the computation of the matrix product transpose(A) * A.

set.seed (1)

m <- 10000

n <- 5000

A <- matrix (runif (m*n),m,n)

system.time (B <- crossprod(A))

The system will respond with a message in this format:

User system elapsed

37.22 0.40 9.68

The “elapsed” times indicate total wall-clock time to run the timed code.

The table above reflects the elapsed time for this and the other benchmark tests. The test system was an INTEL® Xeon® 8-core CPU (model X55600) at 2.5 GHz with 18 GB system RAM running Windows Server 2008 operating system. For the Revolution R benchmarks, the computations were limited to 1 core and 4 cores by calling setMKLthreads(1) and setMKLthreads(4) respectively. Note that Revolution R performs very well even in single-threaded tests: this is a result of the optimized algorithms in the Intel MKL library linked to Revolution R. The slight greater than linear speedup may be due to the greater total cache available to all CPU cores, or simply better OS CPU scheduling–no attempt was made to pin execution threads to physical cores. Consult Revolution R’s documentation to learn how to run benchmarks that use less cores than your hardware offers.

## Cholesky Factorization

The Cholesky matrix factorization may be used to compute the solution of linear systems of equations with a symmetric positive definite coefficient matrix, to compute correlated sets of pseudo-random numbers, and other tasks. We re-use the matrix B computed in the example above:

system.time (C <- chol(B))

## Singular Value Decomposition with Applications

The Singular Value Decomposition (SVD) is a numerically-stable and very useful matrix decompisition. The SVD is often used to compute Principal Components and Linear Discriminant Analysis.

**# Singular Value Deomposition**

m <- 10000

n <- 2000

A <- matrix (runif (m*n),m,n)

system.time (S <- svd (A,nu=0,nv=0))

**# Principal Components Analysis**

m <- 10000

n <- 2000

A <- matrix (runif (m*n),m,n)

system.time (P <- prcomp(A))

**# Linear Discriminant Analysis
**require (‘MASS’)

g <- 5

k <- round (m/2)

A <- data.frame (A, fac=sample (LETTERS[1:g],m,replace=TRUE))

train <- sample(1:m, k)

system.time (L <- lda(fac ~., data=A, prior=rep(1,g)/g, subset=train))

###### Related Articles

- Revolution Analytics Introduces Enterprise-Class Application Integration, Deployment & Administration for R (eon.businesswire.com)
- R on Windows HPC Server (r-bloggers.com)
- Revolution links R stats package to apps (go.theregister.com)
- Introducing RevoDeployR: Web Services for R (r-bloggers.com)