The statistical language R and its Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by some analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing systems offer a solution to this issue. The Simple Parallel R Interface (SPRINT) is a package that provides biostatisticians with easy access to High Performance Computing systems and allows the addition of parallelized functions to R. Previous work has established that the SPRINT implementation of an R permutation testing function has close to optimal scaling on up to 512 processors on a supercomputer. Access to supercomputers, however, is not always possible, and so the work presented here compares the performance of the SPRINT implementation on a supercomputer with benchmarks on a range of platforms including cloud resources and a common desktop machine with multiprocessing capabilities. Copyright © 2011 John Wiley & Sons, Ltd.

The statistical language R and Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by these analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing (HPC) systems offer a solution to this issue. The Simple Parallel R INTerface (SPRINT) is a package that provides biostatisticians with easy access to HPC systems and allows the addition of parallelized functions to R. This paper will present how we added a parallelized permutation testing function in R using SPRINT and how this function performs on a supercomputer for executions of up to 512 processes.

In modern distributed computing, vast amounts of data are stored in many different formats, employing different storage solutions. OGSA-DAI 3.0 is a middleware software solution. It provides application developers with the means to access data distributed across multiple platforms with different native access mechanisms. Data integration can take place at the server and deliver results using a variety of protocols and mechanisms within OGSA-DAI. It accomplishes this by using a highly flexible and extensible framework which can accommodate different types of data resources, such as XML databases, relational databases or files, different operations such as transformation to different formats, selection or filter operations. The framework can be extended by a developer to provide customized functionality for project specific tasks while using generic functions for common tasks such as database querying. This paper presents an overview of OGSA-DAI and how it tackles data access and integration through a set of example use cases.