Optimum Platform Selection and Configuration for Computational Jobs

The performance and cost of many scientific applications which execute on a variety of High Performance Computing (HPC), local cluster environments and cloud services could be enhanced, and costs reduced if the platform was carefully selected on a per-application basis and the application itself was optimally configured for a given platform.

With a wide-variety of computing platforms on offer, each possessing different properties, all too frequently platform decisions are made on an ad-hoc basis with limited ‘black-box’ information. The limitless number of possible application configurations also make it difficult for an individual who wants to achieve cost-effective results with the maximum performance available. Such individuals may include biomedical researchers analysing microarray data, software developers running aviation simulations or bankers performing risk assessments. However in either case, it is likely that many may not have the required knowledge to select the optimum platform and setup for their application; to do so, would require extensive knowledge of their applications and various platforms.

In this paper we describe a framework that aims to resolve such issues by (i) reducing the detail required in the decision making process by placing this information within a selection framework, thereby (ii) maximising an application’s performance gain and/or reducing costs. We present a set of preliminary results where we compare the performance of running the Simple Parallel R INTerface (SPRINT) over a variety of platforms. SPRINT is a framework providing parallel functions of the statistical package R, allowing post genomic data to be easily analysed on HPC resources [1]. We run SPRINT on Amazon’s Elastic Compute Cloud (EC2) to compare the performance with the results obtained from HECToR, the UK’s National Supercomputing Service, and the Edinburgh Compute and Data Facilities (ECDF) cluster.