Archive (2016–2006)

Exploiting Parallel R in the Cloud with SPRINT

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Topic:

Focus Theme
Grid and Cloud Computing Methods in Biomedical Research
Guest Editors: M. J. A. Eugster, M. Schmid, H. Binder,
M. Schmidberger

DOI: https://doi.org/10.3414/ME11-02-0039
Issue: 2013 (Vol. 52): Issue 1 2013
Pages: 80-90

Exploiting Parallel R in the Cloud with SPRINT

Focus Theme - Grid and Cloud Computing Methods in Biomedical Research

M. Piotrowski (1), G. A. McGilvary (2), T. M. Sloan (1), M. Mewissen (3), A. D. Lloyd (4), T. Forster (3), L. Mitchell (1), P. Ghazal (3), J. Hill (5)

(1) EPCC, The University of Edinburgh, Edinburgh, United Kingdom; (2) Edinburgh Data-Intensive Research Group, School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom; (3) Division of Pathway Medicine, The University of Edinburgh, Edinburgh, United Kingdom; (4) The University of Edinburgh Business School, Edinburgh, United Kingdom; (5) Applied Modelling and Computation Group, Imperial College, London, United Kingdom

Keywords

Genomics, computing methodologies

Summary

Background: Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need.

Objectives: Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon’s Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance.

Methods: The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences.

Results: It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of the algorithm. Resource underutilization can further improve the time to result. End-user’s location impacts on costs due to factors such as local taxation.

Conclusions: Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds.

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