Exploiting Parallel R in the Cloud with SPRINT

M. Piotrowski; G. A. McGilvary; T. M. Sloan; M. Mewissen; A. D. Lloyd; T. Forster; L. Mitchell; P. Ghazal; J. Hill

doi:10.3414/ME11-02-0039

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2013; 52(01): 80-90
DOI: 10.3414/ME11-02-0039

Original Articles

Schattauer GmbH

Exploiting Parallel R in the Cloud with SPRINT

Authors

M. Piotrowski

¹EPCC, The University of Edinburgh, Edinburgh, United Kingdom
G. A. McGilvary

²Edinburgh Data-Intensive Research Group, School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
T. M. Sloan

¹EPCC, The University of Edinburgh, Edinburgh, United Kingdom
M. Mewissen

³Division of Pathway Medicine, The University of Edinburgh, Edinburgh, United Kingdom
A. D. Lloyd

⁴The University of Edinburgh Business School, Edinburgh, United Kingdom
T. Forster

³Division of Pathway Medicine, The University of Edinburgh, Edinburgh, United Kingdom
L. Mitchell

¹EPCC, The University of Edinburgh, Edinburgh, United Kingdom
P. Ghazal

³Division of Pathway Medicine, The University of Edinburgh, Edinburgh, United Kingdom
J. Hill

⁵Applied Modelling and Computation Group, Imperial College, London, United Kingdom

Abstract

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 multiprocessor 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.

Keywords

Genomics - computing methodologies

Volltext

Referenzen

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