Exploiting parallel R in the cloud with SPRINT.
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...
| Publicado en: | Methods of Information in Medicine Vol. 52; no. 1; pp. 80 - 91 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Thieme Medical Publishing Inc.
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=107879139&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107879139 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00261270 W7M jtl: Methods of Information in Medicine issn: 00261270 maglogo: N pubinfo: dt: 2013 vid: 52 iid: 1 pid: 2811 pub: Thieme Medical Publishing Inc. place: New York, New York artinfo: ui: 107879139 107879139 NLM23223611 2012273061 10.3414/ME11-02-0039 NLM23223611 PMC3547073 107879139 ppf: 80 ppct: 11 formats: tig: atl: Exploiting parallel R in the cloud with SPRINT. aug: au: Piotrowski M McGilvary GA Sloan TM Mewissen M Lloyd AD Forster T Mitchell L Ghazal P Hill J Piotrowski, M McGilvary, G A Sloan, T M Mewissen, M Lloyd, A D Forster, T Mitchell, L Ghazal, P Hill, J affil: EPCC, The University of Edinburgh, Edinburgh, United Kingdom sug: subj: Computing Methodologies Genomics Economics Information Retrieval Methods Medical Informatics Methods Microarray Analysis Economics Animals Computer Graphics Economics Costs and Cost Analysis Management Information Systems Economics Information Retrieval Economics Internet Economics Medical Informatics Economics Natural Language Processing Sequence Analysis Economics ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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