title: Exploiting parallel R in the cloud with SPRINT.
abstract: 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.
  
  
  OBJECTIVE
  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.
authors.affiliation: EPCC, The University of Edinburgh, Edinburgh, United Kingdom.
authors.forename: M
authors.initials: M
authors.lastname: Piotrowski
authors.forename: G A
authors.initials: GA
authors.lastname: McGilvary
authors.forename: T M
authors.initials: TM
authors.lastname: Sloan
authors.forename: M
authors.initials: M
authors.lastname: Mewissen
authors.forename: A D
authors.initials: AD
authors.lastname: Lloyd
authors.forename: T
authors.initials: T
authors.lastname: Forster
authors.forename: L
authors.initials: L
authors.lastname: Mitchell
authors.forename: P
authors.initials: P
authors.lastname: Ghazal
authors.forename: J
authors.initials: J
authors.lastname: Hill
mesh_terms: D000818:Animals; D003196:Computer Graphics / Q000191:economics; D003205:Computing Methodologies; D003365:Costs and Cost Analysis; D003628:Database Management Systems / Q000191:economics; D023281:Genomics / Q000191:economics; D006801:Humans; D016247:Information Storage and Retrieval / Q000191:economics / Q000379:methods*; D020407:Internet / Q000191:economics; D008490:Medical Informatics / Q000191:economics / Q000379:methods*; D046228:Microarray Analysis / Q000191:economics; D009323:Natural Language Processing; D017422:Sequence Analysis, DNA / Q000191:economics