title: Hybrid cloud and cluster computing paradigms for life science applications.
abstract: BACKGROUND
  Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister.
  
  
  RESULTS
  Comparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications.
  
  
  CONCLUSIONS
  The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications.
  
  
  METHODS
  We used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments.
authors.affiliation: School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA. xqiu@indiana.edu
authors.forename: Judy
authors.initials: J
authors.lastname: Qiu
authors.forename: Jaliya
authors.initials: J
authors.lastname: Ekanayake
authors.forename: Thilina
authors.initials: T
authors.lastname: Gunarathne
authors.forename: Jong Youl
authors.initials: JY
authors.lastname: Choi
authors.forename: Seung-Hee
authors.initials: SH
authors.lastname: Bae
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.forename: Bingjing
authors.initials: B
authors.lastname: Zhang
authors.forename: Tak-Lon
authors.initials: TL
authors.lastname: Wu
authors.forename: Yang
authors.initials: Y
authors.lastname: Ruan
authors.forename: Saliya
authors.initials: S
authors.lastname: Ekanayake
authors.forename: Adam
authors.initials: A
authors.lastname: Hughes
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Fox
mesh_terms: D001690:Biological Science Disciplines; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D056186:Metagenomics; D012984:Software