| Document Date: 2009-10-26 20:50:36
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Company Google / Red Hat / Intel / Microsoft / / / Event Reorganization / Product Release / / Facility University Bloomington / University of Chicago / Pervasive Technology Institute / / IndustryTerm monitoring / appropriate technology / scientific applications / scientific data analysis applications / high energy physics / intensive applications / intensive scientific applications / heterogeneous processing times / shared file systems / certain cluster services / data/computing nodes / finance / cloud technologies / data processing / data analysis applications / data/compute intensive applications / parallel seed-and-extend read-mapping algorithm / similar applications / distributed applications / fault tolerance / local file systems / large computing infrastructures / / OperatingSystem Windows Server / Linux / Red Hat Enterprise Linux / Microsoft Windows / / Organization School of Informatics and Computing / Pervasive Technology Institute / Indiana University / Department of Computer Science / University of Chicago / / Person Christophe Poulaind / Nelson Araujod / Geoffrey Foxa / Roger Bargad / / Position PLINQ scheduler / scheduler / Hadoop Programming Model Data Handling MapReduce Intermediate Data Communication Scheduling HDFS/ Point-to-point / programmer / / Product DryadLINQ application / Kmeans clustering / Linux / DryadLINQ / MapReduce / Microsoft DryadLINQ / / ProgrammingLanguage Java / R / C / C++ / / Technology appropriate technology / Linux / API / Operating System / Shared memory / human genome / parallel seed-and-extend read-mapping algorithm / Ethernet / Java / cloud technologies / HTTP / MapReduce algorithm / parallel processing / only be accessed using Java / / |
DryadLINQ for Scientific Analyses Jaliya Ekanayake1,a, Atilla Soner Balkirc, Thilina Gunarathnea, Geoffrey Foxa,b, Christophe Poulaind, Nelson Araujod, Roger Bargad a School of Informatics and Computing, Indiana Universi