Research Article
High Performance Parallel Computing with Clouds and Cloud Technologies
@INPROCEEDINGS{10.1007/978-3-642-12636-9_2, author={Jaliya Ekanayake and Geoffrey Fox}, title={High Performance Parallel Computing with Clouds and Cloud Technologies}, proceedings={Cloud Computing. First International Conference, CloudComp 2009 Munich, Germany, October 19--21, 2009 Revised Selected Papers}, proceedings_a={CLOUDCOMP}, year={2012}, month={5}, keywords={Cloud Virtualization MapReduce Dryad Parallel Computing}, doi={10.1007/978-3-642-12636-9_2} }- Jaliya Ekanayake
Geoffrey Fox
Year: 2012
High Performance Parallel Computing with Clouds and Cloud Technologies
CLOUDCOMP
Springer
DOI: 10.1007/978-3-642-12636-9_2
Abstract
Infrastructure services (Infrastructure-as-a-service), provided by cloud vendors, allow any user to provision a large number of compute instances fairly easily. Whether leased from public clouds or allocated from private clouds, utilizing these virtual resources to perform data/compute intensive analyses requires employing different parallel runtimes to implement such applications. Among many parallelizable problems, most “pleasingly parallel” applications can be performed using MapReduce technologies such as Hadoop, CGL-MapReduce, and Dryad, in a fairly easy manner. However, many scientific applications, which have complex communication patterns, still require low latency communication mechanisms and rich set of communication constructs offered by runtimes such as MPI. In this paper, we first discuss large scale data analysis using different MapReduce implementations and then, we present a performance analysis of high performance parallel applications on virtualized resources.