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2010
IEEE

Twister: a runtime for iterative MapReduce

9 years 2 months ago
Twister: a runtime for iterative MapReduce
MapReduce programming model has simplified the implementation of many data parallel applications. The simplicity of the programming model and the quality of services provided by many implementations of MapReduce attract a lot of enthusiasm among distributed computing communities. From the years of experience in applying MapReduce to various scientific applications we identified a set of extensions to the programming model and improvements to its architecture that will expand the applicability of MapReduce to more classes of applications. In this paper, we present the programming model and the architecture of Twister an enhanced MapReduce runtime that supports iterative MapReduce computations efficiently. We also show performance comparisons of Twister with other similar runtimes such as Hadoop and DryadLINQ for large scale data parallel applications. Categories and Subject Descriptors
Jaliya Ekanayake, Hui Li, Bingjing Zhang, Thilina
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2010
Where HPDC
Authors Jaliya Ekanayake, Hui Li, Bingjing Zhang, Thilina Gunarathne, Seung-Hee Bae, Judy Qiu, Geoffrey Fox
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