DryadLINQ for Scientific Analyses

Applying high level parallel runtimes to data/compute intensive applications is becoming increasingly common. The simplicity of the MapReduce programming model and the availability of open source MapReduce runtimes such as Hadoop, attract more users to the MapReduce programming model. Recently, Microsoft has released DryadLINQ for academic use, allowing users to experience a new programming model and a runtime that is capable of performing large scale data/compute intensive analyses. In this paper, we present our experience in applying DryadLINQ for a series of scientific data analysis applications, identify their mapping to the DryadLINQ programming model, and compare their performances with Hadoop implementations of the same applications.

eScience09.pdf
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Previous Versions

Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly. Dryad: Distributed Data-parallel Programs from Sequential Building Blocks, Association for Computing Machinery, Inc., March 2007.

Yuan Yu, Michael Isard, Dennis Fetterly, Mihai Budiu, Ăšlfar Erlingsson, Pradeep Kumar Gunda, and Jon Currey. DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language, USENIX, December 2008.

Yuan Yu, Michael Isard, Dennis Fetterly, Mihai Budiu, Ulfar Erlingsson, Pradeep Kumar Gunda, Jon Currey, Frank McSherry, and Kannan Achan. Some sample programs written in DryadLINQ, December 2009.

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