DryadLINQ for Scientific Analyses

  • Jaliya Ekanayake ,
  • Thilina Gunarathne ,
  • Geoffrey Fox ,
  • Atilla Soner Balkir ,
  • ,
  • Nelson Araujo ,
  • Roger Barga

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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.