Iterative Map-Reduce for parallel and scientific computing.

MapReduce programming model has simplified the implementations 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 parallel computing communities. From the years of experience in applying MapReduce programming model 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. Twister is a lightweight MapReduce runtime we have developed by incorporating these enhancements.

Twister provides the following features to support MapReduce computations. (Twister is developed as part of Jaliya Ekanayake's Ph.D. research and is supported by the S A L S A Team @ IU)

  • Distinction on static and variable data

  • Configurable long running (cacheable) map/reduce tasks

  • Pub/sub messaging based communication/data transfers

  • Efficient support for Iterative MapReduce computations (extremely faster than Hadoop or Dryad/DryadLINQ)

  • Combine phase to collect all reduce outputs

  • Data access via local disks

  • Lightweight (~5600 lines of Java code)

  • Support for typical MapReduce computations

  • Tools to manage data

Papers and Presentation


ISE & Data Science 

Indiana University

© 2019 Judy Fox

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