Tuesday, February 09, 2010

Twister: Iterative MapReduce

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 which will expand the applicability of MapReduce to more classes of applications.

Twister is a lightweight MapReduce runtime we have developed by incorporating these enhancements. We have published several scientific papers [1-5] explaining the key concepts and comparing it with other MapReduce implementations such as Hadoop and DryadLINQ. Today we would like to announce its first release.

Key Features of Twister are:
  • Distinction on static and variable data
  • Configurable long running (cacheable) map/reduce tasks
  • Pub/sub messaging based communication/data transfers
  • Combine phase to collect all reduce outputs
  • Efficient support for Iterative MapReduce computations (extremely faster than Hadoop or DryadLINQ)
  • Data access via local disks
  • Lightweight (5600 lines of code)
  • Tools to manage data
For more details please visit www.iterativemapreduce.org and let us know your thoughts and experience using Twister.

Thank you,


[1]. Jaliya Ekanayake, (Advisor: Geoffrey Fox) Architecture and Performance of Runtime Environments for Data Intensive Scalable Computing, Doctoral Showcase, SuperComputing2009.
[2]. Jaliya Ekanayake, Atilla Soner Balkir, Thilina Gunarathne, Geoffrey Fox, Christophe Poulain, Nelson Araujo, Roger Barga, DryadLINQ for Scientific Analyses, Fifth IEEE International Conference on e-Science (eScience2009), Oxford, UK.
[3]. Jaliya Ekanayake, Xiaohong Qiu, Thilina Gunarathne, Scott Beason, Geoffrey Fox High Performance Parallel Computing with Clouds and Cloud Technologies Technical Report August 25 2009 to appear as Book Chapter.
[4]. Geoffrey Fox, Seung-Hee Bae, Jaliya Ekanayake, Xiaohong Qiu, and Huapeng Yuan, Parallel Data Mining from Multicore to Cloudy Grids, High Performance Computing and Grids workshop, 2008. – An extended version of this paper goes to a book chapter.
[5]. Jaliya Ekanayake, Shrideep Pallickara, Geoffrey Fox, MapReduce for Data Intensive Scientific Analyses, Fourth IEEE International Conference on eScience, 2008, pp.277-284.