Scalable parallel computing on clouds using Twister4Azure iterative MapReduce
Future Generation Computer Systems
Visualizing the protein sequence universe
Proceedings of the 3rd international workshop on Emerging computational methods for the life sciences
HyMR: a hybrid MapReduce workflow system
Proceedings of the 3rd international workshop on Emerging computational methods for the life sciences
Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions
Journal of Grid Computing
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Many scientific applications suffer from the lack of a unified approach to support the management and efficient processing of large-scale data. The Twister MapReduce Framework, which not only supports the traditional MapReduce programming model but also extends it by allowing iterations, addresses these problems. This paper describes how Twister is applied to several kinds of scientific applications such as BLAST, MDS Interpolation and GTM Interpolation in a non-iterative style and to MDS without interpolation in an iterative style. The results show the applicability of Twister to data parallel and EM algorithms with small overhead and increased efficiency.