Generalizing MapReduce as a Unified Cloud and HPC Runtime

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Provided by: Association for Computing Machinery
Topic: Cloud
Format: PDF
Computational simulation and analysis were one of the keys to the future in data-intensive science as a \"Fourth paradigm\" of scientific discovery but facing a major challenge as handling the incredible increases in dataset sizes. This paper requires attractive powerful programming models that address issues of portability with scaling performance and fault tolerance. Further, one must meet these challenges for both computation and storage. The authors build on the success of their research on Iterative MapReduce with successful prototypes Twister (on HPC) and Twister4Azure (on clouds).
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