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OpenMP-style parallelism in data-centered multicore computing with R by Lei Jiang; Pragneshkumar B. Patel; George Ostrouchov; Ferdinand Jamitzky is a Computer Science article available to read on EtoBox.

What is OpenMP-style parallelism in data-centered multicore computing with R about?

R ^1^ is a domain specific language widely used for data analysis by the statistics community as well as by researchers in finance, biology, social sciences, and many other disciplines. As R programs are linked to input data, the exponential growth of available data makes high-performance computing with R imperative. To ease the process of writing parallel programs in R, code transformation from a sequential program to a parallel version would bring much convenience to R users. In this paper, we present our work in semi-automatic parallelization of R codes with user-added OpenMP-style pragmas. While such pragmas are used at the frontend, we take advantage of multiple parallel backends with different R packages. We provide flexibility for importing parallelism with plug-in components, impose built-in MapReduce for data processing, and also maintain code reusability. We illustrate the advantage of the on-the-fly mechanisms which can lead to significant applications in data-centered parallel computing.

Who reads OpenMP-style parallelism in data-centered multicore computing with R?

Author

Lei Jiang; Pragneshkumar B. Patel; George Ostrouchov; Ferdinand Jamitzky

Publisher

ACM

Published

2012

Language

EN

Field

Computer Science (Physical Sciences)