OpenMP-style parallelism in data-centered multicore computing with R

Authors:
Lei Jiang;Pragneshkumar B. Patel;George Ostrouchov;Ferdinand Jamitzky
Affiliations:
Louisiana State University, Baton Rouge, LA, USA;University of Tennessee, Knoxville, TN, USA;Oak Ridge National Laboratory, Oak Ridge, TN, USA;Leibniz Supercomputing Centre, Garching, Germany
Venue:
Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
Year:
2012

Citing 4
Cited 0

MapReduce: simplified data processing on large clusters

OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
JCudaMP: OpenMP/Java on CUDA

Proceedings of the 3rd International Workshop on Multicore Software Engineering
A domain-specific approach to heterogeneous parallelism

Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
SpiceC: scalable parallelism via implicit copying and explicit commit

Proceedings of the 16th ACM symposium on Principles and practice of parallel programming

Quantified Score

Hi-index	0.00

Visualization

Abstract

R1 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.