High performance statistical computing with parallel R: applications to biology and climate modelling

Ultrascale computing and high-throughput experimental technologies have enabled the production of scientific data about complex natural phenomena. With this opportunity, comes a new problem – the massive quantities of data so produced. Answers to fundamental questions about the nature of those pheno...

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Bibliographic Details
Published in:Journal of physics. Conference series 2006-09, Vol.46 (1), p.505-509
Main Authors: Samatova, Nagiza F, Branstetter, Marcia, Ganguly, Auroop R, Hettich, Robert, Khan, Shiraj, Kora, Guruprasad, Li, Jiangtian, Ma, Xiaosong, Pan, Chongle, Shoshani, Arie, Yoginath, Srikanth
Format: Article
Language:English
Subjects:
Climate models
Computation
Computer architecture
Data analysis
Distributed memory
Parallel processing
Physics
Routines
Scientists
Statistical analysis
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Summary:Ultrascale computing and high-throughput experimental technologies have enabled the production of scientific data about complex natural phenomena. With this opportunity, comes a new problem – the massive quantities of data so produced. Answers to fundamental questions about the nature of those phenomena remain largely hidden in the produced data. The goal of this work is to provide a scalable high performance statistical data analysis framework to help scientists perform interactive analyses of these raw data to extract knowledge. Towards this goal we have been developing an open source parallel statistical analysis package, called Parallel R, that lets scientists employ a wide range of statistical analysis routines on high performance shared and distributed memory architectures without having to deal with the intricacies of parallelizing these routines.
ISSN:1742-6596
1742-6588
1742-6596
DOI:10.1088/1742-6596/46/1/069