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Title:
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pR: AUTOMATIC PARALLELIZATION OF DATA PARALLEL STATISTICAL COMPUTING CODES FOR R IN HYBRID MULTI-NODE AND MULTI-CORE ENVIRONMENTS |
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Author(s):
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Paul Breimyer , Guruprasad Kora , William Hendrix , Neil Shah , Nagiza F. Samatova |
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ISBN:
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978-972-8924-97-3 |
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Editors:
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Hans Weghorn and Pedro Isaías |
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Year:
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2009 |
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Edition:
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V II, 2 |
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Keywords:
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Statistical Computing, Automatic Parallelization, Data-Parallel |
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Type:
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Short Paper |
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First Page:
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22 |
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Last Page:
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27 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The increasing size and complexity of modern scientific data sets challenge the capabilities of traditional statistical
computing. High-Performance Statistical Parallel Computing is a promising strategy to address these challenges,
especially as multi-core parallel computing architectures become increasingly prevalent. However, parallel statistical
computing introduces implementation complexities and, therefore, an automatic parallelization approach would be ideal.
Data-parallel statistical computations that aim to evaluate the same function on different subsets of data represent natural
candidates for automatic parallelization due to their inherent inter-process independence.
In this paper, we extend the pR middleware for the R open-source statistical environment to support automatic
parallelization of data-parallel tasks in multi-node, multi-core, and hybrid environments. pR requires few or no changes
to existing serial codes and yielded over 50% end-to-end execution time improvements in our tests, compared to the
commonly used snow R package. |
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