Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Norman Matloff
Title: Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones
Abstract: The growth in the use of computationally intensive statistical procedures, especially with big data, has necessitated the usage of parallel computation on diverse platforms such as multicore, GPUs, clusters and clouds. However, slowdown due to interprocess communication costs typically limits such methods to "embarrassingly parallel" (EP) algorithms, especially on non-shared memory platforms. This paper develops a broadlyapplicable method for converting many non-EP algorithms into statistically equivalent EP ones. The method is shown to yield excellent levels of speedup for a variety of statistical computations. It also overcomes certain problems of memory limitations.

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Paper: Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones     Download PDF (Downloads: 1073)
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partools_1.1.5.tar.gz: R source package Download (Downloads: 112; 1MB)
v71i04.R: R replication code Download (Downloads: 150; 5KB)
DOI: 10.18637/jss.v071.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.