R offers several extension packages that allow it to perform parallel computations. These operate on fixed points in the program flow and make it difficult to deal with nested parallelism and to organize parallelism in complex computations in general. In this article we discuss, first, of how to detect parallelism in functions, and second, how to minimize user intervention in that process. We present a solution that requires minimal code changes and enables to flexibly and dynamically choose the degree of parallelization in the resulting computation. An implementation is provided by the R package parallelize.dynamic and practical issues are discussed with the help of examples.
Rsge, foreach, boot, snow, parallelize.dynamic
HighPerformanceComputing, Econometrics, Optimization, SocialSciences, Survival, TimeSeries
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For attribution, please cite this work as
Böhringer, "Dynamic Parallelization of R Functions", The R Journal, 2013
BibTeX citation
@article{RJ-2013-029,
author = {Böhringer, Stefan},
title = {Dynamic Parallelization of R Functions},
journal = {The R Journal},
year = {2013},
note = {https://doi.org/10.32614/RJ-2013-029},
doi = {10.32614/RJ-2013-029},
volume = {5},
issue = {2},
issn = {2073-4859},
pages = {88-96}
}