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| 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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Page views:: 1302. Submitted: 2012-09-05. Published: 2016-07-23. |
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| Paper: |
Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones
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| DOI: |
10.18637/jss.v071.i04
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This work is licensed under the licenses 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. |