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Title: Programming with BIG data in R: Scaling analytics from one to thousands of nodes

Abstract

Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.

Authors:
 [1];  [2];  [3];  [4]
+ Show Author Affiliations
  1. Univ. of Tennessee, Knoxville, TN (United States)
  2. U.S. Food and Drug Administration, Silver Spring, MD (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Univ. of Tennessee, Knoxville, TN (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Joint Institute for Computational Sciences (JICS)
Sponsoring Org.:
Work for Others (WFO); USDOE Office of Science (SC)
OSTI Identifier:
1333101
Alternate Identifier(s):
OSTI ID: 1416808
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Big Data Research
Additional Journal Information:
Journal Name: Big Data Research; Journal ID: ISSN 2214-5796
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Schmidt, Drew, Chen, Wei -Chen, Matheson, Michael A., and Ostrouchov, George. Programming with BIG data in R: Scaling analytics from one to thousands of nodes. United States: N. p., 2016. Web. doi:10.1016/j.bdr.2016.10.002.
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Schmidt, Drew, Chen, Wei -Chen, Matheson, Michael A., & Ostrouchov, George. Programming with BIG data in R: Scaling analytics from one to thousands of nodes. United States. https://doi.org/10.1016/j.bdr.2016.10.002
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Schmidt, Drew, Chen, Wei -Chen, Matheson, Michael A., and Ostrouchov, George. 2016. "Programming with BIG data in R: Scaling analytics from one to thousands of nodes". United States. https://doi.org/10.1016/j.bdr.2016.10.002. https://www.osti.gov/servlets/purl/1333101.
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@article{osti_1333101,
title = {Programming with BIG data in R: Scaling analytics from one to thousands of nodes},
author = {Schmidt, Drew and Chen, Wei -Chen and Matheson, Michael A. and Ostrouchov, George},
abstractNote = {Here, we present a tutorial overview showing how one can achieve scalable performance with R. We do so by utilizing several package extensions, including those from the pbdR project. These packages consist of high performance, high-level interfaces to and extensions of MPI, PBLAS, ScaLAPACK, I/O libraries, profiling libraries, and more. While these libraries shine brightest on large distributed platforms, they also work rather well on small clusters and often, surprisingly, even on a laptop with only two cores. Our tutorial begins with recommendations on how to get more performance out of your R code before considering parallel implementations. Because R is a high-level language, a function can have a deep hierarchy of operations. For big data, this can easily lead to inefficiency. Profiling is an important tool to understand the performance of an R code for both serial and parallel improvements.},
doi = {10.1016/j.bdr.2016.10.002},
url = {https://www.osti.gov/biblio/1333101}, journal = {Big Data Research},
issn = {2214-5796},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {11}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.bdr.2016.10.002
Copyright Statement
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Cited by: 4 works
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Works referenced in this record:

RcppArmadillo: Accelerating R with high-performance C++ linear algebra
journal, March 2014

Singular value decomposition and least squares solutions
journal, April 1970

Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
journal, January 2011

Basic Linear Algebra Subprograms for Fortran Usage
journal, September 1979

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