Optimizing R with SparkR on a commodity cluster for biomedical research

Highlights • R is a popular environment for clinical data analysis. Computational demanding tasks can often be paralelized in computing clusters using the Message Passing Interface. • (MPI) on traditional clusters or the relatively new SparkR variant as part of the Hadoop family. • SparkR allows sup...

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Published in:Computer methods and programs in biomedicine 2016-12, Vol.137, p.321-328
Main Authors: Sedlmayr, Martin, Würfl, Tobias, Maier, Christian, Häberle, Lothar, Fasching, Peter, Prokosch, Hans-Ulrich, Christoph, Jan
Format: Article
Language:English
Subjects:
Big data
Biomedical Research
Cluster Analysis
Cluster computing
Computing Methodologies
Genome-Wide Association Study
Humans
Internal Medicine
Other
SparkR
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cited_by cdi_FETCH-LOGICAL-c455t-deaf23f8938910914101b133799d6ad478030b29232160fbeadff85f390a3e243
cites cdi_FETCH-LOGICAL-c455t-deaf23f8938910914101b133799d6ad478030b29232160fbeadff85f390a3e243
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container_title Computer methods and programs in biomedicine
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creator Sedlmayr, Martin
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description Highlights • R is a popular environment for clinical data analysis. Computational demanding tasks can often be paralelized in computing clusters using the Message Passing Interface. • (MPI) on traditional clusters or the relatively new SparkR variant as part of the Hadoop family. • SparkR allows supporting big data analysis using R even on non-dedicated resources with minimal change to original code. It offers elastic resources and tight integration with Hadoop distributed services for huge files. •Computation in SparkR scales better than with the Message Passing Interface (MPI) due to optimized data communication.
doi_str_mv 10.1016/j.cmpb.2016.10.006
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subjects Big data
Biomedical Research
Cluster Analysis
Cluster computing
Computing Methodologies
Genome-Wide Association Study
Humans
Internal Medicine
Other
SparkR
title Optimizing R with SparkR on a commodity cluster for biomedical research
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