LLMapReduce: Multi-Level Map-Reduce for High Performance Data Analysis

The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a superc...

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Vydané v:arXiv.org
Hlavní autori: Byun, Chansup, Kepner, Jeremy, Arcand, William, Bestor, David, Bergeron, Bill, Gadepally, Vijay, Hubbell, Matthew, Michaleas, Peter, Mullen, Julie, Prout, Andrew, Rosa, Antonio, Yee, Charles, Reuther, Albert
Médium: Paper
Jazyk:English
Vydavateľské údaje: Ithaca Cornell University Library, arXiv.org 22.07.2016
Predmet:
Big Data
Data analysis
Parallel programming
Performance enhancement
Product development
Programming languages
Supercomputers
ISSN:2331-8422
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Shrnutí:The map-reduce parallel programming model has become extremely popular in the big data community. Many big data workloads can benefit from the enhanced performance offered by supercomputers. LLMapReduce provides the familiar map-reduce parallel programming model to big data users running on a supercomputer. LLMapReduce dramatically simplifies map-reduce programming by providing simple parallel programming capability in one line of code. LLMapReduce supports all programming languages and many schedulers. LLMapReduce can work with any application without the need to modify the application. Furthermore, LLMapReduce can overcome scaling limits in the map-reduce parallel programming model via options that allow the user to switch to the more efficient single-program-multiple-data (SPMD) parallel programming model. These features allow users to reduce the computational overhead by more than 10x compared to standard map-reduce for certain applications. LLMapReduce is widely used by hundreds of users at MIT. Currently LLMapReduce works with several schedulers such as SLURM, Grid Engine and LSF.
Bibliografia:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.1607.06543