LLMapReduce: Multi-Level Map-Reduce for High Performance Data Analysis
Abstract
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 10x compared to standard mapreduce. LLMapReduce is widely used by hundreds of users at MIT. Currently LLMapReduce works with several schedulers such as SLURM, Grid Engine and LSF.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 23, 2016
- Accession Number
- AD1033661
Entities
People
- Albert I. Reuther
- Andrew J. Prout
- Antonio Rosa
- Chansup Byun
- Charles Yee
- David Bestor
- Jeremy Kepner
- Julie Mullen
- Matthew Hubbell
- Peter W. Michaleas
- Vijay N. Gadepally
- William Arcand
- William Bergeron
Organizations
- MIT Lincoln Laboratory