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  1. Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems (DISCS-2013)
  2. BDMPI: conquering BigData with small clusters using MPI
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BDMPI: conquering BigData with small clusters using MPI

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BDMPI: conquering BigData with small clusters using MPI

Content Provider ACM Digital Library
Author Karypis, George LaSalle, Dominique
Abstract The problem of processing massive amounts of data on clusters with finite amount of memory has become an important problem facing the parallel/distributed computing community. While MapReduce-style technologies provide an effective means for addressing various problems that fit within the MapReduce paradigm, there are many classes of problems for which this paradigm is ill-suited. In this paper we present a runtime system for traditional MPI programs that enables the efficient and transparent disk-based execution of distributed-memory parallel programs. This system, called BDMPI, leverages the semantics of MPI's API to orchestrate the execution of a large number of MPI processes on much fewer compute nodes, so that the running processes maximize the amount of computation that they perform with the data fetched from the disk. BDMPI enables the development of efficient parallel distributed memory disk-based codes without the high engineering and algorithmic complexities associated with multiple levels of blocking. BDMPI achieves significantly better performance than existing technologies on a single node (GraphChi) as well as on a small cluster (Hadoop).
Starting Page 19
Ending Page 24
Page Count 6
File Format PDF
ISBN 9781450325066
DOI 10.1145/2534645.2534652
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Out-of-core Mpi Bigdata Parallel processing
Content Type Text
Resource Type Article
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