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    Evaluation of SMP Shared Memory Machines for Use with In-Memory and OpenMP Big Data Applications

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    Name:
    Evaluation_of_SMP_Paper.pdf
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    396.0Kb
    Format:
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    Description:
    Final Accepted Manuscript
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    Author
    Younge, Andrew J.
    Reidy, Christopher
    Henschel, Robert
    Fox, Geoffrey C.
    Affiliation
    Res. Comput., Univ. of Arizona
    Issue Date
    2016-05
    Keywords
    Symmetric Multiprocessing
    SMP
    ScaleMP
    vSMP
    SGI UV
    Large Memory
    Big Data
    Virtualization
    
    Metadata
    Show full item record
    Publisher
    IEEE
    Citation
    A. J. Younge, C. Reidy, R. Henschel and G. C. Fox, "Evaluation of SMP Shared Memory Machines for Use with In-Memory and OpenMP Big Data Applications," 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Chicago, IL, 2016, pp. 1597-1606.
    Journal
    2016 IEEE 30TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW)
    Rights
    Copyright © 2016, IEEE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    While distributed memory systems have shaped the field of distributed systems for decades, the demand for many-core shared memory resources is increasing. Symmetric Multiprocessor Systems (SMPs) have become increasingly important recently among a wide array of disciplines, ranging from Bioinformatics to astrophysics, and beyond. With the increase in big data computing, the size and scope of traditional commodity server systems is often outpaced. While some big data applications can be mapped to distributed memory systems found through many cluster and cloud technologies today, this effort represents a large barrier of entry that some projects cannot cross. Shared memory SMP systems look to effectively and efficiently fill this niche within distributed systems by providing high throughput and performance with minimized development effort, as the computing environment often represents what many researchers are already familiar with. In this paper, we look at the use of two common shared memory systems, the ScaleMP vSMP virtualized SMP deployment at Indiana University, and the SGI UV architecture deployed at University of Arizona. While both systems are notably different in their design, their potential impact on computing is remarkably similar. As such, we look to compare each system first under a set of OpenMP threaded benchmarks via the SPEC group, and to follow up with our experience using each machine for Trinity de-novo assembly. We find both SMP systems are well suited to support various big data applications, with the newer vSMP deployment often slightly faster; however, certain caveats and performance considerations are necessary when considering such SMP systems.
    Note
    No embargo.
    DOI
    10.1109/IPDPSW.2016.133
    Version
    Final accepted manuscript
    Additional Links
    http://ieeexplore.ieee.org/document/7530057/
    ae974a485f413a2113503eed53cd6c53
    10.1109/IPDPSW.2016.133
    Scopus Count
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