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Scalable In-Memory Graph Pattern Matching on Symmetric Multiprocessor Systems

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Software Foundations for Data Interoperability and Large Scale Graph Data Analytics (SFDI 2020, LSGDA 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1281))

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Abstract

Graph-structured data can be found in nearly every aspect of today’s world which contributes to an increasing importance of this data structure for storing and processing data. From a processing perspective, finding comprehensive patterns in graph-structured data is a processing primitive in a variety of applications, such as fraud detection, biological engineering or social graph analytics. On the hardware side, multiprocessor systems—consisting of multiple processors in a single scale-up server—are the next important wave on top of multi-core systems. In particular, symmetric multiprocessor systems (SMP) are characterized by the fact, that each processor has the same architecture, e.g., every processor is a multi-core and all multiprocessors share a common and huge main memory space. Moreover, large SMPs will feature a non-uniform memory access (NUMA), whose impact on the design of efficient data processing concepts is considerable. In this paper, we give an overview of NeMeSys, our system for scalable near-memory graph pattern matching (GPM) on SMPs. NeMeSys is built on a synthesis of well-known concepts of database systems including a set of graph-tailored and hardware-oriented optimization techniques for scalable GPM on SMPs.

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Acknowledgment

This work was partly funded by the German Research Foundation (DFG) within the CRC 912 (HAEC).

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Authors and Affiliations

  1. Technische Universität Dresden, Nöthnitzer Straße 46, 01187, Dresden, Germany

    Alexander Krause, Dirk Habich & Wolfgang Lehner

Authors
  1. Alexander Krause
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  2. Dirk Habich
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  3. Wolfgang Lehner
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Corresponding author

Correspondence to Alexander Krause .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Lu Qin

  2. The University of New South Wales, Sydney, NSW, Australia

    Wenjie Zhang

  3. University of Technology Sydney, Sydney, NSW, Australia

    Ying Zhang

  4. The University of New South Wales, Sydney, NSW, Australia

    You Peng

  5. National Institute of Informatics, Tokyo, Japan

    Hiroyuki Kato

  6. The University of New South Wales, Sydney, NSW, Australia

    Wei Wang

  7. Osaka University, Osaka, Japan

    Chuan Xiao

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Krause, A., Habich, D., Lehner, W. (2020). Scalable In-Memory Graph Pattern Matching on Symmetric Multiprocessor Systems. In: Qin, L., et al. Software Foundations for Data Interoperability and Large Scale Graph Data Analytics. SFDI LSGDA 2020 2020. Communications in Computer and Information Science, vol 1281. Springer, Cham. https://doi.org/10.1007/978-3-030-61133-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-61133-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61132-3

  • Online ISBN: 978-3-030-61133-0

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