skip to main content
column

Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries

Authors Info & Claims
Published:03 December 2014Publication History
Skip Abstract Section

Abstract

Graph databases use graph structures to store data sets as nodes, edges, and properties. They are used to store and search the relationships between a large number of nodes, such as social networking services and recommendation engines that use customer social graphs. Since computation cost for graph search queries increases as the graph becomes large, in this pa- per we accelerate the graph search functions (Dijkstra and A* algorithms) of a graph database Neo4j using two ways: multi- threaded library and CUDA library for graphics processing units (GPUs). We use 100,000-node graphs generated based on a degree distribution of Facebook social graph for evaluations. Our multi-threaded and GPU-based implementations require an auxiliary adjacency matrix for a target graph. The results show that, when we do not take into account additional overhead to generate the auxiliary adjacency matrix, multi-threaded version improves the Dijkstra and A* search performance by 16.2x and 13.8x compared to the original implementation. The GPU-based implementation improves the Dijkstra and A* search performance by 26.2x and 32.8x. When we take into account the overhead, although the speed-ups by our implementations are reduced, by reusing the auxiliary adjacency matrix for multiple graph search queries we can significantly improve the graph search performance.

References

  1. J. M. Bull and M. E. Kambites. JOMP - An OpenMP-like Interface for Java. In Proc. of International Conference on Java Grande, pages 44--53, June 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T. H. Hetherington, T. G. Rogers, L. Hsu, M. O'Connor, and T. M. Aamodt. Characterizing and Evaluating a Key-value Store Application on Heterogegenenous CPU-GPU Systems. In Proc. of the International Symposium on Performance Analysis of System and Software, pages 88--98, April 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. jcuda.org. http://www.jcuda.org.Google ScholarGoogle Scholar
  4. D. Merill, M. Garland, and A. Grimshaw. Scalable GPU Graph Traversal. In Proc. of International Symposium on Principles and Practice of Parallel Programming, pages 117--128, August 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Neo4j.org. http://www.neo4j.org.Google ScholarGoogle Scholar
  6. S. Nobari, T.-T. Cao, S. Bressan, and P. Karras. Scalable Parallel Minimum Spanning Forest Computation. In Proc. of International Symposium on Principles and Practice of Parallel Programming, pages 205--214, August 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. H. Ortega-Arranz, Y. Torres, D. R. Llanos, and A. Gonzalez-Escribano. A New GPU-based Approach to the Shortest Path Problem. In Proc. of International Conference on High Performance Computing and Simulation, pages 505--511, July 2013.Google ScholarGoogle ScholarCross RefCross Ref
  8. J. Ugander, B. Karrer, L. BackStrom, and C. Marlow. The Anatomy of the Facebook Social Graph. In Arxiv preprint arXiv:1111.4503, November 2011.Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM SIGARCH Computer Architecture News
    ACM SIGARCH Computer Architecture News  Volume 42, Issue 4
    HEART '14
    Setember 2014
    99 pages
    ISSN:0163-5964
    DOI:10.1145/2693714
    Issue’s Table of Contents

    Copyright © 2014 Authors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 3 December 2014

    Check for updates

    Qualifiers

    • column

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader
About Cookies On This Site

We use cookies to ensure that we give you the best experience on our website.

Learn more

Got it!