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Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries

Published: 03 December 2014 Publication History

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.
[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.
[3]
jcuda.org. http://www.jcuda.org.
[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.
[5]
Neo4j.org. http://www.neo4j.org.
[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.
[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.
[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.

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  • (2016)Accelerating Spark RDD Operations with Local and Remote GPU Devices2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS.2016.0108(791-799)Online publication date: Dec-2016
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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
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 December 2014
Published in SIGARCH Volume 42, Issue 4

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Cited By

View all
  • (2020)GPU in applications with non-relational DBs2020 28th Telecommunications Forum (TELFOR)10.1109/TELFOR51502.2020.9306672(1-4)Online publication date: 24-Nov-2020
  • (2017)Distributed In-GPU Data Cache for Document-Oriented Data Store via PCIe over 10 Gbit EthernetEuro-Par 2016: Parallel Processing Workshops10.1007/978-3-319-58943-5_4(41-55)Online publication date: 28-May-2017
  • (2016)Accelerating Spark RDD Operations with Local and Remote GPU Devices2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS.2016.0108(791-799)Online publication date: Dec-2016
  • (2016)An in-kernel NOSQL cache for range queries using FPGA NIC2016 International Conference on FPGA Reconfiguration for General-Purpose Computing (FPGA4GPC)10.1109/FPGA4GPC.2016.7518528(13-18)Online publication date: May-2016
  • (2015)Performance Evaluations of Document-Oriented Databases Using GPU and Cache StructureProceedings of the 2015 IEEE Trustcom/BigDataSE/ISPA - Volume 0310.1109/Trustcom.2015.619(108-115)Online publication date: 20-Aug-2015
  • (2014)An FPGA-Based Tightly Coupled Accelerator for Data-Intensive ApplicationsProceedings of the 2014 IEEE 8th International Symposium on Embedded Multicore/Manycore SoCs10.1109/MCSoC.2014.47(289-296)Online publication date: 23-Sep-2014

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