Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Benchmarking, Measuring and Optimization

Bench 2019: Benchmarking, Measuring, and Optimizing pp 20–31Cite as

GraphBench: A Benchmark Suite for Graph Computing Systems

  • Conference paper
  • First Online:

  • 1090 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12093))

Abstract

In the Big data and IoT era, graph data processing is widely used. The graph data is a kind of structural data that defined entities as vertices and described dependencies between different entities as edges. Today, a lot of graph computing systems emerge with massive diverse graph applications deployed, evaluating graph computing systems become a challenge work. Existing graph computing benchmarks are constructed with prevalent graph computing applications. However, the graph micro-benchmark is lacking, which is a key for the system fine-grained evaluation and obtaining the upper bound performance of the system. In this paper, we take graph computing applications as the combination of basic operations and user-defined operations. Then, we build the GraphBench benchmark suite with micro-benchmarks (basic operations) and component benchmarks (graph computing applications). At last, we evaluates the current mainstream graph computing frameworks with GraphBench. We found that there is no one-size-fits-all solution for the graph computing system. Using GraphBench, we can evaluate the graph computing system at the fine-grained level and get more insights.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Nai, L., Xia, Y., et al.: GraphBIG: understanding graph computing in the context of industrial solutions. In: SC 2015: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE (2015)

    Google Scholar 

  2. Erling, O., Averbuch, A., Larriba-Pey, J., et al.: The LDBC social network benchmark: interactive workload. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM (2015)

    Google Scholar 

  3. Ahmad, M., Hijaz, F., Shi, Q., et al.: Crono: a benchmark suite for multithreaded graph algorithms executing on futuristic multicores. In: 2015 IEEE International Symposium on Workload Characterization. IEEE (2015)

    Google Scholar 

  4. Guo, Y., Varbanescu, A.L., Iosup, A., et al.: An empirical performance evaluation of gpu-enabled graph-processing systems. In: 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE (2015)

    Google Scholar 

  5. Zou, L., Mo, J., Chen, L., Özsu, M.T., Zhao, D.: gStore: answering SPARQL queries via subgraph matching. Proc. VLDB Endow. 4(8), 482–493 (2011)

    Article  Google Scholar 

  6. Webber, J., Robinson, I.: A Programmatic Introduction to Neo4j. Addison-Wesley Professional, Boston (2018)

    Google Scholar 

  7. Developers O. OrientDB: Hybrid Document-Store and Graph NoSQL Database (2012)

    Google Scholar 

  8. Güting, R.H.: GraphDB: modeling and querying graphs in databases. VLDB 94, 12–15 (1994)

    Google Scholar 

  9. Gonzalez, J.E., Low, Y., Gu, H., et al.: Powergraph: distributed graph-parallel computation on natural graphs. In: Presented as Part of the 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2012) (2012)

    Google Scholar 

  10. Chen, R., Shi, J., Chen, Y., et al.: Powerlyra: differentiated graph computation and partitioning on skewed graphs. In: Proceedings of the Tenth European Conference on Computer Systems. ACM (2015)

    Google Scholar 

  11. Gonzalez, J.E., Xin, R.S., Dave, A., et al.: Graphx: graph processing in a distributed dataflow framework. In: 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2014) (2014)

    Google Scholar 

  12. Zhu, X., Chen, W., Zheng, W., et al.: Gemini: a computation-centric distributed graph processing system. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016) (2016)

    Google Scholar 

  13. Eu email communication network. http://snap.stanford.edu/data/email-EuAll.html

  14. Wikipedia talk network. http://snap.stanford.edu/data/wiki-Talk.html

  15. Pokec social network. https://snap.stanford.edu/data/soc-Pokec.html

  16. Livejournal social network and ground-truth communities. https://snap.stanford.edu/data/com-LiveJournal.html

  17. Shortest path problem. https://en.wikipedia.org/wiki/

  18. Breadth-first search. https://en.wikipedia.org/wiki/

  19. Connected component. https://en.wikipedia.org/wiki/

  20. K-core. https://en.wikipedia.org/wiki/

  21. Pagerank. https://en.wikipedia.org/wiki/

Download references

Acknowledgment

This work is supported by the National Key Research and Development Plan of China Grant No. 2016YFB1000201.

Author information

Authors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Lei Wang & Minghe Yu

Authors
  1. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minghe Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Wang .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Wanling Gao

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Jianfeng Zhan

  3. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Geoffrey Fox

  4. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Xiaoyi Lu

  5. Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA

    Dan Stanzione

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, L., Yu, M. (2020). GraphBench: A Benchmark Suite for Graph Computing Systems. In: Gao, W., Zhan, J., Fox, G., Lu, X., Stanzione, D. (eds) Benchmarking, Measuring, and Optimizing. Bench 2019. Lecture Notes in Computer Science(), vol 12093. Springer, Cham. https://doi.org/10.1007/978-3-030-49556-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-49556-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49555-8

  • Online ISBN: 978-3-030-49556-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation