Advertisement

Efficient Subgraph Matching on Non-volatile Memory

  • Yishu Shen
  • Zhaonian ZouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)

Abstract

The emerging non-volatile memory (NVM) technologies have attracted much attention due to its advantages over the existing DRAM technology such as non-volatility, byte-addressability and high storage density. These promising features make NVM a promising replacement of DRAM. Although the reading cost of NVM is close to that of DRAM, the writing cost is significantly higher than that of DRAM. Existing algorithms designed on DRAM treat read and write equally and thus are not applicable to NVM. In this paper, we investigate efficient algorithms for subgraph matching, a fundamental problem in graph databases, on NVM. We first give a detailed evaluation on several existing subgraph matching algorithms by experiments and theoretical analysis. Then, we propose our write-limited subgraph matching algorithm based on the analysis. We also extend our algorithm to answer subgraph matching on dynamic graphs. Experiments on an NVM simulator demonstrate a significant improvement in efficiency against the existing algorithms.

Keywords

Non-volatile memory Subgraph matching Graph database 

Notes

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Nos. 61532015 and 61672189).

References

  1. 1.
    Raoux, S., Burr, G.W., Breitwisch, M.J., Rettner, C.T., Chen, Y.-C., Shelby, R.M., Salinga, M., et al.: Phase-change random access memory: a scalable technology. IBM J. Res. Dev. 52(4.5), 465–479 (2008)CrossRefGoogle Scholar
  2. 2.
    Strukov, D.B., Snider, G.S., Stewart, D.R., Williams, R.S.: The missing memristor found. Nature 453(7191), 80–83 (2008)CrossRefGoogle Scholar
  3. 3.
    Driskill-Smith, A.: Latest advances and future prospects of STT-RAM. In: Non-volatile Memories Workshop, pp. 11–13 (2010)Google Scholar
  4. 4.
    Ullmann, J.R.: An algorithm for subgraph isomorphism. J. ACM 23(1), 31–42 (1976)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cordella, L.P., Foggia, P., Sansone, C., et al.: A (sub)graph isomorphism algorithm for matching large graphs. IEEE Trans. Pattern Anal. Mach. Intell. 26(10), 1367–1372 (2004)CrossRefGoogle Scholar
  6. 6.
    Shang, H., Zhang, Y., Lin, X., et al.: Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. Proc. VLDB Endow. 1(1), 364–375 (2008)CrossRefGoogle Scholar
  7. 7.
    He, H., Singh, A.K.: Query language and access methods for graph databases. In: ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, pp. 405–418. DBLP, June 2010Google Scholar
  8. 8.
    Han, W.S., Lee, J., Lee, J.H.: Turbo ISO: towards ultrafast and robust subgraph isomorphism search in large graph databases. In: ACM SIGMOD International Conference on Management of Data, pp. 337–348. ACM (2013)Google Scholar
  9. 9.
    Ren, X., Wang, J.: Exploiting vertex relationships in speeding up subgraph isomorphism over large graphs. Proc. VLDB Endow. 8(5), 617–628 (2015)CrossRefGoogle Scholar
  10. 10.
    Bi, F., Chang, L., Lin, X., et al.: Efficient subgraph matching by postponing cartesian products. In: International Conference, pp. 1199–1214 (2016)Google Scholar
  11. 11.
    Poremba, M., Zhang, T., Xie, Y.: NVMain 2.0: architectural simulator to model (non-)volatile memory systems. IEEE Comput. Archit. Lett. 14(2), 140–143 (2015)CrossRefGoogle Scholar
  12. 12.
    Binkert, N., Beckmann, B., Black, G., et al.: The gem5 simulator. ACM SIGARCH Comput. Archit. News 39(2), 1–7 (2011)CrossRefGoogle Scholar
  13. 13.
    Blelloch, G.E., Fineman, J.T., Gibbons, P.B., et al.: Efficient algorithms with asymmetric read and write costs. arXiv preprint arXiv:1511.01038 (2015)
  14. 14.
    Blelloch, G.E., Fineman, J.T., Gibbons, P.B., et al.: Sorting with asymmetric read and write costs. In: ACM on Symposium on Parallelism in Algorithms and Architectures, pp. 1–12. ACM (2016)Google Scholar
  15. 15.
    Viglas, S.D.: Write-limited sorts and joins for persistent memory. Proc. VLDB Endow. 7(5), 413–424 (2014)CrossRefGoogle Scholar
  16. 16.
    Carson, E., Demmel, J., Grigori, L., et al.: Write-avoiding algorithms. In: IEEE International Parallel and Distributed Processing Symposium, pp. 648–658. IEEE (2016)Google Scholar
  17. 17.
    Lee, J., Han, W.S., Kasperovics, R., et al.: An in-depth comparison of subgraph isomorphism algorithms in graph databases. Proc. VLDB Endow. 6(2), 133–144 (2012)CrossRefGoogle Scholar
  18. 18.
    Floyd, R.W.: Algorithm 97: shortest path. Commun. ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  19. 19.
    Rossi, R., Ahmed, N.: The network data repository with interactive graph analytics and visualization. In: AAAI (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

Personalised recommendations