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TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance

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Abstract

It is challenging to model the performance of distributed graph computation. Explicit formulation cannot easily capture the diversified factors and complex interactions in the system. Statistical learning methods require a large number of training samples to generate an accurate prediction model. However, it is time-consuming to run the required graph computation tests to obtain the training samples. In this paper, we propose TransGPerf, a transfer learning based solution that can exploit prior knowledge from a source scenario and utilize a manageable amount of training data for modeling the performance of a target graph computation scenario. Experimental results show that our proposed method is capable of generating accurate models for a wide range of graph computation tasks on PowerGraph and GraphX. It outperforms transfer learning methods proposed for other applications in the literature.

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References

  1. Malewicz G, Austern M H, Bik A J C, Dehnert J C, Horn I, Leiser N, Czajkowski G. Pregel: A system for large-scale graph processing. In Proc. the 2010 ACM SIGMOD International Conference on Management of Data, June 2010, pp.135-146. https://doi.org/10.1145/1807167.1807184.

  2. Gonzalez J E, Low Y, Gu H, Bickson D, Guestrin C. PowerGraph: Distributed graph-parallel computation on natural graphs. In Proc. the 10th USENIX Symposium on Operating Systems Design and Implementation, October 2012, pp.17-30.

  3. Xin R S, Gonzalez J E, Franklin M J, Stoica I. GraphX: A resilient distributed graph system on Spark. In Proc. the 1st International Workshop on Graph Data Management Experiences and Systems, June 2013, Article No. 2. https://doi.org/10.1145/2484425.2484427.

  4. Niu S, Chen S. Optimizing CPU cache performance for Pregel-like graph computation. In Proc. the 31st IEEE International Conference on Data Engineering Workshops, April 2015, pp.149-154. https://doi.org/10.1109/ICDEW.2015.7129568.

  5. Han M, Daudjee K, Ammar K, Özsu M T, Wang X, Jin T. An experimental comparison of Pregel-like graph processing systems. Proc. VLDB Endow., 2014, 7(12): 1047-1058. https://doi.org/10.14778/2732977.2732980.

  6. Iosup A, Hegeman T, Ngai W L et al. LDBC graphalytics: A benchmark for large-scale graph analysis on parallel and distributed platforms. Proc. VLDB Endow., 2016, 9(13): 1317-1328. https://doi.org/10.14778/3007263.3007270.

  7. Ngai W L, Hegeman T, Heldens S, Iosup A. Granula: Toward fine-grained performance analysis of largescale graph processing platforms. In Proc. the 5th International Workshop on Graph Data-management Experiences & Systems, May 2017, Article No. 8. https://doi.org/10.1145/3078447.3078455.

  8. Herodotou H, Lim H, Luo G, Borisov N, Dong L, Cetin F B, Babu S. Starfish: A self-tuning system for big data analytics. In Proc. the 5th Biennial Conference on Innovative Data Systems Research, January 2011, pp.261-272.

  9. Venkataraman S, Yang Z, Franklin M J, Recht B, Stoica I. Ernest: Efficient performance prediction for large-scale advanced analytics. In Proc. the 13th USENIX Symposium on Networked Systems Design and Implementation, March 2016, pp.363-378.

  10. Pan S J, Yang Q. A survey on transfer learning. IEEE Trans. Knowledge and Data Engineering, 2010, 22(10): 1345-1359. https://doi.org/10.1109/TKDE.2009.191.

    Article  Google Scholar 

  11. Weiss K R, Khoshgoftaar T M, Wang D. A survey of transfer learning. J. Big Data, 2016, 3: Article No. 9. https://doi.org/10.1186/s40537-016-0043-6.

  12. Tzeng E, Hoffman J, Zhang N, Saenko K, Darrell T. Deep domain confusion: Maximizing for domain invariance. arXiv:1412.3474, 2014. https://arxiv.org/pdf/141-2.3474.pdf, May 2021.

  13. Long M, Cao Y, Wang J, Jordan M I. Learning transferable features with deep adaptation networks. In Proc. the 32nd International Conference on Machine Learning, July 2015, pp.97-105.

  14. Long M, Zhu H, Wang J, Jordan M I. Deep transfer learning with joint adaptation networks. In Proc. the 34th International Conference on Machine Learning, August 2017, pp.2208-2217.

  15. Sun B, Saenko K. Deep CORAL: Correlation alignment for deep domain adaptation. In Proc. the 2016 European Conference on Computer Vision Workshops, October 2016, pp.443-450. https://doi.org/10.1007/978-3-319-49409-8_35.

  16. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, June 2016, pp.770-778. https://doi.org/10.1109/CVPR.2016.90.

  17. Barker K J, Pakin S, Kerbyson D J. A performance model of the Krak hydrodynamics application. In Proc. the 2006 International Conference on Parallel Processing, August 2006, pp.245-254. https://doi.org/10.1109/ICPP.2006.11.

  18. Kerbyson D J, Alme H J, Hoisie A, Petrini F, Wasserman H J, Gittings M L. Predictive performance and scalability modeling of a large-scale application. In Proc. the 2001 ACM/IEEE Conference on Supercomputing, November 2001, Article No. 37. https://doi.org/10.1145/582034.582071.

  19. Sundaram-Stukel D, Vernon M K. Predictive analysis of a wavefront application using logGP. In Proc. the 1999 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, May 1999, pp.141-150. https://doi.org/10.1145/301104.301117.

  20. Bhattacharyya A, Hoeer T. PEMOGEN: Automatic adaptive performance modeling during program runtime. In Proc. the 23rd International Conference on Parallel Architectures and Compilation, August 2014, pp.393-404. https://doi.org/10.1145/2628071.2628100.

  21. Bhattacharyya A, Kwasniewski G, Hoeer T. Using compiler techniques to improve automatic performance modeling. In Proc. the 2015 International Conference on Parallel Architectures and Compilation, October 2015, pp.468-479. https://doi.org/10.1109/PACT.2015.39.

  22. Calotoiu A, Beckingsale D, Earl C W, Hoeer T, Karlin I, Schulz M, Wolf F. Fast multi-parameter performance modeling. In Proc. the 2016 IEEE International Conference on Cluster Computing, September 2016, pp.172-181. https://doi.org/10.1109/CLUSTER.2016.57.

  23. Sun J, Sun G, Zhan S, Zhang J, Chen Y. Automated performance modeling of HPC applications using machine learning. IEEE Trans. Computers, 2020, 69(5): 749-763. https://doi.org/10.1109/TC.2020.2964767.

    Article  MATH  Google Scholar 

  24. Pan S J, Tsang I W, Kwok J T, Yang Q. Domain adaptation via transfer component analysis. IEEE Trans. Neural Networks, 2011, 22(2): 199-210. https://doi.org/10.1109/TNN.2010.2091281.

    Article  Google Scholar 

  25. Sun B, Feng J, Saenko K. Return of frustratingly easy domain adaptation. In Proc. the 30th AAAI Conference on Artificial Intelligence, February 2016, pp.2058-2065.

  26. Tzeng E, Hoffman J, Darrell T, Saenko K. Simultaneous deep transfer across domains and tasks. In Proc. the 2015 IEEE International Conference on Computer Vision, December 2015, pp.4068-4076. https://doi.org/10.1109/ICCV.2015.463.

  27. Long M, Zhu H, Wang J, Jordan M I. Unsupervised domain adaptation with residual transfer networks. In Proc. the 30th Annual Conference on Neural Information Processing Systems, December 2016, pp.136-144.

  28. Leskovec J, Sosič R. SNAP: A general-purpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol., 2016, 8(1): Article No. 1. https://doi.org/10.1145/2898361.

  29. Chakrabarti D, Zhan Y, Faloutsos C. R-MAT: A recursive model for graph mining. In Proc. the 4th SIAM International Conference on Data Mining, April 2004, pp.442-446. https://doi.org/10.1137/1.9781611972740.43.

  30. Faloutsos M, Faloutsos P, Faloutsos C. On power-law relationships of the Internet topology. In Proc. the 1999 ACM SIGCOMM Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, August 30–September 3, 1999, pp.251-262. https://doi.org/10.1145/316188.316229.

  31. Leskovec J, Chakrabarti D, Kleinberg J M, Faloutsos C. Realistic, mathematically tractable graph generation and evolution, using Kronecker multiplication. In Proc. the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, October 2005, pp.133-145. https://doi.org/10.1007/11564126_17.

  32. Park H, Kim M. TrillionG: A trillion-scale synthetic graph generator using a recursive vector model. In Proc. the 2017 ACM International Conference on Management of Data, May 2017, pp.913-928. https://doi.org/10.1145/3035918.3064014.

  33. Boldi P, Vigna S. The webgraph framework I: Compression techniques. In Proc. the 13th International Conference on World Wide Web, May 2004, pp.595-602. https://doi.org/10.1145/988672.988752.

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Authors and Affiliations

  1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Songjie Niu & Shimin Chen

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Songjie Niu & Shimin Chen

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  1. Songjie Niu
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  2. Shimin Chen
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Correspondence to Shimin Chen.

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Niu, S., Chen, S. TransGPerf: Exploiting Transfer Learning for Modeling Distributed Graph Computation Performance. J. Comput. Sci. Technol. 36, 778–791 (2021). https://doi.org/10.1007/s11390-021-1356-2

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  • DOI: https://doi.org/10.1007/s11390-021-1356-2

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