Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
194 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

WindGP: Efficient Graph Partitioning on Heterogenous Machines (2403.00331v2)

Published 1 Mar 2024 in cs.DC

Abstract: Graph Partitioning is widely used in many real-world applications such as fraud detection and social network analysis, in order to enable the distributed graph computing on large graphs. However, existing works fail to balance the computation cost and communication cost on machines with different power (including computing capability, network bandwidth and memory size), as they only consider replication factor and neglect the difference of machines in realistic data centers. In this paper, we propose a general graph partitioning algorithm WindGP, which can support fast and high-quality edge partitioning on heterogeneous machines. WindGP designs novel preprocessing techniques to simplify the metric and balance the computation cost according to the characteristics of graphs and machines. Also, best-first search is proposed instead of BFS and DFS, in order to generate clusters with high cohesion. Furthermore, WindGP adaptively tunes the partition results by sophisticated local search methods. Extensive experiments show that WindGP outperforms all state-of-the-art partition methods by 1.35 - 27 times on both dense and sparse distributed graph algorithms, and has good scalability with graph size and machine number.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (63)
  1. 2018. Apache Giraph. In Encyclopedia of Social Network Analysis and Mining, 2nd Edition, Reda Alhajj and Jon G. Rokne (Eds.). Springer.
  2. Streaming Graph Partitioning: An Experimental Study. Proc. VLDB Endow. 11, 11 (2018), 1590–1603.
  3. High-Quality Shared-Memory Graph Partitioning. IEEE Trans. Parallel Distributed Syst. 31, 11 (2020), 2710–2722.
  4. Bob Bixby. 2007. The gurobi optimizer. Transp. Re-search Part B 41, 2 (2007), 159–178.
  5. Balanced graph edge partition. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, New York, NY, USA - August 24 - 27, 2014, Sofus A. Macskassy, Claudia Perlich, Jure Leskovec, Wei Wang, and Rayid Ghani (Eds.). ACM, 1456–1465.
  6. Aydin Buluc and Kamesh Madduri. 2011. Parallel Breadth-First Search on Distributed Memory Systems. In IEEE International Conference on High Performance Computing, Data, and Analytics.
  7. Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems (2021).
  8. R-MAT: A Recursive Model for Graph Mining. In Proceedings of the Fourth SIAM International Conference on Data Mining, Lake Buena Vista, Florida, USA, April 22-24, 2004, Michael W. Berry, Umeshwar Dayal, Chandrika Kamath, and David B. Skillicorn (Eds.). SIAM, 442–446.
  9. PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs. ACM Trans. Parallel Comput. 5, 3 (2018), 13:1–13:39.
  10. Integer programming. Vol. 271. Springer.
  11. Eduardo F. D’Azevedo and Neena Imam. 2015. Graph 500 in OpenSHMEM. In OpenSHMEM and Related Technologies. Experiences, Implementations, and Technologies - Second Workshop, OpenSHMEM 2015, Annapolis, MD, USA, August 4-6, 2015. Revised Selected Papers (Lecture Notes in Computer Science), Manjunath Gorentla Venkata, Pavel Shamis, Neena Imam, and M. Graham Lopez (Eds.), Vol. 9397. Springer, 154–163.
  12. Peter J Denning and Ted G Lewis. 2016. Exponential laws of computing growth. Commun. ACM 60, 1 (2016), 54–65.
  13. TigerGraph: A Native MPP Graph Database. arXiv (2019).
  14. Chris Edwards. 2021. Moore’s Law: what comes next? Commun. ACM 64, 2 (2021), 12–14.
  15. GraphScope: A Unified Engine For Big Graph Processing. Proc. VLDB Endow. 14, 12 (2021), 2879–2892.
  16. John Forrest and Robin Lougee-Heimer. 2005. CBC user guide. In Emerging theory, methods, and applications. INFORMS, 257–277.
  17. Sebastian Forster and Danupon Nanongkai. 2017. A Faster Distributed Single-Source Shortest Paths Algorithm. FOCS (2017).
  18. Greg N. Frederickson. 1993. An Optimal Algorithm for Selection in a Min-Heap. Inf. Comput. 104, 2 (1993), 197–214.
  19. The SCIP optimization suite 7.0. (2020).
  20. An Analysis of the Server Characteristics and Resource Utilization in Google Cloud. In 2013 IEEE International Conference on Cloud Engineering, IC2E 2013, San Francisco, CA, USA, March 25-27, 2013. IEEE Computer Society, 124–131.
  21. Alexandros V. Gerbessiotis and Leslie G. Valiant. 1994. Direct Bulk-Synchronous Parallel Algorithms. J. Parallel Distributed Comput. 22, 2 (1994), 251–267.
  22. PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs. In 10th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2012, Hollywood, CA, USA, October 8-10, 2012, Chandu Thekkath and Amin Vahdat (Eds.). USENIX Association, 17–30.
  23. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 1024–1034.
  24. Quantifying Cloud Performance and Dependability: Taxonomy, Metric Design, and Emerging Challenges. ACM Trans. Model. Perform. Evaluation Comput. Syst. 3, 4 (2018), 19:1–19:36.
  25. Juraj Hromkovič. 2013. Algorithmics for hard problems: introduction to combinatorial optimization, randomization, approximation, and heuristics. Springer Science & Business Media.
  26. Whale: Efficient Giant Model Training over Heterogeneous GPUs. In 2022 USENIX Annual Technical Conference (USENIX ATC 22).
  27. George Karypis and Vipin Kumar. 1998. A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SIAM J. Sci. Comput. 20, 1 (1998), 359–392.
  28. What is Twitter, a social network or a news media?. In WWW. ACM, 591–600.
  29. Eugene L Lawler and David E Wood. 1966. Branch-and-bound methods: A survey. Operations research 14, 4 (1966), 699–719.
  30. Graphs over time: densification laws, shrinking diameters and possible explanations. In SIGKDD. ACM, 177–187.
  31. Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.
  32. HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments. Future Generation Computer Systems (2021).
  33. Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation. Proc. VLDB Endow. 8, 3 (2014), 281–292.
  34. A Survey of Data Partitioning and Sampling Methods to Support Big Data Analysis. Big Data Mining and Analytics 2 (2020), 17.
  35. HETS: Heterogeneous Edge and Task Scheduling Algorithm for Heterogeneous Computing Systems. In 17th IEEE International Conference on High Performance Computing and Communications, HPCC 2015, 7th IEEE International Symposium on Cyberspace Safety and Security, CSS 2015, and 12th IEEE International Conference on Embedded Software and Systems, ICESS 2015, New York, NY, USA, August 24-26, 2015. IEEE, 1865–1870.
  36. GrapH: Heterogeneity-Aware Graph Computation with Adaptive Partitioning. In 36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016, Nara, Japan, June 27-30, 2016. IEEE Computer Society, 118–128.
  37. K-Hop Community Search Based On Local Distance Dynamics. KSII Trans. Internet Inf. Syst. 12, 7 (2018), 3041–3063.
  38. GAP: Generalizable Approximate Graph Partitioning Framework. CoRR abs/1903.00614 (2019).
  39. Anil Pacaci and M. Tamer Özsu. 2019. Experimental Analysis of Streaming Algorithms for Graph Partitioning. In Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, Amsterdam, The Netherlands, June 30 - July 5, 2019, Peter A. Boncz, Stefan Manegold, Anastasia Ailamaki, Amol Deshpande, and Tim Kraska (Eds.). ACM, 1375–1392.
  40. HDRF: Stream-Based Partitioning for Power-Law Graphs. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015, James Bailey, Alistair Moffat, Charu C. Aggarwal, Maarten de Rijke, Ravi Kumar, Vanessa Murdock, Timos K. Sellis, and Jeffrey Xu Yu (Eds.). ACM, 243–252.
  41. David Pisinger and Stefan Ropke. 2010. Large neighborhood search. In Handbook of metaheuristics. Springer, 399–419.
  42. Luca Pretto. 2002. A Theoretical Analysis of Google’s PageRank. In String Processing and Information Retrieval, 9th International Symposium, SPIRE 2002, Lisbon, Portugal, September 11-13, 2002, Proceedings (Lecture Notes in Computer Science), Alberto H. F. Laender and Arlindo L. Oliveira (Eds.), Vol. 2476. Springer, 131–144.
  43. Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021, Zhi-Hua Zhou (Ed.). ijcai.org, 1548–1554.
  44. Isabelle Stanton and Gabriel Kliot. 2012. Streaming graph partitioning for large distributed graphs. In The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’12, Beijing, China, August 12-16, 2012, Qiang Yang, Deepak Agarwal, and Jian Pei (Eds.). ACM, 1222–1230.
  45. An Evolutionary Computing-Based Efficient Hybrid Task Scheduling Approach for Heterogeneous Computing Environment. J. Grid Comput. 19, 1 (2021), 11.
  46. Tencent. 2019. Plato. https://github.com/Tencent/plato.
  47. FENNEL: streaming graph partitioning for massive scale graphs. In Seventh ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, February 24-28, 2014, Ben Carterette, Fernando Diaz, Carlos Castillo, and Donald Metzler (Eds.). ACM, 333–342.
  48. An Experimental Comparison of Partitioning Strategies in Distributed Graph Processing. Proc. VLDB Endow. 10, 5 (2017), 493–504.
  49. Yiwei Shen Verma and Guosun Zeng. 2005. An unbalanced partitioning scheme for graph in heterogeneous computing. International Conference on Grid and Cooperative Computing (2005).
  50. Knowledge Graph Convolutional Networks for Recommender Systems. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, Ling Liu, Ryen W. White, Amin Mantrach, Fabrizio Silvestri, Julian J. McAuley, Ricardo Baeza-Yates, and Leila Zia (Eds.). ACM, 3307–3313.
  51. Distributed Power-law Graph Computing: Theoretical and Empirical Analysis. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, and Kilian Q. Weinberger (Eds.). 1673–1681.
  52. GraphX: a resilient distributed graph system on Spark. In First International Workshop on Graph Data Management Experiences and Systems, GRADES 2013, co-located with SIGMOD/PODS 2013, New York, NY, USA, June 24, 2013, Peter A. Boncz and Thomas Neumann (Eds.). CWI/ACM, 2.
  53. When computing meets heterogeneous cluster: Workload assignment in graph computation. In 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29 - November 1, 2015. IEEE Computer Society, 154–163.
  54. Muhammad Zakarya and Lee Gillam. 2019. Modelling resource heterogeneities in cloud simulations and quantifying their accuracy. Simul. Model. Pract. Theory 94 (2019), 43–65.
  55. HTC: Hybrid vertex-parallel and edge-parallel Triangle Counting. HPEC (2022).
  56. SQLG+: Efficient k-hop Query Processing on RDBMS. In Database Systems for Advanced Applications - 27th International Conference, DASFAA 2022, Virtual Event, April 11-14, 2022, Proceedings, Part III (Lecture Notes in Computer Science), Arnab Bhattacharya, Janice Lee, Mong Li, Divyakant Agrawal, P. Krishna Reddy, Mukesh K. Mohania, Anirban Mondal, Vikram Goyal, and Rage Uday Kiran (Eds.), Vol. 13247. Springer, 430–442.
  57. SGSI: A Scalable GPU-Friendly Subgraph Isomorphism Algorithm. IEEE Transactions on Knowledge and Data Engineering (2022).
  58. Graph Edge Partitioning via Neighborhood Heuristic. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13 - 17, 2017. ACM, 605–614.
  59. AGL: A Scalable System for Industrial-purpose Graph Machine Learning. Proc. VLDB Endow. 13, 12 (2020), 3125–3137.
  60. An Efficient and Balanced Graph Partition Algorithm for the Subgraph-Centric Programming Model on Large-scale Power-law Graphs. In 41st IEEE International Conference on Distributed Computing Systems, ICDCS 2021, Washington DC, USA, July 7-10, 2021. IEEE, 68–78.
  61. HAEP: Heterogeneous Environment Aware Edge Partitioning for Power-Law Graphs. In Database Systems for Advanced Applications - 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part III (Lecture Notes in Computer Science), Xin Wang, Maria Luisa Sapino, Wook-Shin Han, Amr El Abbadi, Gill Dobbie, Zhiyong Feng, Yingxiao Shao, and Hongzhi Yin (Eds.), Vol. 13945. Springer, 331–340.
  62. HaSGP: an effective graph partition method for heterogeneous-aware. Computing 105, 2 (2023), 455–481.
  63. Gemini: A Computation-Centric Distributed Graph Processing System. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, November 2-4, 2016, Kimberly Keeton and Timothy Roscoe (Eds.). USENIX Association, 301–316.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets