research-article

ConnectIt: a framework for static and incremental parallel graph connectivity algorithms

Authors:

Laxman Dhulipala,

Julian ShunAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 14, Issue 4

Pages 653 - 667

https://doi.org/10.14778/3436905.3436923

Published: 01 December 2020 Publication History

Abstract

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting.

We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

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Jayanti PJayanti SYavuz UHernandez LDhulipala LSun Y(2024)Meta-Configuration Tracking for Machine-Certified Correctness of Concurrent Data Structures (Abstract)Proceedings of the 2024 ACM Workshop on Highlights of Parallel Computing10.1145/3670684.3673406(21-22)Online publication date: 17-Jun-2024
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Jayanti STarjan RDhulipala LSun Y(2024)Fast, Scalable, and Machine-Verified Multicore Disjoint Set Union Data Structures and their Wide Deployment in Parallel Algorithms (Abstract)Proceedings of the 2024 ACM Workshop on Highlights of Parallel Computing10.1145/3670684.3673405(27-28)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3670684.3673405
Liu QShun JZablotchi ILee IChabbi MSteuwer M(2024)Parallel k-Core Decomposition with Batched Updates and Asynchronous ReadsProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638508(286-300)Online publication date: 2-Mar-2024
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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 14, Issue 4

December 2020

263 pages

ISSN:2150-8097

Editors:
Xin Luna Dong
Amazon
,
Felix Naumann
HPI, University of Potsdam

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Published: 01 December 2020

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Jayanti PJayanti SYavuz UHernandez LDhulipala LSun Y(2024)Meta-Configuration Tracking for Machine-Certified Correctness of Concurrent Data Structures (Abstract)Proceedings of the 2024 ACM Workshop on Highlights of Parallel Computing10.1145/3670684.3673406(21-22)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3670684.3673406
Jayanti STarjan RDhulipala LSun Y(2024)Fast, Scalable, and Machine-Verified Multicore Disjoint Set Union Data Structures and their Wide Deployment in Parallel Algorithms (Abstract)Proceedings of the 2024 ACM Workshop on Highlights of Parallel Computing10.1145/3670684.3673405(27-28)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3670684.3673405
Liu QShun JZablotchi ILee IChabbi MSteuwer M(2024)Parallel k-Core Decomposition with Batched Updates and Asynchronous ReadsProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3627535.3638508(286-300)Online publication date: 2-Mar-2024
https://dl.acm.org/doi/10.1145/3627535.3638508
Concessao KCheramangalath UDev RNasre R(2024)Meerkat: A Framework for Dynamic Graph Algorithms on GPUsInternational Journal of Parallel Programming10.1007/s10766-024-00774-z52:5-6(400-453)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10766-024-00774-z
Wang LDing XGu YSun Y(2023)Fast and Space-Efficient Parallel Algorithms for Influence MaximizationProceedings of the VLDB Endowment10.14778/3632093.363210417:3(400-413)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.14778/3632093.3632104
Wang LDong XGu YSun Y(2023)Parallel Strong Connectivity Based on Faster ReachabilityProceedings of the ACM on Management of Data10.1145/35892591:2(1-29)Online publication date: 20-Jun-2023
https://dl.acm.org/doi/10.1145/3589259
Dong XWang LGu YSun YDehnavi MKulkarni MKrishnamoorthy S(2023)Provably Fast and Space-Efficient Parallel BiconnectivityProceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming10.1145/3572848.3577483(52-65)Online publication date: 25-Feb-2023
https://dl.acm.org/doi/10.1145/3572848.3577483
Fedorov AHashemi DNadiradze GAlistarh DAgrawal KShun J(2023)Provably-Efficient and Internally-Deterministic Parallel Union-FindProceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3558481.3591082(261-271)Online publication date: 17-Jun-2023
https://dl.acm.org/doi/10.1145/3558481.3591082
Dong XWu YWang ZDhulipala LGu YSun YAgrawal KShun J(2023)High-Performance and Flexible Parallel Algorithms for Semisort and Related ProblemsProceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures10.1145/3558481.3591071(341-353)Online publication date: 17-Jun-2023
https://dl.acm.org/doi/10.1145/3558481.3591071
Dhulipala LEisenstat DŁącki JMirrokni VShi JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Hierarchical agglomerative graph clustering in poly-logarithmic depthProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601936(22925-22940)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601936
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