H-INDEX: Hash-Indexing for Parallel Triangle Counting on GPUs

Pandey, Santosh; Li, Xiaoye Sherry; Buluç, Aydın; Xu, Jin; Liu, Huan

doi:10.1109/hpec.2019.8916492

Cited by 34 publications

(25 citation statements)

References 19 publications

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“…Graph mining software systems. Previously, various graph mining problems usually used different hand-optimized specialpurpose algorithms, such as for triangle counting [22,25,27,43,44], clique listing [12,14,21], motif counting [1,35,45], and subgraph listing [5,6,32,33,36,49,52]. However, it requires substantial programming efforts to hand-tune each algorithm individually for correctness and performance.…”

Section: Background and Motivations 21 Graph Mining Algorithms And Sy...mentioning

confidence: 99%

FINGERS: exploiting fine-grained parallelism in graph mining accelerators

Chen

Tian

Gao

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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Graph mining is an emerging application of high importance and also with high complexity, thus requiring efficient hardware acceleration. Current accelerator designs only utilize coarse-grained parallelism, leaving large room for further optimizations. Our key insight is to fully exploit fine-grained parallelism to overcome the existing issues of hardware underutilization, inefficient resource provision, and limited single-thread performance under imbalanced loads. Targeting pattern-aware graph mining algorithms, we first comprehensively identify and analyze the abundant fine-grained parallelism at the branch, set, and segment levels during search tree exploration and set operations. We then propose a novel graph mining accelerator, FINGERS, which effectively exploits these multiple levels of fine-grained parallelism to achieve significant performance improvements. FINGERS mainly enhances the design of each single processing element with parallel compute units for set operations, and efficient techniques for task scheduling, load balancing, and data aggregation. FINGERS outperforms the state-of-the-art design by 2.8× on average and up to 8.9× with the same chip area. We also demonstrate that different patterns and different graphs exhibit drastically different parallelism opportunities, justifying the necessity of exploiting all levels of fine-grained parallelism in FINGERS.

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Section: Background and Motivations 21 Graph Mining Algorithms And Sy...mentioning

confidence: 99%

FINGERS: exploiting fine-grained parallelism in graph mining accelerators

Chen

Tian

Gao

2022

Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems

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“…In particular, CCA c requires no decontraction (Theorem 1) and thus incurs no cost for storing f D at all. We compared the space cost with the indices used by TurboIso, HINDEX [75], PLL [4] and RMC [68].…”

Section: Experimental Studymentioning

confidence: 99%

“…(b) Contraction makes SubIso, TriC, Dist, CC and CD 1.69, 1.44, 1.47, 2.24 and 1.37 times faster, respectively. (c) The total space cost of our contraction scheme for the five accounts only for 12.6% of indices for TurboIso [44], HINDEX [75], PLL [4] and RMC [68]. It is 9.0% when kNN [92] also runs on the same graph.…”

Section: Introductionmentioning

confidence: 99%

Making graphs compact by lossless contraction

Fan

Liu

et al. 2022

The VLDB Journal

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This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete parts and regular structures into supernodes. The supernodes carry a synopsis $$S_\mathcal {Q}$$ S Q for each query class $$\mathcal {Q}$$ Q in use, to abstract key features of the contracted parts for answering queries of $$\mathcal {Q}$$ Q . Moreover, for various types of graphs, we identify regular structures to contract. The contraction scheme provides a compact graph representation and prioritizes up-to-date data. Better still, it is generic and lossless. We show that the same contracted graph is able to support multiple query classes at the same time, no matter whether their queries are label based or not, local or non-local. Moreover, existing algorithms for these queries can be readily adapted to compute exact answers by using the synopses when possible and decontracting the supernodes only when necessary. As a proof of concept, we show how to adapt existing algorithms for subgraph isomorphism, triangle counting, shortest distance, connected component and clique decision to contracted graphs. We also provide a bounded incremental contraction algorithm in response to updates, such that its cost is determined by the size of areas affected by the updates alone, not by the entire graphs. We experimentally verify that on average, the contraction scheme reduces graphs by 71.9% and improves the evaluation of these queries by 1.69, 1.44, 1.47, 2.24 and 1.37 times, respectively.

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“…In community detection research, a large and growing body of literature studied the triangle counting problem in graphs. Some of the papers focused only on counting the number of triangles [12][13][14][15][16][17][18] while others focused on finding k-trusses in a graph [5,7,[19][20][21][22]. Our main focus in this paper is finding k-trusses, and in the following, we review the recent works on the k-truss problem.…”

Section: Related Workmentioning

confidence: 99%

Truss decomposition using triangle graphs

Rezvani

2021

Soft Comput

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H-INDEX: Hash-Indexing for Parallel Triangle Counting on GPUs

Cited by 34 publications

References 19 publications

FINGERS: exploiting fine-grained parallelism in graph mining accelerators

FINGERS: exploiting fine-grained parallelism in graph mining accelerators

Making graphs compact by lossless contraction

Truss decomposition using triangle graphs

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