Trust: Triangle Counting Reloaded on GPUs

Pandey, Santosh; Wang, Zhibin; Zhong, Sheng; Tian, Chen; Zheng, Bolong; Li, Xiaoye; Li, Lingda; Hoisie, Adolfy; Ding, Caiwen; Li, Dong; Liu, Hang

doi:10.1109/tpds.2021.3064892

Trust: Triangle Counting Reloaded on GPUs

Journal Article · Tue Mar 09 00:00:00 EST 2021 · IEEE Transactions on Parallel and Distributed Systems

DOI:https://doi.org/10.1109/tpds.2021.3064892· OSTI ID:1805277

Pandey, Santosh ^[1]; Wang, Zhibin ^[2]; Zhong, Sheng ^[2]; Tian, Chen ^[2]; Zheng, Bolong ^[3]; Li, Xiaoye ^[4]; Li, Lingda ^[5]; Hoisie, Adolfy ^[5]; Ding, Caiwen ^[6]; Li, Dong ^[7]; Liu, Hang ^[1]

Stevens Institute of Technology, Hoboken, NJ (United States)
Nanjing Univ., Jiangsu (China)
Huazhong Univ. of Science and Technology, Hubei (China)
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States)
Univ. of Connecticut, Storrs, CT (United States)
Univ. of California, Merced, CA (United States)

Triangle counting is a building block for a wide range of graph applications. Here, traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric option reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hash-based 2D partitioning to scale vertex-centric triangle counting over 1,000 GPUs with sustained scalability. In this work, we present TRUST, which performs triangle counting with the hash operation and vertex-centric paradigm. To the best of our knowledge, TRUST is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: National Key R&D Program of China; National Natural Science Foundation of China; National Science Foundation; USDOE Office of Science (SC), Advanced Scientific Computing Research; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

Grant/Contract Number:: AC05-00OR22725; SC0012704

OSTI ID:: 1805277

Report Number(s):: BNL--221701-2021-JAAM

Journal Information:: IEEE Transactions on Parallel and Distributed Systems, Journal Name: IEEE Transactions on Parallel and Distributed Systems Journal Issue: 11 Vol. 32; ISSN 1045-9219

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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