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PSMiner : A pattern-aware accelerator for high-performance streaming graph pattern mining
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Qi, Hao, Zhang, Yu, He, Ligang, Luo, Kang, Huang, Jun, Lu, Haoyu, Zhao, Jin and Jin, Hai (2023) PSMiner : A pattern-aware accelerator for high-performance streaming graph pattern mining. In: 2023 Design Automation Conference (DAC2023), San Francisco, CA, 9-13 Jul 2023. Published in: 2023 60th ACM/IEEE Design Automation Conference (DAC) ISBN 9798350323481.
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WRAP-PSMiner-A-pattern-aware-accelerator-high-performance-23.pdf - Accepted Version - Requires a PDF viewer. Download (1947Kb) | Preview |
Abstract
Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.
Item Type: | Conference Item (Paper) | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Data mining, Graph theory -- Data processing, Graphic methods -- Data processing | |||||||||||||||
Journal or Publication Title: | 2023 60th ACM/IEEE Design Automation Conference (DAC) | |||||||||||||||
Publisher: | IEEE Computer Society | |||||||||||||||
ISBN: | 9798350323481 | |||||||||||||||
Official Date: | 15 September 2023 | |||||||||||||||
Dates: |
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Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Re-use Statement: | © 2023 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 17 March 2023 | |||||||||||||||
Date of first compliant Open Access: | 17 March 2023 | |||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||
Title of Event: | 2023 Design Automation Conference (DAC2023) | |||||||||||||||
Type of Event: | Conference | |||||||||||||||
Location of Event: | San Francisco, CA | |||||||||||||||
Date(s) of Event: | 9-13 Jul 2023 | |||||||||||||||
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