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DTSTAMP:20260422T000712Z
LOCATION:405-406-407
DTSTART;TZID=America/Denver:20231114T160000
DTEND;TZID=America/Denver:20231114T163000
UID:submissions.supercomputing.org_SC23_sess173_pap216@linklings.com
SUMMARY:GraphSet: High Performance Graph Mining through Equivalent Set Tra
 nsformations
DESCRIPTION:Tianhui Shi, Jidong Zhai, Haojie Wang, Qiqian Chen, Mingshu Zh
 ai, Zixu Hao, Haoyu Yang, and Wenguang Chen (Tsinghua University, China)\n
 \nGraph mining is of critical use in a number of fields such as social net
 works, knowledge graphs, and fraud detection. As an NP-complete problem, i
 mproving computation performance is the main target for current optimizati
 ons. Due to excellent performance, state-of-the-art graph mining systems m
 ainly rely on pattern-aware algorithms. Despite previous efforts, complex 
 control flows introduced by pattern-aware algorithms bring large overhead 
 and also impede further acceleration on heterogeneous hardware. \n\nTo add
 ress these challenges, we propose a set-based equivalent transformation ap
 proach for the optimization of pattern-aware graph mining applications, wh
 ich can leverage set properties to eliminate most control flows and reduce
  computation overhead exponentially. We implement a high-performance patte
 rn-aware graph mining system supporting both CPU and GPU, namely GraphSet,
  to automatically apply these transformations.  Evaluation results show th
 at GraphSet outperforms state-of-the-art cross-platform and hardware-speci
 fic graph mining frameworks by up to 3384.1x and 243.2x (18.0x and 10.2x o
 n average), respectively.\n\nTag: Accelerators, Applications, Graph Algori
 thms and Frameworks, Performance Measurement, Modeling, and Tools, Program
 ming Frameworks and System Software\n\nRegistration Category: Tech Program
  Reg Pass\n\nReproducibility Badges: Artifact Available, Artifact Function
 al, Results Reproduced\n\nSession Chair: Mehmet E Belviranli (Colorado Sch
 ool of Mines)\n\n
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