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DTSTAMP:20260522T150118Z
LOCATION:C146
DTSTART;TZID=America/Chicago:20181113T140000
DTEND;TZID=America/Chicago:20181113T143000
UID:submissions.supercomputing.org_SC18_sess178_pap140@linklings.com
SUMMARY:TriCore: Parallel Triangle Counting on GPUs
DESCRIPTION:Yang Hu (George Washington University); Hang Liu (University o
 f Massachusetts, Lowell); and H. Howie Huang (George Washington University
 )\n\nTriangle counting algorithm enumerates the triangles in a graph by id
 entifying the common neighbors between two vertices of every edge. In this
  work, we present TriCore, a new GPU-based high-performance and scalable t
 riangle counting system that consists of three main techniques. First, we 
 design a binary search based counting algorithm that tremendously increase
 s both thread parallelism and memory performance. Second, TriCore exploits
  a 2-D partition method to distribute the CSR representation across multip
 le GPUs, combined with a new streaming buffer to load the edge list from o
 utside of GPUs. Third, we develop a dynamic workload management technique 
 to balance the workload across multiple GPUs. Our evaluation demonstrates 
 TriCore is 22× faster than the state-of-the-art parallel triangle counting
  projects. In addition, TriCore can not only process big graphs that are s
 ignificant larger than the memory size of one GPU but also achieve 24× spe
 edup when scaling to 32 GPUs.\n\nTag: Algorithms, Architectures, Data Anal
 ytics, Deep Learning, Networks, Scientific Computing, Visualization\n\nReg
 istration Category: Tech Program Reg Pass\n\nFinalist: BSP Finalist\n\nSes
 sion Chair: Tom Peterka (Argonne National Laboratory (ANL))\n\n
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