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DTSTART:19700308T020000
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DTSTAMP:20260522T150110Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181114T083000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess342_drs101@linklings.com
SUMMARY:Pattern Matching on Massive Metadata Graphs at Scale
DESCRIPTION:Tahsin Reza and Matei Ripeanu (University of British Columbia)
 \n\nPattern matching is a powerful graph analysis tool. Unfortunately, exi
 sting solutions have limited scalability, support only a limited set of pa
 tterns, and/or focus on only a subset of the real-world problems associate
 d with pattern matching. First, we present a new algorithmic pipeline base
 d on graph pruning that: (i) enables highly scalable exact pattern matchin
 g on labeled graphs, (ii) supports arbitrary patterns, (iii) enables trade
 -offs between precision and time-to-solution, and (iv) supports a set of p
 opular analytics scenarios. We implement our approach on top of HavoqGT an
 d demonstrate its advantages through strong and weak scaling experiments o
 n massive-scale real-world (up to 257B edges) and synthetic (up to 4.4T ed
 ges) graphs, respectively, and at scales (1,024 nodes / 36,864 cores) orde
 rs of magnitude larger than used in the past for similar problems. Further
 more, we explore avenues to enable approximate matching within the graph p
 runing model, targeting contemporary and emerging high-impact, real-world 
 applications.\n\nRegistration Category: Workshop Reg Pass, Tutorial Reg Pa
 ss, Tech Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhibit Hall Only
  Reg Pass\n\n
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