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DTSTAMP:20260522T150119Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181113T163000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess212_pap466@linklings.com
SUMMARY:PruneJuice:  Pruning Trillion-Edge Graphs to a Precise Pattern-Mat
 ching Solution
DESCRIPTION:Tahsin Reza, Matei Ripeanu, and Nicolas Tripoul (University of
  British Columbia) and Geoffrey Sanders and Roger Pearce (Lawrence Livermo
 re National Laboratory)\n\nPattern matching is a powerful graph analysis t
 ool. Unfortunately, existing solutions have limited scalability, support o
 nly a limited set of search patterns, and/or focus on only a subset of the
  real-world problems associated with pattern matching. This paper presents
  a new algorithmic pipeline that: (i) enables highly scalable pattern matc
 hing on labeled graphs, (ii) supports arbitrary patterns, (iii) enables tr
 ade-offs between precision and time-to-solution (while always selecting al
 l vertices and edges that participate in matches, thus offering 100% recal
 l), and (iv) supports a set of popular data analytics scenarios. We implem
 ent our approach on top of HavoqGT and demonstrate its advantages through 
 strong and weak scaling experiments on massive-scale real-world (up to 257
  billion edges) and synthetic (up to 4.4 trillion edges) graphs, respectiv
 ely, and at scales (1,024 nodes / 36,864 cores) orders of magnitude larger
  than used in the past for similar problems.\n\nTag: Algorithms, Graph Alg
 orithms, Linear Algebra, Machine Learning, Sparse Computation\n\nRegistrat
 ion Category: Tech Program Reg Pass\n\nFinalist: BSP Finalist\n\nSession C
 hair: Howie Huang (George Washington University)\n\n
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