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DTSTAMP:20181221T160730Z
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:Paper\nAlgorithms, Graph Algorithms, Linear Algebra, Machine L
 earning, Sparse Computation, Tech Program Reg Pass\n\nPruneJuice:  Pruning
  Trillion-Edge Graphs to a Precise Pattern-Matching Solution\n\nReza, Ripe
 anu, Tripoul, Sanders, Pearce\n\nPattern matching is a powerful graph anal
 ysis tool. Unfortunately, existing solutions have limited scalability, sup
 port only a limited set of search patterns, and/or focus on only a subset 
 of the real-world problems associated with pattern matching. This paper pr
 esents a new algorithmic pipeline that: (i) enables highly scalable patter
 n matching on labeled graphs, (ii) supports arbitrary patterns, (iii) enab
 les trade-offs between precision and time-to-solution (while always select
 ing all vertices and edges that participate in matches, thus offering 100%
  recall), and (iv) supports a set of popular data analytics scenarios. We 
 implement our approach on top of HavoqGT and demonstrate its advantages th
 rough strong and weak scaling experiments on massive-scale real-world (up 
 to 257 billion edges) and synthetic (up to 4.4 trillion edges) graphs, res
 pectively, and at scales (1,024 nodes / 36,864 cores) orders of magnitude 
 larger than used in the past for similar problems.
URL:https://sc18.supercomputing.org/presentation/?id=pap466&sess=sess212
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