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DTSTAMP:20181221T160742Z
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DTSTART;TZID=America/Chicago:20181113T103000
DTEND;TZID=America/Chicago:20181113T104500
UID:submissions.supercomputing.org_SC18_sess277_drs101@linklings.com
SUMMARY:Pattern Matching on Massive Metadata Graphs at Scale
DESCRIPTION:Doctoral Showcase\nComputational Biology, Exascale, GPUs, Grap
 h Algorithms, Linear Algebra, Machine Learning, Sparse Computation, Worksh
 op Reg Pass, Tutorial Reg Pass, Tech Program Reg Pass, Exhibits Reg Pass, 
 Exhibits - Exhibit Hall Only Reg Pass, Doctoral Showcase\n\nPattern Matchi
 ng on Massive Metadata Graphs at Scale\n\nReza, Ripeanu\n\nPattern matchin
 g is a powerful graph analysis tool. Unfortunately, existing solutions hav
 e limited scalability, support only a limited set of patterns, and/or focu
 s on only a subset of the real-world problems associated with pattern matc
 hing. First, we present a new algorithmic pipeline based on graph pruning 
 that: (i) enables highly scalable exact pattern matching on labeled graphs
 , (ii) supports arbitrary patterns, (iii) enables trade-offs between preci
 sion and time-to-solution, and (iv) supports a set of popular analytics sc
 enarios. We implement our approach on top of HavoqGT and demonstrate its a
 dvantages through strong and weak scaling experiments on massive-scale rea
 l-world (up to 257B edges) and synthetic (up to 4.4T edges) graphs, respec
 tively, and at scales (1,024 nodes / 36,864 cores) orders of magnitude lar
 ger than used in the past for similar problems. Furthermore, we explore av
 enues to enable approximate matching within the graph pruning model, targe
 ting contemporary and emerging high-impact, real-world applications.
URL:https://sc18.supercomputing.org/presentation/?id=drs101&sess=sess277
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