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DTSTAMP:20230124T171526Z
LOCATION:C1-2-3
DTSTART;TZID=America/Chicago:20221117T083000
DTEND;TZID=America/Chicago:20221117T170000
UID:submissions.supercomputing.org_SC22_sess226_spostg114@linklings.com
SUMMARY:Towards Scalable Identification of Motifs Representing Non-Determi
 nism in HPC Simulations
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nTowards Scalable I
 dentification of Motifs Representing Non-Determinism in HPC Simulations\n\
 nKhan\n\nWe present a novel parallel framework for large scale network ali
 gnment. Network alignment has applications in many disciplines including b
 ioinformatics and social sciences. Our algorithm is one of the first netwo
 rk alignment tools that can not only identify similar networks, but also i
 dentify the differences between nearly similar networks. It is particularl
 y useful in finding regions of non-determinism in event graphs, arising in
  large HPC simulations.\n\nOur algorithm compares similarity between verti
 ces based the number of graphlets (or motifs) to which the vertex belongs.
  Thus, it can also be used to find motifs in a graph. However, compared to
  the state-of-the art algorithms, our algorithm can (i) compute multiple m
 otifs in one execution and (ii) be tuned to graph structure and user speci
 fication. We will present the algorithm, showcase the scalability results,
  and compare its performance and accuracy with other state-of-the art soft
 ware.\n\nRegistration Category: Tech Program Reg Pass
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