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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055353Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211117T083000
DTEND;TZID=America/Chicago:20211117T170000
UID:submissions.supercomputing.org_SC21_sess279_rpost162@linklings.com
SUMMARY:Parallel Framework for Updating Large-Scale Dynamic Networks
DESCRIPTION:Posters, Research Posters\n\nParallel Framework for Updating L
 arge-Scale Dynamic Networks\n\nSrinivasan, Pandey, Khanda, Srinivasan, Das
 \n\nAnalysis of large-scale dynamic networks is vital for understanding th
 e relationship between entities that constantly change over time. Unfortun
 ately, existing algorithms for identifying graph properties are optimized 
 for static networks and resort to recomputing those properties over the en
 tire network every time it evolves. To combat this problem, we introduce a
  parallel framework in this poster that efficiently updates the network pr
 operties as the structure changes in time through edge insertions or delet
 ions. Our framework implements four parallel algorithms for identifying gr
 aph properties, namely: strongly connected components (SCC); single source
  shortest path (SSSP), minimum spanning tree (MST); and page rank on dynam
 ic networks.  All four implementations are enabled with shared-memory para
 llelism, while SCC is also enabled with distributed memory parallelism for
  improved memory utilization and SSSP is implemented on an NVIDIA GPU plat
 form to leverage the data parallelism.\n\nRegistration Category: Tech Prog
 ram Reg Pass, Exhibit Hall Only
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