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DTSTART;TZID=America/Denver:20231114T100000
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UID:submissions.supercomputing.org_SC23_sess291_rpost210@linklings.com
SUMMARY:Scalable Algorithms for Analyzing Large Dynamic Networks Using CAN
 DY
DESCRIPTION:Aashish Pandey (University of North Texas), Arindam Khanda (Mi
 ssouri University of Science and Technology), Sriram Srinivasan and Sudhar
 shan Srinivasan (University of Oregon), S. M. Shovan (Missouri University 
 of Science and Technology), Farahnaz Hosseini (University of North Texas),
  Sajal Das (Missouri University of Science and Technology), Boyana Norris 
 (University of Oregon), and Sanjukta Bhowmick (University of North Texas)\
 n\nAs the dynamic network’s topology undergoes temporal alterations, assoc
 iated graph properties must be updated to ensure their ac- curacy. Address
 ing this requirement efficiently in large dynamic networks led to the prop
 osal of a generic framework, CANDY (Cyberinfrastructure for Accelerating I
 nnovation in Network Dynamics). This paper expounds on the development of 
 algorithms and subsequent performance improvements facilitated by CANDY.\n
 \nTag: Artificial Intelligence/Machine Learning, Architecture and Networks
 , Heterogeneous Computing, I/O and File Systems, Performance Measurement, 
 Modeling, and Tools, Post-Moore Computing, Programming Frameworks and Syst
 em Software, Quantum Computing\n\nRegistration Category: Tech Program Reg 
 Pass, Exhibits Reg Pass\n\n
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