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DTSTART;TZID=America/Chicago:20221117T083000
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UID:submissions.supercomputing.org_SC22_sess275_rpost178@linklings.com
SUMMARY:Exploring FPGA Acceleration of Seed Selection in Influence Maximiz
 ation
DESCRIPTION:Posters, Research Posters\n\nExploring FPGA Acceleration of Se
 ed Selection in Influence Maximization\n\nNeff, Minutoli, Tumeo, Halappana
 var, Becchi\n\nThe Influence Maximization (IM) problem on a social network
  is the problem of identifying a small cohort of vertices that, when initi
 ally activated, results in a cascading effect that will activate the maxim
 um expected number other vertices in the network.  While the problem is NP
 -hard under budget constraints, it has a submodular structure that leads t
 o efficient approximation.\n\nIn this work, we present techniques and our 
 performance analysis that we are using to drive the design of efficient FP
 GA acceleration for the seed selection step within the IMM algorithm. Curr
 ently, we are able to achieve from 0.75x to 4.78x speedup, with the main b
 ottleneck being a static overhead determined by the size of the input grap
 h. We discuss future work to improve on the current architecture, and hope
  to provide techniques for making "almost-regular" applications fast and e
 fficient on FPGAs.\n\nRegistration Category: Tech Program Reg Pass, Exhibi
 ts Reg Pass
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