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DTSTAMP:20220121T042448Z
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DTSTART;TZID=America/Denver:20220120T190000
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DESCRIPTION:Combinatorial algorithms in general\, and graph algorithms in p
 articular\, play a critical enabling role in numerous scientific applicati
 ons. The irregular memory access nature of these algorithms makes them one
  of the hardest algorithmic kernels to implement on parallel systems. To a
 ddress the challenges\, ExaGraph\, the co-design center on combinatorial a
 lgorithms\, was established to design and develop methods and techniques f
 or efficient implementation of key combinatorial (graph) algorithms chosen
  from a set of exascale applications\, targeting accelerator-enabled pre-e
 xascale and exascale systems.\n\nDr. Halappanavar\, a chief computer scien
 tist at PNNL\, will present a brief overview of the latest work on multi-G
 PU systems for two prototypical graph problems — graph clustering and in
 fluence maximization — and demonstrate substantial gains in performance.
  He serves as the group leader of the Data Science and Machine Intelligenc
 e group. His research has spanned multiple technical foci and includes com
 binatorial scientific computing\, parallel graph algorithms\, artificial i
 ntelligence and machine learning\, and the application of graph theory and
  game theory to solve problems in application domains\, such as scientific
  computing\, power grids\, cybersecurity\, and life sciences.\n\nContact C
 hris Gunning\, cgunning@ieee.org\, Boise Computer Society chapter chair\, 
 if you have a section\, chapter\, or student branch that would like to be 
 a co-host for this event.\n\nSpeaker(s): Dr. Mahantesh Halappanavar\, \n\n
 Virtual: https://events.vtools.ieee.org/m/297456
LOCATION:Virtual: https://events.vtools.ieee.org/m/297456
ORGANIZER:cgunning@ieee.org
SEQUENCE:10
SUMMARY:Graph Analytics in the Accelerator-enabled Exascale Era
URL;VALUE=URI:https://events.vtools.ieee.org/m/297456
X-ALT-DESC:Description: <br /><p>Combinatorial algorithms in general\, and 
 graph algorithms in particular\, play a critical enabling role in numerous
  scientific applications. The irregular memory access nature of these algo
 rithms makes them one of the hardest algorithmic kernels to implement on p
 arallel systems. To address the challenges\, ExaGraph\, the co-design cent
 er on combinatorial algorithms\, was established to design and develop met
 hods and techniques for efficient implementation of key combinatorial (gra
 ph) algorithms chosen from a set of exascale applications\, targeting acce
 lerator-enabled pre-exascale and exascale systems.</p>\n<p>Dr. Halappanava
 r\, a chief computer scientist at PNNL\, will present a brief overview of 
 the latest work on multi-GPU systems for two prototypical graph problems &
 mdash\; graph clustering and influence maximization &mdash\; and demonstra
 te substantial gains in performance. He serves as the group leader of the 
 Data Science and Machine Intelligence group. His research has spanned mult
 iple technical foci and includes combinatorial scientific computing\, para
 llel graph algorithms\, artificial intelligence and machine learning\, and
  the application of graph theory and game theory to solve problems in appl
 ication domains\, such as scientific computing\, power grids\, cybersecuri
 ty\, and life sciences.&nbsp\;</p>\n<p>Contact Chris Gunning\, cgunning@ie
 ee.org\, Boise Computer Society chapter chair\, if you have a section\, ch
 apter\, or student branch that would like to be a co-host for this event.<
 /p>
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