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BEGIN:VEVENT
UID:news258@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20180716T215328
DTSTART;TZID=Europe/Zurich:20141212T110000
SUMMARY:Seminar in Numerical Analysis: Olaf Schenk (Universita della Svizze
 ra italiana)
DESCRIPTION:We will review the state-of-the art techniques in the parallel 
 direct  solution of linear systems of equations and present several recent
  new  research directions. This includes (i) fast methods for evaluating  
 certain selected elements of a matrix function that can be used for  solvi
 ng the Kohn-Sham-equation without explicit diagonalization and (ii)  stoch
 astic optimization problems under uncertainty from power grid  problems fr
 om electrical power grid systems. Several algorithmic and  performance eng
 ineering advances are discussed to sove the underlying  sparse linear alge
 bra problems. The new developments include novel  incomplete augmented mul
 ticore sparse factorizations\, multicore- and  GPU-based dense matrix impl
 ementations\, and communication-avoiding  Krylov solvers. We also improve 
 the interprocess communication on Cray  systems to solve e.g. 24-hour hori
 zon power grid problems from  electrical power grid systems of realistic s
 ize with up to 1.95 billion  decision variables and 1.94 billion constrain
 ts.  Full-scale results are  reported on Cray XC30 and BG/Q\, where we ob
 serve very  good parallel  efficiencies and solution times within a opera
 tionally defined time  interval. To our knowledge\, "real-time"-compatible
  performance on a  broad range of architectures for this class of problems
  has not been  possible prior to present work.
X-ALT-DESC:We will review the state-of-the art techniques in the parallel d
 irect  solution of linear systems of equations and present several recent 
 new  research directions. This includes (i) fast methods for evaluating  c
 ertain selected elements of a matrix function that can be used for  solvin
 g the Kohn-Sham-equation without explicit diagonalization and (ii)  stocha
 stic optimization problems under uncertainty from power grid  problems fro
 m electrical power grid systems. Several algorithmic and  performance engi
 neering advances are discussed to sove the underlying  sparse linear algeb
 ra problems. The new developments include novel  incomplete augmented mult
 icore sparse factorizations\, multicore- and  GPU-based dense matrix imple
 mentations\, and communication-avoiding  Krylov solvers. We also improve t
 he interprocess communication on Cray  systems to solve e.g. 24-hour horiz
 on power grid problems from  electrical power grid systems of realistic si
 ze with up to 1.95 billion  decision variables and 1.94 billion constraint
 s.&nbsp\; Full-scale results are  reported on Cray XC30 and BG/Q\, where w
 e observe very&nbsp\; good parallel  efficiencies and solution times withi
 n a operationally defined time  interval. To our knowledge\, &quot\;real-t
 ime&quot\;-compatible performance on a  broad range of architectures for t
 his class of problems has not been  possible prior to present work. 
DTEND;TZID=Europe/Zurich:20141212T120000
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