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DTSTART:19700308T020000
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DTSTAMP:20190719T085744Z
LOCATION:HG EO Nord
DTSTART;TZID=Europe/Stockholm:20190613T195000
DTEND;TZID=Europe/Stockholm:20190613T215000
UID:submissions.pasc-conference.org_PASC19_sess179_post111@linklings.com
SUMMARY:CSM12 - Optimization of the Parallel Solver Execution Time with Ev
 olutionary and Swarm Algorithms
DESCRIPTION:Poster\n\n\nCSM12 - Optimization of the Parallel Solver Execut
 ion Time with Evolutionary and Swarm Algorithms\n\nPanoc, Meca, Říha,
  Brzobohatý\n\nThe development of optimal designs requires access to a gre
 at computational power leading to the use of High Performance Computing. T
 his work is focused on automatized settings of domain decomposition type s
 olvers and algebraic multigrid solver available in ESPRESO, which is an op
 en source computational tool for numerical simulations designed to utilize
  modern supercomputers. The optimal computational resource utilization req
 uires good knowledge of the linear solver settings and might represent a p
 roblem for new users to achieve the best performance. We tried to improve 
 this situation by an automatic setting of linear solver parameters with ev
 olutionary and swarm optimization algorithms. We focused on a set of param
 eters which influences linear solver execution time, the most demanding pa
 rt in simulation workflow. Especially in transient analysis, optimization 
 algorithm is able to try, per each time step, different parameter combinat
 ion representing an individual. With increasing number of processed time s
 teps the execution time of the following time step decreases, as all indiv
 iduals within the algorithm are reaching the best individual continuously.
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