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DTSTAMP:20210916T132451Z
LOCATION:Jean-Jacques Rousseau
DTSTART;TZID=Europe/Stockholm:20210707T120000
DTEND;TZID=Europe/Stockholm:20210707T123000
UID:submissions.pasc-conference.org_PASC21_sess161_msa288@linklings.com
SUMMARY:Algebraic MultiGrid Preconditioners for Sparse Linear Solvers at E
 xtreme Scales on Hybrid Architectures
DESCRIPTION:Minisymposium\n\nAlgebraic MultiGrid Preconditioners for Spars
 e Linear Solvers at Extreme Scales on Hybrid Architectures\n\nD'Ambra, Dur
 astante, Filippone\n\nSparse linear solvers enable technology for reliable
  and high-performance scientific and engineering computing, and Algebraic 
 MultiGrid (AMG) preconditioners are a popular ingredient of such linear so
 lvers. I will present some recent developments on a package of AMG precond
 itioners aimed to improve the efficiency, scalability, and robustness of K
 rylov accelerators in solving extreme-scale problems on parallel hybrid ar
 chitectures. The package, named AMG4PSBLAS, is part of the PSCToolkit (Par
 allel Sparse Computation Toolkit) and includes some parallel AMG methods d
 esigned to provide scalable and easy-to-use preconditioners in the context
  of the PSBLAS (Parallel Sparse Basic Linear Algebra Subprograms) computat
 ional framework, which implements distributed Krylov-type linear solvers. 
 I will focus on flexibility and efficiency of the proposed AMG methods and
  of the related software framework on supercomputers with hybrid nodes whi
 ch embed NVIDIA GPUs, showing results both on model problems with up to te
 ns of billions of unknowns and on test cases arising from the EoCoE-II (En
 ergy oriented Center of Excellence-II funded by EU) flagship applications.
 \n\nDomain: CS and Math, Emerging Applications, Solid Earth Dynamics, Engi
 neering
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