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SUMMARY:David Keyes (KAUST)
DTSTART:20200909T140000Z
DTEND:20200909T150000Z
DTSTAMP:20260423T023052Z
UID:E-NLA/15
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/E-NLA/15/">D
 ata-sparse Linear Algebra Algorithms for Large-scale Applications on Emerg
 ing Architectures</a>\nby David Keyes (KAUST) as part of E-NLA - Online se
 minar series on numerical linear algebra\n\n\nAbstract\nA traditional goal
  of algorithmic optimality\, squeezing out operations\, has been supersede
 d because of evolution in architecture. Algorithms must now squeeze memory
 \, data transfers\, and synchronizations\, while extra operations on local
 ly cached data cost relatively little time or energy. Hierarchically low-r
 ank matrices realize a rarely achieved combination of optimal storage comp
 lexity and high-computational intensity in approximating a wide class of f
 ormally dense operators that arise in exascale applications. They may be r
 egarded as algebraic generalizations of the fast multipole method. Methods
  based on hierarchical tree-based data structures and their simpler cousin
 s\, tile low-rank matrices\, are well suited for early exascale architectu
 res\, which are provisioned for high processing power relative to memory c
 apacity and memory bandwidth. These data-sparse algorithms are ushering in
  a renaissance of numerical linear algebra. We describe modules of a softw
 are toolkit\, Hierarchical Computations on Manycore Architectures (HiCMA)\
 , that illustrate these features on several applications. Early modules of
  this open-source project are distributed in software libraries of major v
 endors. A recent addition\, H2Opus\, extends H2 hierarchical matrix operat
 ions to distributed memory and GPUs.\n
LOCATION:https://researchseminars.org/talk/E-NLA/15/
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