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LOCATION:HG F 30 Audi Max
DTSTART;TZID=Europe/Stockholm:20240604T100100
DTEND;TZID=Europe/Stockholm:20240604T100100
UID:submissions.pasc-conference.org_PASC24_sess158_pos130@linklings.com
SUMMARY:P49 - The Task-Based GPU-Enabled Distributed Eigensolver available
  in DLA-Future
DESCRIPTION:Poster\n\nJohn Biddiscombe, Alberto Invernizzi, Rocco Meli, Au
 riane Reverdell, Mikael Simberg, and Raffaele Solcà (ETH Zurich / CSCS)\n\
 nDLA-Future implements an efficient GPU-enabled distributed eigenvalue sol
 ver using asynchronous methods based on the C++ std::execution API. Using 
 a task-based approach reduces the number of synchronization points and all
 ows for simple overlapping of communication and computation which helps im
 prove performance relative to fork join parallelism techniques as found in
  other libraries such as LAPACK and ScaLAPACK.\nIn certain cases when mult
 iple algorithms with suitable problem sizes are run independently, they ca
 n be co-scheduled to run at the same time producing noticeable improvement
 s in time to solution. \nWe present results of our task-based generalized 
 eigensolver and show the current optimization status using both multicore-
 only and GPU-enabled systems (including both Nvidia and AMD devices). We a
 lso present full application results generated with CP2K and SIRIUS, where
  DLA-future support was easily added thanks to the C-API provided, which i
 s compatible with the widely used ScaLAPACK interface.\n\nSession Chair: I
 va Kavcic (Met Office)
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