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UID:pretalx-juliacon-2026-E7N39Z@pretalx.com
DTSTART;TZID=CET:20260812T161500
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DESCRIPTION:Lattice QCD is one of the most computationally demanding proble
 ms in theoretical physics\, requiring large-scale parallel computation and
  sophisticated numerical algorithms.\nJuliaQCD is an open-source project t
 hat implements lattice QCD simulations in Julia\, with an emphasis on perf
 ormance portability across different computer architectures. By leveraging
  Julia’s abstraction mechanisms and multiple dispatch\, the framework en
 ables rapid prototyping\, flexible algorithm development\, and high-perfor
 mance execution on a wide range of computing platforms.\nIn this project\,
  we focus on the domain-wall fermion formulation and construct a numerical
  optimization framework to tune its free parameters by minimizing selected
  physical observables\, such as the effective mass. We discuss how this ap
 proach can be implemented efficiently within the JuliaQCD code base while 
 maintaining readability and flexibility of the code.\nTo enable large-scal
 e simulations\, we employ MPI-based parallelization and demonstrate produc
 tion runs on the Fugaku supercomputer\, where Julia is not pre-installed. 
 We address practical challenges of deploying Julia on such systems\, inclu
 ding building the Julia runtime\, integrating with the system MPI librarie
 s\, and preparing job scripts and execution environments.\nWe will present
  the physical motivation\, software architecture\, implementation strategi
 es on Fugaku and related HPC systems\, scaling and performance results for
  domain-wall fermions\, and plans for the open-source release of these dev
 elopments within JuliaQCD.
DTSTAMP:20260710T092546Z
LOCATION:Alte Mensa — Audi Max
SUMMARY:Parameter optimization of domain-wall fermion based on machine-lear
 ning framework - Kenta Yoshimura
URL:https://pretalx.com/juliacon-2026/talk/E7N39Z/
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