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UID:pretalx-juliacon-2026-3MV3BW@pretalx.com
DTSTART;TZID=CET:20260814T161500
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DESCRIPTION:Pseudospectra generalize eigenvalue analysis by characterizing 
 how the resolvent norm of (zB - A)^{-1} varies over the complex plane\, re
 vealing transient behavior and stability properties that eigenvalues alone
  miss.\nGrowing demand from non-Hermitian physics\, power systems\, and da
 ta-driven robust control necessitates large-scale pseudospectral computati
 ons that existing CPU-based tools cannot efficiently handle for large dens
 e matrices.\n\nIn this talk\, we present KAPseudospectra.jl\, the first GP
 U-accelerated pseudospectra package\, built on KernelAbstractions.jl for v
 endor-neutral execution across supported backends.\nThe package implements
  a batched Inverse Hermitian Lanczos (IHL) iteration that approximates the
  smallest singular value at each grid point in only a few steps\, requirin
 g O(N^2) operations at each grid point after a single O(N^3) CPU-only Schu
 r decomposition.\nCentral to the IHL iteration is KATRSM.jl\, a submodule 
 providing batched triangular solvers that keep the pencil (zB - A) factore
 d on-device\, largely eliminating host-device data movement.\nMulti-device
  parallelism is achieved by partitioning the complex grid across available
  GPUs with automatic memory-aware batching.\n\nWe demonstrate the package 
 on matrices up to dimension 2^14\, and discuss the design decisions that e
 nable this codebase to target multiple compute backends through Julia's pa
 ckage extension system and KernelAbstractions.jl.
DTSTAMP:20260502T093456Z
LOCATION:Room 3
SUMMARY:KAPseudospectra.jl: GPU-Accelerated Pseudospectra via KernelAbstrac
 tions.jl - Dan Folescu
URL:https://pretalx.com/juliacon-2026/talk/3MV3BW/
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