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UID:pretalx-juliacon2023-YKUD8Q@pretalx.com
DTSTART;TZID=EST:20230728T113000
DTEND;TZID=EST:20230728T120000
DESCRIPTION:We present an efficient and scalable approach to inverse PDE-ba
 sed modelling with the adjoint method. We use automatic differentiation (A
 D) with Enzyme to automaticaly generate the buidling blocks for the invers
 e solver. We utilize the efficient pseudo-transient iterative method to ac
 hieve performance that is\n close to the hardware limit for both forward a
 nd adjont problems. We demonstrate close to optimal parallel efficiency on
  GPUs in series of benchmarks.
DTSTAMP:20260516T232854Z
LOCATION:26-100
SUMMARY:Massively parallel inverse modelling on GPUs with Enzyme - Ludovic 
 Räss\, Samuel Omlin\, Ivan Utkin
URL:https://pretalx.com/juliacon2023/talk/YKUD8Q/
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