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DTSTAMP:20260817T171532Z
LOCATION:Hall B7 (1)\, B Block\, Level 7
DTSTART;TZID=Asia/Tokyo:20241205T092300
DTEND;TZID=Asia/Tokyo:20241205T093400
UID:siggraphasia_SIGGRAPH Asia 2024_sess126_papers_1048@linklings.com
SUMMARY:Barrier-Augmented Lagrangian for GPU-based Elastodynamic Contact
DESCRIPTION:Dewen Guo (Peking University), Minchen Li (Carnegie Mellon Uni
 versity), Yin Yang (University of Utah), and Sheng Li and Guoping Wang (Pe
 king University)\n\nWe propose a GPU-based iterative method for accelerate
 d elastodynamic simulation with the log-barrier-based contact model. While
  Newton's method is a conventional choice for solving the interior-point s
 ystem, the presence of ill-conditioned log barriers often necessitates a d
 irect solution at each linearized substep and costs substantial storage an
 d computational overhead.\nMoreover, constraint sets that vary in each ite
 ration present additional challenges in algorithm convergence. Our method 
 employs a novel barrier-augmented Lagrangian method to improve system cond
 itioning and solver efficiency by adaptively updating an augmentation cons
 traint sets. This enables the utilization of a scalable, inexact Newton-PC
 G solver with sparse GPU storage, eliminating the need for direct factoriz
 ation. We further enhance PCG convergence speed with a domain-decomposed w
 arm start strategy based on an eigenvalue spectrum approximated through ou
 r in-time assembly. Demonstrating significant scalability improvements, ou
 r method makes simulations previously impractical on 128 GB of CPU memory 
 feasible with only 8 GB of GPU memory and orders-of-magnitude faster. Addi
 tionally, our method adeptly handles stiff problems, surpassing the capabi
 lities of existing GPU-based interior-point methods. Our results, validate
 d across various complex collision scenarios involving intricate geometrie
 s and large deformations, highlight the exceptional performance of our app
 roach.\n\nRegistration Category: Full Access, Full Access Supporter\n\nLan
 guage Format: English Language\n\nSession Chair: Paul Kry (McGill Universi
 ty)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_1048&sess=sess126
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