research-article

A Schur Complement Preconditioner for Scalable Parallel Fluid Simulation

Authors:

Nafees Bin Zafar,

Xubo YangAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 36, Issue 5

Article No.: 163, Pages 1 - 11

https://doi.org/10.1145/3092818

Published: 25 July 2017 Publication History

Abstract

We present an algorithmically efficient and parallelized domain decomposition based approach to solving Poisson’s equation on irregular domains. Our technique employs the Schur complement method, which permits a high degree of parallel efficiency on multicore systems. We create a novel Schur complement preconditioner which achieves faster convergence, and requires less computation time and memory. This domain decomposition method allows us to apply different linear solvers for different regions of the flow. Subdomains with regular boundaries can be solved with an FFT-based Fast Poisson Solver. We can solve systems with 1,024³ degrees of freedom, and demonstrate its use for the pressure projection step of incompressible liquid and gas simulations. The results demonstrate considerable speedup over preconditioned conjugate gradient methods commonly employed to solve such problems, including a multigrid preconditioned conjugate gradient method.

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Index Terms

A Schur Complement Preconditioner for Scalable Parallel Fluid Simulation
1. Computing methodologies
  1. Computer graphics
    1. Animation
  2. Modeling and simulation
    1. Simulation types and techniques
      1. Massively parallel and high-performance simulations

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 36, Issue 5

October 2017

161 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3127587

Editor:
Kavita Bala
Cornell University

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Copyright © 2017 ACM.

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Publication History

Published: 25 July 2017

Accepted: 01 April 2017

Revised: 01 April 2017

Received: 01 September 2016

Published in TOG Volume 36, Issue 5

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National High Technology Research and Development Program of China
National Natural Science Foundation of China

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Wang XXu YLiu SRen BKosinka JTelea AWang JSong CChang JLi CZhang JBan X(2024)Physics-based fluid simulation in computer graphics: Survey, research trends, and challengesComputational Visual Media10.1007/s41095-023-0368-y10:5(803-858)Online publication date: 27-Apr-2024
https://doi.org/10.1007/s41095-023-0368-y
Xu WGrande Gutierrez NMcComb C(2023)MegaFlow2D: A Parametric Dataset for Machine Learning Super-resolution in Computational Fluid Dynamics SimulationsProceedings of Cyber-Physical Systems and Internet of Things Week 202310.1145/3576914.3587552(100-104)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3576914.3587552
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