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Efficient GPU Data Structures and Methods to Solve Sparse Linear Systems in Dynamics Applications

Weber, Daniel and Bender, Jan and Schnös, Markus and Stork, André and Fellner, Dieter W. (2013):
Efficient GPU Data Structures and Methods to Solve Sparse Linear Systems in Dynamics Applications.
In: Computer Graphics Forum, 32 (1), pp. 16-26, DOI: 10.1111/j.1467-8659.2012.03227.x,
[Article]

Abstract

We present graphics processing unit (GPU) data structures and algorithms to efficiently solve sparse linear systems that are typically required in simulations of multi-body systems and deformable bodies. Thereby, we introduce an efficient sparse matrix data structure that can handle arbitrary sparsity patterns and outperforms current state-of-the-art implementations for sparse matrix vector multiplication. Moreover, an efficient method to construct global matrices on the GPU is presented where hundreds of thousands of individual element contributions are assembled in a few milliseconds. A finite-element-based method for the simulation of deformable solids as well as an impulse-based method for rigid bodies are introduced in order to demonstrate the advantages of the novel data structures and algorithms. These applications share the characteristic that a major computational effort consists of building and solving systems of linear equations in every time step. Our solving method results in a speed-up factor of up to 13 in comparison to other GPU methods.

Item Type: Article
Erschienen: 2013
Creators: Weber, Daniel and Bender, Jan and Schnös, Markus and Stork, André and Fellner, Dieter W.
Title: Efficient GPU Data Structures and Methods to Solve Sparse Linear Systems in Dynamics Applications
Language: English
Abstract:

We present graphics processing unit (GPU) data structures and algorithms to efficiently solve sparse linear systems that are typically required in simulations of multi-body systems and deformable bodies. Thereby, we introduce an efficient sparse matrix data structure that can handle arbitrary sparsity patterns and outperforms current state-of-the-art implementations for sparse matrix vector multiplication. Moreover, an efficient method to construct global matrices on the GPU is presented where hundreds of thousands of individual element contributions are assembled in a few milliseconds. A finite-element-based method for the simulation of deformable solids as well as an impulse-based method for rigid bodies are introduced in order to demonstrate the advantages of the novel data structures and algorithms. These applications share the characteristic that a major computational effort consists of building and solving systems of linear equations in every time step. Our solving method results in a speed-up factor of up to 13 in comparison to other GPU methods.

Journal or Publication Title: Computer Graphics Forum
Volume: 32
Number: 1
Uncontrolled Keywords: Business Field: Virtual engineering, Research Area: Confluence of graphics and vision, Interactive simulation, GPU computing, Physically based modeling, Linear systems
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Date Deposited: 12 Nov 2018 11:16
DOI: 10.1111/j.1467-8659.2012.03227.x
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