An implementation of direct linear equation solver using a many-core CPU for mechanical dynamic analysis

This research proposes an effective implementation of linear equation solver for an implicit integration on a many-core CPU. Although this implementation is applied to a flexible body simulation in mechanical dynamics, it could be also utilized in a wide range of other fields. BFS-based nested disse...

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Bibliographic Details
Published inJournal of mechanical science and technology Vol. 31; no. 10; pp. 4637 - 4645
Main Authors Jung, Ji-Hyun, Bae, Dae-Sung
Format Journal Article
LanguageEnglish
Published Seoul Korean Society of Steel Construction 01.10.2017
Springer Nature B.V
대한기계학회
Subjects
Adaptive control
Central processing units
Computer memory
Computer simulation
Control
CPUs
Dissection
Dynamical Systems
Engineering
Flexible bodies
Industrial and Production Engineering
Linear equations
Mechanical Engineering
Separators
Sparse matrices
Vibration
기계공학
Linear equation solver
Nested dissection
Mechanical dynamics
Implicit integration
Knights landing
Online AccessGet full text
ISSN1738-494X
1976-3824
DOI10.1007/s12206-017-0910-x

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Summary:This research proposes an effective implementation of linear equation solver for an implicit integration on a many-core CPU. Although this implementation is applied to a flexible body simulation in mechanical dynamics, it could be also utilized in a wide range of other fields. BFS-based nested dissection and its numerical factorization enables adaptive control of setting operational range as well as positive parallelization compared with traditional DFS-based nested dissection. It brings better parallel efficiency when various sized separators are divided into blocks under a certain size. This study presents an experiment to identify an optimal maximum block size. Sparse matrices from mechanical dynamics software are numerically factorized, and the time results show that CACHE memory mode is appropriate for a better performance than FLAT mode. And it is recommended to split the operational region in accordance with MCDRAM size in this experiment. Our research shows fairly similar performance to DSS included in MKL and speeds up the time approximately 8 - 14 times in comparison with CHOLMOD, a part of SuiteSparse.
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ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-017-0910-x