Parallel collision detection of ellipsoids with applications in large scale multibody dynamics

Pazouki, Arman; Mazhar, Hammad; Negrut, Dan

doi:10.1016/j.matcom.2011.11.005

Cited by 21 publications

(17 citation statements)

References 22 publications

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“…Recent granular dynamics results [11] indicate reduction in simulation times on the order of 50-60, and a factor of 60-70 for collision detection of ellipsoids [21]. Discrete Element Method simulation results reported by [10] suggest more than two orders of magnitude speedup when going from sequential to GPU-enabled parallel computing.…”

Section: Discussion What Comes Next?mentioning

confidence: 99%

Enabling Computational Dynamics in Distributed Computing Environments Using a Heterogeneous Computing Template

Negrut

Heyn

Seidl

et al. 2011

Volume 4: 8th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts a and B

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This paper describes a software infrastructure made up of tools and libraries designed to assist developers in implementing computational dynamics applications running on heterogeneous INTRODUCTIONOver the last five years it has become apparent that future increases in computational speed are not going to be fueled by advances in sequential computing technology. There are three main walls that the sequential computing model has hit [1]. Firstly, there is the power dissipation wall caused by the amount of energy that is dissipated per unit area by ever smaller transistors. On a per unit area basis, the amount of energy dissipated by a Pentium 4 processor comes slightly short of that associated with a nuclear power plant. Since the amount of power dissipated scales with the square of the clock frequency, steady further clock frequency increases, which in the past were responsible for most of the processing speed gains, are unlikely. Forced cooling solutions could increase absolute clock rates, but come at a prohibitively high price.The second wall, that is, the memory wall, arose in sequential computing as a manifestation of the gap between

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Section: Discussion What Comes Next?mentioning

confidence: 99%

Enabling Computational Dynamics in Distributed Computing Environments Using a Heterogeneous Computing Template

Negrut

Heyn

Seidl

et al. 2011

Volume 4: 8th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts a and B

View full text Add to dashboard Cite

show abstract

“…FSI applications lead to large problems owing to the number of SPH markers that need to be considered in the solution. The results reported in [64,65] drew on models with close to one million degrees of freedom and thus provide ample opportunities for parallel computing. For the specifics of a parallel implementation of the FSI solution, the interested reader is referred to [42].…”

Section: Mbd: the Need For Parallel Computingmentioning

confidence: 99%

Parallel Computing in Multibody System Dynamics: Why, When, and How

Negrut

Serban

Mazhar

et al. 2014

Journal of Computational and Nonlinear Dynamics

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This paper addresses three questions related to the use of parallel computing in Multibody Dynamics (MBD) simulation. The "why parallel computing?" question is answered based on the argument that in the upcoming decade parallel computing represents the main source of speed improvement in MBD simulation. The answer to "when is it relevant?" is built around the observation that MBD software users are increasingly interested in multi-physics problems that cross disciplinary boundaries and lead to large sets of equations. The "how?" question is addressed by providing an overview of the state of the art in parallel computing. Emphasis is placed on parallelization approaches and support tools specific to MBD simulation. Three MBD applications are presented where parallel computing has been used to increase problem size and/or reduce time to solution. The paper concludes with a summary of best practices relevant when mapping MBD solutions onto parallel computing hardware.

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“…Alternatively [28], the principle of action and reaction could be leveraged to calculate and store the acceleration terms for each inter-penetration. Although the second algorithm reduces the amount of work by re-using the acceleration terms calculated for each inter-penetration, it can not be applied to the SPH method due to the massive amount of memory required to store the data associated with all inter-penetration events.…”

Section: Proximity Calculation and Neighbor Searchmentioning

confidence: 99%

A High Performance Computing Approach to the Simulation of Fluid-Solid interaction Problems with Rigid and Flexible Components

Pazouki¹,

Serban²,

Negrut³

2014

Archive of Mechanical Engineering

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This work outlines a unified multi-threaded, multi-scale High Performance Computing (HPC) approach for the direct numerical simulation of Fluid-Solid Interaction (FSI) problems. The simulation algorithm relies on the extended Smoothed Particle Hydrodynamics (XSPH) method, which approaches the fluid flow in a Lagrangian framework consistent with the Lagrangian tracking of the solid phase. A general 3D rigid body dynamics and an Absolute Nodal Coordinate Formulation (ANCF) are implemented to model rigid and flexible multibody dynamics. The twoway coupling of the fluid and solid phases is supported through use of Boundary Condition Enforcing (BCE) markers that capture the fluid-solid coupling forces by enforcing a no-slip boundary condition. The solid-solid short range interaction, which has a crucial impact on the small-scale behavior of fluid-solid mixtures, is resolved via a lubrication force model. The collective system states are integrated in time using an explicit, multi-rate scheme. To alleviate the heavy computational load, the overall algorithm leverages parallel computing on Graphics Processing Unit (GPU) cards. Performance and scaling analysis are provided for simulations scenarios involving one or multiple phases with up to tens of thousands of solid objects. The software implementation of the approach, called Chrono::Fluid, is part of the Chrono project and available as an open-source software.

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Parallel collision detection of ellipsoids with applications in large scale multibody dynamics

Cited by 21 publications

References 22 publications

Enabling Computational Dynamics in Distributed Computing Environments Using a Heterogeneous Computing Template

Enabling Computational Dynamics in Distributed Computing Environments Using a Heterogeneous Computing Template

Parallel Computing in Multibody System Dynamics: Why, When, and How

A High Performance Computing Approach to the Simulation of Fluid-Solid interaction Problems with Rigid and Flexible Components

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