Interoperable mesh components for large-scale, distributed-memory simulations

Devine, Karen Dragon; Diachin, Lori Freitag; Kraftcheck, Jason A.; Jansen, Kenneth E.; Leung, Vitus J.; Luo, Xiang; Miller, Mark; Ollivier‐Gooch, Carl; Ovcharenko, A. G.; Sahni, Onkar; Shephard, Mark S.; Tautges, Timothy J.; Xie, Ting; Zhou, Min

doi:10.1088/1742-6596/180/1/012011

Cited by 6 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The various ITAPS iMesh implementations (Devine et al, 2009;Chand et al, 2008;Ollivier-Gooch et al, 2010), and others, are based on such a topological abstraction of the mesh. Although it is possible to base the representations used on only the specific mesh entities and adjacencies used in the linkage of specific components, many implementations employ a complete representation in the sense that any of the desired 12 mesh adjacencies can be determined in O(1) time (i.e., are not a function of the number of mesh entities) (Beall and Shephard, 1997;Remacle and Shephard, 2003;Seol and Shephard, 2006).…”

Section: Relating Multiphysics Spatial Discretizationsmentioning

confidence: 99%

Multiphysics simulations: challenges and opportunities.

Keyes¹,

McInnes²,

Woodward³

et al. 2012

149

View full text Add to dashboard Cite

We consider multiphysics applications from algorithmic and architectural perspectives, where "algorithmic" includes both mathematical analysis and computational complexity and "architectural" includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

show abstract

Section: Relating Multiphysics Spatial Discretizationsmentioning

confidence: 99%

Multiphysics simulations: challenges and opportunities.

Keyes¹,

McInnes²,

Woodward³

et al. 2012

149

View full text Add to dashboard Cite

show abstract

“…When linking independently developed components with different internal data representations, this approach must employ a lingua franca perspective in which the information passed in the calls can be easily extracted from, and placed into, the internal data structures of the individual components. Such an approach was adopted by the ITAPS SciDAC center for dealing with unstructured meshes and the field (solution) information defined over those meshes (Chand et al, 2008; Devine et al, 2009; Ollivier-Gooch et al, 2010).…”

Section: Multiphysics Softwarementioning

confidence: 99%

Multiphysics simulations

Keyes

McInnes

Woodward

et al. 2013

The International Journal of High Performance Computing Applica

273

View full text Add to dashboard Cite

We consider multiphysics applications from algorithmic and architectural perspectives, where “algorithmic” includes both mathematical analysis and computational complexity, and “architectural” includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

show abstract

“…The ghost copy must also store information about its owner entity and the owning part. This provides a mechanism to keep the owner entity synchronized with their ghost copies on other parts and vice versa [6,16]. By storing this information locally, the following queries can be answered without involving remote communication between parts (i) the ownership information of a ghost entity or (ii) the information about the ghost copies of an original entity.…”

Section: Ownership Of Ghostsmentioning

confidence: 99%

A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database

Mubarak

Seol

et al. 2013

Scientific Programming

View full text Add to dashboard Cite

Critical to the scalability of parallel adaptive simulations are parallel control functions including load balancing, reduced inter-process communication and optimal data decomposition. In distributed meshes, many mesh-based applications frequently access neighborhood information for computational purposes which must be transmitted efficiently to avoid parallel performance degradation when the neighbors are on different processors. This article presents a parallel algorithm of creating and deleting data copies, referred to as ghost copies, which localize neighborhood data for computation purposes while minimizing inter-process communication. The key characteristics of the algorithm are: (1) It can create ghost copies of any permissible topological order in a 1D, 2D or 3D mesh based on selected adjacencies. (2) It exploits neighborhood communication patterns during the ghost creation process thus eliminating all-to-all communication. (3) For applications that need neighbors of neighbors, the algorithm can creatennumber of ghost layers up to a point where the whole partitioned mesh can be ghosted. Strong and weak scaling results are presented for the IBM BG/P and Cray XE6 architectures up to a core count of 32,768 processors. The algorithm also leads to scalable results when used in a parallel super-convergent patch recovery error estimator, an application that frequently accesses neighborhood data to carry out computation.

show abstract

Interoperable mesh components for large-scale, distributed-memory simulations

Cited by 6 publications

References 7 publications

Multiphysics simulations: challenges and opportunities.

Multiphysics simulations: challenges and opportunities.

Multiphysics simulations

A Parallel Ghosting Algorithm for The Flexible Distributed Mesh Database

Contact Info

Product

Resources

About