A Comprehensive Set of Tools for Solving Partial Differential Equations; Diffpack

Bruaset, Are Magnus; Langtangen, Hans Petter

doi:10.1007/978-1-4612-1984-2_4

Cited by 51 publications

(30 citation statements)

References 15 publications

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“…For example, the codes SPECFEM3D [Carrington et al 2008], CitcomS [Tan et al 2008], and Rhea [Burstedde et al 2008a] have been written for particular geophysical applications and are not based on general-purpose finite element libraries. The reason, of course, is that none of the libraries widely used in academic and applied finite element simulations-such as PLTMG [Bank 1998], DiffPack [Bruaset and Langtangen 1997;Langtangen 2003], libMesh [Kirk et al 2006], Getfem++ [Renard and Pommier 2006], OOFEM [Patzák and Bittnar 2001], FEniCS/DOLFIN [Logg 2007;Logg and Wells 2010], or deal.II up to version 6.x [Bangerth et al 2007;Bangerth and Kanschat 2011]-support massively parallel computations that will run on thousands of processors and routinely solve problems with hundreds of millions or billions of cells and several billion unknowns.…”

Section: Introductionmentioning

confidence: 99%

Algorithms and data structures for massively parallel generic adaptive finite element codes

Bangerth

Burstedde

Heister

et al. 2011

ACM Trans. Math. Softw.

173

154

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Today's largest supercomputers have 100,000s of processor cores and offer the potential to solve partial differential equations discretized by billions of unknowns. However, the complexity of scaling to such large machines and problem sizes has so far prevented the emergence of generic software libraries that support such computations, although these would lower the threshold of entry and enable many more applications to benefit from large-scale computing.We are concerned with providing this functionality for mesh-adaptive finite element computations. We assume the existence of an "oracle" that implements the generation and modification of an adaptive mesh distributed across many processors, and that responds to queries about its structure. Based on querying the oracle, we develop scalable algorithms and data structures for generic finite element methods. Specifically, we consider the parallel distribution of mesh data, global enumeration of degrees of freedom, constraints, and postprocessing. Our algorithms remove the bottlenecks that typically limit large-scale adaptive finite element analyses.We demonstrate scalability of complete finite element workflows on up to 16,384 processors. An implementation of the proposed algorithms, based on the open source software p4est as mesh oracle, is provided under an open source license through the widely used deal.II finite element software library.

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Section: Introductionmentioning

confidence: 99%

Algorithms and data structures for massively parallel generic adaptive finite element codes

Bangerth

Burstedde

Heister

et al. 2011

ACM Trans. Math. Softw.

173

154

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“…A variation on this is the use of the Diffpack software package [7]. It is a support environment for the development of object-oriented numerical software, and has embodied many good software engineering features and practices.…”

Section: A Discussion Of Relevant Coding Techniquesmentioning

confidence: 99%

“…Currently, there is therefore, a considerable amount of research being undertaken on the restructuring and redevelopment of numerical software and a genuine in-terest in using modern software engineering practices in the process, e.g. [1,2,[6][7][8]33,34].…”

Section: Introductionmentioning

confidence: 99%

Coordinate Free Programming of Computational Fluid Dynamics Problems

Grant

Haveraaen

Webster

2000

Scientific Programming

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It has long been acknowledged that the development of scientific applications is in need of better software engineering practices. Here we contrast the difference between conventional software development of CFD codes with a method based on coordinate free mathematics. The former approach leads to programs where different aspects, such as the discretisation technique and the coordinate systems, can get entangled with the solver algorithm. The latter approach yields programs that segregate these concerns into fully independent software modules. Such considerations are important for the construction of numerical codes for practical problems. The two approaches are illustrated on the coating problem: the simulation of coating a wire with a polymer.

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“…Object-oriented software methods enable developers to focus on a small part of a complex system, rather than attempting to develop and maintain a monolithic application. Furthermore, reuse justifies expending significant effort on the development of highly optimized object toolkits encapsulating expert knowledge, such as Diffpack [12], Overture [13], ParPre [14], SAMRAI [15], and PETSc [16,17].…”

Section: Parallel Numerical Libraries and Common Interface Effortsmentioning

confidence: 99%

Parallel components for PDEs and optimization: some issues and experiences

et al. 2002

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High-performance simulations in computational science often involve the combined software contributions of multidisciplinary teams of scientists, engineers, mathematicians, and computer scientists. One goal of component-based software engineering in large-scale scientific simulations is to help manage such complexity by enabling better interoperability among codes developed by different groups. This paper discusses recent work on building component interfaces and implementations in parallel numerical toolkits for mesh manipulations, discretization, linear algebra, and optimization. We consider several motivating applications involving partial differential equations and unconstrained minimization to demonstrate this approach and evaluate performance.

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A Comprehensive Set of Tools for Solving Partial Differential Equations; Diffpack

Cited by 51 publications

References 15 publications

Algorithms and data structures for massively parallel generic adaptive finite element codes

Algorithms and data structures for massively parallel generic adaptive finite element codes

Coordinate Free Programming of Computational Fluid Dynamics Problems

Parallel components for PDEs and optimization: some issues and experiences

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