Scalable implicit finite element solver for massively parallel processing with demonstration to 160K cores

Implicit methods for partial differential equations using unstructured meshes allow for an efficient solution strategy for many real-world problems (e.g., simulation-based virtual surgical planning). Scalable solvers employing these methods not only enable solution of extremely-large practical probl...

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Vydáno v:Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis s. 1 - 12
Hlavní autoři: Sahni, Onkar, Zhou, Min, Shephard, Mark S., Jansen, Kenneth E.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 14.11.2009
Edice:ACM Conferences
Témata:
Applied computing > Physical sciences and engineering > Mathematics and statistics
Computer systems organization > Architectures > Distributed architectures > Grid computing
Computer systems organization > Architectures > Parallel architectures > Multicore architectures
Computer systems organization > Architectures > Serial architectures > Superscalar architectures
Computing methodologies > Modeling and simulation > Simulation types and techniques > Massively parallel and high-performance simulations
Computing methodologies > Parallel computing methodologies > Parallel algorithms
Finite element analysis
Iterative methods
Magnetic cores
Mathematical models
Mathematics of computing > Mathematical analysis > Numerical analysis
Mathematics of computing > Mathematical analysis > Numerical analysis > Computations in finite fields
Numerical models
Partial differential equations
Peer-to-peer computing
Scalability
Software and its engineering > Software organization and properties > Software system structures > Distributed systems organizing principles > Grid computing
Solids
Theory of computation > Design and analysis of algorithms > Parallel algorithms
Topology
ISBN:1605587443, 9781605587448
ISSN:2167-4329
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Shrnutí:Implicit methods for partial differential equations using unstructured meshes allow for an efficient solution strategy for many real-world problems (e.g., simulation-based virtual surgical planning). Scalable solvers employing these methods not only enable solution of extremely-large practical problems but also lead to dramatic compression in time-to-solution. We present a parallelization paradigm and associated procedures that enable our implicit, unstructured flow-solver to achieve strong scalability. We consider fluid-flow examples in two application areas to show the effectiveness of our procedures that yield near-perfect strong-scaling on various (including near-petascale) systems. The first area includes a double-throat nozzle (DTN) whereas the second considers a patient-specific abdominal aortic aneurysm (AAA) model. We present excellent strong-scaling on three cases ranging from relatively small to large; a DTN model with O(106) elements up to 8,192 cores (9 core-doublings), an AAA model with O(108) elements up to 32,768 cores (6 core-doublings) and O(109) elements up to 163,840 cores.
ISBN:1605587443
9781605587448
ISSN:2167-4329
DOI:10.1145/1654059.1654129