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

Abstract

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(10<sup>6</sup>) elements up to 8,192 cores (9 core-doublings), an AAA model with O(10<sup>8</sup>) elements up to 32,768 cores (6 core-doublings) and O(10<sup>9</sup>) elements up to 163,840 cores.

DOI: 10.1145/1654059.1654129

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Cite this paper

@article{Sahni2009ScalableIF, title={Scalable implicit finite element solver for massively parallel processing with demonstration to 160K cores}, author={Onkar Sahni and Min Zhou and Mark S. Shephard and Kenneth E. Jansen}, journal={Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis}, year={2009}, pages={1-12} }