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Towards extreme-scale simulations for low mach fluids with second-generation trilinos

Parallel Processing Letters

Lin, Paul L.; Bettencourt, Matthew T.; Domino, Stefan P.; Fisher, Travis C.; Hoemmen, Mark F.; Hu, Jonathan J.; Phipps, Eric T.; Prokopenko, Andrey V.; Rajamanickam, Sivasankaran R.; Siefert, Christopher S.; Kennon, Stephen

Trilinos is an object-oriented software framework for the solution of large-scale, complex multi-physics engineering and scientific problems. While Trilinos was originally designed for scalable solutions of large problems, the fidelity needed by many simulations is significantly greater than what one could have envisioned two decades ago. When problem sizes exceed a billion elements even scalable applications and solver stacks require a complete revision. The second-generation Trilinos employs C++ templates in order to solve arbitrarily large problems. We present a case study of the integration of Trilinos with a low Mach fluids engineering application (SIERRA low Mach module/Nalu). Through the use of improved algorithms and better software engineering practices, we demonstrate good weak scaling for up to a nine billion element large eddy simulation (LES) problem on unstructured meshes with a 27 billion row matrix on 524,288 cores of an IBM Blue Gene/Q platform.

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TYPE Journal Article YEAR 2014
ScopusOSTIDOI

Numerical Continuation Methods for Intrusive Uncertainty Quantification Studies

Safta, Cosmin S.; Najm, H.N.; Phipps, Eric T.

Rigorous modeling of engineering systems relies on efficient propagation of uncertainty from input parameters to model outputs. In recent years, there has been substantial development of probabilistic polynomial chaos (PC) Uncertainty Quantification (UQ) methods, enabling studies in expensive computational models. One approach, termed ”intrusive”, involving reformulation of the governing equations, has been found to have superior computational performance compared to non-intrusive sampling-based methods in relevant large-scale problems, particularly in the context of emerging architectures. However, the utility of intrusive methods has been severely limited due to detrimental numerical instabilities associated with strong nonlinear physics. Previous methods for stabilizing these constructions tend to add unacceptably high computational costs, particularly in problems with many uncertain parameters. In order to address these challenges, we propose to adapt and improve numerical continuation methods for the robust time integration of intrusive PC system dynamics. We propose adaptive methods, starting with a small uncertainty for which the model has stable behavior and gradually moving to larger uncertainty where the instabilities are rampant, in a manner that provides a suitable solution.

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TYPE SAND Report YEAR 2014
OSTIDOI

Exploring Heterogeneous Multicore Architectures for Advanced Embedded Uncertainty Quantification

Phipps, Eric T.; Edwards, Harold C.; Hu, Jonathan J.

We explore rearrangements of classical uncertainty quantification methods with the aim of achieving higher aggregate performance for uncertainty quantification calculations on emerging multicore and many core architectures. We show a rearrangement of the stochastic Galerkin method leads to improved performance and scalability on several computational architectures whereby uncertainty information is propagated at the lowest levels of the simulation code improving memory access patterns, exposing new dimensions of fine grained parallelism, and reducing communication. We also develop a general framework for implementing such rearrangements for a diverse set of uncertainty quantification algorithms as well as computational simulation codes to which they are applied.

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TYPE SAND Report YEAR 2014
OSTIDOI

Efficient expression templates for operator overloading-based automatic differentiation

Lecture Notes in Computational Science and Engineering

Phipps, Eric T.; Pawlowski, Roger P.

Expression templates are a well-known set of techniques for improving the efficiency of operator overloading-based forward mode automatic differentiation schemes in the C++ programming language by translating the differentiation from individual operators to whole expressions. However standard expression template approaches result in a large amount of duplicate computation, particularly for large expression trees, degrading their performance. In this paper we describe several techniques for improving the efficiency of expression templates and their implementation in the automatic differentiation package Sacado (Phipps et al., Advances in automatic differentiation, Lecture notes in computational science and engineering, Springer, Berlin, 2008; Phipps and Gay, Sacado automatic differentiation package. http://trilinos.sandia.gov/packages/sacado/, 2011). We demonstrate their improved efficiency through test functions as well as their application to differentiation of a large-scale fluid dynamics simulation code. © 2012 Springer-Verlag.

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TYPE Conference YEAR 2012
ScopusOSTI
Results 51–100 of 144
Results 51–100 of 144