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ML Enhancements via the Calculation of Rigid Body Modes (RBMs) for Mechanics Problems Implemented within the Albany Code Base

Kalashnikova, Irina

The algebraic multigrid approach known as smoothed aggregation is very efficient at solv- ing systems that arise from elasticity problems [1]. In order to construct an efficient algebraic multilevel method, a multigrid solver should be provided with a small set of vectors that repre- sent the error components that are difficult to resolve. It is well-known [2, 5] that for linear elas- ticity problems, these components correspond to the the so-called rigid body modes (RBMs). The present document summarizes some new development within the Albany code base that has enabled the application of algebraic multigrid preconditioners from the ML package [2] of Trilinos to mechanics problems implemented within Albany via a new function that calculates the RBMs using information about the problem's underlying mesh. The performance of these preconditioners is evaluated on four problems: a 3D static elasticity problem, a 3D non-linear elasticity problem, a 3D thermo-elasticity problem, and a 3D thermo-poro-plasticity problem. The tests reveal the superiority of the ML preconditioners over ILU preconditioners from the Trilinos Ifpack package [4] for mechanics problems in Albany. Draft -- Draft -- Draft -- Draft -- Draft -- Draft -- Draf

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TYPE SAND Report YEAR 2016
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Performance Portability of the Aeras Atmosphere Model to Next Generation Architectures using Kokkos

Watkins, Jerry E.; Kalashnikova, Irina

The subject of this report is the performance portability of the Aeras global atmosphere dynamical core (implemented within the Albany multi-physics code) to new and emerging architecture ma- chines using the Kokkos library and programming model. We describe the process of refactoring the finite element assembly process for the 3D hydrostatic model in Aeras and highlight common issues associated with development on GPU architectures. After giving detailed build and execute instructions for Aeras with MPI, OpenMP and CUDA on the Shannon cluster at Sandia National Laboratories and the Titan supercomputer at Oak Ridge National Laboratory, we evaluate the per- formance of the code on a canonical test case known as the baroclinic instability problem. We show a speedup of up to 4 times on 8 OpenMP threads, but we were unable to achieve a speedup on the GPU due to memory constraints. We conclude by providing methods for improving the performance of the code for future optimization.

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TYPE SAND Report YEAR 2016
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The Aeras Next Generation Global Atmosphere Model

Bosler, Peter A.; Bova, S.W.; Demeshko, Irina P.; Fike, Jeffrey A.; Guba, Oksana G.; Overfelt, James R.; Roesler, Erika L.; Salinger, Andrew G.; Smith, Thomas M.; Kalashnikova, Irina; Watkins, Jerry E.

The Next Generation Global Atmosphere Model LDRD project developed a suite of atmosphere models: a shallow water model, an x - z hydrostatic model, and a 3D hydrostatic model, by using Albany, a finite element code. Albany provides access to a large suite of leading-edge Sandia high- performance computing technologies enabled by Trilinos, Dakota, and Sierra. The next-generation capabilities most relevant to a global atmosphere model are performance portability and embedded uncertainty quantification (UQ). Performance portability is the capability for a single code base to run efficiently on diverse set of advanced computing architectures, such as multi-core threading or GPUs. Embedded UQ refers to simulation algorithms that have been modified to aid in the quantifying of uncertainties. In our case, this means running multiple samples for an ensemble concurrently, and reaping certain performance benefits. We demonstrate the effectiveness of these approaches here as a prelude to introducing them into ACME.

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TYPE SAND Report YEAR 2016
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Model Reduction for Compressible Cavity Simulations Towards Uncertainty Quantification of Structural Loading

Kalashnikova, Irina; Balajewicz, Maciej B.; Barone, Matthew F.; Carlberg, Kevin T.; Fike, Jeffrey A.; Mussoni, Erin E.

This report summarizes FY16 progress towards enabling uncertainty quantification for compress- ible cavity simulations using model order reduction (MOR). The targeted application is the quan- tification of the captive-carry environment for the design and qualification of nuclear weapons systems. To accurately simulate this scenario, Large Eddy Simulations (LES) require very fine meshes and long run times, which lead to week -long runs even on parallel state-of-the-art super- computers. MOR can reduce substantially the CPU-time requirement for these simulations. We describe two approaches for model order reduction for nonlinear systems, which can yield sig- nificant speed-ups when combined with hyper-reduction: the Proper Orthogonal Decomposition (POD)/Galerkin approach and the POD/Least-Squares Petrov Galerkin (LSPG) approach. The im- plementation of these methods within the in-house compressible flow solver SPARC is discussed. Next, a method for stabilizing and enhancing low-dimensional reduced bases that was developed as a part of this project is detailed. This approach is based on a premise termed "minimal sub- space rotation", and has the advantage of yielding ROMs that are more stable and accurate for long-time compressible cavity simulations. Numerical results for some laminar cavity problems aimed at gauging the viability of the proposed model reduction methodologies are presented and discussed.

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TYPE SAND Report YEAR 2016
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Results 76–100 of 156
Results 76–100 of 156