Publications

Lin, Paul L.; Shadid, John N.

Abstract not provided.

Shadid, John N.

This brief paper explores the development of scalable, nonlinear, fully-implicit solution methods for a stabilized unstructured finite element (FE) discretization of the 2D incompressible (reduced) resistive MHD system. The discussion considers the stabilized FE formulation in context of a fully-implicit time integration and direct-to-steady-state solution capability. The nonlinear solver strategy employs Newton-Krylov methods, which are preconditioned using fully-coupled algebraic multilevel (AMG) techniques and a new approximate block factorization (ABF) preconditioner. The intent of these preconditioners is to enable robust, scalable and efficient solution approaches for the large-scale sparse linear systems generated by the Newton linearization. We present results for the fully-coupled AMG preconditioner for two prototype problems, a low Lundquist number MHD Faraday conduction pump and moderately-high Lundquist number incompressible magnetic island coalescence problem. For the MHD pump results we explore the scaling of the fully-coupled AMG preconditioner for up to 4096 processors for problems with up to 64M unknowns on a CrayXT3/4. Using the island coalescence problem we explore the weak scaling of the AMG preconditioner and the influence of the Lundquist number on the iteration count. Finally we present some very recent results for the algorithmic scaling of the ABF preconditioner.

Shadid, John N.; Tuminaro, Raymond S.

Abstract not provided.

Shadid, John N.; Tuminaro, Raymond S.; Pawlowski, Roger P.

Abstract not provided.

Lin, Paul L.; Shadid, John N.

Abstract not provided.

Shadid, John N.

Abstract not provided.

Lin, Paul L.; Shadid, John N.

Abstract not provided.

Cyr, Eric C.; Shadid, John N.; Pawlowski, Roger P.; Tuminaro, Raymond S.

Abstract not provided.

Journal of Computational Physics

Lin, Paul T.; Shadid, John N.; Sala, Marzio; Tuminaro, Raymond S.; Hennigan, Gary L.; Hoekstra, Robert J.

In this study results are presented for the large-scale parallel performance of an algebraic multilevel preconditioner for solution of the drift-diffusion model for semiconductor devices. The preconditioner is the key numerical procedure determining the robustness, efficiency and scalability of the fully-coupled Newton-Krylov based, nonlinear solution method that is employed for this system of equations. The coupled system is comprised of a source term dominated Poisson equation for the electric potential, and two convection-diffusion-reaction type equations for the electron and hole concentration. The governing PDEs are discretized in space by a stabilized finite element method. Solution of the discrete system is obtained through a fully-implicit time integrator, a fully-coupled Newton-based nonlinear solver, and a restarted GMRES Krylov linear system solver. The algebraic multilevel preconditioner is based on an aggressive coarsening graph partitioning of the nonzero block structure of the Jacobian matrix. Representative performance results are presented for various choices of multigrid V-cycles and W-cycles and parameter variations for smoothers based on incomplete factorizations. Parallel scalability results are presented for solution of up to 108 unknowns on 4096 processors of a Cray XT3/4 and an IBM POWER eServer system. © 2009 Elsevier Inc. All rights reserved.

Computer Methods in Applied Mechanics and Engineering

Shadid, John N.; Love, Edward L.; Rider, William J.

Abstract not provided.

Shadid, John N.

This preliminary study considers the scaling and performance of a finite element (FE) semiconductor device simulator on a capacity cluster with 272 compute nodes based on a homogeneous multicore node architecture utilizing 16 cores. The inter-node communication backbone for this Tri-Lab Linux Capacity Cluster (TLCC) machine is comprised of an InfiniBand interconnect. The nonuniform memory access (NUMA) nodes consist of 2.2 GHz quad socket/quad core AMD Opteron processors. The performance results for this study are obtained with a FE semiconductor device simulation code (Charon) that is based on a fully-coupled Newton-Krylov solver with domain decomposition and multilevel preconditioners. Scaling and multicore performance results are presented for large-scale problems of 100+ million unknowns on up to 4096 cores. A parallel scaling comparison is also presented with the Cray XT3/4 Red Storm capability platform. The results indicate that an MPI-only programming model for utilizing the multicore nodes is reasonably efficient on all 16 cores per compute node. However, the results also indicated that the multilevel preconditioner, which is critical for large-scale capability type simulations, scales better on the Red Storm machine than the TLCC machine.

SIAM Journal on Numerical Analysis

Pawlowski, Roger P.; Simonis, Joseph P.; Walker, Homer F.; Shadid, John N.

The dogleg method is a classical trust-region technique for globalizing Newton's method. While it is widely used in optimization, including large-scale optimization via truncated-Newton approaches, its implementation in general inexact Newton methods for systems of nonlinear equations can be problematic. In this paper, we first outline a very general dogleg method suitable for the general inexact Newton context and provide a global convergence analysis for it. We then discuss certain issues that may arise with the standard dogleg implementational strategy and propose modified strategies that address them. Newton-Krylov methods have provided important motivation for this work, and we conclude with a report on numerical experiments involving a Newton-GMRES dogleg method applied to benchmark CFD problems. © 2008 Society for Industrial and Applied Mathematics.

Tuminaro, Raymond S.; Shadid, John N.

Abstract not provided.

Hoekstra, Robert J.; Hennigan, Gary L.; Pawlowski, Roger P.; Phipps, Eric T.; Shadid, John N.; Musson, Lawrence M.; Keiter, Eric R.

Abstract not provided.

Bochev, Pavel B.; Hu, Jonathan J.; Siefert, Christopher S.; Shadid, John N.; Lin, Paul L.

Abstract not provided.

Shadid, John N.; Hoekstra, Robert J.

Abstract not provided.

Pawlowski, Roger P.; Shadid, John N.; Phipps, Eric T.

Abstract not provided.

Shadid, John N.

Abstract not provided.

Lin, Paul L.; Shadid, John N.; Hoekstra, Robert J.; Hennigan, Gary L.

Abstract not provided.

Pawlowski, Roger P.; Shadid, John N.; Phipps, Eric T.; Bartlett, Roscoe B.

Abstract not provided.

Shadid, John N.

Abstract not provided.

Journal of Computational Physics

Elman, Howard; Howle, Victoria E.; Shadid, John N.; Shuttleworth, Robert R.; Tuminaro, Raymond S.

In recent years, considerable effort has been placed on developing efficient and robust solution algorithms for the incompressible Navier-Stokes equations based on preconditioned Krylov methods. These include physics-based methods, such as SIMPLE, and purely algebraic preconditioners based on the approximation of the Schur complement. All these techniques can be represented as approximate block factorization (ABF) type preconditioners. The goal is to decompose the application of the preconditioner into simplified sub-systems in which scalable multi-level type solvers can be applied. In this paper we develop a taxonomy of these ideas based on an adaptation of a generalized approximate factorization of the Navier-Stokes system first presented in [A. Quarteroni, F. Saleri, A. Veneziani, Factorization methods for the numerical approximation of Navier-Stokes equations, Computational Methods in Applied Mechanical Engineering 188 (2000) 505-526]. This taxonomy illuminates the similarities and differences among these preconditioners and the central role played by efficient approximation of certain Schur complement operators. We then present a parallel computational study that examines the performance of these methods and compares them to an additive Schwarz domain decomposition (DD) algorithm. Results are presented for two and three-dimensional steady state problems for enclosed domains and inflow/outflow systems on both structured and unstructured meshes. The numerical experiments are performed using MPSalsa, a stabilized finite element code. © 2007 Elsevier Inc. All rights reserved.

Journal of Computational Physics

Banks, Jeffrey W.; Shadid, John N.

Abstract not provided.

Journal of Computational Physics

Banks, Jeffrey W.; Shadid, John N.

Abstract not provided.

Physics of Plasmas

Shadid, John N.; Banks, Jeffrey W.

Abstract not provided.