Performance of a parallel algebraic multilevel preconditioner for stabilized finite element semiconductor device modeling

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

doi:10.1016/j.jcp.2009.05.024

Cited by 50 publications

(60 citation statements)

References 44 publications

(74 reference statements)

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“…Charon is a semiconductor device simulation computer program [17,29] developed at SNL. It is a transport reaction code used to simulate the performance of semiconductor devices under irradiation.…”

Section: A Semiconductor Device Simulation Codementioning

confidence: 99%

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Barrett

Doerfler

Heroux

et al. 2015

Journal of Parallel and Distributed Computing

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Computational science and engineering application programs are typically large, complex, and dynamic, and are often constrained by distribution limitations. As a means of making tractable rapid explorations of scientific and engineering application programs in the context of new, emerging, and future computing architectures, a suite of "miniapps" has been created to serve as proxies for full scale applications. Each miniapp is designed to represent a key performance characteristic that does or is expected to significantly impact the runtime performance of an application program. In this paper we introduce a methodology for assessing the ability of these miniapps to effectively represent these performance issues. We applied this methodology to four miniapps, examining the linkage between them and an application they are intended to represent. Herein we evaluate the fidelity of that linkage. This work represents the initial steps required to begin to answer the question, "Under what conditions does a miniapp represent a key performance characteristic in a full app?"

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Section: A Semiconductor Device Simulation Codementioning

confidence: 99%

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Barrett

Doerfler

Heroux

et al. 2015

Journal of Parallel and Distributed Computing

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“…We also see this behavior with scalable preconditioners such as an algebraic multilevel preconditioner. Figure 1 shows an example of this difficulty for Charon, a semiconductor device simulation code [1][2][3], with a three level multigrid preconditioner. As the number of MPI tasks increases, the number of linear solver iterations increases ( Figure 1(b)).…”

Section: Motivationmentioning

confidence: 99%

Factors Impacting Performance of Multithreaded Sparse Triangular Solve

Wolf

Heroux

Boman

2011

Lecture Notes in Computer Science

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Abstract. As computational science applications grow more parallel with multi-core supercomputers having hundreds of thousands of computational cores, it will become increasingly difficult for solvers to scale. Our approach is to use hybrid MPI/threaded numerical algorithms to solve these systems in order to reduce the number of MPI tasks and increase the parallel efficiency of the algorithm. However, we need efficient threaded numerical kernels to run on the multi-core nodes in order to achieve good parallel efficiency. In this paper, we focus on improving the performance of a multithreaded triangular solver, an important kernel for preconditioning. We analyze three factors that affect the parallel performance of this threaded kernel and obtain good scalability on the multi-core nodes for a range of matrix sizes.

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“…Charon [39], developed at Sandia National Laboratories, is a semiconductor device simulation code. Charon employs the drift-diffusion model, a coupled system of nonlinear partial differential equations that relate the electric potential to the electron and hole concentrations.…”

Section: Chapter 4 Applicationsmentioning

confidence: 99%

Measuring and tuning energy efficiency on large scale high performance computing platforms.

Laros¹

2011

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Recognition of the importance of power in the field of High Performance Computing, whether it be as an obstacle, expense or design consideration, has never been greater and more pervasive. While research has been conducted on many related aspects, there is a stark absence of work focused on large scale High Performance Computing. Part of the reason is the lack of measurement capability currently available on small or large platforms. Typically, research is conducted using coarse methods of measurement such as inserting a power meter between the power source and the platform, or fine grained measurements using custom instrumented boards (with obvious limitations in scale). To collect the measurements necessary to analyze real scientific computing applications at large scale, an in-situ measurement capability must exist on a large scale capability class platform.In response to this challenge, we exploit the unique power measurement capabilities of the Cray XT architecture to gain an understanding of power use and the effects of tuning. We apply these capabilities at the operating system level by deterministically halting cores when idle. At the application level, we gain an understanding of the power requirements of a range of important DOE/NNSA production scientific computing applications running at large scale (thousands of nodes), while simultaneously collecting current and voltage measurements on the hosting nodes. We examine the effects of both CPU and network bandwidth tuning and demonstrate energy savings opportunities of up to 39% with little or no impact on run-time performance. Capturing scale effects in our 3 experimental results was key. Our results provide strong evidence that next generation large-scale platforms should not only approach CPU frequency scaling differently, but could also benefit from the capability to tune other platform components, such as the network, to achieve energy efficient performance. 5Acknowledgments I would like to acknowledge the following people who have contributed in various ways, all of which significant, to the successful completion of this work: Dr.

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Performance of a parallel algebraic multilevel preconditioner for stabilized finite element semiconductor device modeling

Cited by 50 publications

References 44 publications

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Factors Impacting Performance of Multithreaded Sparse Triangular Solve

Measuring and tuning energy efficiency on large scale high performance computing platforms.

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