Nek5000 Enhancements for Faster Running Analysis

Min, Misun; Tomboulides, Ananias; Fischer, Paul; Merzari, Elia; Shaver, Dillon; Martínez, Javier; Yuan, Hongyong; Lan, Yu-Hsiang

doi:10.2172/1670708

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…The Brown et al 2017;Tomov et al 2018;Min et al 2019a;Shephard et al 2019;Tomov et al 2019;Kolev et al 2020) and other project reports (Merzari et al 2017;Min et al 2019b;Ameen et al 2020). Some of the results are shown in Figures 17-18.…”

Section: Application Integrationmentioning

confidence: 96%

Efficient Exascale Discretizations: High-Order Finite Element Methods

Kolev,

Fischer,

Min

et al. 2021

Preprint

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Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free / partially-assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of nextgeneration discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

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Section: Application Integrationmentioning

confidence: 96%

Efficient Exascale Discretizations: High-Order Finite Element Methods

Kolev,

Fischer,

Min

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The outcomes of the CEED technologies have been integrated into the open source codes, Nek5000/CEM/RS, MFEM, libCEED and libParanumal. Their impact in various application problems have been demonstrated in the CEED milestone reports (Brown et al, 2017; Kolev et al, 2020; Min et al, 2017, 2019a; Shephard et al, 2019; Tomov et al, 2018, 2019) and other project reports (Ameen et al, 2020; Merzari et al, 2017; Min et al, 2019b). Some of the results are shown in Figures 17 and 18.…”

Section: Application Integrationmentioning

confidence: 96%

Efficient exascale discretizations: High-order finite element methods

Kolev

Fischer

Min

et al. 2021

The International Journal of High Performance Computing Applica

Self Cite

View full text Add to dashboard Cite

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

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Nek5000 Enhancements for Faster Running Analysis

Abstract: 60439. For information about Argonne and its pioneering science and technology programs, see www.anl.gov.

Cited by 2 publications

References 10 publications

Efficient Exascale Discretizations: High-Order Finite Element Methods

Efficient Exascale Discretizations: High-Order Finite Element Methods

Efficient exascale discretizations: High-order finite element methods

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