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29 results on '"Kasia Swirydowicz"'

7. Efficient exascale discretizations: High-order finite element methods.

Author

Tzanio V. Kolev, Paul F. Fischer, Misun Min, Jack J. Dongarra, Jed Brown, Veselin Dobrev, Tim Warburton, Stanimire Tomov, Mark S. Shephard, Ahmad Abdelfattah, Valeria Barra, Natalie Beams, Jean-Sylvain Camier, Noel Chalmers, Yohann Dudouit, Ali Karakus, Ian Karlin, Stefan Kerkemeier, Yu-Hsiang Lan, David S. Medina, Elia Merzari, Aleksandr Obabko, Will Pazner, Thilina Rathnayake, Cameron W. Smith, Lukas Spies, Kasia Swirydowicz, Jeremy L. Thompson, Ananias Tomboulides, and Vladimir Z. Tomov

Published
2021
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19. A Survey of Numerical Methods Utilizing Mixed Precision Arithmetic.

Author

Ahmad Abdelfattah, Hartwig Anzt, Erik G. Boman, Erin C. Carson, Terry Cojean, Jack J. Dongarra, Mark Gates, Thomas Grützmacher, Nicholas J. Higham, Xiaoye Sherry Li, Neil Lindquist, Yang Liu 0179, Jennifer A. Loe, Piotr Luszczek, Pratik Nayak, Srikara Pranesh, Sivasankaran Rajamanickam, Tobias Ribizel, Barry Smith 0002, Kasia Swirydowicz, Stephen J. Thomas, Stanimire Tomov, Yaohung M. Tsai, Ichitaro Yamazaki, and Ulrike Meier Yang

Published
2020

25. Efficient Exascale Discretizations: High-Order Finite Element Methods

Author

Noel Chalmers, Jean-Sylvain Camier, Tzanio V. Kolev, Yohann Dudouit, Kasia Swirydowicz, Lukas Spies, Thilina Rathnayake, Tim Warburton, Valeria Barra, Misun Min, Veselin Dobrev, Ahmad Abdelfattah, Ian Karlin, Jeremy Thompson, Jack Dongarra, Mark S. Shephard, Stanimire Tomov, Vladimir Tomov, Will Pazner, Ananias G. Tomboulides, Jed Brown, Paul Fischer, Ali Karakus, Natalie Beams, Elia Merzari, Stefan Kerkemeier, David Medina, Yu-Hsiang Lan, Cameron W. Smith, and Aleksandr Obabko

Subjects
FOS: Computer and information sciences, Floating point, Discretization, Computer science, 010103 numerical & computational mathematics, Parallel computing, 01 natural sciences, Theoretical Computer Science, Data visualization, Software, FOS: Mathematics, Mathematics - Numerical Analysis, 0101 mathematics, business.industry, Adaptive mesh refinement, Numerical Analysis (math.NA), Finite element method, Exascale computing, 010101 applied mathematics, Range (mathematics), Computer Science - Distributed, Parallel, and Cluster Computing, Hardware and Architecture, Computer Science - Mathematical Software, Distributed, Parallel, and Cluster Computing (cs.DC), business, Mathematical Software (cs.MS)
Abstract

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., 22 pages, 18 figures

Published
2021

26. Advances in Mixed Precision Algorithms: 2021 Edition

Author

Jennifer A. Loe, Xiaoye Li, Y Liu, Barry Smith, Stephen Thomas, S Kruger, A Ayala, Kasia Swirydowicz, Jack Dongarra, Hartwig Anzt, Robert D. Falgout, Ulrike Meier Yang, Daniel Osei-Kuffuor, Terry Cojean, Y Tsai, Erin Carson, N Higham, Mark Gates, Ahmad Abdelfattah, T Gruetzmacher, Tobias Ribizel, Ichitaro Yamazaki, S Cayrols, N Lindquist, Piotr Luszczek, Pratik Nayak, Sri Pranesh, Siva Rajamanickam, Erik G. Boman, and Stan Tomov

Subjects
Computer science, Mixed precision, Algorithm
Published
2021
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28. Compare linear-system solver and preconditioner stacks with emphasis on GPU performance and propose phase-2 NGP solver development pathway

Author

Michael A. Sprague, Kasia Swirydowicz, Paul Mullowney, Luc Berger-Vergiat, Stephen Thomas, Sivasankaran Rajamanickam, Jonathan Joseph Hu, Shreyas Ananthan, Ichitaro Yamazaki, and Jay Sitaraman

Subjects
Development (topology), Preconditioner, Computer science, Linear system, Emphasis (telecommunications), Phase (waves), Solver, Computational science
Published
2020
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29. Deploy threading in Nalu solver stack

Author

Michael A. Sprague, Jonathan Joseph Hu, Kasia Swirydowicz, Alan B. Williams, Andrey Prokopenko, Shreyas Ananthan, and Stephen Thomas

Subjects
Computer science, Threading (manufacturing), Parallel computing, Solver
Published
2018
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