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Replicated Computational Results (RCR) Report for “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software”

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Sarah OsbornAuthors Info & Claims

ACM Transactions on Mathematical Software (TOMS), Volume 47, Issue 2

Article No.: 15, Pages 1 - 4

https://doi.org/10.1145/3446000

Published: 01 April 2021 Publication History

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Abstract

The article by Flegar et al. titled “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software” presents a novel, practical implementation of an adaptive precision block-Jacobi preconditioner. Performance results using state-of-the-art GPU architectures for the block-Jacobi preconditioner generation and application demonstrate the practical usability of the method, compared to a traditional full-precision block-Jacobi preconditioner. A production-ready implementation is provided in the Ginkgo numerical linear algebra library.

In this report, the Ginkgo library is reinstalled and performance results are generated to perform a comparison to the original results when using Ginkgo’s Conjugate Gradient solver with either the full or the adaptive precision block-Jacobi preconditioner for a suite of test problems on an NVIDIA GPU accelerator. After completing this process, the published results are deemed reproducible.

References

[1]

GitHub. n.d. Instructions to Reproduce Experiments. Retrieved October 23, 2020 from https://github.com/ginkgo-project/ginkgo/blob/2019toms-adaptive-bj-solver/Reproduce_Experiments.md.

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[2]

Goran Flegar, Hartwig Anzt, Terry Cojean, and Enrique S. Quintana-Orti. 2020. Adaptive precision block-Jacobi for high performance preconditioning in the Ginkgo linear algebra software. ACM Transactions on Mathematical Software 1, 1 (Aug. 2020), Article 1, 27 pages.

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Index Terms

Replicated Computational Results (RCR) Report for “Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software”
1. General and reference
  1. Cross-computing tools and techniques
    1. Verification
2. Mathematics of computing
  1. Mathematical software
    1. Mathematical software performance

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Information

Published In

cover image ACM Transactions on Mathematical Software

ACM Transactions on Mathematical Software Volume 47, Issue 2

June 2021

243 pages

ISSN:0098-3500

EISSN:1557-7295

DOI:10.1145/3459727

Editors:
Zhaojun Bai
University of California at Davis, USA
,
Wolfgang Bangerth
Colorado State University, USA

Issue’s Table of Contents

This paper is authored by an employee(s) of the United States Government and is in the public domain. Non-exclusive copying or redistribution is allowed, provided that the article citation is given and the authors and agency are clearly identified as its source.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2021

Accepted: 01 December 2020

Received: 01 November 2020

Published in TOMS Volume 47, Issue 2

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U.S. Department of Energy by Lawrence Livermore National Laboratory
Neither the United States government nor Lawrence Livermore National Security, LLC
United States government or Lawrence Livermore National Security, LLC
United States government

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