A Performance and Energy Study of GPU-Resident Preconditioners for Conjugate Gradient Solvers: In the Context of Existing and Novel Approaches

Authors

Joseph Manzano (ORCID: https://orcid.org/0000-0002-6616-0125)
Kevin Barker (ORCID: https://orcid.org/0000-0003-4947-0559)
Jesun Firoz (ORCID: https://orcid.org/0000-0002-8174-2545)
Stephen Thomas (ORCID: https://orcid.org/0000-0001-8007-0057)
Kasia Świrydowicz
Mahantesh Halappanavar

Institutions

Pacific Northwest National Laboratory (US)
Advanced Micro Devices (United States) (US)

Publication Details

Published: 2024-11-13
DOI: https://doi.org/10.1109/sbac-pad63648.2024.00015
Citations: 1
Primary Topic: Advanced Battery Materials and Technologies
Type: article
Field-Weighted Citation Impact: 0.21

Funders

Office of Science
Advanced Scientific Computing Research

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article

A Performance and Energy Study of GPU-Resident Preconditioners for Conjugate Gradient Solvers: In the Context of Existing and Novel Approaches

Joseph Manzano, Kevin Barker, Jesun Firoz, Stephen Thomas et al.

Nov 13, 2024

1 citations

Advanced Battery Materials and Technologies

0.21

article

A Performance and Energy Study of GPU-Resident Preconditioners for Conjugate Gradient Solvers: In the Context of Existing and Novel Approaches

Joseph Manzano, Kevin Barker, Jesun Firoz, Stephen Thomas, Kasia Świrydowicz, Mahantesh Halappanavar

November 13, 2024article en

1 citations

Abstract

No abstract available for this paper.

Pacific Northwest National Laboratory (US), Advanced Micro Devices (United States) (US)

Office of Science, Advanced Scientific Computing Research

Affordable and clean energy

Openalex Percentile: Top 46%

Advanced Battery Materials and Technologies

0.21

DOI Source OpenAlex

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