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Review of CPU and GPU Faddeeva Implementations
The Faddeeva error function is frequently used when computing electric fields generated by two-dimensional Gaussian charge distributions. Numeric evaluation of the Faddeeva function is particularly challenging since there is no single expansion that converges rapidly over the whole complex domain. V...
Autores principales: | , , , , , , , |
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Lenguaje: | eng |
Publicado: |
2016
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.18429/JACoW-IPAC2016-WEPOY044 http://cds.cern.ch/record/2207430 |
_version_ | 1780951699701104640 |
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author | Oeftiger, Adrian Aviral, Anshu De Maria, Riccardo Deniau, Laurent Hegglin, Stefan Li, Kevin McIntosh, Eric Moneta, Lorenzo |
author_facet | Oeftiger, Adrian Aviral, Anshu De Maria, Riccardo Deniau, Laurent Hegglin, Stefan Li, Kevin McIntosh, Eric Moneta, Lorenzo |
author_sort | Oeftiger, Adrian |
collection | CERN |
description | The Faddeeva error function is frequently used when computing electric fields generated by two-dimensional Gaussian charge distributions. Numeric evaluation of the Faddeeva function is particularly challenging since there is no single expansion that converges rapidly over the whole complex domain. Various algorithms exist, even in the recent literature there have been new proposals. The many different implementations in computer codes offer different trade-offs between speed and accuracy. We present an extensive benchmark of selected algorithms and implementations for accuracy, speed and memory footprint, both for CPU and GPU architectures. |
id | oai-inspirehep.net-1470416 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2016 |
record_format | invenio |
spelling | oai-inspirehep.net-14704162022-08-10T12:48:16Zdoi:10.18429/JACoW-IPAC2016-WEPOY044http://cds.cern.ch/record/2207430engOeftiger, AdrianAviral, AnshuDe Maria, RiccardoDeniau, LaurentHegglin, StefanLi, KevinMcIntosh, EricMoneta, LorenzoReview of CPU and GPU Faddeeva ImplementationsAccelerators and Storage RingsThe Faddeeva error function is frequently used when computing electric fields generated by two-dimensional Gaussian charge distributions. Numeric evaluation of the Faddeeva function is particularly challenging since there is no single expansion that converges rapidly over the whole complex domain. Various algorithms exist, even in the recent literature there have been new proposals. The many different implementations in computer codes offer different trade-offs between speed and accuracy. We present an extensive benchmark of selected algorithms and implementations for accuracy, speed and memory footprint, both for CPU and GPU architectures.CERN-ACC-2016-193oai:inspirehep.net:14704162016 |
spellingShingle | Accelerators and Storage Rings Oeftiger, Adrian Aviral, Anshu De Maria, Riccardo Deniau, Laurent Hegglin, Stefan Li, Kevin McIntosh, Eric Moneta, Lorenzo Review of CPU and GPU Faddeeva Implementations |
title | Review of CPU and GPU Faddeeva Implementations |
title_full | Review of CPU and GPU Faddeeva Implementations |
title_fullStr | Review of CPU and GPU Faddeeva Implementations |
title_full_unstemmed | Review of CPU and GPU Faddeeva Implementations |
title_short | Review of CPU and GPU Faddeeva Implementations |
title_sort | review of cpu and gpu faddeeva implementations |
topic | Accelerators and Storage Rings |
url | https://dx.doi.org/10.18429/JACoW-IPAC2016-WEPOY044 http://cds.cern.ch/record/2207430 |
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