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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...

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Detalles Bibliográficos
Autores principales: Oeftiger, Adrian, Aviral, Anshu, De Maria, Riccardo, Deniau, Laurent, Hegglin, Stefan, Li, Kevin, McIntosh, Eric, Moneta, Lorenzo
Lenguaje:eng
Publicado: 2016
Materias:
Acceso en línea:https://dx.doi.org/10.18429/JACoW-IPAC2016-WEPOY044
http://cds.cern.ch/record/2207430
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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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