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Arpra: An Arbitrary Precision Range Analysis Library

Motivated by the challenge of investigating the reproducibility of spiking neural network simulations, we have developed the Arpra library: an open source C library for arbitrary precision range analysis based on the mixed Interval Arithmetic (IA)/Affine Arithmetic (AA) method. Arpra builds on this...

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Detalles Bibliográficos
Autores principales: Turner, James Paul, Nowotny, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267943/
https://www.ncbi.nlm.nih.gov/pubmed/34248530
http://dx.doi.org/10.3389/fninf.2021.632729
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author Turner, James Paul
Nowotny, Thomas
author_facet Turner, James Paul
Nowotny, Thomas
author_sort Turner, James Paul
collection PubMed
description Motivated by the challenge of investigating the reproducibility of spiking neural network simulations, we have developed the Arpra library: an open source C library for arbitrary precision range analysis based on the mixed Interval Arithmetic (IA)/Affine Arithmetic (AA) method. Arpra builds on this method by implementing a novel mixed trimmed IA/AA, in which the error terms of AA ranges are minimised using information from IA ranges. Overhead rounding error is minimised by computing intermediate values as extended precision variables using the MPFR library. This optimisation is most useful in cases where the ratio of overhead error to range width is high. Three novel affine term reduction strategies improve memory efficiency by merging affine terms of lesser significance. We also investigate the viability of using mixed trimmed IA/AA and other AA methods for studying reproducibility in unstable spiking neural network simulations.
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spelling pubmed-82679432021-07-10 Arpra: An Arbitrary Precision Range Analysis Library Turner, James Paul Nowotny, Thomas Front Neuroinform Neuroscience Motivated by the challenge of investigating the reproducibility of spiking neural network simulations, we have developed the Arpra library: an open source C library for arbitrary precision range analysis based on the mixed Interval Arithmetic (IA)/Affine Arithmetic (AA) method. Arpra builds on this method by implementing a novel mixed trimmed IA/AA, in which the error terms of AA ranges are minimised using information from IA ranges. Overhead rounding error is minimised by computing intermediate values as extended precision variables using the MPFR library. This optimisation is most useful in cases where the ratio of overhead error to range width is high. Three novel affine term reduction strategies improve memory efficiency by merging affine terms of lesser significance. We also investigate the viability of using mixed trimmed IA/AA and other AA methods for studying reproducibility in unstable spiking neural network simulations. Frontiers Media S.A. 2021-06-25 /pmc/articles/PMC8267943/ /pubmed/34248530 http://dx.doi.org/10.3389/fninf.2021.632729 Text en Copyright © 2021 Turner and Nowotny. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Turner, James Paul
Nowotny, Thomas
Arpra: An Arbitrary Precision Range Analysis Library
title Arpra: An Arbitrary Precision Range Analysis Library
title_full Arpra: An Arbitrary Precision Range Analysis Library
title_fullStr Arpra: An Arbitrary Precision Range Analysis Library
title_full_unstemmed Arpra: An Arbitrary Precision Range Analysis Library
title_short Arpra: An Arbitrary Precision Range Analysis Library
title_sort arpra: an arbitrary precision range analysis library
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8267943/
https://www.ncbi.nlm.nih.gov/pubmed/34248530
http://dx.doi.org/10.3389/fninf.2021.632729
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