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Arpra: an arbitrary precision range analysis library
Version 2 2023-06-12, 09:53
Version 1 2023-06-10, 00:05
journal contribution
posted on 2023-06-12, 09:53 authored by James Turner, Thomas NowotnyThomas NowotnyMotivated 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.
Funding
Brains on Board: Neuromorphic Control of Flying Robots; G1980; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/P006094/1
HBP SGA3 - Human Brain Project Specific Grant Agreement 3; G2945; EUROPEAN UNION; 945539
Human Brain Project Specific Grant Agreement 2 — HBP SGA2; G2410; EUROPEAN UNION; 785907
History
Publication status
- Published
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- Published version
Journal
Frontiers in NeuroinformaticsISSN
1662-5196Publisher
Frontiers MediaExternal DOI
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15Page range
1-21Article number
a632729Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2021-06-14First Open Access (FOA) Date
2021-06-29First Compliant Deposit (FCD) Date
2021-06-12Usage metrics
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