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.

中文翻译：

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Arpra：任意精度范围分析库


受到研究尖峰神经网络模拟重现性的挑战的激励，我们开发了 Arpra 库：一个开源 C 库，用于基于混合区间算术 (IA)/仿射算术 (AA) 方法的任意精度范围分析。 Arpra 在此方法的基础上实现了一种新颖的混合修剪 IA/AA，其中使用 IA 范围的信息来最小化 AA 范围的误差项。通过使用 MPFR 库将中间值计算为扩展精度变量，可以最大限度地减少开销舍入误差。在开销误差与范围宽度之比较高的情况下，此优化最有用。三种新颖的仿射项减少策略通过合并不太重要的仿射项来提高记忆效率。我们还研究了使用混合修剪 IA/AA 和其他 AA 方法来研究不稳定尖峰神经网络模拟中的再现性的可行性。