高性能 Stingray
High performance Stingray
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中文总结 AI 辅助
本文测试开源Python库Stingray最新版本的性能与鲁棒性，针对大小数据集分别实现并行处理方案与GPU移植可能性探索，提升其适配性与扩展性。
中文摘要 AI 辅助
X射线天体物理天体在从几分之一秒到数年的广泛时标上表现出变异性。开源Python库Stingray专注于高能天体物理学的时间序列分析，包含最常用的傅里叶分析技术，还支持一系列额外扩展，可用于分析脉冲星数据、模拟数据集和执行统计建模。在代码的最新版本中，已实现新的傅里叶方法并支持更多任务，使Stingray更易于适配和扩展到其他用例。本文重点测试最新版本Stingray的性能与鲁棒性，考虑代码处理小于或大于内存（RAM）的数据集时的不同表现，解决处理大数据集的问题并实现该场景下最慢方法的并行版本；对于小数据集，研究将代码关键函数移植到GPU的可能性。
英文摘要
X-ray astrophysical objects show variability on a wide range of timescales, i.e. from fractions of second to years. The open-source Python library stingray is able to perform time series analyses with a focus on high-energy astrophysics. Comprising the most commonly used Fourier analyses techniques, it also supports a range of additional extensions able to analyse pulsar data, simulate data sets and perform statistical modelling. With the latest release of the code, new Fourier methods and support for additional missions have been implemented, making Stingray more easily adaptable and extendable to other use cases. In this paper we focus on testing the performance and robustness of the latest version of stingray . We consider the possible different behaviour of the code when dealing with data sets smaller or larger than the RAM. We address the problem of dealing with large data sets and implemented parallel versions of the slowest methods in this regime. For small data sets, we investigate the possibility of a porting in GPU of key functions of the code.
发表机构
- INAF-Osservatorio Astronomico di Cagliari（意大利国家天体物理研究所卡利亚里天文台）
- Anton Pannekoek Institute for Astronomy, University of Amsterdam（阿姆斯特丹大学安东·潘内科克天文学研究所）
- Scuola Universitaria Superiore IUSS Pavia（帕维亚高等大学学院）
机构由 AI 辅助整理，请以论文原文为准。
