Compositional Performance Analysis in Python with pyCPA
Compositional Performance Analysis in Python with pyCPA
复制标题
使用 pyCPA 在 Python 中进行成分性能分析
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
R. Ernst
中科院分区：
文献类型：
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作者：
Jonas Diemer;Philip Axer;R. Ernst
当前和未来的嵌入式和分布式系统的时序行为变得越来越复杂。同时，许多应用领域，如安全关键系统，需要验证最坏情况下的时序行为。获得声音保证是一项复杂的任务，可以通过作曲演奏分析来解决。这种方法正式计算系统的每个组件上的最坏情况下的时序场景，并从这些本地分析中得出端到端的系统时序。在本文中，我们提出了pyCPA，一个开源的实现的组合性能分析方法。针对学术界，pyCPA提供了一些功能，例如支持最常见的实时任务，通信任务的路径分析，导入和导出功能以及不同的可视化。因此，pyCPA是一个有价值的贡献的研究领域。
The timing behavior of current and future embedded and distributed systems becomes increasingly complex. At the same time, many application fields such as safety-critical systems require a verification of worst-case timing behavior. Deriving sound guarantees is a complex task, which can be solved by Compositional Performance Analysis. This approach formally computes worst-case timing scenarios on each component of the system and derives end-to-end system timing from these local analyses. In this paper, we present pyCPA, an open-source implementation of the Compositional Performance Analysis approach. Targeted towards academia, pyCPA offers features such as support for the most common real-time schedulers, path analysis for communicating tasks, import and export functionality, and different visualizations. Thus, pyCPA is a valuable contribution to the research domain.