具有参数依赖性的建筑性能模型的增量校准

Manar Mazkatli, David Monschein, Johannes Grohmann, A. Koziolek
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引用次数: 9

摘要

基于体系结构的性能预测(AbPP)允许对系统的性能进行评估,并在不测量所有替代方案的情况下回答假设问题。创建模型时的一个困难是性能模型参数(Performance Model Parameters,如资源需求、循环迭代次数和分支概率)取决于各种影响因素,如输入数据、使用的硬件和应用的工作负载。为了实现大范围的假设问题,性能模型(Performance Models, pm)需要具有预测能力,而不仅仅是用来校准模型的测量结果。因此,pmp需要对可能变化的影响因素进行参数化。现有的方法允许通过测量整个系统来估计参数化的pmp。因此,它们的成本太高,不能经常应用,直到每次代码更改之后。此外,它们在重新校准时不会对模型进行手动更改。在这项工作中,我们提出了性能模型的持续集成(CIPM),它增量地提取和校准性能模型,包括参数依赖性。CIPM通过更新PM和自适应地检测更改的部分来响应源代码更改。为了允许AbPP, CIPM使用测量(由性能测试或在生产中执行系统产生)和统计分析(例如,回归分析和决策树)来估计参数化的pmp。此外,我们的方法响应产品变更(例如,负载或部署变更),并相应地校准pm的使用和部署部分。为了评估,我们使用了两个案例研究。评估结果表明,我们能够逐步准确地校准PM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incremental Calibration of Architectural Performance Models with Parametric Dependencies
Architecture-based Performance Prediction (AbPP) allows evaluation of the performance of systems and to answer what-if questions without measurements for all alternatives. A difficulty when creating models is that Performance Model Parameters (PMPs, such as resource demands, loop iteration numbers and branch probabilities) depend on various influencing factors like input data, used hardware and the applied workload. To enable a broad range of what-if questions, Performance Models (PMs) need to have predictive power beyond what has been measured to calibrate the models. Thus, PMPs need to be parametrized over the influencing factors that may vary. Existing approaches allow for the estimation of the parametrized PMPs by measuring the complete system. Thus, they are too costly to be applied frequently, up to after each code change. Moreover, they do not keep manual changes to the model when recalibrating. In this work, we present the Continuous Integration of Performance Models (CIPM), which incrementally extracts and calibrates the performance model, including parametric dependencies. CIPM responds to source code changes by updating the PM and adaptively instrumenting the changed parts. To allow AbPP, CIPM estimates the parametrized PMPs using the measurements (generated by performance tests or executing the system in production) and statistical analysis, e.g., regression analysis and decision trees. Additionally, our approach responds to production changes (e.g., load or deployment changes) and calibrates the usage and deployment parts of PMs accordingly. For the evaluation, we used two case studies. Evaluation results show that we were able to calibrate the PM incrementally and accurately.
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