Hardware-Validated CPU Performance and Energy Modelling
Hardware-Validated CPU Performance and Energy Modelling
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经过硬件验证的 CPU 性能和能耗建模
DOI：
10.1109/ispass.2018.00013
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
B. Al
中科院分区：
文献类型：
--
作者：
M. J. Walker;Sascha Bischoff;S. Diestelhorst;G. Merrett;B. Al
全系统仿真框架（例如GEM5）被广泛用于评估研究思想和设计空间探索。此外，近年来能源效率已成为关键设计的限制，许多作品使用单独的功率建模框架来评估能源消耗。尽管此类工具很方便且灵活，但已知它们包含误差源，这些错误源通常不完全理解，并可能影响调查得出的结论。这项工作使CPU的准确，硬件验证的性能，功率和能源建模首先提出一种方法，以评估和识别CPU性能模型中的错误源，其次开发了用于与此类性能模型一起使用的经验功率模型。分层聚类，相关分析和回归技术用于识别错误源，而无需详细的CPU规范并使现有模型得到改进，开发新模型，模拟器更改的验证以及模型适用性的特定用例。此外，提出了宝石开源软件工具，该工具可以自动化表征硬件平台，识别GEM5模型中的错误源，应用功率分析以及量化错误对性能，功率和能量估计的影响。此外，发现执行时间中的平均百分比误差从同一GEM5模型的两个版本之间从-51％降至 +10％，这强调了对自动化工具的需求，以验证模型免受参考硬件的验证，以确保准确性和一致性。
Full-system simulation frameworks such as gem5 are used extensively to evaluate research ideas and for design-space exploration. Moreover, energy-efficiency has become the key design constraint in recent years and many works use a separate power modelling framework to evaluate energy consumption. While such tools are convenient and flexible, they are known to contain sources of error which are often not fully understood and potentially impact the conclusions drawn from investigations. This work enables accurate, hardware-validated performance, power, and energy modelling of CPUs by first presenting a methodology to evaluate and identify sources of error in CPU performance models, and secondly developing empirical power models optimised for use with such performance models. Hierarchical clustering, correlation analysis, and regression techniques are used to identify sources of error without requiring detailed CPU specifications and enable existing models to be improved, new models to be developed, validation of simulator changes, and testing of model suitability for specific use-cases. Furthermore, the GemStone open-source software tool is presented, which automates the process of characterising hardware platforms, identifying sources of error in gem5 models, applying power analysis, and quantifying the effect of errors on the performance, power, and energy estimations. In addition, the mean percentage error in execution time was found to swing from -51% to +10% between two versions of the same gem5 model, underlining the need for an automated tool to validate models against reference hardware, ensuring accuracy and consistency.