基于机器学习技术的异构复合核架构多线程工作负载能效预测
Energy-Efficiency Prediction of Multithreaded Workloads on Heterogeneous Composite Cores Architectures using Machine Learning Techniques
摘要：
异构体系结构已经成为同构体系结构的一个有前途的替代方案，以提高计算机系统的能量效率。复合核架构（CCA），一类动态异构架构，其使得计算机系统能够通过将核组合在一起以构建更大的核或将大的核分解成多个更小的核来在运行时为每个应用构建正确的核。虽然这种架构为运行的应用程序提供了更大的灵活性，以找到最佳的运行时设置，以最大限度地提高能源效率，由于各种调整参数的相互依赖性，如核心的类型，运行时电压和频率以及线程的数量，它使调度更具挑战性。以前的研究主要是解决调度问题，在CCA通过查看这些调整参数中的一个或两个。然而，正如我们将在本文中展示的那样，同时优化和微调这些参数是很重要的。此外，大多数以前的CCA工作主要是研究传统的单线程CPU应用程序。本文介绍了一种系统的方法来预测正确的配置运行多线程工作负载的CCA。它通过开发一种基于机器学习的方法来预测核心类型，电压和频率设置，以最大限度地提高能源效率。我们的预测器从大量的训练多线程工作负载中离线学习。然后，它被应用到预测的最佳处理器配置在运行时，考虑到多线程应用程序的特性和优化目标。为此，五个著名的机器学习模型实现的能源效率优化和精确比较的准确性和硬件开销，以指导调度决策CCA。
英文摘要：
Heterogeneous architectures have emerged as a promising alternative for homogeneous architectures to improve the energy-efficiency of computer systems. Composite Cores Architecture (CCA), a class of dynamic heterogeneous architectures enabling the computer system to construct the right core at run-time for each application by composing cores together to build larger core or decomposing a large core into multiple smaller cores. While this architecture provides more flexibility for the running application to find the best run-time settings to maximize energy-efficiency, due to the interdependence of various tuning parameters such as the type of the core, run-time voltage and frequency and the number of threads, it makes it more challenging for scheduling. Prior studies mainly addressed the scheduling problem in CCAs by looking at one or two of these tuning parameters. However, as we will show in this paper, it is important to concurrently optimize and fine-tune these parameters. In addition, most previous works on CCA mainly study traditional single threaded CPU applications. This paper describes a systematic approach to predict the right configurations for running multithreaded workloads on CCAs. It achieves this by developing a machine learning-based approach to predict core type, voltage and frequency setting to maximize the energy-efficiency. Our predictor learns offline from an extensive set of training multithreaded workloads. It is then applied to predict the optimal processor configuration at run-time by taking into account the multithreaded application characteristics and the optimization objective. For this purpose, five well-known machine learning models are implemented for energy-efficiency optimization and precisely compared in terms of accuracy and hardware overhead to guide the scheduling decisions in a CCA.
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