多核soc的模块化和分布式管理

Marcelo Ruaro, A. Sant'Ana, A. Jantsch, F. Moraes
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摘要

多核片上系统对资源动态多目标管理(DMOM)的要求越来越高。DMOM对目标和资源使用不同的管理组件,实现全面的、自适应的系统资源管理。dmom具有挑战性,因为它们需要一个可伸缩且组织良好的框架来使每个组件模块化,从而允许对其进行实例化或重新设计,而对其他组件的影响有限。这项工作评估了两种最先进的分布式管理范式,并根据它们的缺点,提出了一种称为管理应用程序(MA)的新范式,以及基于MA的DMOM框架。MA是一种分布式应用程序,专门用于管理,其中每个任务实现一个管理角色。这种范例有利于可伸缩性和模块化,因为管理设计采用不同的并行模块,与操作系统解耦。一个任务映射案例研究的实验表明,与最先进的每个应用程序管理相比,MA减少了管理资源的开销(-61.5%)、延迟(-66%)和通信量(-96%)。与直接作为操作系统一部分实现的基于集群的管理(CBM)相比,MA在资源和通信量方面相似,只是增加了映射延迟(+16%)。结果显示,与CBM相比,针对多达三个不同目标的完整DMOM控制回路显示了系统大小和自适应频率的可扩展性,总体管理延迟减少了17.2%,总体监控消息延迟减少了90.2%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modular and Distributed Management of Many-Core SoCs
Many-Core Systems-on-Chip increasingly require Dynamic Multi-objective Management (DMOM) of resources. DMOM uses different management components for objectives and resources to implement comprehensive and self-adaptive system resource management. DMOMs are challenging because they require a scalable and well-organized framework to make each component modular, allowing it to be instantiated or redesigned with a limited impact on other components. This work evaluates two state-of-the-art distributed management paradigms and, motivated by their drawbacks, proposes a new one called Management Application (MA), along with a DMOM framework based on MA. MA is a distributed application, specific for management, where each task implements a management role. This paradigm favors scalability and modularity because the management design assumes different and parallel modules, decoupled from the OS. An experiment with a task mapping case study shows that MA reduces the overhead of management resources (-61.5%), latency (-66%), and communication volume (-96%) compared to state-of-the-art per-application management. Compared to cluster-based management (CBM) implemented directly as part of the OS, MA is similar in resources and communication volume, increasing only the mapping latency (+16%). Results targeting a complete DMOM control loop addressing up to three different objectives show the scalability regarding system size and adaptation frequency compared to CBM, presenting an overall management latency reduction of 17.2% and an overall monitoring messages’ latency reduction of 90.2%.
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