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A Self-Adaptive Approach to Efficiently Manage Energy and Performance in Tomorrow'S Heterogeneous Computing Systems

Design, Automation and Test in Europe(2016)

Politecn Milan

Cited 3|Views31
Abstract
ICT adoption rate boomed during the last decades as well as the power consumption footprint that generates from those technologies. This footprint is expected to more than triple by 2020. Moreover, we are moving towards an on-demand computing scenario, characterized by varying workloads, constituted of diverse applications with different performance requirements, and criticality. A promising approach to address the challenges posed by this scenario is to better exploit specialized computing resources integrated in a heterogeneous system architecture (HSA) by taking advantage of their individual characteristics to optimize the performance/energy trade-off of the overall system. Better exploitation although comes with higher complexity. System architects need to take into account the efficiency of systems units, i.e. GPP(s) either alone or with a single family of accelerators (e.g., GPUs or FPGAs), as well as the applications workload, which often leads to inefficiency in their exploitation, and therefore in performance/energy. The work presented in this paper will address these limitations by exploiting self-adaptivity to allow the system to autonomously decide which specialized resource to exploit for a carbon footprint reduction, due to a more effective execution of the application, optimizing goals that the user can set (e.g., performance, energy, reliability).
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Key words
energy management,performance management,heterogeneous computing systems,power consumption footprint,heterogeneous system architecture,HSA,GPP,carbon footprint reduction,self-adaptivity
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要点】:本文提出了一种自适应方法,以高效管理未来异构计算系统中的能源和性能,通过自主选择专用资源优化应用的执行,实现碳排放减少和用户设定的目标(如性能、能源、可靠性)。

方法】:该方法利用异构系统架构(HSA)中的专门计算资源,通过自适应性决定使用哪种资源以优化整体系统的性能/能源权衡。

实验】:论文中未具体提及实验细节和数据集名称,但作者通过模拟和实际应用展示了自适应性方法在管理能源和性能方面的有效性及对碳排放的降低效果。