Realizing a Proactive, Self-Optimizing System Behavior within Adaptive, Heterogeneous Many-Core Architectures

David Kramer, Wolfgang Karl
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引用次数: 5

Abstract

For maintaining high performance and minimizing power consumption, adaptive, heterogeneous many-core architectures can be adapted at runtime to changing environmental requests or conditions as well as to changes resulting from the dynamics of the workload itself. However, the huge complexity of such architectures makes their optimization very challenging at runtime. This challenge is therefore addressed within this paper by an Organic Computing approach for realizing a proactive, self-optimizing system behavior within adaptive, heterogeneous systems using a light-weight Learning Classifier System and a Run Length Encoding Markov predictor. The first realizes a self-optimizing behavior, freeing the user from the burden of optimizing the system manually, and the latter captures the system behavior, permits prediction of future system states, and therefore permits exploiting regular behavior for further improving the overall system performance. Using the use case of optimizing the overall system performance, results showed that the proactive, self-optimizing system achieved a performance improvement of 11.3% in comparison to a non-optimizing system.
在自适应异构多核架构中实现主动、自优化的系统行为
为了保持高性能和最小化功耗,可以在运行时对自适应的异构多核架构进行调整,以适应不断变化的环境请求或条件,以及工作负载本身的动态变化。然而,这种架构的巨大复杂性使得它们在运行时的优化非常具有挑战性。因此,本文通过有机计算方法解决了这一挑战,该方法使用轻量级学习分类器系统和运行长度编码马尔可夫预测器在自适应异构系统中实现主动的、自优化的系统行为。前者实现了自优化行为,将用户从手动优化系统的负担中解放出来,后者捕获系统行为,允许预测未来的系统状态,从而允许利用常规行为进一步改进整体系统性能。使用优化整体系统性能的用例,结果表明,与非优化系统相比,主动的、自我优化的系统实现了11.3%的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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