Task allocation for minimum system power in a homogenous multi-core processor

Yang Ge, Qinru Qiu
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引用次数: 9

Abstract

In this paper we address the impact of task allocation to the system power consumption of a homogenous multi-core processor with a main focus on its impact on the leakage power and fan power. Although the leakage power is determined by the average die temperature and the fan power is determined by the peak temperature, our analysis shows that the overall power can be minimized if a task allocation with minimum peak temperature is adopted together with an intelligent fan speed adjustment technique that finds the optimal tradeoff between fan power and leakage power. We further propose a multi-agent distributed task migration technique that searches for the best task allocation during runtime. By choosing only those migration requests that will result chip maximum temperature reduction, the proposed framework achieves large fan power savings as well as overall power reduction. Experimental results show that, our agent-based distributed task migration policy can save up to 37.2% fan power and 17.9% system overall power compared to the random mapping policy when the temperature constraint is tight. When the temperature constraint is loose, the overall system power is insensitive to the task allocation.
均匀多核处理器中最小系统功耗的任务分配
本文讨论了任务分配对同质多核处理器系统功耗的影响,重点讨论了任务分配对泄漏功率和风扇功率的影响。虽然泄漏功率由模具平均温度决定,风扇功率由峰值温度决定,但我们的分析表明,如果采用峰值温度最小的任务分配,并采用风扇智能调速技术,在风扇功率和泄漏功率之间找到最佳权衡,则可以使总体功率最小。我们进一步提出了一种在运行时搜索最佳任务分配的多智能体分布式任务迁移技术。通过只选择那些将导致芯片最大温度降低的迁移请求,所提出的框架实现了大量风扇功耗节省以及整体功耗降低。实验结果表明,在温度约束较紧的情况下,与随机映射策略相比,基于agent的分布式任务迁移策略可节省37.2%的风扇功率和17.9%的系统总功率。当温度约束较松时,系统整体功率对任务分配不敏感。
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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