热约束下多核处理器的频率和电压规划

M. Kadin, S. Reda
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引用次数: 21

摘要

时钟频率和晶体管密度的增加导致芯片温度升高。为了满足温度限制,同时仍然利用持续扩展所带来的性能机会,芯片设计师已经转向多核架构。多核架构使用在中等时钟频率下运行的多个内核来并发运行多个线程,从而提高了整体系统吞吐量。在这项工作中,我们提出了新的方法来找到最佳的工作参数,即频率和电压,在热约束下最大化多核系统吞吐量。通过调整内核时钟频率和电压,可以在空间和时间上分布芯片上的功耗,以最大限度地提高芯片在运行时的物理性能。我们提出了一个简单而有效的模型,可以准确地表征时钟频率和电压变化对片上温度的影响。使用该模型,我们找到了以下场景的最佳操作条件:(1)标准处理器性能,其中各种核心使用相同的操作参数运行;(2)最佳处理器性能,其中每个核心可以拥有自己的频率和电压;(3)具有线程优先级的最佳处理器性能,其中每个核心运行不同重要性的线程。我们在六个不同的技术节点上进行了几个实验来验证工作,确保我们的模型和方法是准确的。我们的方法表明,在不违反最高温度约束的情况下,多核系统的总物理性能可以提高33.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Frequency and voltage planning for multi-core processors under thermal constraints
Clock frequency and transistor density increases have resulted in elevated chip temperatures. In order to meet temperature constraints while still exploiting the performance opportunities enabled by continued scaling, chip designers have migrated towards multi-core architectures. Multi-core architectures use multiple cores running at moderate clock frequencies to run several threads concurrently, which increases overall system throughput. In this work, we propose novel methods to find the optimal operating parameters, i.e., frequency and voltage, that maximize a multi-core system throughput under thermal constraints. By adjusting core clock frequencies and voltages, on-chip power dissipation can be spatially and temporally distributed to maximize the chippsilas physical performance during runtime. We propose a simple, yet efficient model that accurately characterize the effects that changes in clock frequency and voltage have on on-chip temperatures. Using the model, we find the optimal operating conditions for the following scenarios: (1) standard processor performance, where various cores operate using identical operating parameters, (2) optimal processor performance where each core can have its own frequency and voltage, and (3) optimal processor performance with thread priorities, where each core runs a thread of varied importance. We run several experiments across six different technology nodes to validate the work, assuring that our models and methods are accurate. Our methods demonstrate the total physical performance of a multi-core system can be increased by up to 33.4% without violating the maximum temperature constraints.
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