PTSMT: SMT处理器的跨能级功率、性能和热研究工具

D. Kannan, Aseem Gupta, Aviral Shrivastava, N. Dutt, F. Kurdahi
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引用次数: 0

摘要

同步多线程(SMT)处理器正变得越来越流行,因为它们通过在同一周期内从不同线程发出指令来利用指令级和线程级并行性。然而,功率和热管理问题阻碍了纳米级SMT处理器的制造。SMT处理器中的功耗和热问题不仅限制了可实现的性能,而且对这些处理器的成本和可行性也有直接影响。虽然存在一些性能仿真工具,可以在SMT处理器的设计阶段早期探索其性能方面,但缺乏用于SMT处理器的早期功率和性能评估工具。为此,我们开发了PTSMT:一种紧密耦合的SMT处理器功率,性能和热勘探工具。在本文中,我们证明了PTSMT可以自动有效地完成SMT处理器在设计层次、应用层、微体系结构层和物理层的功耗、性能和热探测。我们的实验结果表明:在应用程序级别,将应用程序划分为上下文的数量可能会影响性能2.2倍,能量降低52%,峰值温度降低35℃;在微体系结构级别上,运行时的上下文交换可以减少9%的能量并提高8%的性能。这些观察结果表明,设计空间的大小,可以探索使用PTSMT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PTSMT: A Tool for Cross-Level Power, Performance, and Thermal Exploration of SMT Processors
Simultaneous Multi-Threading (SMT) processors are becoming popular because they exploit both instruction-level and thread- level parallelism by issuing instructions from different threads in the same cycle. However, the issues of power and thermal management hinder SMT processors fabricated in nano-scale technologies. Power and thermal issues in SMT processors not only limit the achievable performance, but also have a direct impact on the cost and viability of these processors. While several performance simulation tools to explore the performance aspect of SMT processors early in their design phase exist, there is a lack of early power and performance evaluation tools for SMT processors. To this end, we have developed PTSMT: a tightly coupled power, performance and thermal exploration tool for SMT processors. In this paper, we demonstrate that PTSMT can automatically and effectively accomplish power, performance and thermal exploration of SMT processors at various levels of design hierarchy, at the application level, microarchitecture level, and physical level. Our experimental results show that: at the application level, number of contexts into which an application is divided could affect performance by 2.2times, energy by 52%, and peak temperature by 35degC; and at the microarchitecture level, context swapping during run time could reduce energy by 9% and improve performance by 8%. These observations indicate the size of the design space which can be explored using PTSMT.
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