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DiST: A Simple, Reliable and Scalable Method to Significantly Reduce Processor Architecture Simulation Time

机译:DiST:一种简单,可靠且可扩展的方法,可显着减少处理器体系结构仿真时间

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摘要

While architecture simulation is often treated as a methodology issue, it is at the core of most processor architecture research works, and simulation speed is often the bottleneck of the typical trial-and-error research process. To speedup simulation during this research process and get trends faster, researchers usually reduce the trace size. More sophisticated techniques like trace sampling or distributed simulation are scarcely used because they are considered unreliable and complex due to their impact on accuracy and the associated warm-up issues. In this article, we present DiST, a practical distributed simulation scheme where, unlike in other simulation techniques that trade accuracy for speed, the user is relieved from most accuracy issues thanks to an automatic and dynamic mechanism for adjusting the warm-up interval size. Moreover, the mechanism is designed so as to always privilege accuracy over speedup. The speedup scales with the amount of available computing resources, bringing an average 7.35 speedup on 10 machines with an average IPC error of 1.81 % and a maximum IPC error of 5.06%. Besides proposing a solution to the warm-up issues in distributed simulation, we experimentally show that our technique is significantly more accurate than trace size reduction or trace sampling for identical speedups. We also show that not only the error always remains small for IPC and other metrics, but that a researcher can reliably base research decisions on DiST simulation results. Finally, we explain how the DiST tool is designed to be easily pluggable into existing architecture simulators with very few modifications.
机译:尽管架构仿真通常被视为方法论问题,但它是大多数处理器架构研究工作的核心,而仿真速度通常是典型的反复试验研究过程的瓶颈。为了在此研究过程中加快仿真速度并更快地获得趋势,研究人员通常会减小迹线的大小。很少使用更复杂的技术,例如跟踪采样或分布式仿真,因为它们会影响准确性和相关的预热问题,因此被认为不可靠且复杂。在本文中,我们介绍了DiST,这是一种实用的分布式仿真方案,与其他以牺牲精度换取速度的仿真技术不同,该方案通过自动和动态的机制来调节预热间隔大小,从而使用户摆脱了大多数精度问题。此外,该机制的设计使其始终优先考虑精度而不是加速。加速与可用计算资源的数量成比例,在10台计算机上实现平均7.35的加速,平均IPC误差为1.81%,最大IPC误差为5.06%。除了为分布式仿真中的预热问题提出解决方案外,我们还通过实验表明,对于相同的加速,我们的技术比跟踪尺寸减小或跟踪采样准确得多。我们还表明,不仅对于IPC和其他指标,误差始终保持很小,而且研究人员可以可靠地将研究决策基于DiST仿真结果。最后,我们说明DiST工具是如何设计的,只需很少的修改即可轻松插入现有的架构模拟器中。

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