基于noc的多处理器设计的性能建模

Takashi Nakada, Shinobu Miwa, K. Yano, Hiroshi Nakamura
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引用次数: 2

摘要

基于片上网络(NoC)的多处理器已经成为经典总线架构的可扩展替代方案。基于noc的多处理器的性能评估主要基于仿真。然而,精确的模拟是非常缓慢的。此外,还有许多设计参数会影响总体性能。因此,用精确的仿真来设计空间探索实际上是不可能的。为了缓解这一问题,原型NoC系统和评估其性能是至关重要的。在本文中,我们提出了一个通用的新型性能模型,并结合仿真设计了基于noc的多处理器。我们发现缓存和网络延迟对性能的影响占主导地位。此外,在适当的配置下,网络拥塞很少发生。因此,性能模型主要使用硬件参数和从与网络行为分离的简单缓存模拟中获得的统计数据来构建。所提出的性能模型不仅可以获得快速准确的性能,而且可以指导基于noc的多处理器设计空间探索。通过仿真说明了该方法的准确性和实际应用。结果表明,该模型的平均误差为3.4%,最坏误差为21%。我们还证实,我们的评估框架的估计速度比蛮力全系统模拟快360倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance modeling for designing NoC-based multiprocessors
Network-on-Chip (NoC) based multiprocessors have become popular as a scalable alternative to classical bus architectures. The performance evaluation of NoC-based multiprocessors is largely based on simulation. However, precise simulation is extremely slow. Additionally, there are many design parameters that affect the total performance. Therefore, it is practically impossible to use the precise simulation for the design space exploration purposes. To alleviate this problem, prototyping NoC systems and estimating their performances are critically important. In this paper, we present a generalized novel performance model that combined with the simulations for designing NoC-based multiprocessors. We revealed that the performance impact of cache and network latencies are dominant. Moreover, network congestion rarely happens under near appropriate configuration. Thus, the performance model is mainly constructed using the hardware parameters and the statistics that obtained from a simple cache simulation that is separated from the network behavior. The proposed performance model is used not only to obtain fast and accurate performance, but also to guide the NoC-based multiprocessor design space exploration. The accuracy of our approach and its practical use are illustrated through simulation. The results showed that proposed model can estimate performance with only 3.4% error on average and 21% at worst. We also confirmed that our evaluation framework can estimate 360 times faster than the brute force full system simulation.
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