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Performance modeling for designing NoC-based multiprocessors

机译:用于设计基于NoC的多处理器的性能建模

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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.
机译:基于片上网络(NoC)的多处理器已成为流行的经典总线体系结构的替代产品。基于NoC的多处理器的性能评估主要基于仿真。但是,精确的模拟非常慢。此外,还有许多设计参数会影响总体性能。因此,实际上不可能将精确模拟用于设计空间探索目的。为了缓解此问题,对NoC系统进行原型设计并评估其性能至关重要。在本文中,我们提出了一种通用的新型性能模型,该模型结合了用于设计基于NoC的多处理器的仿真。我们发现,缓存和网络延迟对性能的影响是主要的。而且,在适当的配置下,网络拥塞很少发生。因此,主要使用硬件参数和统计信息来构建性能模型,该统计信息是从与网络行为分离的简单缓存模拟中获得的。所提出的性能模型不仅用于获得快速,准确的性能,而且还指导基于NoC的多处理器设计空间的探索。通过仿真说明了我们方法的准确性及其实际应用。结果表明,提出的模型可以估计性能,平均误差只有3.4%,最差的是21%。我们还证实,我们的评估框架可以比蛮力完整系统仿真快360倍。

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