用机器学习模型预测处理器性能

A. Beg
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引用次数: 10

摘要

传统上,架构模拟器用于研究处理器系统的设计权衡。仿真器是用高级编程语言或硬件描述语言实现的,用于在硬件实现之前估计系统性能。然而,即使是很小的设计变化,模拟也可能需要运行很长一段时间。在本文中,我们提出了一个机器学习(神经网络/NN)模型来估计一个超标量处理器的执行性能。与基于仿真的方法需要几天或几周的时间相比,模型的多次运行在不到几毫秒的时间内完成。在六个SPEC2000 CPU整数基准测试中,该模型能够预测处理器系统的执行吞吐量,准确率超过85%。该模型在计算机体系结构研究和教学中具有一定的应用价值。
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Predicting processor performance with a machine learnt model
Architectural simulators are traditionally used to study the design trade-offs for processor systems. The simulators are implemented in a high-level programming language or a hardware descriptive language, and are used to estimate the system performance prior to the hardware implementation. The simulations, however, may need to run for long periods of time for even a small set of design variations. In this paper, we propose a machine learnt (neural network/NN) model for estimating the execution performance of a superscalar processor. Multiple runs for the model are finished in less than a few milliseconds as compared to days or weeks required for simulation-based methods. The model is able to predict the execution throughput of a processor system with over 85% accuracy when tested with six SPEC2000 CPU integer benchmarks. The proposed model has possible applications in computer architecture research and teaching.
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