Mechanistic-empirical processor performance modeling for constructing CPI stacks on real hardware
Mechanistic-empirical processor performance modeling for constructing CPI stacks on real hardware
复制标题
用于在真实硬件上构建 CPI 堆栈的机械经验处理器性能建模
DOI：
10.1109/ispass.2011.5762738
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
L. Eeckhout
中科院分区：
文献类型：
--
作者：
Stijn Eyerman;Kenneth Hoste;L. Eeckhout
在过去的几年中，分析的处理器绩效建模基本上有两种构建分析模型的方法：机械模型和经验模型。盒子方法 - 而经验建模通过培训数据中的统计推断和机器学习构建了分析模型，例如回归建模或神经网络 - 虽然经验模型通常更容易构造，但与机械模型相比，它提供的洞察力更少。 ）从受到机械建模启发的通用，参数化的性能模型开始，回归建模侵入未知参数，经验模型。 ，Core 2和Core IL，使用Spec CPU2000和CPU2006，并报告9％至13％的平均预测错误。与纯粹的经验模型相比，模型更健壮，不适合过度拟合。优化和分析。
Analytical processor performance modeling has received increased interest over the past few years. There are basically two approaches to constructing an analytical model: mechanistic modeling and empirical modeling. Mechanistic modeling builds up an analytical model starting from a basic understanding of the underlying system — white-box approach — whereas empirical modeling constructs an analytical model through statistical inference and machine learning from training data, e.g., regression modeling or neural networks — black-box approach. While an empirical model is typically easier to construct, it provides less insight than a mechanistic model. This paper bridges the gap between mechanistic and empirical modeling through hybrid mechanistic-empirical modeling (gray-box modeling). Starting from a generic, parameterized performance model that is inspired by mechanistic modeling, regression modeling infers the unknown parameters, alike empirical modeling. Mechanistic-empirical models combine the best of both worlds: they provide insight (like mechanistic models) while being easy to construct (like empirical models). We build mechanistic-empirical performance models for three commercial processor cores, the Intel Pentium 4, Core 2 and Core il, using SPEC CPU2000 and CPU2006, and report average prediction errors between 9% and 13%. In addition, we demonstrate that the mechanistic-empirical model is more robust and less subject to overfitting than purely empirical models. A key feature of the proposed mechanistic-empirical model is that it enables constructing CPI stacks on real hardware, which provide insight in commercial processor performance and which offer opportunities for software and hardware optimization and analysis.