MicroStAT: A Microarchitect's Statistically-based Analytical Toolkit

缓慢的模拟速度和大量用户可配置的处理器和编译器参数阻碍了对设计空间的详细探索。虽然架构师已经提出了减少输入集合和采样等解决方案，以最大限度地减少模拟速度的影响，但尚未提出基于系统统计的方法来削减设计空间。此外，在评估新处理器功能的效率或比较两个处理器的性能时，架构师通常孤立地查看高级单一指标或特定处理器组件的行为。虽然这些度量和技术可以让架构师深入了解处理器-S的行为，但除了不是基于统计之外，它们还可能受到过于抽象或关注范围狭窄的限制。这两种情况的根本问题都是他们没有系统地分析处理器-S的性能。来解决模拟速度慢的双重问题？尤其是因为它涉及到设计空间的减少？和随意的性能分析，这个项目的目标是开发一套基于统计的模拟和性能分析工具。该套件的一些关键工具包括用于识别处理器的工具-S性能瓶颈(及其相对排序)、用于评估新处理器功能的效果的工具、以及用于通过使用信号处理和统计概念找到理想的采样频率来提高模拟采样技术的精度水平的工具。由于MicroStat将在本项目结束时公开提供，学术研究人员和工业界将能够改进他们的模拟方法的统计严格性，从而提高他们的模拟结果的质量。此外，该项目将为教育发展做出实质性贡献。它将支持电气与计算机工程和计算机科学的博士生的论文研究，以及几个硕士S项目和几个本科生的研究机会。

Slow simulation speed and a vast number of user-configurable processor and compiler parameters preclude a detailed exploration of the design space. Although architects have proposed solutions such as reduced input sets and sampling to minimize the effect of slow simulation speed, what has not been proposed are systematic statistically-based approaches to pare the design space. Moreover, when evaluating the efficacy of a new processor feature or when comparing the performance of two processors, architects typically look at high-level single metrics or the behavior of a specific processor component in isolation. While these metrics and techniques can give the architect insight into the processor?s behavior, in addition to not being statistically-based, they are potentially limited by being too abstract or narrowly focused. The fundamental problem with both cases is that they do not systematically analyze the processor?s performance. To address the dual problems of poor simulation speed ? especially as it pertains to design space reduction ? and haphazard performance analysis, the goal of this project is to develop a suite of statistically-based simulation and performance analysis tools. Some of the key tools of this suite include a tool for identifying a processor?s performance bottlenecks (and their relative ordering); a tool for evaluating the effect of a new processor feature, and a tool for improving level of accuracy of simulation sampling techniques by finding an ideal sampling frequency using signal-processing and statistical concepts. Since MicroStAT will be publicly available at the end of this project, academic researchers and industry will be able to improve the statistical-rigor of their simulation methodology and, thus, the quality of their simulation results. Furthermore, this project will make substantial contributions to educational development. It will support the dissertation research of Ph.D. students in electrical and computer engineering and computer science, along with several master?s projects and research opportunities for several undergraduate students.