MicroStAT: A Microarchitect's Statistically-based Analytical Toolkit

缓慢的仿真速度和大量的用户可配置的处理器和编译器参数排除了设计空间的详细探索。虽然架构师已经提出了减少输入集和采样等解决方案，以最大限度地减少模拟速度慢的影响，但还没有提出系统的基于几何的方法来帕雷设计空间。此外，在评估新处理器功能的功效或比较两个处理器的性能时，架构师通常会孤立地查看高级单一指标或特定处理器组件的行为。虽然这些指标和技术可以让架构师深入了解处理器？的行为，除了不是基于理论的，他们是潜在的限制过于抽象或狭隘的重点。这两种情况的根本问题是，他们没有系统地分析处理器？的表现。为了解决模拟速度差的双重问题？特别是当它涉及到设计空间的减少？和随机性能分析，本项目的目标是开发一套基于计算机的仿真和性能分析工具。该套件的一些关键工具包括用于识别处理器的工具？的性能瓶颈（及其相对顺序）;一个工具，用于评估新的处理器功能的影响，并通过使用信号处理和统计概念找到一个理想的采样频率，提高仿真采样技术的准确性水平的工具。由于MicroStAT将在该项目结束时公开提供，学术研究人员和行业将能够提高其模拟方法的精确性，从而提高其模拟结果的质量。此外，该项目将对教育发展作出重大贡献。它将支持博士学位的论文研究。学生在电气和计算机工程和计算机科学，沿着几个硕士？的项目和研究机会，为几个本科生。

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.