CSR-SMA: Toward Model-Driven Multilevel Analysis and Optimization of Multicomponent Computer Systems

CSR-SMA：迈向模型驱动的多组件计算机系统的多级分析和优化

基本信息

批准号：
0720749
负责人：
Padma Raghavan
金额：
$ 70万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-01 至 2012-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720749&HistoricalAwards=false
关键词：
CSR SMA Toward Model Driven

项目摘要

This project seeks to enable model-driven optimizations spanningmultiple levels of a computing system including the architecture,compiler, algorithm and application layers, for multiple objectivessuch as performance, power and productivity. A primary goal is todevelop a comprehensive framework for model-driven multilevel,multiobjective optimizations with a focus on chip multiprocessors(CMPs) and large-scale, sparse engineering and scientific applications.Key activities concern developing (i) parameterized models tocompose models of the application, architecture and compilertransformations, (ii) an optimization framework to determinemultiobjective, optimal or pareto-optimal designs while modelinguncertainties, and (iii) undergraduate and graduate courses on themethodology for multilevel optimizations of computing systems, The proposed techniques yield metrics at coarse- and medium-scalesthat can be used with stochastic optimization techniques to determineoptimal design choices. The medium-scale metrics are obtained bysimulating a concatenated discrete time Markov Chain model (C-DT-MCM)which incorporates both the deterministic and stochastic aspectsof multilevel optimizations and their impacts. Such C-DT-MCMs canbe simulated very efficiently to obtain traces which can then becompared using statistical techniques with those from detailedhardware simulation. Using this approach, only promising designoptions need be studied in detail, using current modalities, suchas detailed hardware simulators, which can be prohibitively slowfor larger CMP architectures.

该项目旨在实现跨计算系统多个级别（包括架构、编译器、算法和应用程序层）的模型驱动优化，以实现性能、功耗和生产力等多个目标。主要目标是开发一个用于模型驱动的多级、多目标优化的综合框架，重点关注芯片多处理器 (CMP) 以及大规模、稀疏工程和科学应用。关键活动涉及开发 (i) 参数化模型以组成应用程序模型、体系结构和编译器转换，(ii) 在对不确定性进行建模时确定多目标、最优或帕累托最优设计的优化框架，以及 (iii) 有关多级计算优化方法的本科生和研究生课程系统中，所提出的技术产生粗尺度和中等尺度的度量，可以与随机优化技术一起使用来确定最佳设计选择。中等规模的指标是通过模拟级联离散时间马尔可夫链模型（C-DT-MCM）获得的，该模型结合了多级优化的确定性和随机性及其影响。可以非常有效地模拟此类 C-DT-MCM 以获得轨迹，然后使用统计技术将其与详细硬件模拟的轨迹进行比较。使用这种方法，只需要使用当前的模式（例如详细的硬件模拟器）详细研究有前途的设计选项，这对于较大的 CMP 架构来说可能会慢得令人望而却步。