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

• 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; CSR; SMA; Toward; Model; Driven

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.

该项目旨在启用模型驱动的优化，包括计算系统的数量级别，包括体系结构，编译器，算法和应用程序层，以进行多种客观性，例如性能，功率和生产力。 一个主要目标是为模型驱动的多级，多目标优化的综合框架，重点关注芯片多处理器（CMPS）和大规模，稀疏工程和科学应用程序。关注的开发（I）参数化的应用程序（i）参数化的模型的应用程序和组合模型，以确定优化的构建和构造框架，（II），（II），（II），（II），（II），（II II），（II II）的框架，II）。在建模时，帕累托最佳设计以及（iii）关于计算系统多级优化的本科和研究生课程，该建议的技术可以在粗糙和中等范围的降级技术中生产指标，以确定可替代技术选择。中等尺度的指标是通过想象串联离散的时间马尔可夫链模型（C-DT-MCM）获得的，该模型（C-DT-MCM）结合了多级优化及其影响的确定性和随机方面。 这样的C-DT-MCMS可以非常有效地模拟以获取轨迹，然后使用详细信息模拟的统计技术来获得轨迹。 使用这种方法，只需要使用当前的模态，详细的硬件模拟器来详细研究有希望的DesignOptions，对于更大的CMP体系结构，可以过分放慢详细的硬件模拟器。