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

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.

该项目旨在使模型驱动的优化跨越多个层次的计算系统，包括架构，编译器，算法和应用程序层，多个目标，如性能，功率和生产力。 主要目标是开发一个模型驱动的多级多目标优化的综合框架，重点是芯片多处理器（CMP）和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，体系结构和编译器转换的模型，（ii）优化框架，以确定多目标，最优或帕累托最优的设计，而modelinguncertainty，和（iii）本科生和研究生课程的方法论多级优化的计算系统，所提出的技术产生的指标，在粗和中等规模，可用于随机优化技术，以确定最优的设计选择。中等规模的指标是通过模拟一个级联的离散时间马尔可夫链模型（C-DT-MCM），它结合了确定性和随机方面的多级优化及其影响。 这样的C-DT-MCM可以非常有效地模拟，以获得轨迹，然后可以使用统计技术与来自详细硬件模拟的轨迹进行对比。 使用这种方法，只有有前途的设计需要详细研究，使用当前的方式，如详细的硬件模拟器，这可能是非常缓慢的较大的CMP架构。