EAGER: Foundations for Predictive Resource Management in Next-Generation Multicore Processor Systems

EAGER：下一代多核处理器系统中预测资源管理的基础

• 批准号: 1059283
• 负责人: Sangyeun Cho
• 金额: $ 10万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1059283&HistoricalAwards=false
• 关键词: EAGER; Foundations; Predictive; Resource; Management

Future multicore processor systems will have a growing amount of system-wide shared resources. However, shared resources will present significant undesirable asymmetry. For example, the capabilities of processor cores, cache access latency, and memory access cost will differ depending on the time and the location of their usage. If such asymmetry is not properly managed, the full potential of the multicore computing paradigm will not be achieved. This exploratory research will investigate a novel predictive resource management framework called MAESTRO. The proposed framework automatically learns asymmetry in the system and useful application behavior; the learned knowledge is accumulated and refined; and resource management decisions, such as cache capacity allocation, are made in a predictive manner by exploiting the accumulated knowledge. It is expected that MAESTRO's predictive strategies with detailed system and application knowledge will be a more effective solution to new multicore resource management problems than conventional reactive strategies with limited knowledge. The PI will validate this expectation with solid system prototyping and by studying two target resource management problems. The project has the potential to impact the way future computer systems are designed and managed. It is inter-disciplinary by nature and requires understating of applications, computer architecture, OS and machine learning. Students working on this project will receive rigorous inter-disciplinary training.

未来的多核处理器系统将具有越来越多的系统范围的共享资源。然而，共享资源将呈现显著的不期望的不对称性。例如，处理器核心的能力、高速缓存访问延迟和存储器访问成本将根据其使用的时间和位置而不同。如果这种不对称性没有得到适当的管理，多核计算范式的全部潜力将无法实现。这项探索性的研究将探讨一种新的预测资源管理框架称为MAESTRO。所提出的框架自动学习系统中的不对称性和有用的应用程序行为;所学到的知识的积累和完善;和资源管理决策，如缓存容量分配，是在预测的方式，通过利用积累的知识。预计MAESTRO的预测策略与详细的系统和应用知识将是一个更有效的解决方案，新的多核资源管理问题比传统的反应策略与有限的知识。PI将通过研究两个目标资源管理问题，用坚实的系统原型来验证这一期望。该项目有可能影响未来计算机系统的设计和管理方式。它本质上是跨学科的，需要了解应用程序，计算机体系结构，操作系统和机器学习。参与该项目的学生将接受严格的跨学科培训。