MCDA: Collaborative Research: A Multi-Element and Multi-Objective Optimization Approach for Allocating tasks to Multi-Core Processors

MCDA：协作研究：一种将任务分配给多核处理器的多元素和多目标优化方法

• 批准号: 0903430
• 负责人: Sanjay Ranka
• 金额: $ 27万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0903430&HistoricalAwards=false
• 关键词: MCDA; Collaborative; Research; Multi; Element

The objective of the proposed research is to design innovative algorithms and tools for energy-aware scheduling and mapping of tasks onto homogeneous and heterogeneous multi-core processor (HeMP) architectures. The research proposes to develop a new theoretical and experimental framework, called multi-element and multi-objective (MEMO) optimization, that will simultaneously and flexibly optimize the goals of energy minimization and performance maximization while taking into account constraints due to multiple architectural elements such as cores and caches of current and emerging multi-core processors. The project will develop CorePac, a toolkit that will provide a flexible and friendly environment to schedule task-parallel applications on HeMPs under various performance/energy trade-offs and demonstrate the usefulness of the algorithms and CorePac. Benchmarking of the algorithms will be conducted using a diverse suite of scientific, multimedia, and bioinformatics applications.Through its production of new algorithms and software toolkit, this work will have a direct and immediate impact on a number of communities. At the collaborating institutions, this project will have an educational impact by involving undergraduate and graduate students. This situation also presents excellent opportunities for interaction with postdoctoral researchers as well as with colleagues in academic, government and industry research labs. The CorePac software toolkit will be the basis for subsequent development of production quality software for energy-performance tradeoffs. Developing means to manage energy consumption in computers is imperative from both environmental and economical perspectives.

所提出的研究的目的是设计创新的算法和工具，能源感知调度和映射到同构和异构的多核处理器（HeMP）架构的任务。该研究提出开发一种新的理论和实验框架，称为多元素和多目标（MEMO）优化，该框架将同时灵活地优化能量最小化和性能最大化的目标，同时考虑到当前和新兴多核处理器的核心和缓存等多个架构元素的约束。 该项目将开发CorePac，这是一个工具包，将提供一个灵活友好的环境，在各种性能/能量权衡下在HeMP上调度任务并行应用程序，并展示算法和CorePac的实用性。算法的基准测试将使用科学，多媒体和生物信息学的各种应用程序进行。通过其新算法和软件工具包的生产，这项工作将对一些社区产生直接和直接的影响。在合作机构，该项目将通过让本科生和研究生参与产生教育影响。这种情况也为与博士后研究人员以及学术，政府和行业研究实验室的同事进行互动提供了极好的机会。CorePac软件工具包将成为随后开发生产质量软件的基础，用于能源性能权衡。从环境和经济的角度来看，开发管理计算机能耗的方法势在必行。