SHF:Small:Collaborative Research: Application-aware Energy Modeling and Power Management for Parallel and High Performance Computing

SHF:Small：协作研究：用于并行和高性能计算的应用感知能源建模和电源管理

• 批准号: 1422712
• 负责人: Kirk Cameron
• 金额: $ 25万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422712&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Application

One of the critical challenges in scaling out current and future high performance computing (HPC) and enterprise computing systems is the requirement that their power envelope remain comparable to that of today?s systems. This project addresses this ?power wall? challenge from the system software aspect by developing application-aware methodologies of energy modeling and power management. The project optimizes system efficiency by tuning performance and energy consumption to resonate with application runtime behavior while staying below the system power envelope. The project develops user interfaces and new compiler models and runtime tuning techniques to manage the tradeoffs between performance and energy consumption. The approach enables cooperative, application-specific control of energy consumption between hardware, system software and applications. The investigations and solutions deepen understanding of application power usage and guide users to customized performance and energy consumption behavior.This collaborative project integrates the development, education, and outreach efforts of collaborating University partners and is well positioned to have a substantial impact on both the HPC research community and hardware designers and vendors. All findings are published in peer-reviewed conferences and journals while source code and results are available through a project web site. This work addresses the need for energy efficiency improvements in large-scale systems in support of high-end simulations used to design pharmaceuticals, aircraft, global warming scenarios, etc. The techniques we propose influence the design of future directions HPC and enterprise computing systems from industry and government. The project engages and trains graduate and undergraduate students, including underrepresented minority students, in the area of energy efficient computing, parallel and high performance computing, and computer architecture and systems. The open source evaluation platforms are used in teaching related coursework in graduate and undergraduate classes.

在扩展当前和未来的高性能计算（HPC）和企业计算系统时，关键挑战之一是要求其功率包络保持与当今相当？s系统。该项目针对这一？电力墙？通过开发应用感知的能源建模和电源管理方法，从系统软件方面的挑战。该项目通过调整性能和能耗来优化系统效率，以与应用程序运行时行为保持一致，同时保持低于系统功耗。该项目开发用户界面和新的编译器模型和运行时调优技术，以管理性能和能耗之间的权衡。该方法能够在硬件、系统软件和应用程序之间对能耗进行协作的、特定于应用程序的控制。调查和解决方案加深了对应用功耗的理解，并指导用户定制性能和能耗行为。这个合作项目整合了合作大学合作伙伴的开发，教育和推广工作，并有能力对HPC研究社区和硬件设计师和供应商产生重大影响。所有研究结果都发表在同行评议的会议和期刊上，而源代码和结果可通过项目网站获得。这项工作解决了需要在大规模系统的能源效率的提高，以支持高端模拟用于设计制药，飞机，全球变暖的情况下，我们提出的技术，影响未来的方向HPC和企业计算系统的设计从行业和政府。该项目在节能计算、并行和高性能计算以及计算机体系结构和系统领域吸引和培训研究生和本科生，包括代表性不足的少数民族学生。开源评估平台用于研究生和本科生课程的教学相关课程。