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: 2001580
• 负责人: Yonghong Yan
• 金额: $ 2.33万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-15 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2001580&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 proposed techniques 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.

扩展当前和未来的高性能计算 (HPC) 和企业计算系统的关键挑战之一是要求其功率范围与当今系统的功率范围保持相当。这个项目解决了这个“电源墙”问题。通过开发应用感知的能源建模和电源管理方法来应对系统软件方面的挑战。该项目通过调整性能和能耗来与应用程序运行时行为产生共鸣，同时保持在系统功耗范围以下，从而优化系统效率。该项目开发用户界面、新的编译器模型和运行时调整技术，以管理性能和能耗之间的权衡。该方法能够对硬件、系统软件和应用程序之间的能耗进行协作式、特定于应用程序的控制。这些调查和解决方案加深了对应用程序电源使用情况的了解，并指导用户定制性能和能耗行为。该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，并且有能力对 HPC 和 HPC 产生重大影响。研究社区以及硬件设计者和供应商。所有研究结果均发表在同行评审的会议和期刊上，而源代码和结果可通过项目网站获取。这项工作满足了大规模系统中提高能源效率的需求，以支持用于设计药品、飞机、全球变暖场景等的高端模拟。所提出的技术影响了工业界 HPC 和企业计算系统的未来方向设计和政府。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族裔学生。开源评估平台用于研究生和本科生课程的相关课程教学。