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

扩展当前和未来高性能计算（HPC）和企业计算系统的关键挑战之一是要求它们的功率包络保持与今天的功率包络相当。s系统。这个项目解决了这个问题？权力墙?通过开发应用感知的能源建模和电源管理方法，从系统软件方面提出挑战。该项目通过调整性能和能耗来优化系统效率，从而与应用程序运行时行为产生共鸣，同时保持在系统功率包络以下。该项目开发用户界面和新的编译器模型以及运行时调优技术，以管理性能和能耗之间的权衡。这种方法可以在硬件、系统软件和应用程序之间实现协作性的、特定于应用程序的能耗控制。调查和解决方案加深了对应用程序功率使用情况的理解，并指导用户定制性能和能耗行为。这个合作项目整合了大学合作伙伴的开发、教育和推广工作，并很好地定位于对高性能计算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。这项工作解决了大规模系统中能源效率改进的需求，以支持用于设计药品、飞机、全球变暖情景等的高端模拟。所提出的技术影响着工业界和政府对高性能计算和企业计算系统未来发展方向的设计。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招收和培训研究生和本科生，包括代表性不足的少数民族学生。将开源测评平台应用于研究生和本科生的相关课程教学中。

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.