NGS: Collaborative Research: Performance Measurement & Modeling of Deep Hierarchy Systems

NGS：合作研究：绩效衡量

• 批准号: 0406312
• 负责人: Allan Snavely
• 金额: $ 26.25万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0406312&HistoricalAwards=false
• 关键词: NGS; Collaborative; Research; Performance; Measurement

CNS-0406312The goal of this project is to advance methods for understanding the performance of scientific applications. Because of current architectural trends the performance of applications depends largely on their interactions with memory and interconnect subsystems of today's large-scale HEC (High-End Computing) platforms. The dynamics of these interactions are increasingly complex and subtle; therefore means for understanding them are needed so that applications can be better tuned to improve time-to-solution, and future machines can be designed to better meet the needs of applications. The Principal Investigators (PIs) will carry out a program of research and development to provide the computational science community with EMPS (Environment for Memory Performance Studies), an integrated simulation environment for posing and answering "what if" performance questions.Capabilities that this project will provide include:1. Tools for capturing program's performance data with special emphasis on memory and communications.2. Tools for abstracting and summarizing the essential performance behaviors of applications from the performance data.3. Facilities for forming and evaluating performance models to identify the performance sensitivity of applications to changes in machine, or algorithm, or implementation.4. An EMPS API to enable plug-and play between performance tool components the PIs develop and also allowing the environment to be extensible to include components developed by others.Intellectual Merit: As part of this work the PIs will develop a science for understanding the performance of applications running on deep and wide memory hierarchy machines. They will answer the question "what are the factors that affect the performance of scientific applications on such machines?" Going a step further, they will quantify the effects of performance factors, thus increasing the exactitude of performance models for these architectures. They will also develop predictive methods to evaluate how the performance of applications would benefit from new architectural features, and how much performance would improve if different algorithms and/or optimizations were used- thus to evaluate options and tradeoffs prior to carrying out expensive development efforts.Broader Impact:The proposed work will develop a variety of techniques that will permit application scientists to better understand their applications and allow computer scientists to develop computers that are better able to accommodate applications. This will potentially allow faster and better scientific simulations that enable new research to be done in many scientific disciplines. In addition by providing a close working relationship between a university, an NSF center, and a major HPC vendor this project will produce students who are trained in the many facets that impact HPC system development.

CNS-0406312该项目的目标是推进理解科学应用性能的方法。由于当前的架构趋势，应用程序的性能在很大程度上取决于它们与当今大规模HEC（高端计算）平台的存储器和互连子系统的交互。这些相互作用的动力学越来越复杂和微妙;因此，需要了解它们的方法，以便更好地调整应用程序以改善解决方案的时间，并且未来的机器可以设计为更好地满足应用程序的需求。主要研究者（PI）将开展一项研究和开发计划，为计算科学界提供EMPS（记忆性能研究环境），这是一个用于提出和回答“如果”性能问题的集成模拟环境。用于捕获程序性能数据的工具，特别强调内存和通信。用于从性能数据中抽象和总结应用程序基本性能行为的工具。用于形成和评估性能模型的设施，以确定应用程序对机器或算法或实现的变化的性能敏感度。一个EMPS API，使PI开发的性能工具组件之间的即插即用，并允许环境可扩展，以包括其他人开发的组件。智力优点：作为这项工作的一部分，PI将开发一门科学，用于理解运行在深宽内存层次机器上的应用程序的性能。他们将回答这样一个问题：“影响这些机器上科学应用性能的因素是什么？”更进一步，他们将量化性能因素的影响，从而提高这些架构的性能模型的准确性。他们还将开发预测方法，以评估应用程序的性能将如何受益于新的架构功能，以及如果使用不同的算法和/或优化，性能将提高多少-从而在进行昂贵的开发工作之前评估选项和权衡。拟议的工作将开发各种技术，使应用科学家能够更好地了解其应用，并使计算机科学家能够开发出能够更好地适应应用程序。这将有可能实现更快、更好的科学模拟，使许多科学学科的新研究得以完成。此外，通过在大学、NSF中心和主要HPC供应商之间建立密切的工作关系，该项目将培养出在影响HPC系统开发的许多方面接受培训的学生。