NGS: Collaborative Research: Reusable, Observation-based Performance Prediction across Platforms

NGS：协作研究：跨平台可重复使用、基于观察的性能预测

• 批准号: 0406408
• 负责人: Jiawei Han
• 金额: $ 2.34万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0406408&HistoricalAwards=false
• 关键词: NGS; Collaborative; Research; Reusable; Observation

This project, investigate an observation-based execution time estimation, approach for resource planning and usage estimation in the grid environment for application and resource scheduling. More specifically, the proposed approaches will collect/manage/utilize application characteristics and performance results, and equally transfer such information across disjoint applications and hardware platforms. With these approaches, performance data from one application's executions on one platform helps predict the performance of another application on another platform. The expected outcome of this research to be a meta-predictor, an effective, efficient and sufficiently accurate cross-platform performance prediction tool that can provide performance predictions as a general service to assist grid users in both their long-term research planning and their everyday job execution. These approaches will be validated and evaluated on production platforms with applications representative for nationally relevant high-end applications, such as National Lab production codes.

本计画探讨一种基于观察的执行时间估计方法，用于在网格环境中应用程序与资源调度的资源规划与使用估计。更具体地说，所提出的方法将收集/管理/利用应用程序的特性和性能结果，并平等地跨分离的应用程序和硬件平台传输这些信息。通过这些方法，来自一个应用程序在一个平台上的执行的性能数据有助于预测另一个应用程序在另一个平台上的性能。本研究的预期成果是一个元预测器，一个有效的，高效的和足够准确的跨平台的性能预测工具，可以提供性能预测作为一般服务，以帮助网格用户在他们的长期研究规划和日常工作执行。这些方法将在生产平台上进行验证和评估，其中应用程序代表国家相关的高端应用程序，例如国家实验室生产代码。