SHF: Small: Collaborative Research: Experimental-based Research on Effective Models of Parallel Application Execution Time, Power, and Resilience

SHF：小型：协作研究：基于实验的并行应用程序执行时间、功耗和弹性有效模型的研究

• 批准号: 1618776
• 负责人: Zhiling Lan
• 金额: $ 20万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618776&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Experimental

The increasing scale and complexity of parallel systems present enormous challenges to parallel applications. One such challenge is the integration and balancing of execution time, power, and resilience for parallel applications. The MuMMI_R project seeks to advance the scientific understanding of the interdependence among power, execution time, and resilience for various application-system configurations. The broader impacts include training of undergraduate and graduate students and the participation in programs such as REUs, CREU, and DREU to increase the participation of students from underrepresented groups in the project.The MuMMI_R research aims to develop effective techniques for quantifying the complicated tradeoffs among execution time, power, and resilience, and to provide a tuning mechanism for user-defined metrics. Toward this goal, the research focuses on three interrelated research thrusts: (1) experimental research to conduct extensive experiments of a suite of representative application under different resilience strategies on various parallel architectures, (2) application-level co-modeling to develop analytical models and colored Petri net based simulation for quantifying the correlations and tradeoffs between execution time, power, and resilience, and (3) model-based analysis to examine the tradeoffs among resilience, execution time, and power for different application-system configurations, and to tune application implementations for a user-defined target metric on current and future systems. The resulting framework, MuMMI_R, will provide valuable insights into application-system interactions and aid in the design of efficient parallel applications (with respect to execution time, power requirements, and resilience), runtime systems, and computer architectures.

并行系统规模和复杂性的不断增加给并行应用带来了巨大的挑战。其中一项挑战是并行应用程序的执行时间、功率和弹性的集成和平衡。 MuMMI_R 项目旨在促进对各种应用系统配置的功率、执行时间和弹性之间相互依赖关系的科学理解。更广泛的影响包括培训本科生和研究生以及参与 REU、CREU 和 DREU 等项目，以增加来自代表性不足群体的学生对项目的参与。 MuMMI_R 研究旨在开发有效的技术来量化执行时间、功率和弹性，并为用户定义的指标提供调整机制。为了实现这一目标，该研究重点关注三个相互关联的研究重点：（1）实验研究，在各种并行架构上的不同弹性策略下对一组代表性应用程序进行广泛的实验，（2）应用程序级联合建模以开发分析模型以及基于彩色 Petri 网的模拟，用于量化执行时间、功率和弹性之间的相关性和权衡，以及 (3) 基于模型的分析，用于检查不同应用程序系统配置的弹性、执行时间和功率之间的权衡，并调整应用程序实现用于当前和未来系统的用户定义的目标指标。由此产生的框架 MuMMI_R 将为应用程序系统交互提供有价值的见解，并帮助设计高效的并行应用程序（在执行时间、功耗要求和弹性方面）、运行时系统和计算机体系结构。

项目成果

Union: An Automatic Workload Manager for Accelerating Network Simulation

Union：用于加速网络仿真的自动工作负载管理器

• DOI: 10.1109/ipdps47924.2020.00089
• 发表时间: 2020-05
• 期刊: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS
• 作者: Wang, Xin;Mubarak, Misbah;Kang, Yao;Ross, Robert B.;Lan, Zhiling
• 通讯作者: Lan, Zhiling