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

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。其中一个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耗、执行时间和弹性之间相互依赖关系的科学理解。MuMMI_R研究旨在开发有效的技术来量化执行时间、功耗和弹性之间的复杂权衡，并为用户定义的指标提供调优机制。为实现这一目标，本研究侧重于三个相互关联的研究方向：（1）实验研究，以在各种并行体系结构上在不同的弹性策略下对一套代表性应用进行广泛的实验，（2）应用级协同建模，以开发分析模型和基于有色Petri网的仿真，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，以检查针对不同应用系统配置的弹性、执行时间和功率之间的权衡，并且针对当前和未来系统上的用户定义的目标度量来调整应用实现。由此产生的框架，MuMMI_R，将提供有价值的见解，应用程序系统的交互和高效的并行应用程序的设计（相对于执行时间，功耗要求和弹性），运行时系统，和计算机架构的援助。更广泛的影响包括培训本科生和研究生，以及参与雷乌斯、CREU和DREU等方案，以增加代表性不足群体的学生参与该项目。

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 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. 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.