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

并行系统的规模和复杂性不断增加，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、能力和弹性的集成和平衡。MuMMI_R项目旨在促进对不同应用系统配置的能力、执行时间和弹性之间相互依赖的科学理解。MuMMI_R研究旨在开发有效的技术来量化执行时间、能力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。基于这一目标，本研究集中于三个相互关联的研究方向：(1)实验研究，在不同的并行体系结构上对一套不同的弹性策略下的典型应用进行广泛的实验；(2)应用级联合建模，以开发分析模型和基于着色Petri网的仿真，以量化执行时间、能力和弹性之间的相关性和权衡；(3)基于模型的分析，以检查不同应用系统配置的弹性、执行时间和能力之间的权衡，并针对用户定义的目标度量在当前和未来的系统上调整应用实现。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(关于执行时间、功率需求和弹性)、运行时系统和计算机体系结构。更广泛的影响包括对本科生和研究生的培训，以及参与REUS、CREU和DREU等项目，以增加来自代表性不足群体的学生对该项目的参与。

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.