System and Application Performance Modeling and Simulation in the AI Era

The increasing complexity and heterogeneity of systems at large scale, combined with challenging characteristics of applications driven by data dominated by adaptivity and irregularity, pose a need for fundamental rethinking and retooling of modeling and simulation (ModSim) for systems and applications. ModSim as a science and practice will be discussed from the perspectives of methods and tools and its myriad of uses, such as system-application co-design, performance prediction, or system and application optimization. To achieve this demanding goal, the presentation initially will offer an analysis and critique of the traditional methodologies and their state of the art. Then, attention will focus on new ideas related to machine learning-both as an increasingly important application workload and a method for ModSim. The context of mapping these applications to leading-edge systems will include analysis and the need for "dynamic performance modeling" as an actionable way to optimize effectively for performance during execution. Throughout, particular emphasis will be on methods and practices that are practical, accurate, and can be applied to extreme-scale computing (as broadly defined).