Asynchronous Runtimes in Action: An Introspective Framework for a Next Gen Runtime
Joshua Suetterlein, Joshua Landwehr, Andrés Márquez, Joseph Manzano, Guang R. Gao · 2016
One of the most critical challenges that new highperformance systems face is the lack of system software supportfor these large scale systems. Investment on system stack componentsis essential in the development, debugging and optimizationof the new emerging programming models. These emergingmodels have the promise to better utilize the vast hardwareresources available in current and future systems. To aid in thedevelopment of applications and new system stacks, runtimes, asinstances of their respective execution models, need to producefacilities to introspect their inner workings and allow an indepthattribution of performance bottlenecks and computationalpatterns. In other words, the runtime systems need to reducetheir opacity to observers so that users of a novel programexecution model can adapt their designs to fit the intended modelusage, regardless of the layer that they are working on. Thisdesign/development loop (akin to co-design) enables synergisticopportunities across the entire computational stack. This paper presents the design and implementation of a simple"gray" box performance attribution harness running inside a finegrain runtime system: the Open Community Runtime (OCR). We showcase what such a framework can indicate regarding theruntime behavior while running at scale. To this end, we havedesigned a set of synthetic scenarios aimed to test the runtime attheir best and worst cases. We present an analysis of the mostimportant runtime features, properties and idiosyncrasies thatwill affect the development of new runtime features, algorithmicselection, and application development.