Speaker
Description
Exascale systems draw a significant amount of power. As each application
deployed map to the various heterogeneous computing elements of these
platforms, managing how power is distributed across components becomes a
priority. The ECP Argo project is developing an infrastructure for node-local control loops that can observe application behavior and adjust resources dynamically, power included, for better performance. We have recently developed a control loop using reinforcement learning, with a proximal policy optimization algorithm, trained on an existing mathematical model of application progress response to power capping. This dependency on the mathematical model is a hindrance: progress/instantaneous performance is stochastic (noisy) under a dynamic workload and therefore a good approximation model demands more data, and lengthy characterization studies. Therefore, we are exploring methods for bypassing this mathematical model, like actor-critic methods, and are looking for collaborations with know-how on other options, for example: real-time training, existing fully characterized applications, alternative control loop designs.
