HESSLE-FREE

Abstract

As computing platforms increasingly embrace heterogeneity, runtime resource managers need to efficiently, dynamically, and robustly manage shared resources (e.g., cores, power budgets, memory bandwidth). To address the complexities in heterogeneous systems, state-of-the-art techniques that use heuristics or machine learning have been proposed. On the other hand, conventional control theory can be used for formal guarantees, but may face unmanageable complexity for modeling system dynamics of complex heterogeneous systems. We address this challenge through HESSLE-FREE (Heterogeneous Systems Leveraging Fuzzy Control for Runtime Resource Management): an approach leveraging fuzzy control theory that combines the strengths of classical control theory together with heuristics to form a light-weight, agile, and efficient runtime resource manager for heterogeneous systems. We demonstrate the efficacy of HESSLE-FREE executing on a NVIDIA Jetson TX2 platform (containing a heterogeneous multi-processor with a GPU) to show that HESSLE-FREE: 1) provides opportunity for optimization in the controller and stability analysis to enhance the confidence in the reliability of the system; 2) coordinates heterogeneous compute units to achieve desired objectives (e.g., QoS, optimal power references, FPS) efficiently and with lower complexity, and 3) eases the burden of system specification.