Work-In-Progress: Energy and Thermal-Aware Scheduling based on HMARL for OpenMP DAG Workloads

With technological advancement, the leakage power of multicore platforms which correlates exponentially with chip temperature becomes more than dynamic power. Energy-aware hardware solutions use dynamic voltage and frequency scaling (DVFS) to avoid overheating in aggressive performance-boosting scenarios, and software solutions assign high-utilization tasks to different core configurations to conserve processor package power. Since the current energy-aware heuristics are not based on core-by-core frequency monitoring, they do not address overheating when some cores are more active than others. Also, assigning tasks to cores without comprehensive profiling does not address irregular task execution. In this article, we investigate potential of applying an online reinforcement learning method to efficiently allocate tasks based on their profiling data, including makespan, to appropriate core combinations according to their temperature status, aiming to reduce energy consumption. We propose using a hierarchical multi-agent reinforcement learning (HMARL) approach, where one agent selects cores and frequency scaling as a function of profiler results, while another agent selects core combinations as a function of temperature sensor results.