Thermal-aware Adaptive Energy Minimization of OpenMP Parallel Applications

This paper proposes an adaptive energy minimization approach that hierarchically applies DVFS, thread-to-core affinity and dynamic concurrency controls (DCT) to minimize the energy consumption and improve lifetime reliability through balanced thermal controls, while meeting a specified power budget requirement. Fundamental to this approach is an iterative learning-based control algorithm that adapts the VFS and core allocations dynamically based on the CPU workloads and thermal distributions, guided by the CPU performance counters at regular intervals. The adaptation is facilitated through modified OpenMP library-based power budget annotations. The proposed approach is extensively validated on an Intel Xeon E5-2630 platform with up to 12 CPUs running NAS parallel benchmark applications.