Accelerating Concurrent Priority Scheduling Using Adaptive in-Hardware Task Distribution in Multicores

Task parallel algorithms execute tasks in some programmer-specified order on shared-memory multicores. A Concurrent Priority Scheduler (CPS) selects high priority tasks, and schedules them among cores to exploit parallelism. The main objective of a CPS is to deliver work efficient algorithmic execution at low communication cost. Selecting high priority tasks among cores requires high synchronizations, but results in near-optimal work-efficiency. The communication cost can be reduced by processing tasks without a strict priority order, but it potentially degrades work-efficiency. This letter proposes HAPS, a novel CPS architecture that utilizes in-hardware core-to-core messages to accelerate task distribution among cores. Moreover, a set of dynamically tunable heuristics are proposed to co-optimize work-efficiency and communication in shared-memory multicores.