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.

中文翻译：

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使用多核中的自适应硬件内任务分配来加速并发优先级调度

任务并行算法在共享内存多核上以程序员指定的顺序执行任务。并发优先级调度程序（CPS）选择高优先级任务，并在内核之间调度它们以利用并行性。CPS的主要目标是以较低的通信成本提供高效的算法执行。在内核之间选择高优先级的任务需要高度的同步，但会导致接近最佳的工作效率。无需严格的优先级顺序即可通过处理任务来减少通信成本，但它可能会降低工作效率。这封信提出了HAPS，这是一种新颖的CPS体系结构，它利用硬件内部的核心到核心消息来加速核心之间的任务分配。此外，