As modern HPC computing platforms become increasingly heterogeneous, it is challenging for programmers to fully leverage the computation power of massive parallelism offered by such heterogeneity. Consequently, task-based runtime systems have been proposed as an intermediate layer to hide the complex heterogeneity from the application programmers. The core functionality of these systems is to realize efficient task-to-resource mapping in the form of Directed Acyclic Graph (DAG) scheduling. However, existing scheduling schemes face several drawbacks to determine task priorities due to the heavy reliance on domain knowledge or failure to efficiently exploit the interaction of application and hardware characteristics. In this paper, we propose INSPIRIT, an efficient and lightweight scheduling framework with adaptive priority designed for task-based runtime systems. INSPIRIT introduces two novel task attributes \textit{inspiring ability} and \textit{inspiring efficiency} for dictating scheduling, eliminating the need for application domain knowledge. In addition, INSPIRIT jointly considers runtime information such as ready tasks in worker queues to guide task scheduling. This approach exposes more performance opportunities in heterogeneous hardware at runtime while effectively reducing the overhead for adjusting task priorities. Our evaluation results demonstrate that INSPIRIT achieves superior performance compared to cutting edge scheduling schemes on both synthesized and real-world task DAGs.

中文翻译：

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INSPIRIT：通过基于任务的运行时系统中的自适应优先级优化异构任务调度

随着现代 HPC 计算平台变得越来越异构，程序员如何充分利用这种异构性提供的大规模并行计算能力面临着挑战。因此，基于任务的运行时系统被建议作为中间层，以向应用程序员隐藏复杂的异构性。这些系统的核心功能是以有向无环图（DAG）调度的形式实现高效的任务到资源映射。然而，由于严重依赖领域知识或未能有效利用应用程序和硬件特性的交互，现有的调度方案在确定任务优先级时面临着几个缺点。在本文中，我们提出了 INSPIRIT，一种高效、轻量级的调度框架，具有自适应优先级，专为基于任务的运行时系统而设计。 INSPIRIT 引入了两个新颖的任务属性 \textit{启发能力} 和 \textit{启发效率} 来指示调度，从而消除了对应用领域知识的需求。此外，INSPIRIT还联合考虑工作队列中的就绪任务等运行时信息来指导任务调度。这种方法在运行时在异构硬件中提供了更多的性能机会，同时有效地减少了调整任务优先级的开销。我们的评估结果表明，与合成任务 DAG 和实际任务 DAG 上的尖端调度方案相比，INSPIRIT 实现了卓越的性能。