异构众核系统中基于模仿学习的并行任务调度
Runtime Task Scheduling using Imitation Learning for Heterogeneous Many-Core Systems
摘要：
特定领域的片上系统是一类异构众核系统，被认为是缩小定制硬件加速器和可编程处理器之间性能和能效差距的关键方法。这些架构的全部潜力的实现关键取决于在运行时将应用程序最佳地调度到可用资源。现有的基于优化的技术不能在运行时实现这一目标，由于组合性质的任务调度问题。作为主要的理论贡献，本文提出调度作为一个分类问题，并提出了一个分层的模仿学习（IL）为基础的调度，学习从Oracle的多个特定领域的应用程序的性能最大化。从无线通信和雷达领域的六个流媒体应用程序的广泛评估表明，拟议的IL为基础的调度器近似的离线Oracle的政策，超过99%的准确性为性能和能源为基础的优化目标。此外，它还以较低的运行时开销实现了与Oracle几乎相同的性能，并成功适应新应用程序、众核系统配置以及应用程序特征的运行时变化。
英文摘要：
Domain-specific systems-on-chip, a class of heterogeneous many-core systems, are recognized as a key approach to narrow down the performance and energy-efficiency gap between custom hardware accelerators and programmable processors. Reaching the full potential of these architectures depends critically on optimally scheduling the applications to available resources at runtime. Existing optimization-based techniques cannot achieve this objective at runtime due to the combinatorial nature of the task scheduling problem. As the main theoretical contribution, this paper poses scheduling as a classification problem and proposes a hierarchical imitation learning (IL)-based scheduler that learns from an Oracle to maximize the performance of multiple domain-specific applications. Extensive evaluations with six streaming applications from wireless communications and radar domains show that the proposed IL-based scheduler approximates an offline Oracle policy with more than 99% accuracy for performance- and energy-based optimization objectives. Furthermore, it achieves almost identical performance to the Oracle with a low runtime overhead and successfully adapts to new applications, many-core system configurations, and runtime variations in application characteristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
