论文标题
在动态不对称环境中调度任务并行应用
Scheduling Task-parallel Applications in Dynamically Asymmetric Environments
论文作者
论文摘要
通过应用程序将共享资源干扰视为动态性能不对称。先前的ART已开发出降低主要不对称影响的方法，主要是在操作系统和建筑层面上。在这项工作中，我们研究了应用程序级调度技术如何利用可可的性能（即灵活地作为单线程或多线程任务起作用），以及有关任务关键性的明确知识来处理系统性能不仅是未知的场景，而且随着时间的推移而变化。我们提出的任务调度程序动态地了解了基础平台的性能特征，并利用这些知识来设计更好的时间表，以了解动态性能不对称性，从而减少干扰的影响。我们的评估表明，批判性感知的调度和并行性调整都是解决共享和分布式内存应用程序干扰的有效方案
Shared resource interference is observed by applications as dynamic performance asymmetry. Prior art has developed approaches to reduce the impact of performance asymmetry mainly at the operating system and architectural levels. In this work, we study how application-level scheduling techniques can leverage moldability (i.e. flexibility to work as either single-threaded or multithreaded task) and explicit knowledge on task criticality to handle scenarios in which system performance is not only unknown but also changing over time. Our proposed task scheduler dynamically learns the performance characteristics of the underlying platform and uses this knowledge to devise better schedules aware of dynamic performance asymmetry, hence reducing the impact of interference. Our evaluation shows that both criticality-aware scheduling and parallelism tuning are effective schemes to address interference in both shared and distributed memory applications
