Constraint Programming (CP) is a well-established area in AI as a programming paradigm for modelling and solving discrete optimization problems, and it has been been successfully applied to tackle the on-line job dispatching problem in HPC systems including those running modern applications. The limitations of the available CP-based job dispatchers may hinder their practical use in today's systems that are becoming larger in size and more demanding in resource allocation. In an attempt to bring basic AI research closer to a deployed application, we present a new CP-based on-line job dispatcher for modern HPC systems and applications. Unlike its predecessors, our new dispatcher tackles the entire problem in CP and its model size is independent of the system size. Experimental results based on a simulation study show that with our approach dispatching performance increases significantly in a large system and in a system where allocation is nontrivial.

中文翻译：

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用于现代 HPC 系统和应用程序的基于约束编程的作业调度器

约束编程 (CP) 是 AI 中一个成熟的领域，作为建模和解决离散优化问题的编程范式，它已成功应用于解决 HPC 系统（包括运行现代应用程序的系统）中的在线作业调度问题。可用的基于 CP 的作业调度程序的局限性可能会阻碍它们在当今规模越来越大且对资源分配要求越来越高的系统中的实际使用。为了使基础 AI 研究更接近已部署的应用程序，我们为现代 HPC 系统和应用程序提供了一种新的基于 CP 的在线作业调度程序。与其前辈不同，我们的新调度程序解决了 CP 中的整个问题，其模型大小与系统大小无关。