  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.
