  Jobs on high-performance computing (HPC) clusters can suffer significant
performance degradation due to inter-job network interference. Topology-aware
job allocation problem (TJAP) is such a problem that decides how to dedicate
nodes to specific applications to mitigate inter-job network interference. In
this paper, we study the window-based TJAP on a fat-tree network aiming at
minimizing the cost of communication hop, a defined inter-job interference
metric. The window-based approach for scheduling repeats periodically taking
the jobs in the queue and solving an assignment problem that maps jobs to the
available nodes. Two special allocation strategies are considered, i.e., static
continuity assignment strategy (SCAS) and dynamic continuity assignment
strategy (DCAS). For the SCAS, a 0-1 integer programming is developed. For the
DCAS, an approach called neural simulated algorithm (NSA), which is an
extension to simulated algorithm (SA) that learns a repair operator and employs
them in a guided heuristic search, is proposed. The efficacy of NSA is
demonstrated with a computational study against SA and SCIP. The results of
numerical experiments indicate that both the model and algorithm proposed in
this paper are effective.
