Optimization of Topology-Aware Job Allocation on a High-Performance Computing Cluster by Neural Simulated Annealing

TL;DR

This paper introduces a neural simulated annealing approach for topology-aware job allocation on HPC clusters, aiming to reduce network interference and improve communication efficiency.

Contribution

It proposes a novel neural simulated annealing algorithm for dynamic job allocation, extending traditional simulated annealing with learned repair operators.

Findings

NSA outperforms standard SA and SCIP in experiments.

Both models effectively reduce inter-job network interference.

The approach improves communication hop costs in HPC job scheduling.

Abstract

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.