Topology-aware resource management for HPC applications

The Resource and Job Management System (RJMS) is a crucial system software partof the HPC stack. It is responsible for efficiently delivering computing power to applications insupercomputing environments. Its main intelligence relies on resource selection techniques to findthe most adapted resources to schedule the users' jobs. Improper resource selection operations maylead to poor performance executions and global system utilization along with increase of systemfragmentation and jobs starvation. These phenomenas play a role in the increase of the platforms'total cost of ownership and should be minimized. This paper introduces a new topology-aware re-source selection algorithm to determine the best choice among the available nodes of the platformbased upon their position within the network and taking into account the applications commu-nication matrix. To validate our approach, we integrated this algorithm as a plugin for Slurm,a popular and widespread HPC resource and job management system (RJMS). We validated ourplugin with different optimization schemes by comparing with the default Slurmalgorithm using both emulation of a large-scale platform, and by carrying out experiments in a real cluster.Nous montrons comment la prise en compte de la topologie au moment de lasélection des ressources permet d'augmenter les performances des batch Schedule