Topology-Aware Job Mapping

Abstract : A Resource and Job Management System (RJMS) is a crucial system software part of the HPC stack. It is responsible for eciently delivering computing power to applications in supercomputing environments. Its main intelligence relies on resource selection techniques to find the most adapted resources to schedule the users' jobs. This paper introduces a new method that takes into account the topology of the machine and the application characteristics to determine the best choice among the available nodes of the platform, based upon the network topology and taking into account the applications communication pattern. To validate our approach, we integrate this algorithm as a plugin for Slurm, a well-known and widespread RJMS. We assess our plugin with di↵erent optimization schemes by comparing with the default topology-aware Slurm algorithm, using both emulation and simulation of a large-scale platform and by carrying out experiments in a real cluster. We show that transparently taking into account a job communication pattern and the topology allows for relevant performance gains.
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Yiannis Georgiou, Emmanuel Jeannot, Guillaume Mercier, Adèle Villiermet. Topology-Aware Job Mapping. International Journal of High Performance Computing Applications, SAGE Publications, 2018, 32 (1), pp.14-27. ⟨10.1177/1094342017727061⟩. ⟨hal-01621325⟩

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