The 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 nd the most adapted resources to schedule the users' jobs. Improper resource selection operations may lead to poor performance executions and global system utilization along with increase of system fragmentation 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 resource selection algorithm to determine the best choice among the available nodes of the platform based upon their position within the network and taking into account the applications communication 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 our plugin with dierent optimization schemes by comparing with the default Slurm algorithm using both emulation of a large-scale platform, and by carrying out experiments in a real cluster.
A Resource and Job Management System (RJMS) is a crucial system software part of the HPC stack. It is responsible for e ciently 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 topologyaware 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.
A Resource and Job Management System (RJMS) is a crucial system software part of the HPC stack. It is responsible for efficiently delivering computing power to applications in supercomputing environments and its main intelligence relies on resource selection techniques to find the most adapted resources to schedule the users' jobs. In [8], we introduced a new topology-aware resource selection algorithm to determine the best choice among the available nodes of the platform based on their position in the network and on application behaviour (expressed as a communication matrix). We did integrate this algorithm as a plugin in Slurm and validated it with several optimization schemes by making comparisons with the default Slurm algorithm. This paper presents further experiments with regard to this selection process.
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