Cluster schedulers are crucial in high-performance computing (HPC). They determine when
and which user jobs should be allocated to available system resources. Existing cluster …

High-Performance Computing (HPC) systems are resources utilized for data capture,
sharing, and analysis. The majority of our HPC users come from other disciplines than …

Dragonfly is a highly scalable, low-diameter, and cost-efficient network topology, which has
been adopted in new exascale High Performance Computing (HPC) systems. However …

There are massive jobs submitted in the supercomputer, and the job management system is
typically deployed to schedule these jobs and allocate compute resources. FCFS (First …

Accommodating long-running deep learning (DL) training and inference jobs is challenging
on GPU clusters that use traditional batch schedulers, such as Slurm. Given fixed wall clock …

Having a precise and a fast job scheduler model that resembles the real-machine job
scheduling software behavior is extremely important in the field of job scheduling. The idea …

Using large computer systems such as HPC clusters up to their full potential can be hard.
Many problems and inefficiencies relate to the interactions of user workloads and system …

In this paper, we present a novel methodology for predicting job resources (memory and
time) for submitted jobs on HPC systems. Our methodology based on historical jobs data …

Energy costs are a critical consideration for operating High-Performance Computing (HPC)
systems, with significant efforts dedicated to reducing the energy expenditure of active …

A Slurm simulator was used to study the potential benefits of using multiple Slurm controllers
and node-sharing on the TACC Stampede 2 system. Splitting a large cluster into smaller sub …