Maintaining a high rate of productivity, in terms of completed jobs per unit of time, in High-
Performance Computing (HPC) facilities is a cornerstone in the next generation of exascale …

As high-performance computing infrastructures move towards exascale, the role of resource
and job management systems is more critical now than ever. Simulating batch systems to …

Traditionally, on-demand, rigid, and malleable applications have been scheduled and
executed on separate systems. The ever-growing workload demands and rapidly …

As the complexity of deep learning (DL) networks and training data grows enormously,
methods that scale with computation are becoming the future of artificial intelligence (AI) …

Resource elasticity allows to dynamically change the resources of running jobs, which may
significantly improve the throughput on supercomputers. Elasticity requires support from …

Aggregated HPC resources have rigid allocation systems and programming models which
struggle to adapt to diverse and changing workloads. Consequently, HPC systems fail to …

In situ processing aims to alleviate the growing gap between computation and I/O
capabilities by performing data processing close to the data source. In situ processing is …

In recent years, supercomputers have experienced significant advancements in
performance and have grown in size, now comprising several thousands nodes. To unlock …

This paper presents an adaptive data delivery method for data science pipelines. While this
method is feasible for processes communicating over any network, in this work we focus on …

In current scientific workflows, the computational needs of tasks might not be known when it
is submitted to a system for execution. Current resource management (RM) systems and …