Cascajo García, AlbertoExpósito Singh, DavidCarretero Pérez, Jesús2020-02-062020-02-062019-09-02Electronics, 2019, 8, 982, 21 pp.2079-9292https://hdl.handle.net/10016/29648ASPIDE: Exascale programIng models for extreme data processingThis work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform's compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.21eng© 2019 by the authors. Licensee MDPI, Basel, SwitzerlandThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) licenseAtribución 3.0 EspañaScalable toolsMonitoring toolsSchedulingMalleabilityresearch articleInformáticahttps://doi.org/10.3390/electronics8090982open access19 (982)21Electronics8AR/0000024221