Publication: Performance-aware scheduling of parallel applications on non-dedicated clusters
Loading...
Identifiers
Publication date
2019-09-02
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Impact
Export
Abstract
This 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.
Description
ASPIDE: Exascale programIng models for extreme data processing
Keywords
Scalable tools, Monitoring tools, Scheduling, Malleability
Bibliographic citation
Electronics, 2019, 8, 982, 21 pp.