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Open AccessFeature PaperArticle

Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters

Computer Science and Engineering Department, Universidad Carlos III de Madrid, Avenida Universidad 30, Leganés, 28911 Madrid, Spain
Author to whom correspondence should be addressed.
Electronics 2019, 8(9), 982;
Received: 1 August 2019 / Revised: 22 August 2019 / Accepted: 29 August 2019 / Published: 2 September 2019
(This article belongs to the Section Computer Science & Engineering)
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. View Full-Text
Keywords: scalable tools; monitoring tools; scheduling; malleability scalable tools; monitoring tools; scheduling; malleability
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MDPI and ACS Style

Cascajo, A.; Singh, D.E.; Carretero, J. Performance-Aware Scheduling of Parallel Applications on Non-Dedicated Clusters. Electronics 2019, 8, 982.

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