SEANERGYS at JSC: Software for Efficient and Energy-Aware Supercomputers

SEANERGYS creates an integrated European software solution that optimises resource utilisation and reduces the energy used for real- world workload mixes. It therefore improves the throughput of HPC systems, generating more R&D results for a given energy budget. The solution consists of a comprehensive monitoring infrastructure (CMI), an Artificial Intelligence data analytics system (AIDAS), and a dynamic scheduling and resource management system (DSRM).The CMI gathers data from hardware and software sensors, and correlates it with scheduler information to identify jobs that do not fully utilize allocated resources. Users receive automatic feedback on energy and resource use for each run, plus information on how to optimize these. The AIDAS leverages AI models trained with a vast set of operational data from the participating HPC sites. It fingerprints resource usage patterns, predicts future job behaviour, and identifies complementary job profiles for potential co-scheduling. Finally, the DSRM utilizes these insights to develop scheduling policies that maximize resource utilization and energy efficiency, and supports jobs/applications with dynamic and adaptable resource profiles.

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Publication Details

Journal
JuSER Publikationsportal
Published
2025-01-01
DOI
https://doi.org/10.34734/fzj-2025-03027
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.00
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article

SEANERGYS at JSC: Software for Efficient and Energy-Aware Supercomputers

JuSER Publikationsportal
Cloud Computing and Resource Management
article

SEANERGYS at JSC: Software for Efficient and Energy-Aware Supercomputers

article en

Abstract

SEANERGYS creates an integrated European software solution that optimises resource utilisation and reduces the energy used for real- world workload mixes. It therefore improves the throughput of HPC systems, generating more R&D results for a given energy budget. The solution consists of a comprehensive monitoring infrastructure (CMI), an Artificial Intelligence data analytics system (AIDAS), and a dynamic scheduling and resource management system (DSRM).The CMI gathers data from hardware and software sensors, and correlates it with scheduler information to identify jobs that do not fully utilize allocated resources. Users receive automatic feedback on energy and resource use for each run, plus information on how to optimize these. The AIDAS leverages AI models trained with a vast set of operational data from the participating HPC sites. It fingerprints resource usage patterns, predicts future job behaviour, and identifies complementary job profiles for potential co-scheduling. Finally, the DSRM utilizes these insights to develop scheduling policies that maximize resource utilization and energy efficiency, and supports jobs/applications with dynamic and adaptable resource profiles.

JuSER Publikationsportal
Forschungszentrum Jülich (DE)
Openalex Percentile: Top 38%
Cloud Computing and Resource Management
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