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001     1044104
005     20250801202300.0
024 7 _ |a 10.34734/FZJ-2025-03027
|2 datacite_doi
037 _ _ |a FZJ-2025-03027
041 _ _ |a English
100 1 _ |a Maloney, Samuel
|0 P:(DE-Juel1)200390
|b 0
|e Corresponding author
|u fzj
111 2 _ |a IAS Retreat 2025
|c Jülich
|d 2025-05-27 - 2025-05-27
|w Germany
245 _ _ |a SEANERGYS at JSC: Software for Efficient and Energy-Aware Supercomputers
260 _ _ |c 2025
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a CONFERENCE_POSTER
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336 7 _ |a Output Types/Conference Poster
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336 7 _ |a Poster
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|s 1754035156_22822
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520 _ _ |a 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.
536 _ _ |a 5122 - Future Computing & Big Data Systems (POF4-512)
|0 G:(DE-HGF)POF4-5122
|c POF4-512
|f POF IV
|x 0
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5112
|c POF4-511
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536 _ _ |a SEANERGYS - Software for Efficient and Energy-Aware Supercomputers (101177590)
|0 G:(EU-Grant)101177590
|c 101177590
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856 4 _ |u https://juser.fz-juelich.de/record/1044104/files/SEANERGYS_Poster_IAS_Retreat_2025.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:1044104
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910 1 _ |a Forschungszentrum Jülich
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|6 P:(DE-Juel1)200390
913 1 _ |a DE-HGF
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|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
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|0 G:(DE-HGF)POF4-512
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Supercomputing & Big Data Infrastructures
|9 G:(DE-HGF)POF4-5122
|x 0
913 1 _ |a DE-HGF
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|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
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|v Enabling Computational- & Data-Intensive Science and Engineering
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914 1 _ |y 2025
915 _ _ |a OpenAccess
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920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
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|l Jülich Supercomputing Center
|x 0
980 1 _ |a FullTexts
980 _ _ |a poster
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)JSC-20090406

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