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@INPROCEEDINGS{Maloney:1044104,
      author       = {Maloney, Samuel},
      title        = {{SEANERGYS} at {JSC}: {S}oftware for {E}fficient and
                      {E}nergy-{A}ware {S}upercomputers},
      reportid     = {FZJ-2025-03027},
      year         = {2025},
      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.},
      month         = {May},
      date          = {2025-05-27},
      organization  = {IAS Retreat 2025, Jülich (Germany),
                       27 May 2025 - 27 May 2025},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5122 - Future Computing $\&$ Big Data Systems (POF4-512) /
                      5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / SEANERGYS - Software for
                      Efficient and Energy-Aware Supercomputers (101177590)},
      pid          = {G:(DE-HGF)POF4-5122 / G:(DE-HGF)POF4-5112 /
                      G:(EU-Grant)101177590},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2025-03027},
      url          = {https://juser.fz-juelich.de/record/1044104},
}