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@INPROCEEDINGS{Maloney:1031786,
      author       = {Maloney, Samuel and Suarez, Estela and Eicker, Norbert and
                      Guimaraes, Filipe and Frings, Wolfgang},
      title        = {{A}nalyzing {HPC} {M}onitoring {D}ata {W}ith a {V}iew
                      {T}owards {E}fficient {R}esource {U}tilization},
      journal      = {2643-3001},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-05813},
      pages        = {170-181},
      year         = {2024},
      note         = {The data used for this study are available at:
                      https://doi.org/10.26165/JUELICH-DATA/BDFBPQ
                      979-8-3503-5616-8/24/$31.00 © 2024 IEEE. Personal use of
                      this material is permitted. Permission from IEEE must be
                      obtained for all other uses, in any current or future media,
                      including reprinting/republishing this material for
                      advertising or promotional purposes, creating new collective
                      works, for resale or redistribution to servers or lists, or
                      reuse of any copyrighted component of this work in other
                      works.},
      comment      = {2024 IEEE 36th International Symposium on Computer
                      Architecture and High Performance Computing (SBAC-PAD)},
      booktitle     = {2024 IEEE 36th International Symposium
                       on Computer Architecture and High
                       Performance Computing (SBAC-PAD)},
      abstract     = {Compute nodes in modern HPC systems are growing in size and
                      their hardware has become ever more diverse. Still, many HPC
                      centers allocate the resources of full nodes exclusively to
                      avoid contention, despite the associated risk of
                      underutilization. This paper describes a thorough resource
                      utilization study of CPU and GPU compute and memory
                      capacity, and interconnect bandwidth on JUWELS, a mature
                      leadership-class modular supercomputer, with the aim of
                      identifying opportunities for improving utilization through
                      advanced scheduling and node sharing. Separate analysis of
                      CPU-only and GPU-accelerated nodes finds that CPU compute
                      usage is already close to optimal for the CPU-only nodes,
                      whereas there is plenty of scope for co-scheduling CPU-based
                      jobs on GPU-accelerated nodes. Memory capacity and
                      node-level interconnect bandwidth are sufficient to
                      provision co-scheduled jobs. We analyze multiple one-month
                      datasets to validate robustness of conclusions over time and
                      compare with previous studies on other systems to establish
                      generalizability of results.},
      month         = {Nov},
      date          = {2024-11-13},
      organization  = {2024 IEEE 36th International Symposium
                       on Computer Architecture and High
                       Performance Computing, Hilo, HI (USA),
                       13 Nov 2024 - 15 Nov 2024},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5122 - Future Computing
                      $\&$ Big Data Systems (POF4-512) / DEEP-SEA - DEEP –
                      SOFTWARE FOR EXASCALE ARCHITECTURES (955606) / ATMLAO - ATML
                      Application Optimization and User Service Tools (ATMLAO)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5122 /
                      G:(EU-Grant)955606 / G:(DE-Juel-1)ATMLAO},
      typ          = {PUB:(DE-HGF)16 / PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1109/SBAC-PAD63648.2024.00023},
      url          = {https://juser.fz-juelich.de/record/1031786},
}