Topics of interest

Working from our premise that the deluge of HPC monitoring data necessitates a move toward data-driven intelligent modeling, we solicit contributions including, but not limited to:
  • Use of machine learning or data science in the context of better understanding any of the following HPC issues:
    • Hardware faults and errors
    • Software errors
    • Telemetry data (temperature, voltages, cooling apparatus)
    • Power consumption
    • Facilities / building control
    • Job scheduling
    • Filesystem logs
    • Network logs
    • Syslog or console logs
    • Error detection and correction
    • Resilience and fault tolerance
    • Failure troubleshooting / assistance of human experts
    • Assistance of non-expert users
    • HPC system security​
  • Use of interpretable machine learning models for HPC-related decision support
    • Including user/human-subject studies​
  • Modeling techniques incorporating human expert knowledge along with knowledge extracted from data
    • Use of these models to evaluate, confirm, or refute human assumptions
  • New or improved machine learning models particularly suited for HPC problems
  • Tools, at any stage of development, using data-driven technologies for some aspect of systems monitoring or design
  • Experience reports detailing successes and failures of machine learning applied to HPC
  • Formulations of unsolved data-related HPC problems with the potential for machine learning
    • Especially including the public release of HPC-related datasets for use by the machine learning community