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DTSTAMP:20250822T115804Z
LOCATION:Campussaal - Plenary Room
DTSTART;TZID=Europe/Stockholm:20250616T102000
DTEND;TZID=Europe/Stockholm:20250616T105000
UID:submissions.pasc-conference.org_PASC25_sess149_pos109@linklings.com
SUMMARY:P24 - How to Build an Energy Dataset for HPC
DESCRIPTION:Mathilde Gianolli, Massimo Benini, Jean-Guillaume Piccinali, G
 ianna Marano, and Dino Conciatore (ETH Zurich / CSCS)\n\nQuantifying the e
 nergy consumption in HPC domain is becoming increasingly critical nowadays
 , driven by rising energy costs. To gain a comprehensive understanding of 
 the energy footprint created by the significant power demand of modern sys
 tems like Alps, which exceeds its predecessor Piz Daint in energy usage, t
 he Swiss National Supercomputing Centre (CSCS) decided to collect and aggr
 egate energy consumption data from various sources associated with each jo
 b executed on Alps to create an energy dataset. This dataset is contained 
 in a MySQL database and consists of: - Job Metadata: This includes fields 
 such as job ID, CPU hours consumed, nodes utilized, start time, end time, 
 and elapsed time. This data is sourced from the SLURM workload manager via
  the jobcompletion plugin component. Energy Metrics: energy consumption da
 ta is derived from telemetry sensors installed on Alps supercomputer. Thes
 e sensors, provided by DMTF’s Redfish®  standard , capture raw data then p
 rocessed to calculate energy consumption for all nodes associated with a j
 ob and aggregate the results. SLURM Energy data: added through energy plug
 in to the Job Metadata. Quality Indicators: Additional fields are included
  to assess and ensure reliability and accuracy of computed energy consumpt
 ion metrics. DCGM data:  Nvidia GPU collection metrics system.\n\nSession 
 Chair: Chris Cantwell (Imperial College London)\n\n
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