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LOCATION:B312
DTSTART;TZID=America/New_York:20241117T113000
DTEND;TZID=America/New_York:20241117T120000
UID:submissions.supercomputing.org_SC24_sess739_ws_ss111@linklings.com
SUMMARY:hws: A Tool for Monitoring Hardware Metrics Across Diverse Vendors
 : A Case Study on Hyperparameter Optimization Algorithms
DESCRIPTION:Marcel Breyer, Alexander Van Craen, Peter Domanski, and Dirk P
 flüger (University of Stuttgart, Germany; University of Stuttgart, Institu
 te for Parallel and Distributed Systems)\n\nDue to modern hardware's const
 antly growing energy demands, it is important to consider energy efficienc
 y and power consumption. Especially in the age of AI, where a massive amou
 nt of computational power is necessary, energy consumption and the costs i
 nvolved can become a significant problem. However, gathering this power in
 formation in a vendor-independent and portable way is far from trivial. \n
 \nTherefore, we propose hws a hardware sampling library for Python and C++
 , which makes it extremely easy to gather hardware information like the cu
 rrent power draw or total power consumption, as well as other metrics like
  clock frequencies, memory consumption, or utilizations, for CPUs and GPUs
  from NVIDIA, AMD, and Intel. In a case study, we use our library to analy
 ze three common hyperparameter optimization algorithms for two Neural Netw
 ork architectures and one GPU-accelerated SVM implementation.\n\nTag: Ener
 gy Efficiency, HPC Infrastructure, Sustainability\n\nRegistration Category
 : Workshop Reg Pass\n\nSession Chairs: Cate Berard (US Department of Energ
 y); James H. Rogers (Oak Ridge National Laboratory (ORNL)); Fumiyoshi Shoj
 i (RIKEN Center for Computational Science (R-CCS), Center for Computationa
 l Science); Michèle Weiland (EPCC, The University of Edinburgh; The Univer
 sity of Edinburgh); and Mike Woodacre (Hewlett Packard Enterprise (HPE))\n
 \n
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