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DTSTART;TZID=America/New_York:20241117T161500
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UID:submissions.supercomputing.org_SC24_sess745_ws_scalah107@linklings.com
SUMMARY:Leveraging Hybrid Classical-Quantum Methods for Efficient Load Reb
 alancing in HPC
DESCRIPTION:Justyna Zawalska (AGH University of Krakow, ACC Cyfronet AGH);
  Minh Chung (Leibniz Supercomputing Centre (LRZ); MNM-Team, Ludwig-Maximil
 ians-Universität München); Katarzyna Rycerz (AGH University of Krakow, ACC
  Cyfronet AGH); Laura Schulz and Martin Schulz (Leibniz Supercomputing Cen
 tre (LRZ)); and Dieter Kranzlmüller (Ludwig-Maxmilians-Universität München
 , Leibniz Supercomputing Centre (LRZ))\n\nLoad balancing (LB) is a challen
 ge for parallel applications in High Performance Computing (HPC). Dependin
 g on various constraints, LB is an optimization problem. This paper focuse
 s on the context of a given task distribution in distributed memory system
 s, where load imbalance might happen at runtime due to a weak performance 
 model. In this imbalance context, LB refers to the Load Rebalancing Proble
 m (LRP). Tasks should be migrated from one machine to another to improve t
 he load. Our paper presents a formulation of LRP to be solved in a hybrid 
 classical-quantum approach. We compare the quantum-based methods with the 
 classical methods using heuristic algorithms. The experiments revolve arou
 nd imbalance ratio and speedup based on the results of the applied methods
 , where the number of migrated tasks is a concern because task migration o
 verhead is expensive. The quantum-based methods show positive performance 
 gain even better than classical methods.\n\nTag: Algorithms, Heterogeneous
  Computing\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: 
 Vassil Alexandrov (Hartree Centre, STFC); Jack Dongarra (University of Ten
 nessee, Knoxville; Oak Ridge National Laboratory (ORNL)); Erik Draeger (La
 wrence Livermore National Laboratory (LLNL), Center for Applied Scientific
  Computing); Christian Engelmann (Oak Ridge National Laboratory (ORNL)); a
 nd Dieter A. Kranzlmueller (Ludwig-Maxmilians-Universität München, Leibniz
  Supercomputing Centre (LRZ))\n\n
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