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@INPROCEEDINGS{Sumner:909957,
      author       = {Sumner, Eric Michael and Aach, Marcel and Lintermann,
                      Andreas and Unnthorsson, Runar and Riedel, Morris},
      title        = {{S}peed-{U}p of {M}achine {L}earning for {S}ound
                      {L}ocalization via {H}igh-{P}erformance {C}omputing},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03547},
      pages        = {1-4},
      year         = {2022},
      abstract     = {Sound localization is the ability of humans to determine
                      the source direction of sounds that they hear. Emulating
                      this capability in virtual environments can have various
                      societally relevant applications enabling more realistic
                      virtual acoustics. We use a variety of artificial
                      intelligence methods, such as machine learning via an
                      Artificial Neural Network (ANN) model, to emulate human
                      sound localization abilities. This paper addresses the
                      particular challenge that the training and optimization of
                      these models is very computationally-intensive when working
                      with audio signal datasets. It describes the successful
                      porting of our novel ANN model code for sound localization
                      from limiting serial CPU-based systems to powerful,
                      cutting-edge High-Performance Computing (HPC) resources to
                      obtain significant speed-ups of the training and
                      optimization process. Selected details of the code
                      refactoring and HPC porting are described, such as adapting
                      hyperparameter optimization algorithms to efficiently use
                      the available HPC resources and replacing third-party
                      libraries responsible for audio signal analysis and linear
                      algebra. This study demonstrates that using innovative HPC
                      systems at the Jülich Supercomputing Centre, equipped with
                      high-tech Graphics Processing Unit (GPU) resources and based
                      on the Modular Supercomputing Architecture, enables
                      significant speed-ups and reduces the time-to-solution for
                      sound localization from three days to three hours per ANN
                      model.},
      month         = {Feb},
      date          = {2022-02-16},
      organization  = {26th International Conference on
                       Information Technology (IT), Zabljak
                       (Montenegro), 16 Feb 2022 - 19 Feb
                       2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733) /
                      PhD no Grant - Doktorand ohne besondere Förderung
                      (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1109/IT54280.2022.9743519},
      url          = {https://juser.fz-juelich.de/record/909957},
}