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@INPROCEEDINGS{Riedel:893827,
      author       = {Riedel, Morris and Sedona, Rocco and Barakat, Chadi and
                      Einarsson, Petur and Hassanian, Reza and Cavallaro, Gabriele
                      and Book, Matthias and Neukirchen, Helmut and Lintermann,
                      Andreas},
      title        = {{P}ractice and {E}xperience in using {P}arallel and
                      {S}calable {M}achine {L}earning with {H}eterogenous
                      {M}odular {S}upercomputing {A}rchitectures},
      publisher    = {IEEE},
      reportid     = {FZJ-2021-02866},
      pages        = {76-85},
      year         = {2021},
      abstract     = {We observe a continuously increased use of Deep Learning
                      (DL) as a specific type of Machine Learning (ML) for
                      data-intensive problems (i.e., ’big data’) that requires
                      powerful computing resources with equally increasing
                      performance. Consequently, innovative heterogeneous
                      High-Performance Computing (HPC) systems based on multi-core
                      CPUs and many-core GPUs require an architectural design that
                      addresses end user communities’ requirements that take
                      advantage of ML and DL. Still the workloads of end user
                      communities of the simulation sciences (e.g., using
                      numerical methods based on known physical laws) needs to be
                      equally supported in those architectures. This paper offers
                      insights into the Modular Supercomputer Architecture (MSA)
                      developed in the Dynamic Exascale Entry Platform (DEEP)
                      series of projects to address the requirements of both
                      simulation sciences and data-intensive sciences such as High
                      Performance Data Analytics (HPDA). It shares insights into
                      implementing the MSA in the Jülich Supercomputing Centre
                      (JSC) hosting Europe No. 1 Supercomputer Jülich Wizard for
                      European Leadership Science (JUWELS). We augment the
                      technical findings with experience and lessons learned from
                      two application communities case studies (i.e., remote
                      sensing and health sciences) using the MSA with JUWELS and
                      the DEEP systems in practice. Thus, the paper provides
                      details into specific MSA design elements that enable
                      significant performance improvements of ML and DL
                      algorithms. While this paper focuses on MSA-based HPC
                      systems and application experience, we are not losing sight
                      of advances in Cloud Computing (CC) and Quantum Computing
                      (QC) relevant for ML and DL.},
      month         = {Jun},
      date          = {2021-06-17},
      organization  = {IEEE International Parallel and
                       Distributed Processing Symposium
                       Workshops (IPDPSW), Portland (USA), 17
                       Jun 2021 - 21 Jun 2021},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5111 - Domain-Specific
                      Simulation $\&$ Data Life Cycle Labs (SDLs) and Research
                      Groups (POF4-511) / DEEP-EST - DEEP - Extreme Scale
                      Technologies (754304) / AISee - AI- and Simulation-Based
                      Engineering at Exascale (951733) / DEEP-SEA - DEEP –
                      SOFTWARE FOR EXASCALE ARCHITECTURES (955606) / EUROCC -
                      National Competence Centres in the framework of EuroHPC
                      (951732)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111 /
                      G:(EU-Grant)754304 / G:(EU-Grant)951733 / G:(EU-Grant)955606
                      / G:(EU-Grant)951732},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000689576200008},
      doi          = {10.1109/IPDPSW52791.2021.00019},
      url          = {https://juser.fz-juelich.de/record/893827},
}