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@INPROCEEDINGS{Erlingsson:851271,
      author       = {Erlingsson, E. and Cavallaro, G. and Galonska, A. and
                      Riedel, Morris and Neukirchen, Helmut},
      title        = {{M}odular {S}upercomputing {D}esign {S}upporting {M}achine
                      {L}earning {A}pplications},
      publisher    = {IEEE},
      reportid     = {FZJ-2018-04966},
      pages        = {0159-0163},
      year         = {2018},
      abstract     = {The DEEP-EST (DEEP - Extreme Scale Technologies) project
                      designs and creates a Modular Supercomputer Architecture
                      (MSA) whereby each module has different characteristics to
                      serve as blueprint for future exascale systems. The design
                      of these modules is driven by scientific applications from
                      different domains that take advantage of a wide variety of
                      different functionalities and technologies in High
                      Performance Computing (HPC) systems today. In this context,
                      this paper focuses on machine learning in the remote sensing
                      application domain but uses methods like Support Vector
                      Machines (SVMs) that are also used in life sciences and
                      other scientific fields. One of the challenges in remote
                      sensing is to classify land cover into distinct classes
                      based on multi-spectral or hyper-spectral datasets obtained
                      from airborne and satellite sensors. The paper therefore
                      describes how several of the innovative DEEP-EST modules are
                      co-designed by this particular application and subsequently
                      used in order to not only improve the performance of the
                      application but also the utilization of the next generation
                      of HPC systems. The paper results show that the different
                      phases of the classification technique (i.e. training, model
                      generation and storing, testing, etc.) can be nicely
                      distributed across the various cluster modules and thus
                      leverage unique functionality such as the Network Attached
                      Memory (NAM).},
      month         = {May},
      date          = {2018-05-21},
      organization  = {41st International Convention on
                       Information and Communication
                       Technology, Electronics and
                       Microelectronics, Opatija (Croatia), 21
                       May 2018 - 25 May 2018},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / DEEP-EST - DEEP - Extreme Scale Technologies
                      (754304)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)754304},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.23919/MIPRO.2018.8400031},
      url          = {https://juser.fz-juelich.de/record/851271},
}