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@INPROCEEDINGS{Cavallaro:859351,
      author       = {Cavallaro, Gabriele and Erlingsson, Ernir and Riedel,
                      Morris and Neukirchen, Helmut},
      title        = {{E}nhancing {R}emote {S}ensing {A}pplications towards
                      {E}xascalewith the {DEEP}-{EST} {M}odular {S}upercomputer
                      {A}rchitecture},
      reportid     = {FZJ-2019-00219},
      year         = {2018},
      note         = {digital poster presentation},
      abstract     = {Due to the advancement of the latest-generation remote
                      sensing instruments, a wealth of information is generated
                      almost on a continuous basis and with an increasing rate at
                      global scale. This sheer volume and variety of sensed data
                      leads to a necessary re-definition of the challenges within
                      the entire lifecycle of remote sensing data. Trends in
                      parallel High-Performance Computing (HPC) architectures are
                      constantly developing to tackle the growing demand of
                      domain-specific applications for handling computationally
                      intensive problems. In the context of large scale remote
                      sensing applications, where the interpretation of the data
                      is not straightforward and near-real-time answers are
                      required, HPC can overcome the limitations of serial
                      algorithms. The Dynamic Exascale Entry Platform - Extreme
                      Scale Technologies (DEEP-EST) aims at delivering a
                      pre-exascale platform based on a Modular Supercomputer
                      Architecture (MSA) wherein each module has different
                      characteristics. The MSA provides not only a standard CPU
                      cluster module, but a many-core Extreme Scale Booster (ESB),
                      a Global Collective Engine (GCE) to speed-up MPI collective
                      operations in hardware, a Network Attached Memory (NAM) as a
                      fast scratch file replacement, and a hardware accelerated
                      Data Analytics Module (DAM). As partner in the DEEP-EST
                      consortium, we aim at enhancing machine learning in the
                      remote sensing application domain towards exascale
                      performance. Several of the innovative DEEP-EST modules are
                      co-designed by particular methods such as the clustering
                      algorithm Density-Based Spatial Clustering (DBSCAN) and
                      classification algorithms like Support Vector Machines
                      (SVMs) and Convolutional Neural Networks (CNNs). We intend
                      to present how the different phases of these algorithms
                      (i.e., training, model generation and storing, testing,
                      etc.) can be neatly distributed across the various cluster
                      modules and thus leverage their unique functionality. The
                      MSA will be used to not only improve the performance of
                      these methods but also to serve as blueprint for the next
                      generation of exascale HPC systems.},
      month         = {Nov},
      date          = {2018-11-12},
      organization  = {Phi-week - ESA, Frascati (Italy), 12
                       Nov 2018 - 16 Nov 2018},
      subtyp        = {Other},
      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)24},
      url          = {https://juser.fz-juelich.de/record/859351},
}