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@MISC{Cavallaro:851266,
      author       = {Cavallaro, Gabriele and Erlingsson, Ernir and Memon, Ahmed},
      title        = {{H}igh {P}erformance and {C}loud {C}omputing for {R}emote
                      {S}ensing {D}ata},
      reportid     = {FZJ-2018-04961},
      year         = {2018},
      abstract     = {The development of the latest-generation sensors mounted on
                      board of Earth observation platforms has led to a necessary
                      re-definition of the challenges within the entire lifecycle
                      of remote sensing data. The acquisition, processing and
                      application phases face problems, which are well described
                      by the Vs big data definitions: (1) Volume - the increasing
                      scale of archived data (i.e., hundreds of Terabytes per day)
                      raises not only data storage but also massive analysis
                      issues, (2) Variety - the data are delivered by sensors
                      acting over different spatial, spectral, radiometric and
                      temporal resolutions, (3) Velocity - the data processing and
                      analysis must confront the rapidly growing rate of data
                      generation, (4) Veracity - the massive amount of sensed data
                      coming in at high speed is associated with uncertainty and
                      accuracy measurement and (5) Value - the acquired data are
                      not straightforward to interpret and they require a powerful
                      yet highly accurate processing scheme in order to extract
                      reliable and valuable information. Traditional serial
                      methods (i.e., desktop approaches, such as MATLAB, R, SAS,
                      etc.) present several limitations and they become
                      ineffective when considering these challenges. Despite
                      modern desktop computers and laptops becoming increasingly
                      multi core and performing better, they preserve limitations
                      in terms of memory and core availability. Trends in parallel
                      architectures like many-core systems (e.g. GPUs) are in
                      continuous expansion to satisfy the growing demands of
                      domain-specific applications for handling computationally
                      intensive problems. In the context of large scale remote
                      sensing applications, High Performance Computing (HPC) and
                      Cloud Computing have the chance of overcoming the
                      limitations of serial algorithms. Parallel architectures
                      such as clusters, grids, or clouds provide tremendous
                      computation capacity and outstanding scalability underpinned
                      by strong and stable standards used for decades, e.g.
                      message passing interface (MPI). The tutorial aims at
                      providing a complete overview for an audience that is not
                      familiar with these topics. The tutorial will follow a
                      twofold approach: selected background lectures (morning
                      session) followed by practical hands-on exercises (afternoon
                      session) in order to enable you to perform your own
                      research. The tutorial will discuss the fundamentals of
                      supercomputing as well as cloud computing, and how we can
                      take advantage of such systems to solve remote sensing
                      problems that require fast and highly scalable solutions.},
      month         = {Jul},
      date          = {2018-07-21},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium (IGARSS),
                       Valencia (Spain), 21 Jul 2018 - 21 Jul
                       2018},
      subtyp        = {After Call},
      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) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)754304 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)17},
      url          = {https://juser.fz-juelich.de/record/851266},
}