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@ARTICLE{Asch:862062,
      author       = {Asch, M. and Moore, T. and Badia, R. and Beck, M. and
                      Beckman, P. and Bidot, T. and Bodin, F. and Cappello, F. and
                      Choudhary, A. and de Supinski, B. and Deelman, E. and
                      Dongarra, J. and Dubey, A. and Fox, G. and Fu, H. and
                      Girona, S. and Gropp, W. and Heroux, M. and Ishikawa, Y. and
                      Keahey, K. and Keyes, D. and Kramer, W. and Lavignon, J-F
                      and Lu, Y. and Matsuoka, S. and Mohr, B. and Reed, D. and
                      Requena, S. and Saltz, J. and Schulthess, T. and Stevens, R.
                      and Swany, M. and Szalay, A. and Tang, W. and Varoquaux, G.
                      and Vilotte, J-P and Wisniewski, R. and Xu, Z. and Zacharov,
                      I.},
      title        = {{B}ig data and extreme-scale computing: {P}athways to
                      {C}onvergence-{T}oward ashaping strategy for a future
                      software and data ecosystem for scientific inquiry},
      journal      = {The international journal of high performance computing
                      applications},
      volume       = {32},
      number       = {4},
      issn         = {1741-2846},
      address      = {Thousand Oaks, Calif.},
      publisher    = {Sage Science Press},
      reportid     = {FZJ-2019-02426},
      pages        = {435 - 479},
      year         = {2018},
      abstract     = {Over the past four years, the Big Data and Exascale
                      Computing (BDEC) project organized a series of five
                      international workshops that aimed to explore the ways in
                      which the new forms of data-centric discovery introduced by
                      the ongoing revolution in high-end data analysis (HDA) might
                      be integrated with the established, simulation-centric
                      paradigm of the high-performance computing (HPC) community.
                      Based on those meetings, we argue that the rapid
                      proliferation of digital data generators, the unprecedented
                      growth in the volume and diversity of the data they
                      generate, and the intense evolution of the methods for
                      analyzing and using that data are radically reshaping the
                      landscape of scientific computing. The most critical
                      problems involve the logistics of wide-area, multistage
                      workflows that will move back and forth acrossthe computing
                      continuum, between the multitude of distributed sensors,
                      instruments and other devices at the networks edge, and the
                      centralized resources of commercial clouds and HPC centers.
                      We suggest that the prospects for the future integration of
                      technological infrastructures and research ecosystems need
                      to be considered at three different levels. First, we
                      discuss the convergence of research applications and
                      workflows that establish a research paradigm that combines
                      both HPC and HDA, where ongoing progress is already
                      motivating efforts at the other two levels. Second, we offer
                      an accountof some of the problems involved with creating a
                      converged infrastructure for peripheral environments, that
                      is, a shared infrastructure that can be deployed throughout
                      the network in a scalable manner to meet the highly diverse
                      requirements for processing, communication, and
                      buffering/storage of massive data workflows of many
                      different scientific domains. Third, we focus on some
                      opportunities for software ecosystem convergence in big,
                      logically centralized facilities that execute large-scale
                      simulations and models and/or perform large-scale data
                      analytics. We close by offering some conclusions and
                      recommendations for future investment and policy review.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511)},
      pid          = {G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000438959200001},
      doi          = {10.1177/1094342018778123},
      url          = {https://juser.fz-juelich.de/record/862062},
}