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@ARTICLE{MorenoAlvarez:909066,
      author       = {Moreno-Alvarez, Sergio and Paoletti, Mercedes E. and
                      Cavallaro, Gabriele and Rico, Juan A. and Haut, Juan M.},
      title        = {{R}emote {S}ensing {I}mage {C}lassification {U}sing {CNN}s
                      {W}ith {B}alanced {G}radient for {D}istributed
                      {H}eterogeneous {C}omputing},
      journal      = {IEEE geoscience and remote sensing letters},
      volume       = {19},
      issn         = {1545-598X},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-02984},
      pages        = {1 - 5},
      year         = {2022},
      abstract     = {Land-cover classification methods are based on the
                      processing of large image volumes to accurately extract
                      representative features. Particularly, convolutional models
                      provide notable characterization properties for image
                      classification tasks. Distributed learning mechanisms on
                      high-performance computing platforms have been proposed to
                      speed up the processing, while achieving an efficient
                      feature extraction. High-performance computing platforms are
                      commonly composed of a combination of central processing
                      units (CPUs) and graphics processing units (GPUs) with
                      different computational capabilities. As a result, current
                      homogeneous workload distribution techniques for deep
                      learning (DL) become obsolete due to their inefficient use
                      of computational resources. To address this, new
                      computational balancing proposals, such as heterogeneous
                      data parallelism, have been implemented. Nevertheless, these
                      techniques should be improved to handle the peculiarities of
                      working with heterogeneous data workloads in the training of
                      distributed DL models. The objective of handling
                      heterogeneous workloads for current platforms motivates the
                      development of this work. This letter proposes an innovative
                      heterogeneous gradient calculation applied to land-cover
                      classification tasks through convolutional models,
                      considering the data amount assigned to each device in the
                      platform while maintaining the acceleration. Extensive
                      experimentation has been conducted on multiple datasets,
                      considering different deep models on heterogeneous platforms
                      to demonstrate the performance of the proposed methodology.},
      cin          = {JSC},
      ddc          = {550},
      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) / HPC-EUROPA3 - Transnational Access
                      Programme for a Pan-European Network of HPC Research
                      Infrastructures and Laboratories for scientific computing
                      (730897) / DEEP-EST - DEEP - Extreme Scale Technologies
                      (754304)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111 /
                      G:(EU-Grant)730897 / G:(EU-Grant)754304},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000800169600001},
      doi          = {10.1109/LGRS.2022.3173052},
      url          = {https://juser.fz-juelich.de/record/909066},
}