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@INPROCEEDINGS{GarciadeGonzalo:1033842,
      author       = {Garcia de Gonzalo, Simon and Herten, Andreas and Hrywniak,
                      Markus and Kraus, Jiri and Oden, Lena},
      title        = {{E}fficient {D}istributed {GPU} {P}rogramming for
                      {E}xascale},
      reportid     = {FZJ-2024-06683},
      year         = {2024},
      abstract     = {Over the past decade, GPUs became ubiquitous in HPC
                      installations around the world, delivering the majority of
                      performance of some of the largest supercomputers (e.g.
                      Summit, Sierra, JUWELS Booster). This trend continues in the
                      recently deployed and upcoming Pre-Exascale and Exascale
                      systems (JUPITER, LUMI, Leonardo; El Capitan, Frontier,
                      Aurora): GPUs are chosen as the core computing devices to
                      enter this next era of HPC.To take advantage of future
                      GPU-accelerated systems with tens of thousands of devices,
                      application developers need to have the proper skills and
                      tools to understand, manage, and optimize distributed GPU
                      applications.In this tutorial, participants will learn
                      techniques to efficiently program large-scale multi-GPU
                      systems. While programming multiple GPUs with MPI is
                      explained in detail, also advanced tuning techniques and
                      complementing programming models like NCCL and NVSHMEM are
                      presented. Tools for analysis are shown and used to motivate
                      and implement performance optimizations. The tutorial
                      teaches fundamental concepts that apply to GPU-accelerated
                      systems in general, taking the NVIDIA platform as an
                      example. It is a combination of lectures and hands-on
                      exercises, using a development system for JUPITER (JEDI),
                      for interactive learning and discovery.},
      month         = {Nov},
      date          = {2024-11-17},
      organization  = {The International Conference for High
                       Performance Computing, Networking,
                       Storage, and Analysis 2024, Atlanta, GA
                       (USA), 17 Nov 2024 - 22 Nov 2024},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5122 - Future Computing $\&$ Big Data Systems (POF4-512) /
                      5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / ATML-X-DEV - ATML
                      Accelerating Devices (ATML-X-DEV)},
      pid          = {G:(DE-HGF)POF4-5122 / G:(DE-HGF)POF4-5112 /
                      G:(DE-Juel-1)ATML-X-DEV},
      typ          = {PUB:(DE-HGF)6},
      doi          = {10.5281/ZENODO.12586484},
      url          = {https://juser.fz-juelich.de/record/1033842},
}