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Scalable, Data Driven Plasma Simulations with PIConGPU

Huebl, Axel; Widera, René; Garten, Marco; Pausch, Richard; Steiniger, Klaus; Bastrakov, Sergei; Meyer, Felix; Bastrakova, Ksenia; Debus, Alexander; Kluge, Thomas; Ehrig, Simeon; Werner, Matthias; Worpitz, Benjamin; Matthes, Alexander; Rudat, Sophie; Starke, Sebastian; Bussmann, Michael


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{
  "issued": {
    "date-parts": [
      [
        2019, 
        6, 
        13
      ]
    ]
  }, 
  "publisher": "Rodare", 
  "id": "131", 
  "note": "Invited minisymposium talk at the Platform for Advanced Scientific Computing (PASC) Conference (PASC19) at ETH Zurich (Zurich, Switzerland).", 
  "author": [
    {
      "family": "Huebl, Axel"
    }, 
    {
      "family": "Widera, Ren\u00e9"
    }, 
    {
      "family": "Garten, Marco"
    }, 
    {
      "family": "Pausch, Richard"
    }, 
    {
      "family": "Steiniger, Klaus"
    }, 
    {
      "family": "Bastrakov, Sergei"
    }, 
    {
      "family": "Meyer, Felix"
    }, 
    {
      "family": "Bastrakova, Ksenia"
    }, 
    {
      "family": "Debus, Alexander"
    }, 
    {
      "family": "Kluge, Thomas"
    }, 
    {
      "family": "Ehrig, Simeon"
    }, 
    {
      "family": "Werner, Matthias"
    }, 
    {
      "family": "Worpitz, Benjamin"
    }, 
    {
      "family": "Matthes, Alexander"
    }, 
    {
      "family": "Rudat, Sophie"
    }, 
    {
      "family": "Starke, Sebastian"
    }, 
    {
      "family": "Bussmann, Michael"
    }
  ], 
  "DOI": "10.14278/rodare.131", 
  "title": "Scalable, Data Driven Plasma Simulations with PIConGPU", 
  "abstract": "<p>PIConGPU is an open source, multi-platform particle-in-cell code scaling to the fastest supercomputers in the TOP500 list. We present the architecture, novel developments, and workflows that enable high-precision, fast turn-around computations on Exascale-machines. Furthermore, we present our strategies to handle extreme data flows from thousands of GPUs for analysis with in situ processing and open data formats (openPMD). PIConGPU is since recently furthermore natively controlled by a Python Jupyter interface and we research just-in-time kernel generation for C++ with our Cling-CUDA extensions.</p>", 
  "language": "eng", 
  "type": "speech"
}
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