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DTSTAMP:20211207T055411Z
LOCATION:229
DTSTART;TZID=America/Chicago:20211119T113900
DTEND;TZID=America/Chicago:20211119T114200
UID:submissions.supercomputing.org_SC21_sess504_ws_rsdha104@linklings.com
SUMMARY:Distributed Training for High Resolution Images:  A Domain and Spa
 tial Decomposition Approach
DESCRIPTION:Workshop\n\nDistributed Training for High Resolution Images:  
 A Domain and Spatial Decomposition Approach\n\nTsaris, Hinkle, Lunga, Dias
 \n\nIn this work we developed two Pytorch libraries using the PyTorch RPC 
 interface for distributed deep learning approaches on high resolution imag
 es. The spatial decomposition library allows for distributed training on v
 ery large images, which otherwise won’t be possible on a single GPU. The d
 omain parallelism library allows for distributed training across multiple 
 domain unlabeled data, by leveraging the domain separation architecture. B
 oth of those libraries where tested on the Summit supercomputer at Oak Rid
 ge National Laboratory at a moderate scale.\n\nTag: Architectures, Extreme
  Scale Computing, Heterogeneous Systems\n\nRegistration Category: Workshop
  Reg Pass
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