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DTSTART:19700308T020000
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DTSTAMP:20210402T160557Z
LOCATION:Track 7
DTSTART;TZID=America/New_York:20201111T150000
DTEND;TZID=America/New_York:20201111T153000
UID:submissions.supercomputing.org_SC20_sess199_ws_dls105@linklings.com
SUMMARY:Towards a Scalable and Distributed Infrastructure for Deep Learnin
 g Applications
DESCRIPTION:Workshop\n\nTowards a Scalable and Distributed Infrastructure 
 for Deep Learning Applications\n\nHasheminezhad, Shirzad, Wu, Diehl, Schul
 z...\n\nAlthough recent scaling up approaches to train deep neural network
 s have proven to be effective, the computational intensity of large and co
 mplex models, as well as the availability of large-scale datasets require 
 deep learning frameworks to utilize scaling out techniques. Parallelizatio
 n approaches and distribution requirements are not considered in the prima
 ry designs of most available distributed deep learning frameworks and most
  of them still are not able to perform effective and efficient fine-graine
 d inter-node communication. We present Phylanx, which has the potential to
  alleviate these shortcomings. Phylanx presents a productivity-oriented fr
 ontend where user Python code is translated to a futurized execution tree 
 that can be executed efficiently on multiple nodes using the C++ standard 
 library for parallelism and concurrency (HPX), leveraging fine-grained thr
 eading and an active messaging task-based runtime system.\n\nRegistration 
 Category: Workshop Reg Pass
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