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LOCATION:D166
DTSTART;TZID=America/Chicago:20181111T140000
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UID:submissions.supercomputing.org_SC18_sess174_ws_exampi104@linklings.com
SUMMARY:AITuning: Machine Learning-Based Tuning Tool for Run-Time Communic
 ation Libraries
DESCRIPTION:Alessandro Fanfarillo and Davide Del Vento (National Center fo
 r Atmospheric Research)\n\nIn this work, we address the problem of tuning 
 communication libraries by using a deep reinforcement learning approach.  
 Reinforcement learning is a machine learning technique incredibly effectiv
 e in solving game-like situations.  In fact, tuning a set of parameters in
  a communication library in order to get better performance in a parallel 
 application can be expressed as a game: find the right combination/path th
 at provides the best reward.  Even though AITuning has been designed to be
  utilized with different run-time libraries, we focused this work on apply
 ing it to the OpenCoarrays run-time communication library, built on top of
  MPI-3.  This work not only shows the potential of using a reinforcement l
 earning algorithm for tuning communication libraries, but also demonstrate
 s how the MPI Tool Information Interface, introduced by the MPI-3 standard
 , can be used effectively by run-time libraries to improve the performance
  without human intervention.\n\nTag: Exascale, MPI, Networks, System Softw
 are\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Purusho
 tham Bangalore (University of Alabama); Ryan Grant (Queen's University, Ca
 nada; Power API); Daniel Holmes (Collis-Holmes Innovations); and Anthony S
 kjellum (Tennessee Technological University, ASCEND-TNTECH)\n\n
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