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DTSTART:19700308T020000
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DTSTART;TZID=America/Chicago:20181113T133000
DTEND;TZID=America/Chicago:20181113T134500
UID:submissions.supercomputing.org_SC18_sess278_drs110@linklings.com
SUMMARY:Productive Data Locality Optimizations in Distributed Memory
DESCRIPTION:Engin Kayraklioglu and Tarek El-Ghazawi (George Washington Uni
 versity)\n\nWith deepening memory hierarchies in HPC systems, the challeng
 e of managing data locality gains more importance. Coincidentally, increas
 ing ubiquity of HPC systems and wider range of disciplines utilizing HPC i
 ntroduce more programmers to the HPC community. Given these two trends, it
  is imperative to have scalable and productive ways to manage data localit
 y.<br /><br />In this research, we address the problem in multiple ways. W
 e propose a novel language feature that programmers can use to transform s
 hared memory applications to distributed memory applications easily. We in
 troduce a high-level profiling tool to help understand how distributed arr
 ays are used in an application. As next steps, we are designing a model to
  describe the implementation of data locality optimizations as an engineer
 ing process, which can lend itself to combinatorial optimization. We are a
 lso implementing a profile-based automatic optimization framework that uti
 lizes AI to replace the programmer completely in implementing optimization
 s for distributed memory.\n\nTag: Compilers, Data Analytics, Data Manageme
 nt, GPUs, MPI, OS and Runtime Systems, Doctoral Showcase\n\nRegistration C
 ategory: Workshop Reg Pass, Tutorial Reg Pass, Tech Program Reg Pass, Exhi
 bits Reg Pass, Exhibits - Exhibit Hall Only Reg Pass\n\nSession Chair: Kam
 esh Madduri (Pennsylvania State University)\n\n
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