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DTSTAMP:20181221T160742Z
LOCATION:D221
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:Doctoral Showcase\nCompilers, Data Analytics, Data Management,
  GPUs, MPI, OS and Runtime Systems, Workshop Reg Pass, Tutorial Reg Pass, 
 Tech Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhibit Hall Only Reg
  Pass, Doctoral Showcase\n\nProductive Data Locality Optimizations in Dist
 ributed Memory\n\nKayraklioglu, El-Ghazawi\n\nWith deepening memory hierar
 chies in HPC systems, the challenge of managing data locality gains more i
 mportance. Coincidentally, increasing ubiquity of HPC systems and wider ra
 nge of disciplines utilizing HPC introduce more programmers to the HPC com
 munity. Given these two trends, it is imperative to have scalable and prod
 uctive ways to manage data locality.<br /><br />In this research, we addre
 ss the problem in multiple ways. We propose a novel language feature that 
 programmers can use to transform shared memory applications to distributed
  memory applications easily. We introduce a high-level profiling tool to h
 elp understand how distributed arrays are used in an application. As next 
 steps, we are designing a model to describe the implementation of data loc
 ality optimizations as an engineering process, which can lend itself to co
 mbinatorial optimization. We are also implementing a profile-based automat
 ic optimization framework that utilizes AI to replace the programmer compl
 etely in implementing optimizations for distributed memory.
URL:https://sc18.supercomputing.org/presentation/?id=drs110&sess=sess278
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