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DTSTAMP:20260522T150123Z
LOCATION:D220
DTSTART;TZID=America/Chicago:20181111T143000
DTEND;TZID=America/Chicago:20181111T143300
UID:submissions.supercomputing.org_SC18_sess160_ws_whpc125@linklings.com
SUMMARY:Error Analysis in HPC Applications Using Algorithmic Differentiati
 on
DESCRIPTION:Harshitha Menon (Lawrence Livermore National Laboratory)\n\nCo
 mputer applications running on supercomputers are used to solve critical p
 roblems. These systems are expected to perform tasks not just quickly, but
  also correctly.  Various factors that can affect correctness of programs 
 include faults, reduced precision, lossy data reduction, iteration and tru
 ncation. In the presence of these errors, how do we know whether our progr
 am is producing correct results?  I have developed a method to understand 
 the impact of these errors on a computer program. The method employs algor
 ithmic differentiation (AD) to analyze the sensitivity of the simulation o
 utput to errors in program variables. A tool that we developed based on th
 is method evaluates a given computer program and identifies vulnerable reg
 ions that need to be protected from errors.  We use this to selectively pr
 otect variables against Silent Data Corruptions (SDC). We also use this me
 thod to study floating point sensitivity of the code and develop mixed-pre
 cision configurations to achieve performance improvement without affecting
  accuracy.  Using this tool we can ensure that the computer simulation app
 lications give us the correct results in the presence of these errors, so 
 that scientists and policy makers relying on these results can make accura
 te predictions that can have lasting impact.\n\nTag: Diversity, Education,
  Hot Topics\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs:
  Toni Collis (Women in High Performance Computing); Weronika Filinger (Edi
 nburgh Parallel Computing Centre (EPCC); University of Edinburgh, Scotland
 ); and Misbah Mubarak (Amazon Web Services)\n\n
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