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DTSTAMP:20181221T160904Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181113T083000
DTEND;TZID=America/Chicago:20181113T170000
UID:submissions.supercomputing.org_SC18_sess322_post144@linklings.com
SUMMARY:Detection of Silent Data Corruptions in Smooth Particle Hydrodynam
 ics Simulations
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nDetection 
 of Silent Data Corruptions in Smooth Particle Hydrodynamics Simulations\n\
 nCavelan, Ciorba, Cabezón\n\nSoft errors, such as silent data corruptions 
 (SDCs) hinder the correctness of large-scale scientific applications. Ghos
 t replication (GR) is proposed herein as the first SDCs detector relying o
 n the fast error propagation inherent to applications that employ the smoo
 th particle hydrodynamics (SPH) method. GR follows a two-steps selective r
 eplication scheme. First, an algorithm selects which particles to replicat
 e on a different process. Then, a different algorithm detects SDCs by comp
 aring the data of the selected particles with the data of their ghost. The
  overhead and scalability of the proposed approach are assessed through a 
 set of strong-scaling experiments conducted on a large HPC system under er
 ror-free conditions, using upwards of 3, 000 cores. The results show that 
 GR achieves a recall and precision similar to that of full replication met
 hods, at only a fraction of the cost, with detection rates of 91−99.
 9%, no false-positives, and an overhead of 1−10%.
URL:https://sc18.supercomputing.org/presentation/?id=post144&sess=sess322
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