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DTSTAMP:20211207T055339Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211114T120500
DTEND;TZID=America/Chicago:20211114T121500
UID:submissions.supercomputing.org_SC21_sess428_ws_ftxs104@linklings.com
SUMMARY:Accelerating Checkpoint/Restart with Lossy Methods
DESCRIPTION:Workshop\n\nAccelerating Checkpoint/Restart with Lossy Methods
 \n\nIldes, Kastoras, Keller, Bautista Gomez\n\nApproximate computing targe
 ts applications with the ability to tolerate losses of accuracy in the com
 putational results. The essence of approximate computing is to use a data 
 representation, that allows to reduce the data size or speed up computatio
 ns at the cost of data accuracy. \n\nData reduction is a desirable goal wh
 en leveraging checkpoint-and-restart to ensure resiliency of an HPC applic
 ation. As the performance of the IO subsystem of supercomputers increases 
 slowly compared to the computing resources like CPU and dynamic memory, IO
  poses a bottleneck. Reducing the data before writing a checkpoint can hel
 p to provide viable checkpointing solutions for extreme scale applications
  that need frequent checkpointing. \n\nIn this work, we implement and eval
 uate two approximate checkpoint mechanisms: Precision Bound Differential C
 heckpointing and Checkpointing with Lossy Compression. Both methods reduce
  the amount of data and restore an approximate representation of the appli
 cation state upon recovery.\n\nTag: Online Only, Extreme Scale Computing, 
 Reliability and Resiliency\n\nRegistration Category: Workshop Reg Pass
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