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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210402T160556Z
LOCATION:Track 10
DTSTART;TZID=America/New_York:20201112T164500
DTEND;TZID=America/New_York:20201112T170500
UID:submissions.supercomputing.org_SC20_sess218_ws_pawatm103@linklings.com
SUMMARY:Task-Parallel In Situ Data Compression of Large-Scale Computationa
 l Fluid Dynamics Simulations
DESCRIPTION:Workshop\n\nTask-Parallel In Situ Data Compression of Large-Sc
 ale Computational Fluid Dynamics Simulations\n\nPacella, Dunton\n\nPresent
  day computational fluid dynamics simulations generate extremely large amo
 unts of data; most of this data is discarded because current storage syste
 ms are unable to keep pace. Data compression algorithms can be applied to 
 this data to reduce the overall amount of storage while either exactly ret
 aining the original dataset (lossless compression) or retaining an approxi
 mate representation of the original dataset (lossy compression). Interpola
 tive decomposition (ID) is a type of lossy compression that factors the or
 iginal data matrix as the product of two (smaller) matrices; one of these 
 matrices consists of columns of the original data matrix, while the other 
 is a coefficient matrix. The structure of ID algorithms makes them a natur
 al fit for task-based parallelism. Our presented work will specifically fo
 cus on using the task-based Legion programming model to implement a single
 -pass ID algorithm (SPID) in several fluid dynamics applications. Performa
 nce studies, scalability and the accuracy of the compressed results will b
 e discussed in detail during our presentation.\n\nRegistration Category: W
 orkshop Reg Pass
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