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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20210402T160555Z
LOCATION:Track 8
DTSTART;TZID=America/New_York:20201112T133000
DTEND;TZID=America/New_York:20201112T135500
UID:submissions.supercomputing.org_SC20_sess214_ws_lasalss109@linklings.co
 m
SUMMARY:A Survey of Singular Value Decomposition Methods for Distributed  
 Tall/Skinny Data
DESCRIPTION:Workshop\n\nA Survey of Singular Value Decomposition Methods f
 or Distributed  Tall/Skinny Data\n\nSchmidt\n\nThe Singular Value Decompos
 ition (SVD) is one of the most important matrix \nfactorizations, enjoying
  a wide variety of applications across numerous \napplication domains. In 
 statistics and data analysis, the common applications of \nSVD such as Pri
 ncipal Components Analysis (PCA) and linear regression. Usually \nthese ap
 plications arise on data that has far more rows than columns, so-called\n"
 tall/skinny" matrices. In the big data analytics context, this may take th
 e \nform of hundreds of millions to billions of rows with only a few hundr
 ed \ncolumns. There is a need, therefore, for fast, accurate, and scalable
  \ntall/skinny SVD implementations which can fully utilize modern computin
 g \nresources. To that end, we present a survey of three different algorit
 hms for \ncomputing the SVD for these kinds of tall/skinny data layouts us
 ing MPI for \ncommunication. We contextualize these with common big data a
 nalytics \ntechniques, principally PCA. Finally, we present both CPU and G
 PU timing \nresults from the Summit supercomputer, and discuss possible al
 ternative \napproaches.\n\nTag: Algorithms, Extreme Scale Computing, Perfo
 rmance/Productivity Measurement and Evaluation, Scalable Computing, Scient
 ific Computing\n\nRegistration Category: Workshop Reg Pass
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