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BEGIN:VEVENT
DTSTAMP:20211207T055414Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211119T094000
DTEND;TZID=America/Chicago:20211119T100000
UID:submissions.supercomputing.org_SC21_sess338_ws_xloop102@linklings.com
SUMMARY:Bridging Data Center AI Systems with Edge Computing for Actionable
  Information Retrieval
DESCRIPTION:Workshop\n\nBridging Data Center AI Systems with Edge Computin
 g for Actionable Information Retrieval\n\nLiu, Ali, Kenesei, Miceli, Sharm
 a...\n\nExtremely high data rates at modern synchrotron and X-ray free-ele
 ctron laser light source beamlines motivate the use of machine learning me
 thods for data reduction, feature detection, and other purposes. Regardles
 s of the application, the basic concept is the same: data collected in ear
 ly stages of an experiment, data from past similar experiments, and/or dat
 a simulated for the upcoming experiment are used to train machine learning
  models that, in effect, learn specific characteristics of those data; the
 se models are then used to process subsequent data more efficiently than w
 ould general-purpose models that lack knowledge of the specific dataset or
  data class. Thus, a key challenge is to be able to train models with suff
 icient rapidity that they can be deployed and used within useful timescale
 s. We describe here how specialized data center AI (DCAI) systems can be u
 sed for this purpose through a geographically distributed workflow. Experi
 ments show that although there are data movement cost and service overhead
  to use remote DCAI systems for DNN training, the turnaround time is still
  less than 1/30 of using a locally deploy-able GPU.\n\nTag: Online Only, A
 pplications, Scientific Computing\n\nRegistration Category: Workshop Reg P
 ass
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