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DTSTAMP:20200129T163556Z
LOCATION:506
DTSTART;TZID=America/Denver:20191118T153000
DTEND;TZID=America/Denver:20191118T155500
UID:submissions.supercomputing.org_SC19_sess137_ws_xloop107@linklings.com
SUMMARY:Distributed Global Digital Volume Correlation by Optimal Transport
DESCRIPTION:Workshop\n\nDistributed Global Digital Volume Correlation by O
 ptimal Transport\n\nMacNeil, Morozov, Panerai, Parkinson, Barnard...\n\nBe
 cause of the speed and data rates of timeresolved experiments at facilitie
 s such as synchrotron beamlines, automation is critical during time-resolv
 ed experiments.  In 3D imaging experiments like microCT (CT), this includ
 es recognizing features of interest and “zooming in” spatially and tempora
 lly to those features; ideally without requiring advanced information abou
 t which features are being imaged.  Digital Volume Correlation (DVC) can a
 chieve this by measuring the deformation field between images, but has not
  been used during autonomous experiments because of the scalability of the
  codes. In this work, we propose a model for global DVC and a parallel alg
 orithm for solving it for large-scale images, suitable for giving feedback
  for autonomous experiments at synchrotron-based microCT beamlines. In par
 ticular, we leverage recent advancements in entropy-regularized optimal tr
 ansport to develop efficient, simple-to-implement, parallel algorithms whi
 ch scale linearly (O(N)) in space and time, where N is the number of voxel
 s, and well with an increasing number of processors. As a demonstration, w
 e compute the deformation field for every voxel from a CT volume with dim
 ensions 2560x2560x2160. We discuss implementation details, drawbacks and f
 uture directions.\n\nTag: Workshop Reg Pass, Experimental Datasets, High-f
 idelity Simulations, Scalable Computing\n\nRegistration Category: Workshop
  Reg Pass, Experimental Datasets, High-fidelity Simulations, Scalable Comp
 uting
URL:https://sc19.supercomputing.org/presentation/?id=ws_xloop107&sess=sess
 137
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