TY - GEN
T1 - Distributed global digital volume correlation by optimal transport
AU - MacNeil, John Michael L.
AU - Morozov, Dmitriy
AU - Panerai, Francesco
AU - Parkinson, Dilworth
AU - Barnard, Harold
AU - Ushizima, Daniela
N1 - Funding Information:
the Advanced Light Source, a DOE Office of Science User Facility. We acknowledge the support of the NASA Asteroid Threat Assessment Project (Dr. E. Stern) for the characterization of meteorite materials.
Funding Information:
IX. ACKNOWLEDGEMENTS This work was supported by the Office of Science, of the U.S. Department of Energy (DOE) under Contract No. DE-AC02-05CH11231, and by the use of resources of the National Energy Research Scientific Computing Center (NERSC) and
Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Because of the speed and data rates of time-resolved experiments at facilities such as synchrotron beamlines, automation is critical during time-resolved experiments. In 3D imaging experiments like microCT (CT), this includes recognizing features of interest and 'zooming in' spatially and temporally to those features; ideally without requiring advanced information about which features are being imaged. Digital Volume Correlation (DVC) can achieve 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 algorithm for solving it for large-scale images, suitable for giving feedback for autonomous experiments at synchrotron-based microCT beamlines. In particular, we leverage recent advancements in entropy-regularized optimal transport to develop efficient, simple-to-implement, parallel algorithms which scale linearly (O(N)) in space and time, where N is the number of voxels, and well with an increasing number of processors. As a demonstration, we compute the deformation field for every voxel from a CT volume with dimensions 2560x2560x2160. We discuss implementation details, drawbacks and future directions.
AB - Because of the speed and data rates of time-resolved experiments at facilities such as synchrotron beamlines, automation is critical during time-resolved experiments. In 3D imaging experiments like microCT (CT), this includes recognizing features of interest and 'zooming in' spatially and temporally to those features; ideally without requiring advanced information about which features are being imaged. Digital Volume Correlation (DVC) can achieve 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 algorithm for solving it for large-scale images, suitable for giving feedback for autonomous experiments at synchrotron-based microCT beamlines. In particular, we leverage recent advancements in entropy-regularized optimal transport to develop efficient, simple-to-implement, parallel algorithms which scale linearly (O(N)) in space and time, where N is the number of voxels, and well with an increasing number of processors. As a demonstration, we compute the deformation field for every voxel from a CT volume with dimensions 2560x2560x2160. We discuss implementation details, drawbacks and future directions.
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U2 - 10.1109/XLOOP49562.2019.00008
DO - 10.1109/XLOOP49562.2019.00008
M3 - Conference contribution
AN - SCOPUS:85078167908
T3 - Proceedings of XLOOP 2019: 1st Annual Workshop on Large-Scale Experiment-in-the-Loop Computing, Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis
SP - 14
EP - 19
BT - Proceedings of XLOOP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st IEEE/ACM Annual Workshop on Large-Scale Experiment-in-the-Loop Computing, XLOOP 2019
Y2 - 18 November 2019
ER -