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DTSTART:19700308T020000
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DTSTAMP:20210402T160559Z
LOCATION:Track 8
DTSTART;TZID=America/New_York:20201113T155000
DTEND;TZID=America/New_York:20201113T161000
UID:submissions.supercomputing.org_SC20_sess227_ws_pyhpc106@linklings.com
SUMMARY:Accelerating Microstructural Analytics with Dask for Volumetric X-
 Ray Imaging
DESCRIPTION:Workshop\n\nAccelerating Microstructural Analytics with Dask f
 or Volumetric X-Ray Imaging\n\nUshizima\n\nWhile X-ray microtomography has
  become indispensable in 3D inspections of materials, efficient processing
  of such volumetric datasets continues to be a challenge. This paper descr
 ibes a computational environment for HPC to facilitate parallelization of 
 algorithms in computer vision and machine learning needed for microstructu
 re characterization and interpretation. The contribution is to accelerate 
 microstructural analytics by employing Dask high-level parallel abstractio
 ns, which scales Numpy workflows to enable multi-dimensional image analysi
 s of diverse specimens. We illustrate our results using an example from ma
 terials sciences, emphasizing the benefits of parallel execution of image-
 dependent tasks. Preliminary results show that the proposed environment co
 nfiguration and scientific software stack deployed using JupyterLab at NER
 SC Cori enables near-real time analyses of complex, high-resolution experi
 ments.\n\nRegistration Category: Workshop Reg Pass
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