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Title: Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Trace provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less thanmore » 5 minutes per iteration.« less
Authors:
ORCiD logo [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [2]
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Chicago, Chicago, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Published Article
Journal Name:
Advanced Structural and Chemical Imaging
Additional Journal Information:
Journal Volume: 3; Journal Issue: 1; Journal ID: ISSN 2198-0926
Publisher:
Springer
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; Big Data; High-Throughput; Reconstruction; Tomography
OSTI Identifier:
1344024
Alternate Identifier(s):
OSTI ID: 1375811