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Volume: 28 | Article ID: art00018
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Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessible
  DOI :  10.2352/ISSN.2470-1173.2016.19.COIMG-155  Published OnlineFebruary 2016
Abstract

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are user facilities - they are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a“super-facility”, giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography).

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  Cite this article 

S. V Venkatakrishnan, K. Aditya Mohan, Keith Beattie, Joaquin Correa, Eli Dart, Jack R Deslippe, Alexander Hexemer, Harinarayan Krishnan, Alastair A MacDowell, Stefano Marchesini, Simon J Patton, Talita Perciano, James A Sethian, Rune Stromsness, Brian L Tierney, Craig E Tull, Daniela Ushizima, Dilworth Y Parkinson, "Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessiblein Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XIV,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.19.COIMG-155

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