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Proceedings Paper

Irregular large-scale computed tomography on multiple graphics processors improves energy-efficiency metrics for industrial applications
Author(s): Edward S. Jimenez Jr.; Eric L. Goodman; Ryeojin Park; Laurel J. Orr; Kyle R. Thompson
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Paper Abstract

This paper will investigate energy-efficiency for various real-world industrial computed-tomography reconstruction algorithms, both CPU- and GPU-based implementations. This work shows that the energy required for a given reconstruction is based on performance and problem size. There are many ways to describe performance and energy efficiency, thus this work will investigate multiple metrics including performance-per-watt, energy-delay product, and energy consumption. This work found that irregular GPU-based approaches1 realized tremendous savings in energy consumption when compared to CPU implementations while also significantly improving the performance-per- watt and energy-delay product metrics. Additional energy savings and other metric improvement was realized on the GPU-based reconstructions by improving storage I/O by implementing a parallel MIMD-like modularization of the compute and I/O tasks.

Paper Details

Date Published: 4 September 2014
PDF: 9 pages
Proc. SPIE 9215, Radiation Detectors: Systems and Applications XV, 921509 (4 September 2014); doi: 10.1117/12.2060721
Show Author Affiliations
Edward S. Jimenez Jr., Sandia National Labs. (United States)
Eric L. Goodman, Sandia National Labs. (United States)
Ryeojin Park, ASML (United States)
Laurel J. Orr, Sandia National Labs. (United States)
Kyle R. Thompson, Sandia National Labs. (United States)

Published in SPIE Proceedings Vol. 9215:
Radiation Detectors: Systems and Applications XV
Gary P. Grim; H. Bradford Barber, Editor(s)

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