4 September 2014 Irregular large-scale computed tomography on multiple graphics processors improves energy-efficiency metrics for industrial applications
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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.
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Edward S. Jimenez, Eric L. Goodman, Ryeojin Park, Laurel J. Orr, Kyle R. Thompson, "Irregular large-scale computed tomography on multiple graphics processors improves energy-efficiency metrics for industrial applications", Proc. SPIE 9215, Radiation Detectors: Systems and Applications XV, 921509 (4 September 2014); doi: 10.1117/12.2060721; https://doi.org/10.1117/12.2060721
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