Irregular large-scale computed tomography on multiple graphics processors improves energy-efficiency metrics for industrial applications

Edward S. Jimenez Jr.; Eric L. Goodman; Ryeojin Park; Laurel J. Orr; Kyle R. Thompson

doi:10.1117/12.2060721

4 September 2014 Irregular large-scale computed tomography on multiple graphics processors improves energy-efficiency metrics for industrial applications

Edward S. Jimenez Jr., Eric L. Goodman, Ryeojin Park, Laurel J. Orr, Kyle R. Thompson

Author Affiliations +

Proceedings Volume 9215, Radiation Detectors: Systems and Applications XV; 921509 (2014) https://doi.org/10.1117/12.2060721
Event: SPIE Optical Engineering + Applications, 2014, San Diego, California, United States

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.

Citation Download Citation

Edward S. Jimenez Jr., Eric L. Goodman, Ryeojin Park, Laurel J. Orr, and 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); https://doi.org/10.1117/12.2060721

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