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Rethinking the Union of Computed Tomography Reconstruction and GPGPU Computing

Edward S. Jimenez, Laurel J. Orr
Sandia National Laboratories, PO BOX 5800, Mail stop 0933, Albuquerque NM, USA
SPIE Optics+Photonics, 2013

@article{jimeneza2013rethinking,

   title={Rethinking the Union of Computed Tomography Reconstruction and GPGPU Computing},

   author={Jimeneza, Edward S and Orrb, Laurel J},

   year={2013}

}

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This work will present the utilization of the massively multi-threaded environment of graphics processors (GPUs) to improve the computation time needed to reconstruct large computed tomography (CT) datasets and the arising challenges for system implementation. Intelligent algorithm design for massively multi-threaded graphics processors differs greatly from traditional CPU algorithm design. Although a brute force port of a CPU algorithm to a GPU kernel may yield non-trivial performance gains, further measurable gains could be achieved by designing the algorithm with consideration given to the computing architecture. Previous work has shown that CT reconstruction on GPUs becomes an irregular problem for large datasets (10GB-4TB), 1 thus memory band-width at the host and device levels becomes a significant bottleneck for industrial CT applications. We present a set of GPU reconstruction kernels that utilize various GPU-specific optimizations and measure performance impact.
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