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SPIE Medical Imaging 2007 Hardware acceleration vs. algorithmic acceleration: Can GPU-based processing beat complexity optimization for CT?
BibTeX
@MISC{Neophytou_spiemedical,
author = {Neophytos Neophytou and Fang Xu and Klaus Mueller},
title = {SPIE Medical Imaging 2007 Hardware acceleration vs. algorithmic acceleration: Can GPU-based processing beat complexity optimization for CT?},
year = {}
}
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Abstract
Three-dimensional computed tomography (CT) is a compute-intensive process, due to the large amounts of source and destination data, and this limits the speed at which a reconstruction can be obtained. There are two main approaches to cope with this problem: (i) lowering the overall computational complexity via algorithmic means, and/or (ii) running CT on specialized high-performance hardware. Since the latter requires considerable capital investment into rather inflexible hardware, the former option is all one has typically available in a traditional CPU-based computing environment. However, the emergence of programmable commodity graphics hardware (GPUs) has changed this situation in a decisive way. In this paper, we show that GPUs represent a commodity high-performance parallel architecture that resonates very well with the computational structure and operations inherent to CT. Using formal arguments as well as experiments we demonstrate that GPU-based ‘brute-force ’ CT (i.e., CT at regular complexity) can be significantly faster than CPU-based as well as GPU-based CT with optimal complexity, at least for practical data sizes. Therefore, the answer to the title question: “Can GPU-based processing beat complexity optimization for CT? ” is “Absolutely!” Keywords: computed tomography, CT, 3D reconstruction, filtered backprojection, inverse Radon Transform, programmable graphics hardware, GPU
Keyphrases
gpu-based processing beat complexity optimization spie medical imaging algorithmic acceleration hardware acceleration v programmable commodity graphic hardware practical data size traditional cpu-based computing environment gpu-based brute-force ct algorithmic mean overall computational complexity computational structure compute-intensive process regular complexity main approach destination data specialized high-performance hardware programmable graphic hardware inverse radon transform commodity high-performance parallel architecture formal argument considerable capital investment title question optimal complexity gpu-based ct inflexible hardware former option decisive way large amount
