• Home
  • Labs
    • Computation Vision Recognition Lab
    • Image Analysis Lab
    • Motion Capture Lab
    • Multimedia Lab
    • Medical Imaging Lab
    • Virtual Reality Lab
    • Visual Analytics Lab
    • Visualization Lab
    • Vison Group
  • Members
  • Publications
  • Intranet
    • Publications
    • Authors
    • Labs
    • Projects
 

Hardware acceleration vs. algorithmic acceleration: can GPU-based processing beat complexity optimization for CT?

Neophytos Neophytou, Fang Xu, Klaus Mueller
Proceedings of SPIE, Volume 6510, page 65105-65105 - jan 2007
Download the publication : 27SPIE-MedImg07.pdf [282Ko]  

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!"

Images and movies

27SPIE-MedImg07.jpg [5Ko]
default.jpg [4Ko]
 

BibTex references

@Article {NXM07,
  author       = "Neophytou, Neophytos and Xu, Fang and Mueller, Klaus",
  title        = "Hardware acceleration vs. algorithmic acceleration: can GPU-based processing beat complexity optimization for CT?",
  journal      = "Proceedings of SPIE",
  volume       = "6510",
  pages        = "65105--65105",
  month        = "jan",
  year         = "2007",
  publisher    = "Spie",
  url          = "http://cvc.cs.stonybrook.edu/Publications/2007/NXM07"
}

Other publications in the database

» Neophytos Neophytou
» Fang Xu
» Klaus Mueller
 
Department of Computer Science • Stony Brook University, Stony Brook, NY 11794-4400 • 631-632-8470 or 631-632-8471

 Stony Brook University Home Page    |    Search Stony Brook   |   SOLAR