Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection

Christian B. Mendl, Steven Eliuk, Michelle Noga, Pierre Boulanger
Mathematics Department, Technische Universitat Munchen, Boltzmannstrasse 3, 85748 Garching, Germany
Electronic Letters on Computer Vision and Image Analysis 12(1):1-16, 2013

   title={Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection},

   author={Mendl, Christian B and Eliuk, Steven and Noga, Michelle and Boulanger, Pierre},

   journal={Electronic Letters on Computer Vision and Image Analysis},






Download Download (PDF)   View View   Source Source   



This paper provides an extensive runtime, accuracy, and noise analysis of Computed Tomography (CT) reconstruction algorithms using various High-Performance Computing (HPC) frameworks such as: "conventional" multi-core, multi threaded CPUs, Compute Unified Device Architecture (CUDA), and DirectX or OpenGL graphics pipeline programming. The proposed algorithms exploit various built-in hardwired features of GPUs such as rasterization and texture filtering. We compare implementations of the Filtered Back-Projection (FBP) algorithm with fan-beam geometry for all frameworks. The accuracy of the reconstruction is validated using an ACR-accredited phantom, with the raw attenuation data acquired by a clinical CT scanner. Our analysis shows that a single GPU can run a FBP reconstruction 23 time faster than a 64-core multi-threaded CPU machine for an image of 1024 x 1024. Moreover, directly programming the graphics pipeline using DirectX or OpenGL can further increases the performance compared to a CUDA implementation.
VN:F [1.9.22_1171]
Rating: 5.0/5 (2 votes cast)
Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection, 5.0 out of 5 based on 2 ratings

* * *

* * *

Follow us on Twitter

HGPU group

1584 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

299 people like HGPU on Facebook

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: