Generating Device-specific GPU Code for Local Operators in Medical Imaging

Beitrag bei einer Tagung


Details zur Publikation

Autor(en): Membarth R, Hannig F, Teich J, Körner M, Eckert W
Titel Sammelwerk: Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium, IPDPS 2012
Verlag: IEEE Press
Verlagsort: New York, NY, USA
Jahr der Veröffentlichung: 2012
Tagungsband: Proc. of the 26th IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Seitenbereich: 569-581
ISBN: 978-1-4673-0975-2


Abstract


To cope with the complexity of programming GPU accelerators for medical imaging computations, we developed a framework to describe image processing kernels in a domain-specific language, which is embedded into C++. The description uses decoupled access/execute metadata, which allow the programmer to specify both execution constraints and memory access patterns of kernels. A source-to-source compiler translates this high-level description into low-level CUDA and Open CL code with automatic support for boundary handling and filter masks. Taking the annotated metadata and the characteristics of the parallel GPU execution model into account, two-layered parallel implementations - utilizing SPMD and MPMD parallelism - are generated. An abstract hardware model of graphics card architectures allows to model GPUs of multiple vendors like AMD and NVIDIA, and to generate device-specific code for multiple targets. It is shown that the generated code is faster than manual implementations and those relying on hardware support for boundary handling. Implementations from Rapid Mind, a commercial framework for GPU programming, are outperformed and similar results achieved compared to the GPU backend of the widely used image processing library Open CV. © 2012 IEEE.



FAU-Autoren / FAU-Herausgeber

Hannig, Frank PD Dr.-Ing.
Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)
Membarth, Richard
Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)
Teich, Jürgen Prof. Dr.-Ing.
Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)


Zitierweisen

APA:
Membarth, R., Hannig, F., Teich, J., Körner, M., & Eckert, W. (2012). Generating Device-specific GPU Code for Local Operators in Medical Imaging. In Proc. of the 26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 569-581). Shanghai, CN: New York, NY, USA: IEEE Press.

MLA:
Membarth, Richard, et al. "Generating Device-specific GPU Code for Local Operators in Medical Imaging." Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium (IPDPS), Shanghai New York, NY, USA: IEEE Press, 2012. 569-581.

BibTeX: 

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