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Reviewing GPU architectures to build efficient back projection for parallel geometries

Chilingaryan, Suren ORCID iD icon 1; Ametova, E.; Kopmann, Andreas ORCID iD icon; Mirone, A.
1 Karlsruher Institut für Technologie (KIT)


Back-Projection is the major algorithm in Computed Tomography to reconstruct images from a set of recorded projections. It is used for both fast analytical methods and high-quality iterative techniques. X-ray imaging facilities rely on Back-Projection to reconstruct internal structures in material samples and living organisms with high spatial and temporal resolution. Fast image reconstruction is also essential to track and control processes under study in real-time. In this article, we present efficient implementations of the Back-Projection algorithm for parallel hardware. We survey a range of parallel architectures presented by the major hardware vendors during the last 10 years. Similarities and differences between these architectures are analyzed and we highlight how specific features can be used to enhance the reconstruction performance. In particular, we build a performance model to find hardware hotspots and propose several optimizations to balance the load between texture engine, computational and special function units, as well as different types of memory maximizing the utilization of all GPU subsystems in parallel. We further show that targeting architecture-specific features allows one to boost the performance 2–7 times compared to the current state-of-the-art algorithms used in standard reconstructions codes. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000096506
Veröffentlicht am 06.12.2020
DOI: 10.1007/s11554-019-00883-w
Zitationen: 3
Web of Science
Zitationen: 2
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Prozessdatenverarbeitung und Elektronik (IPE)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 1861-8200, 1861-8219
KITopen-ID: 1000096506
HGF-Programm 54.02.02 (POF III, LK 01) Ultraschnelle Datenauswertung
Erschienen in Journal of real-time image processing
Verlag Springer
Band 17
Heft 5
Seiten 1331–1373
Vorab online veröffentlicht am 26.06.2019
Schlagwörter Parallel algorithms, Hardware architecture, GPU computing, Synchrotron tomography, Back-projection, CUDA, OpenCL
Nachgewiesen in Scopus
Web of Science
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