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Erschienen in: Journal of Visualization 2/2021

09.02.2021 | Regular Paper

Cache-aware volume rendering methods with dynamic data reorganization

verfasst von: Ruiyun Zhu, Yuji Misaki, Marcus Walldén, Fumihiko Ino

Erschienen in: Journal of Visualization | Ausgabe 2/2021

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Abstract

 We present two cache-aware methods for accelerating volume rendering on graphics processing units (GPUs). The proposed methods extend a previous method such that they improve the worst frame rate typically observed at oblique viewing angles. The key technique for realizing this extension is a dynamic reorganization of the volume data that improves the data locality based on the expected data access pattern. The proposed methods are: (1) a basic method which requires three times the amount of memory to store the volume and (2) an adaptive method which utilizes on-the-fly data reorganization to achieve similar performance without increasing the memory consumption. Experimental results indicate that the GPU texture cache hit rate increased from 58.8 to 71.8% for oblique viewing angles using a 1 GB data set, compared to the previous method. Furthermore, the maximum speedup of the proposed adaptive method was 3.6\(\times\) that of the previous method.

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Metadaten
Titel
Cache-aware volume rendering methods with dynamic data reorganization
verfasst von
Ruiyun Zhu
Yuji Misaki
Marcus Walldén
Fumihiko Ino
Publikationsdatum
09.02.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Journal of Visualization / Ausgabe 2/2021
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI
https://doi.org/10.1007/s12650-020-00712-4

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