Abstract
In this paper, we show how we have coded and optimized a complex and not trivially parallelizable case study: a 3D curve-skeleton calculation algorithm. For this we use NVIDIA CUDA, which allows the programmer to easily code algorithms for executing in a parallel way on NVIDIA GPU devices. However, when working with algorithms that have high data-sharing or data-dependence requirements, like the curve-skeleton calculation, it is not always a trivial task to achieve acceptable acceleration rates. So we detail step by step a comprehensive collection of optimizations to be considered in this class of algorithms, and in general in any CUDA implementation. Two different GPU architectures have been used to test the implications of each optimization, the NVIDIA GT200 architecture and the Fermi GF100. As a result, although the first direct CUDA implementation of our algorithm ran even slower than its CPU version, overall speedups of 19x (GT200) and 68x (Fermi GF100) were finally achieved.
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Jiménez, J., Ruiz de Miras, J. (2013). Optimizations with CUDA: A Case Study on 3D Curve-Skeleton Extraction from Voxelized Models. In: Csurka, G., Kraus, M., Laramee, R.S., Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Application. Communications in Computer and Information Science, vol 359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38241-3_6
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DOI: https://doi.org/10.1007/978-3-642-38241-3_6
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