We spot on the performance of two acceleration data structures for electromagnetic ray tracing purposes on GPU using the CUDA programming language, namely the KD-tree and the SBVH. Our implementations have been based on the approach made available by NVIDIA which takes into account for the programming optimizations made possible by the latest version of CUDA and for the latest NVIDIA GPU architectures.
Why does SBVH outperform KD-tree on parallel platforms? / Breglia, Alfonso; Capozzoli, Amedeo; Curcio, Claudio; Liseno, Angelo. - (2016), pp. 1-2. (Intervento presentato al convegno IEEE/ACES International Conference on Wireless Information Technology, ICWITS 2016 and System and Applied Computational Electromagnetics, ACES 2016 tenutosi a Honolulu, HI (USA) nel March 13-17, 2016) [10.1109/ROPACES.2016.7465401].
Why does SBVH outperform KD-tree on parallel platforms?
BREGLIA, ALFONSO;CAPOZZOLI, AMEDEO;CURCIO, CLAUDIO;LISENO, ANGELO
2016
Abstract
We spot on the performance of two acceleration data structures for electromagnetic ray tracing purposes on GPU using the CUDA programming language, namely the KD-tree and the SBVH. Our implementations have been based on the approach made available by NVIDIA which takes into account for the programming optimizations made possible by the latest version of CUDA and for the latest NVIDIA GPU architectures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
