RT Journal Article
T1 CPU and GPU oriented optimizations for LiDAR data processing
A1 Muñoz, Felipe
A1 Asenjo, Rafael
A1 Navarro, Ángeles
A1 Cabaleiro Domínguez, José Carlos
K1 LiDAR data processing
K1 Digital Terrain Model
K1 Tree data structures
K1 Parallel optimization
K1 GPU
K1 SYCL
K1 CUDA
K1 oneAPI
AB Digital Terrain Models (DTM) can be accurately obtained from clouds of LiDAR points but the corresponding cloud processing time can be prohibitive. This paper describes several optimization techniques that have been applied to the Overlap Window Method (OWM) that is a key component in DTM applications. OWM was originally implemented in R which translates into serious limitations in terms of the size of the LiDAR point cloud that can be processed. We have ported the code to C++, significantly optimized the data structure to minimize memory accesses, and developed parallel implementations for CPU and GPU commodity devices using oneAPI libraries and tools. This results in CPU and GPU versions that are up to 19x and 83x faster, respectively, than an OpenMP baseline that uses eight CPU cores. Most importantly, the proposed optimizations for CPU and GPU can be paramount to get the most out of other LiDAR-based algorithms in which the careful selection of the right data structure, parallelization strategies and memory access reduction techniques will certainly result in significant performance improvements.
PB Elsevier
SN 1877-7503
YR 2024
FD 2024
LK https://hdl.handle.net/10347/40796
UL https://hdl.handle.net/10347/40796
LA eng
NO Muñoz, F., Asenjo, R., Navarro, A., Cabaleiro, J. C. (2024). CPU and GPU oriented optimizations for LiDAR data processing. "Journal Of Computational Science", vol. 79, 102317
NO This work was supported by the Ministry of Economy and Competitiveness, Government of Spain (Grant Numbers PID2019-104834GBI00, PID2022-141623NB-I00 and TED2021-131527B-I00), Junta de Andalucía, Spain (Grant Number P20-00395-R) and National FPU Grant FPU20/03735. This work has received financial support from the Consellería de Cultura, Educación e Ordenación Universitaria, Spain (accreditation 2019–2022 ED431G-2019/04, reference competitive group 2019–2021, ED431C 2022/16) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System. The authors would like to thank Babcock International for providing the point clouds and LaboraTe group (USC) for their help. Funding for open access charge: Universidad de Málaga / CBUA. The authors report there are no competing interests to declare.
DS Minerva
RD 24 abr 2026