Date of Award
4-26-2024
Publication Type
Thesis
Degree Name
M.A.Sc.
Department
Electrical and Computer Engineering
Keywords
Data Parallel C++;Hardware Acceleration;Heterogeneous Computing;High Performance Computing;Iterative Closest Point;SYCL
Supervisor
Mohammed Khalid
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
Ever since the Integrated Circuit was used in the Apollo Guidance Computer, which helped put man on the moon, computers have evolved into different forms to meet various requirements. Hardware accelerators are one such evolution in the realm of computing. They represent specialized hardware components designed to execute specific tasks more efficiently than traditional general-purpose processors. Combining the powers of both general-purpose processors and hardware accelerators is a bleeding-edge research area called heterogeneous computing. Advancements in vision technology today demand faster computation on an unprecedented scale. Shape registration is one such operation predominantly carried out by an algorithm called iterative closest point. It is a computationally intensive algorithm with a lot of inherent parallelism, making it a suitable candidate for hardware acceleration through heterogeneous computing. In this thesis, SYCL- based DPC++, a latest heterogeneous computing C++ compiler, was used to accelerate two variants of the ICP algorithm using GPU. A speedup of 24.66X and 35.59X were achieved by our bruteforce-ICP and KD-tree based ICP implementations respectively, compared to the CPU implementation on the Stanford bunny model point cloud - 35k resolution, a standard dataset used for shape registration. Additionally, our GPU implementation of KD-Tree ICP is 2.6 times faster than the widely used state of the art ICP implementation by the Point Cloud Library.
Recommended Citation
Christopher, Roche Periyanayagam, "Hardware Acceleration of Iterative Closest Point Algorithm using DPC++" (2024). Electronic Theses and Dissertations. 9417.
https://scholar.uwindsor.ca/etd/9417