Acceleration Data Structures for Ray Tracing on Mobile Devices

Nuno Sousa, David Sena, Nikolaos Papadopoulos, João Pereira

2019

Abstract

Mobile devices are continuously becoming more efficient at performing computationally expensive tasks, such as ray tracing. A lot of research effort has been put into using acceleration data structures to minimize the computational cost of ray tracing and optimize the use of GPU resources. However, with the vast majority of research focusing on desktop GPUs, there is a lack of data regarding how such optimizations scale on mobile architectures where there are a different set of challenges and limitations. Our work bridges the gap by providing a performance analysis of not only ray tracing as a whole, but also of different data structures and techniques. We implemented and profiled the performance of multiple acceleration data structures across different instrumentation tools using a set of representative test scenes. Our investigation concludes that a hybrid rendering approach is more suitable for current mobile environments, with greater performance benefits observed when using data structures that focus on reducing memory bandwidth and ALU usage.

Paper Citation

in Harvard Style

Sousa N., Sena D., Papadopoulos N. and Pereira J. (2019). Acceleration Data Structures for Ray Tracing on Mobile Devices. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP; ISBN 978-989-758-354-4, SciTePress, pages 332-339. DOI: 10.5220/0007575403320339

in Bibtex Style

@conference{grapp19,
author={Nuno Sousa and David Sena and Nikolaos Papadopoulos and João Pereira},
title={Acceleration Data Structures for Ray Tracing on Mobile Devices},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP},
year={2019},
pages={332-339},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007575403320339},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 1: GRAPP
TI - Acceleration Data Structures for Ray Tracing on Mobile Devices
SN - 978-989-758-354-4
AU - Sousa N.
AU - Sena D.
AU - Papadopoulos N.
AU - Pereira J.
PY - 2019
SP - 332
EP - 339
DO - 10.5220/0007575403320339
PB - SciTePress