Understanding the efficiency of ray traversal on GPUs

By Timo Aila, Samuli Laine. High Performance Graphics, 2009

517 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/818236b1f1ac6fe18b00044e6d50ad8caa6a6420

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

1. I. Wald
2. David P. Luebke

Citations in the corpus

• OptiX: a general purpose ray tracing engine (2010, methodology): “On the other hand, several researchers have shown how to map ray tracing algorithms efficiently to GPUs and the NVIDIA® CUDATM architecture [Aila and Laine 2009; Horn et al. 2007; Popov et al. 2007], but these systems have focused on performance rather than flexibility.”
• GPU-based out-of-core many-lights rendering (2013, unknown): “Ray reordering approaches reduce memory bandwidth by improving the ray traversal orders [Aila and Laine 2009], such as Z-curves, scheduling grids or coarse ray sampling.”
• Embree (2014, methodology): “Here, the rays of a packet are independently traversed through the BVH, and each ray is potentially tested against different BVH nodes or triangles [Aila and Laine 2009].”
• Crowdsourcing in Computer Vision (2016, unknown): “Aila and collaborators have explored ray tracing efficiency on GPUs [2, 75].”