Abstract
Direct volume rendering of large and unstructured datasets demands high computational power and memory bandwidth. Developing an efficient parallel algorithm requires a deep understanding of the bottlenecks involved in the solutions for this problem. In this work, we make a thorough analysis of the overhead components involved in parallel volume raycasting of unstructured grids for high-resolution images on distributed environments. This evaluation has revealed potential opportunities for performance improvements. The result is a novel approach to distributed memory raycasting that includes different acceleration techniques to enhance ray distribution, face projection, memory locality, and message exchanging, while maintaining load balance. We report the gains achieved in each phase and in the complete parallel algorithm when compared with a conventional approach.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call