Data-Aware Predictive Scheduling for Distributed-Memory Ray Tracing
| Autor: | Hyung-Man Park, Paul A. Navrátil, Donald S. Fussell |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer science
Astrophysics::High Energy Astrophysical Phenomena Concurrency Distributed computing Tracing Computer Graphics and Computer-Aided Design Rendering (computer graphics) Scheduling (computing) Tree (data structure) Signal Processing Distributed memory Ray tracing (graphics) Computer Vision and Pattern Recognition Throughput (business) Software ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 28:1172-1181 |
| ISSN: | 2160-9306 1077-2626 |
| Popis: | Scientific ray tracing now can include realistic shading and material properties, but tracing rays of various depths to conclusion through partitioned data is inefficient. For such data, many ray scheduling methods have demonstrated improved rendering performance. However, synchronicity and non-adaptivity inherent in prior methods hinder further performance optimizations. In this paper, we attempt to relax these constraints. Specifically, we incorporate prediction models capable of dynamically adjusting levels of speculation in ray-data queries, making ray scheduling highly adaptable to a spectrum of scene characteristics. In addition, we organize rays in a tree of speculation nodes, where speculation is coordinated pairwise within a subtree of adaptive ray groups, facilitating concurrency and parallelism. Compared to prior non-predictive methods, we achieve up to three times higher throughput for volume and geometry rendering on a distributed system, making our method fit for both interactive and offline applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|