4.7 Article

Data-Aware Predictive Scheduling for Distributed-Memory Ray Tracing

期刊

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2021.3114838

关键词

Task analysis; Distributed databases; Rendering (computer graphics); Data visualization; Processor scheduling; Parallel processing; Ray tracing; Ray tracing; distributed data visualization

资金

  1. US NSF [ACI-1339863]
  2. Intel Graphics and Visualization Institute of eXcellence award

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This paper presents a new ray scheduling method that improves rendering performance by incorporating prediction models and a tree of speculation nodes. Compared to prior methods, this approach achieves higher throughput on a distributed system, making it suitable for both interactive and offline applications.
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.

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