Dynamic Scheduling for Data-Parallel Path Tracing of Large-Scale Instanced Scenes

Data-parallel ray tracing is a crucial technique for rendering large-scale scenes that exceed the memory capacity of a single compute node. It partitions scene data across multiple nodes and accesses remote data through inter-node communication. However, the resulting communication overhead remains a significant bottleneck for practical performance. Existing approaches mitigate this bottleneck by enhancing data locality through dynamic scheduling during rendering, typically employing spatial partitioning to enable access prediction. Although effective in some scenarios, these methods incur significant redundancy in base geometry when applied to large-scale instanced scenes. In this paper, we introduce the first object-space-based dynamic scheduling algorithm, which uses object groups as the scheduling units to eliminate redundant storage of base data in instanced scenes. Additionally, we propose two data access frequency prediction methods to guide asynchronous data prefetching, enhancing rendering efficiency. Compared to the state-of-the-art method, our approach achieves an average rendering speedup of 77.6%, with a maximum improvement of up to 146.1%, while incurring only a 5% increase in scene memory consumption.