Efficient data management for incoherent ray tracing

To obtain good performance on the GPU hardware, it is necessary to design algorithms to manage data, access memory under GPU memory hierarchy, and schedule more efficient threads. In this paper, we propose an efficient data management and task management designed for GPU based ray tracing. Due to the dynamic and uncertainty in ray tracing, we design data-management layer and task-management layer combined with fuzzy spatial analysis, use the two-level ray sorting and a ray bucket structure to reorganize ray data, then a warp's threads can be scheduled to access coherent geometry and nodes data, reduce memory bandwidth, and dispatch the data locally. We schedule tasks in data-driven execution according to coherent data, propose an adaptive ray compaction to eliminate inactive threads, maintain task efficiency of threads in a warp, and design two heuristics to decrease the compaction cost. On the basis of it, we also introduce a memory-optimized dynamic traversal management to reduce incoherent memory access, and avoid frequent sorting computation and compaction operations. Our experiments demonstrate all of these work combined can achieve good performance.

As shown in the figure, we introduce the bucket structure and two-level ray resorting at the data-management layer. Then, we schedule tasks in data-driven execution according to coherent data, and use adaptive ray compaction and memory-optimized dynamic traversal management at the management layer.Figure optionsDownload as PowerPoint slideHighlights
► An efficient data management designed for GPU based ray tracing is proposed.
► Data-management layer and task-management layer process the uncertainty in ray tracing.
► We introduce the two-level ray sorting and a ray bucket structure to reorganize ray data.
► We schedule tasks in data-driven execution according to coherent data.
► The memory-optimized dynamic traversal management reduces incoherent memory access.