Parallel Locally Ordered Clustering for Bounding Volume Hierarchy Construction
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Solution Overview
Problem
Current ray tracing techniques in image synthesis face inefficiencies in building bounding volume hierarchies, particularly due to the need for global barrier operations that hinder parallel processing and increase computational resources required for nearest neighbor searches and merging clusters.
Innovation Solution
The technique employs a parallel locally ordered clustering method that performs nearest neighbor searches and merges clusters without global barriers, allowing for efficient generation and compaction of bounding volume hierarchies, optimizing the process by utilizing wavefronts and local data sharing to reduce memory traffic and enhance parallelism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If global barrier operations are used for nearest neighbor searches and merging clusters, then correctness of BVH construction is ensured, but parallel processing efficiency deteriorates and computational overhead increases
Solution Approach 1:
The patent segments the BVH construction process into independent workgroups, each handling a specific range of clusters. Within each workgroup, threads cooperate locally to perform nearest neighbor searches and merging without requiring global synchronization. This segmentation allows different workgroups to proceed independently in parallel, eliminating the need for global barrier operations while maintaining correctness through local coordination.
Solution Approach 2:
The patent introduces intermediate data structures and temporary storage mechanisms that allow threads within a workgroup to exchange information without global barriers. By using local shared memory and intermediate results that can be independently computed and merged, the system maintains data consistency without requiring all threads to synchronize at each step, thus improving parallel efficiency while ensuring correctness.
2Stability of the object's composition
If global barrier operations are performed during cluster merging, then data consistency is maintained, but memory traffic increases and processing time deteriorates
Solution Approach 1:
The merging process is divided into workgroup-level operations where each workgroup independently merges its local clusters using local shared memory. This eliminates the need for global barrier operations that would require all threads to wait and synchronize, thereby reducing processing time while maintaining data consistency through controlled local access patterns and proper memory management within each workgroup.
3Ease of manufacture
If traditional sequential BVH construction is used, then implementation simplicity is maintained, but processing speed deteriorates
Solution Approach 1:
The patent transforms the static sequential BVH construction into a dynamic parallel process using workgroups and wavefront execution. Each workgroup dynamically processes a portion of the clusters, and the system adapts to available hardware resources (GPUs, TPUs, or multi-core CPUs). This dynamic approach maintains relative implementation simplicity by using standardized parallel programming patterns while achieving significant speedups through concurrent execution across multiple processing units.
Data Source
AI summary
A technique for building a bounding volume hierarchy is disclosed. The technique includes performing a nearest neighbor search for a set of clusters to generate a set of nearest neighbors; without performing a global barrier operation, performing a merge operation for the set of clusters, based on the set of nearest neighbors to generate merge results for the set of clusters; and without performing a global barrier operation, outputting clusters for a level of the bounding volume hierarchy, based on the merge results.