Parallel Ray-Tracing Modular Mapping Load Balancing
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Solution Overview
Problem
Current data-parallel ray tracing technologies face challenges in load balancing and efficiency, particularly with large models, due to uneven workload distribution and high processing costs associated with acceleration structures, leading to inefficiencies in rendering complex scenes.
Innovation Solution
The implementation of a novel method using modular mapping of scene data into a uniform grid, allowing for dynamic load balancing through neighbor-based data exchange and minimizing inter-processor communication, which enables efficient ray tracing on commodity architectures like multicore chips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If data-parallel ray tracing is used with uniform grid distribution, then data locality is improved and massive data moves are reduced, but load balancing deteriorates due to varying ray costs and hot spots
Solution Approach 1:
The patent segments the uniform grid into variable-sized cells based on object density and ray cost estimates. Each processor receives a customized number of cells rather than equal portions, allowing fine-grained control over data distribution while maintaining data locality benefits of uniform grid structure.
Solution Approach 2:
The patent implements dynamic load balancing where processors can request additional cells from overloaded neighbors during execution. The system monitors processor workload and dynamically adjusts the number of cells each processor handles, transitioning from static uniform distribution to adaptive variable distribution based on actual rendering progress.
2Loss of energy
If static data distribution is used, then data locality is maintained, but load balancing becomes severe due to hot spots and varying viewpoints
Solution Approach 1:
The patent enables dynamic workload adjustment where processors can dynamically request additional cells from neighbors when becoming idle. This creates a living data distribution system that adapts to changing rendering needs, viewpoints, and object densities in real-time rather than being fixed beforehand.
Solution Approach 2:
The system implements feedback mechanisms where processors report their workload status and receive feedback about cell allocation. Overloaded processors can be identified and have their cell assignments adjusted, while idle processors receive additional tasks, creating a self-regulating load balancing system based on actual execution feedback.
3Adaptability or versatility
If demand driven approach is used with large models, then processor flexibility is improved, but data copying and multiplication increase massively
Solution Approach 1:
The patent segments the scene into a uniform grid of cells, with each cell containing a subset of geometric data. This segmentation allows processors to work with manageable data portions while maintaining a consistent data structure that reduces copying overhead compared to unstructured approaches.
Solution Approach 2:
The system performs preliminary cell assignment based on uniform grid distribution before rendering begins. This pre-assignment establishes a fixed data partitioning scheme that reduces the need for dynamic data copying during execution, as processors know in advance which cells they will process.
Data Source
AI summary
Novel method and system for distributed database ray-tracing is presented, based on modular mapping of scene-data among processors. Its inherent properties include scattering data among processors for improved load balancing, and matching between geographical proximity in the scene with communication proximity between processors. High utilization is enabled by unique mechanism of cache sharing. The resulting improved performance enables deep level of ray tracing for real time applications.