Modular Scene Data Mapping for Ray Tracing Load Balancing
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Solution Overview
Problem
Current data-parallel ray tracing technologies face challenges in load balancing and efficient data distribution, particularly with large models, leading to inefficiencies due to severe load imbalances and high processing costs associated with acceleration structures.
Innovation Solution
The implementation of a novel method using modular mapping of scene data into a uniform grid, allowing for dynamic load balancing and efficient data exchange between neighboring processors through mutual cache-memory access, reducing the need for global communication and optimizing the distribution of scene data across multiple processing elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data-parallel ray tracing is used with scene data distributed over processors, then processing large models is enabled, but severe load imbalances occur
Solution Approach 1:
The scene data is divided into multiple cells using uniform spatial subdivision, and each cell is further divided into sub-cells. Processors are assigned groups of cells, allowing flexible distribution of workloads. This segmentation enables handling large models while balancing the processing load across multiple processors by assigning different numbers of cells to different processors based on their workload capacity.
Solution Approach 2:
The system implements dynamic load balancing where processors can request additional cells from the cell manager when their current workload is complete. The cell manager distributes idle cells to processors based on their availability and workload status. This dynamic mechanism allows the system to adapt to varying workload conditions and maintain high processing efficiency even when models are large and complex.
2Ease of operation
If demand driven approach is used with image space subdivision, then rendering is simplified, but massive copies of geometric data are required
Solution Approach 1:
The scene is divided into multiple cells with each cell containing a subset of geometric data. Processors are assigned specific cells to process, allowing each processor to work with only the data it needs rather than requiring all processors to have complete copies of the entire scene. This reduces data redundancy while maintaining the simplicity of the demand-driven rendering approach.
Solution Approach 2:
Each processor has access to specific local data (cells assigned to it) rather than requiring global data access. The cell manager facilitates selective data access where processors only load and process the cells they are currently working on, reducing the amount of data that needs to be copied and stored across the system while maintaining ease of operation.
3Device complexity
If uniform distribution of data is used, then data distribution is simplified, but load imbalances occur due to varying ray costs
Solution Approach 1:
The scene space is divided into multiple cells using uniform spatial subdivision, creating a hierarchical structure where each cell can be independently assigned to processors. This segmentation allows simple uniform distribution at the cell level while enabling fine-grained load balancing at the sub-cell level, resolving the contradiction between distribution simplicity and load balance effectiveness.
Solution Approach 2:
The system uses dynamic load balancing where the cell manager monitors processor status and workload, and redistributes cells dynamically based on actual processing needs. This dynamic mechanism allows the system to maintain simple uniform distribution structure while achieving effective load balancing through runtime adaptation, preventing idle time and maximizing processing throughput.
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 matching between geographical proximity in the scene with communication proximity between processors.