SIMD Ray Tracing Query Testing via Multi-Way Tree Segmentation
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Solution Overview
Problem
In single instruction multiple data (SIMD) processing architectures, divergence occurs when different threads in a warp execute different instructions, leading to idle threads and reduced computational throughput, particularly in ray tracing applications where efficient traversal of tree-structured data structures is challenging.
Innovation Solution
A method for testing a query against multiple sets of objects using a SIMD processing architecture, where a query is tested against a first set of objects, and based on the result, a second set of objects is selected for further testing, utilizing cohesive traversal techniques to minimize divergence by assigning tasks to threads in a way that all threads within a warp execute the same instruction on different data, and using multi-way tree data structures to optimize ray tracing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different threads within a warp execute different instructions to handle diverse query traversal needs, then task flexibility and correctness are improved, but computational throughput deteriorates due to idle threads from divergence
Solution Approach 1:
The patent segments the traversal task by organizing objects into a multi-way tree data structure where each node can be independently processed. Threads are divided into sub-groups, each handling specific child nodes of a parent node, allowing independent execution while maintaining overall task coherence and reducing divergence.
Solution Approach 2:
The patent performs preliminary organization of objects into a multi-way tree data structure before query traversal. This pre-organization enables threads to follow predetermined traversal paths based on query results from parent nodes, reducing the need for divergent instructions during execution and improving computational throughput.
2Productivity
If all threads within a warp execute the same instruction on different data to maximize throughput, then computational throughput is improved, but task flexibility deteriorates due to inability to handle different traversal decisions
Solution Approach 1:
The patent segments both the data (objects into multi-way tree) and the thread workload (into sub-groups handling specific child nodes). This segmentation allows threads to execute the same instruction type on different data segments, maintaining SIMD efficiency while handling diverse traversal needs through data organization rather than instruction diversity.
Solution Approach 2:
The patent applies local quality by assigning different child nodes to different thread sub-groups based on their parent node's query results. Each thread sub-group processes its assigned data with the same instruction type, but the data itself has local variations that reflect the specific traversal needs of different query paths.
3Productivity
If a single instruction multiple data (SIMD) processing architecture is used to process multiple rays simultaneously, then processing efficiency is improved, but handling complex tree traversal decisions deteriorates due to divergence constraints
Solution Approach 1:
The patent segments the tree traversal control by organizing objects into a multi-way tree structure where each node's children are assigned to specific thread sub-groups. This segmentation simplifies the control logic for SIMD processing by pre-determining which threads handle which child nodes, reducing the complexity of making traversal decisions in parallel.
Solution Approach 2:
The patent performs preliminary setup of the multi-way tree data structure and thread-to-node assignments before ray tracing execution. This preliminary action transfers the decision-making complexity from runtime parallel execution to static preprocessing, allowing SIMD architecture to efficiently execute the determined traversal paths without complex runtime control logic.
Data Source
AI summary
A system, method, and computer program product are provided for testing a query against multiple sets of objects. In operation, a query is tested against a first set of objects, utilizing a single instruction multiple data processing architecture. Additionally, a second set of objects is selected based on a result of testing the query against the first set of objects. Furthermore, the query is tested against the second set of objects, utilizing the single instruction multiple data processing architecture.