Ray Tracing Storage Identification Through Workload Estimation
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Solution Overview
Problem
Ray tracing simulations face challenges due to high memory and computational resource demands, particularly in scenarios with large numbers of diffuse and diffraction interactions, leading to incomplete simulations or memory overflow.
Innovation Solution
A method is introduced where lightweight ray tracing is performed prior to full ray tracing, estimating workload and partitioning rays across multiple processing units based on available memory, allowing for distributed computation to ensure completion and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ray tracing is performed with large numbers of rays and interactions, then simulation accuracy and completeness are improved, but memory requirements and computational resources increase significantly
Solution Approach 1:
The patent divides the ray tracing workload into segments by partitioning rays into groups and distributing them across multiple processing units (GPUs). Each processing unit handles a subset of rays, allowing the system to achieve complete simulation of large numbers of rays while keeping individual unit memory requirements manageable. This segmentation enables simulation completeness without requiring any single unit to allocate excessive memory.
Solution Approach 2:
The patent introduces a distributed computing dimension by utilizing multiple GPUs in parallel. Instead of attempting to process all rays on a single processing unit, the system leverages additional computational dimensions through parallel processing across multiple devices. This dimensional expansion allows the system to handle larger total ray counts while maintaining feasible memory requirements per unit through the collective capacity of the distributed system.
2Reliability
If ray tracing is performed with large numbers of rays and interactions, then simulation accuracy and completeness are improved, but computational time and processing resources increase significantly
Solution Approach 1:
The patent segments the computational workload by dividing rays into groups and assigning them to different processing units. This segmentation enables parallel execution of ray tracing operations across multiple GPUs, significantly reducing total computational time compared to sequential processing on a single unit. Each processing unit works independently on its assigned ray group, and results are aggregated to produce complete simulation results.
Solution Approach 2:
The patent leverages the temporal and spatial dimensions of parallel computing by executing ray tracing operations simultaneously across multiple GPUs. This dimensional approach transforms a sequential time-consuming process into a parallel execution model where computational time is divided among multiple processing units, achieving complete simulation of large ray sets in significantly reduced time.
3Measurement precision
If scene size and number of interactions are increased, then simulation accuracy are improved, but available memory is insufficient
Solution Approach 1:
The patent segments the simulation workload by partitioning rays into manageable groups that can be processed by individual processing units with limited memory. This segmentation allows the system to maintain high simulation accuracy for large scenes with many interactions while distributing memory requirements across multiple units, preventing any single unit from being constrained by insufficient memory capacity.
Solution Approach 2:
The patent resolves the memory constraint by introducing a distributed computing dimension. Instead of requiring a single processing unit to accommodate the entire scene and all interactions in its memory, the system distributes the data and computational load across multiple GPUs. This dimensional shift enables high-accuracy simulation of large scenes while maintaining feasible memory requirements per unit through collective memory capacity.
Data Source
AI summary
Apparatuses, systems, and techniques to perform ray tracing. In at least one embodiment, ray tracing workload is estimated prior to ray tracing simulation based on, for example, less than all data associated with rays to be traced.