Ray Tracing Instruction Processing with Packed Dequantization
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Solution Overview
Problem
Existing ray tracing technologies are resource-intensive and inefficient for real-time performance, particularly in distributed systems where denoising frameworks operate on a single machine, limiting access to rendered pixels across multiple devices.
Innovation Solution
A distributed denoising system across multiple devices, utilizing a machine-learning engine for efficient ray tracing operations, including beam tracing and hybrid ray tracing, with a bounding volume hierarchy for improved visibility queries and denoising across a network of nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ray tracing is performed using traditional methods, then rendering quality can be achieved, but computational resources are excessively consumed and real-time performance cannot be achieved
Solution Approach 1:
The patent segments the ray tracing computation into multiple independent workgroups, where each workgroup processes a specific portion of the scene or ray bundle. This parallelization enables distributed processing across multiple devices, reducing the computational burden on any single device and achieving real-time performance while maintaining rendering quality
Solution Approach 2:
The patent introduces a distributed memory system and communication protocol as intermediaries between multiple rendering devices. These intermediaries coordinate the exchange of rendering data (such as denoised pixels and visibility information) across devices, enabling collaborative ray tracing computation that reduces overall resource consumption while maintaining real-time performance
2Adaptability or versatility
If denoising is performed on a single machine, then denoising computation is simple, but access to rendered pixels across multiple devices is limited
Solution Approach 1:
The patent designs a universal denoising framework that can operate across different device types and architectures. The framework uses standardized data formats and communication protocols that enable any device in the distributed system to participate in denoising operations, whether performing local denoising or accessing denoised pixels from other devices
Solution Approach 2:
The patent extends the denoising operation from a single-machine context to a multi-device spatial dimension. By organizing devices into a distributed network with defined communication topologies, the system enables denoising computations to leverage data from multiple devices simultaneously, effectively adding a spatial dimension to the denoising process
3Productivity
If visibility queries are performed without optimized data structures, then implementation is simpler, but ray-scene intersection computation is inefficient
Solution Approach 1:
The patent performs preliminary organization of scene geometry into a bounding volume hierarchy (BVH) data structure before ray tracing operations. This pre-computed hierarchical structure enables efficient culling of invisible regions and rapid identification of potential ray-scene intersections, significantly improving traversal efficiency without adding complexity during the actual rendering phase
Solution Approach 2:
The patent applies different levels of bounding volume refinement to different regions of the scene based on their importance and complexity. High-priority regions with detailed geometry receive more refined bounding volumes, while less important regions use coarser approximations, optimizing the trade-off between traversal efficiency and computational complexity
Data Source
AI summary
An apparatus and method to execute ray tracing instructions. For example, one embodiment of an apparatus comprises execution circuitry to execute a dequantize instruction to convert a plurality of quantized data values to a plurality of dequantized data values, the dequantize instruction including a first source operand to identify a plurality of packed quantized data values in a source register and a destination operand to identify a destination register in which to store a plurality of packed dequantized data values, wherein the execution circuitry is to convert each packed quantized data value in the source register to a floating point value, to multiply the floating point value by a first value to generate a first product and to add the first product to a second value to generate a dequantized data value, and to store the dequantized data value in a packed data element location in the destination register.