Speculative Ray Tracing Shader Execution for Distributed Denoising
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Solution Overview
Problem
Existing ray tracing techniques are resource-intensive for real-time performance, and denoising frameworks operate on a single instance, limiting access to rendered pixels across multiple devices.
Innovation Solution
A distributed denoising algorithm utilizing machine learning engines that continuously train and update weights during runtime, combined with a method to share ghost regions of image data across nodes for comprehensive denoising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ray tracing is performed in real-time across multiple devices, then rendering performance and image quality are improved, but access to rendered pixels for denoising becomes limited
Solution Approach 1:
The system divides the rendering workload across multiple independent devices or nodes, each processing specific portions of the scene. Each node maintains local rendering state and processes rays independently, allowing parallel execution while preserving access to necessary pixel data through localized buffers and ghost region sharing mechanisms.
Solution Approach 2:
A distributed memory system and communication protocol act as intermediaries between multiple rendering nodes. These intermediaries enable exchange of ghost regions (boundary pixel data) between nodes without requiring centralized access to all rendered pixels, thus maintaining information availability while supporting distributed processing.
2Device complexity
If denoising is performed on a single instance, then processing simplicity is maintained, but image quality and noise reduction capability are limited
Solution Approach 1:
Multiple denoising instances running on different devices are merged through a distributed framework. Each instance processes local pixel data independently, then results are combined through synchronization and ghost region exchange, achieving superior noise reduction comparable to centralized denoising while maintaining distributed processing benefits.
Solution Approach 2:
The system transitions from single-device denoising to multi-device distributed denoising by adding the spatial dimension of device distribution. This allows parallel denoising operations across multiple instances while maintaining coordination through inter-node communication, effectively increasing processing capacity without proportionally increasing complexity.
3Measurement precision
If ray traversal processes all nodes comprehensively, then intersection accuracy is improved, but computational resources and time are excessive
Solution Approach 1:
Each rendering node maintains high-quality intersection detection for its local scene portion using detailed geometric data, while other nodes use simplified or approximate representations. This allows accurate processing where needed while reducing overall computational burden through spatial partitioning and selective detail levels.
Solution Approach 2:
Bounding volume hierarchies and scene acceleration structures are pre-computed and distributed to relevant nodes before ray traversal begins. This preliminary preparation enables faster intersection testing during actual rendering by avoiding redundant computation, thus maintaining accuracy while reducing traversal time.
Data Source
AI summary
Apparatus and method for speculative execution of hit and intersection shaders on programmable ray tracing architectures. For example, one embodiment of an apparatus comprises: single-instruction multiple-data (SIMD) or single-instruction multiple-thread (SIMT) execution units (EUs) to execute shaders; and ray tracing circuitry to execute a ray traversal thread, the ray tracing engine comprising: traversal/intersection circuitry, responsive to the traversal thread, to traverse a ray through an acceleration data structure comprising a plurality of hierarchically arranged nodes and to intersect the ray with a primitive contained within at least one of the nodes; and shader deferral circuitry to defer and aggregate multiple shader invocations resulting from the traversal thread until a particular triggering event is detected, wherein the multiple shaders are to be dispatched on the EUs in a single shader batch upon detection of the triggering event.