Ray Tracing Instruction Execution with Specialized BVH Intersection Cores
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Solution Overview
Problem
Existing ray tracing techniques 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 is implemented across multiple devices, utilizing a bounding volume hierarchy (BVH) for ray-scene intersections and employing machine learning engines for continuous training and updating during runtime, enabling efficient ray tracing operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ray tracing is performed using traditional single-machine denoising frameworks, then denoising operations can be executed, but access to rendered pixels across multiple devices is limited and computational overhead increases
Solution Approach 1:
The patent divides the denoising system into multiple independent nodes distributed across different devices. Each node processes local rendered pixels independently while communicating with other nodes through a distributed framework, allowing parallel processing of ray tracing operations across multiple devices rather than concentrating all computation on a single machine.
Solution Approach 2:
The patent creates a universal distributed denoising framework that can operate across heterogeneous devices and platforms. The system provides multi-functional capabilities by supporting both local denoising operations and cross-device pixel access, enabling the same framework to handle various ray tracing workloads across different hardware configurations.
2Productivity
If distributed denoising is implemented across multiple devices, then access to rendered pixels improves and computational overhead is reduced, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components including a distributed memory manager and communication protocol layer that facilitate coordination between multiple devices. These intermediaries handle pixel data transfer, synchronization, and resource management, reducing the complexity burden on individual devices while enabling efficient distributed computation across the network.
Data Source
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AI summary
Methods and a graphics processing unit to execute an instruction are described. For example, one embodiment of a graphics processing unit comprises a plurality of multi-core groups. A multi-core group comprises: a plurality of graphics cores to process one or more shader programs; a plurality of tensor cores, apart from the plurality of graphics cores, to perform matrix operations including matrix multiplication operations for neural network training and inferencing; one or more ray tracing cores, apart from the plurality of graphics cores and the plurality of tensor cores, to perform all ray tracing operations to save the plurality of graphics cores from overloading; a cache shared among the plurality of graphics cores, the plurality of tensor cores, and the one or more ray tracing cores; and a set of register files to store operand values. A ray tracing core includes a first set of specialized circuitry for performing bounding box tests and a second set of specialized circuitry for performing the ray-triangle intersection tests, and wherein the ray tracing core independently performs all calculations for bounding box tests and ray traversal and intersection of a ray.