Concurrent Depth Surface Rendering Across GPU Pipelines
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Solution Overview
Problem
Existing graphics processing units (GPUs) face inefficiencies during depth-only or Z-only passes, which result in low GPU utilization and high memory bandwidth demand, leading to underutilization of hardware resources.
Innovation Solution
Implementing a workload distribution scheme that moves depth-only or Z-only workloads to additional hardware pipelines, such as binning render (BR) and binning visibility (BV) pipelines, allowing concurrent execution with other workloads to balance GPU resources and improve utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If depth-only or Z-only passes are executed on a single graphics pipeline, then the rendering task is completed, but GPU utilization is low and memory bandwidth demand is high
Solution Approach 1:
The patent divides the depth-only or Z-only workload into multiple segments and distributes them across different graphics pipelines (e.g., binning render pipeline and binning visibility pipeline). This segmentation allows parallel processing of depth surfaces, increasing GPU utilization while reducing the memory bandwidth demand on any single pipeline by sharing the load across multiple pipelines.
2Productivity
If depth-only workloads are concentrated in one pipeline, then processing is simplified, but hardware resources are underutilized
Solution Approach 1:
The patent makes multiple graphics pipelines (binning render pipeline and binning visibility pipeline) universally capable of handling depth-only or Z-only workloads. By enabling these pipelines to share the depth surface processing tasks, the system achieves better hardware resource utilization without significantly increasing complexity, as the pipelines already exist for other purposes.
3Productivity
If multiple pipelines are used for concurrent execution, then GPU utilization improves, but workload assignment complexity increases
Solution Approach 1:
The patent implements a self-service mechanism where the binning visibility pipeline automatically identifies and processes depth-only or Z-only surfaces that are not rendered by the binning render pipeline. This self-service approach enables concurrent execution across multiple pipelines without requiring complex centralized workload assignment management, as each pipeline independently determines its own tasks based on the workload characteristics.
Data Source
AI summary
Aspects presented herein relate to methods and devices for graphics processing including an apparatus, e.g., a GPU. The apparatus may obtain an indication of a set of workloads. The apparatus may also assign a first subset of the set of workloads to a first graphics pipeline and a second subset of the set of workloads to a second graphics pipeline. Further, the apparatus may render the first subset of the set of workloads in the first graphics pipeline and the second subset of the set of workloads in the second graphics pipeline.


