Compositor Learning Model for GPU Composition Bottlenecks
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Solution Overview
Problem
Current graphics processing units (GPUs) face challenges in efficiently composing display layers, leading to increased processing time, power consumption, and interference with other application processing, particularly when partial GPU composition is required, resulting in janks and backlog in the display pipeline.
Innovation Solution
A learning model is implemented within the compositor process to study GPU composition decision patterns and determine which layers require GPU or full DPU composition, reducing inter-process communication and optimizing layer geometry, thereby minimizing power usage and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If partial GPU composition is used to handle display layers, then composition flexibility is improved, but processing time increases and causes backlog in the display pipeline
Solution Approach 1:
The system performs preliminary analysis of layer composition requirements before actual composition occurs. By pre-determining which layers need GPU composition and which can use DPU composition, the system avoids runtime decision-making delays and pipeline backlogs while maintaining composition flexibility.
Solution Approach 2:
The composition task is segmented into different types: layers requiring GPU composition and layers suitable for DPU composition. This segmentation allows parallel processing where DPU handles simple layers while GPU focuses on complex layers, reducing overall processing time and preventing pipeline backlog.
2Reliability
If GPU is used for composition to maintain display quality, then composition quality is improved, but power consumption increases
Solution Approach 1:
Different composition quality approaches are applied locally to different layers based on their requirements. Layers requiring high quality and complex processing use GPU composition, while simpler layers use power-efficient DPU composition. This local differentiation maintains overall display quality while significantly reducing total power consumption.
Solution Approach 2:
The system dynamically changes composition parameters (GPU vs. DPU selection) based on layer characteristics and system state. By adjusting which processor handles each layer, the system optimizes the balance between display quality and power consumption in real-time.
3Manufacturing precision
If GPU composition is used to ensure proper layer rendering, then rendering accuracy is improved, but interference with other application processing increases
Solution Approach 1:
The rendering workload is segmented between GPU and DPU based on layer complexity. Only layers requiring precise rendering are assigned to the GPU, while simpler layers are handled by the DPU. This segmentation ensures rendering accuracy for critical layers while freeing up GPU resources for other application processing, thereby improving overall system productivity.
Solution Approach 2:
The system applies GPU composition only partially - specifically to layers where it is truly needed - rather than using it for all layers. This partial application of GPU resources maintains rendering accuracy where required while minimizing interference with other GPU-accelerated application processing.
4Use of energy by moving object
If more layers are composed by DPU to reduce power usage, then power consumption is reduced, but composition capability for complex layers is limited
Solution Approach 1:
The system applies different composition capabilities locally to different layers. DPU composition is used for layers where it is sufficient, while GPU composition is applied to complex layers requiring advanced capabilities. This local differentiation maximizes power efficiency while maintaining composition capability where needed.
Solution Approach 2:
The system introduces an intermediary decision-making mechanism that evaluates each layer's composition requirements and routes it to the appropriate processor (DPU or GPU). This intermediary layer ensures that power consumption is minimized by default while maintaining the ability to handle complex layers when necessary.
Data Source
AI summary
The present disclosure relates to methods and apparatus for display processing. The apparatus can determine at least one data parameter corresponding to each of a plurality of layers in a display frame. The apparatus can also calculate a model for the at least one data parameter corresponding to each of the plurality of layers. Additionally, the apparatus can modify the model for the at least one data parameter based on one or more application use cases of the display frame. Moreover, the apparatus can implement the modified model on each of the plurality of layers in the display frame. In some aspects, the apparatus can also determine one or more composition settings for each of the plurality of layers based on the modified model. The apparatus can also apply the one or more composition settings to each of the plurality of layers based on the modified model.