Map Rendering Attribute Batching for Multi-Color GPU Draw Calls
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The high CPU load and degraded rendering performance in map rendering engines due to the need for separate rendering instructions for each color in regions requiring multiple color effects, leading to inefficient use of resources and longer rendering times.
Innovation Solution
Integrate attribute data from multiple model data sets into organized attribute data and index data, allowing a single rendering instruction to render multiple models in a batch, reducing the number of rendering instructions and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate rendering instructions are generated for each color effect, then rendering accuracy is maintained, but CPU load increases and rendering performance degrades
Solution Approach 1:
The patent merges multiple rendering instructions for different color effects into a single unified rendering instruction. The CPU generates one rendering instruction that includes multiple color effects, and the GPU processes all color effects simultaneously in a single batch operation, eliminating the need for separate instructions for each color.
Solution Approach 2:
The rendering instruction is designed with multi-functionality to handle multiple color effects within a single instruction. The uniform variables and rendering parameters are structured to accommodate multiple color effects, allowing the same instruction to perform multiple rendering functions that previously required separate instructions.
2Adaptability or versatility
If multiple separate rendering instructions are used for multiple colors, then each color can be rendered independently, but the number of rendering batches increases and CPU workload increases
Solution Approach 1:
The patent combines multiple color rendering operations into a single rendering batch. Instead of issuing separate rendering instructions for each color, the system creates one rendering instruction that batches all color effects together, reducing the number of CPU-GPU communication cycles and rendering batches.
Solution Approach 2:
The CPU pre-processes and organizes color effect data before generating the rendering instruction. Attribute data for multiple colors is prepared and organized in advance, allowing the GPU to process all color effects efficiently in a single batch without requiring multiple sequential rendering operations.
3Manufacturing precision
If separate rendering instructions are generated for each color effect, then rendering precision is maintained, but rendering time increases
Solution Approach 1:
The patent merges multiple color effect rendering operations into a single parallel processing batch on the GPU. This allows all color effects to be processed simultaneously rather than sequentially, maintaining rendering precision while significantly reducing total rendering time through parallel execution.
Solution Approach 2:
The rendering system maintains continuous useful action by processing all color effects in a single uninterrupted GPU batch operation. Instead of completing one color rendering then starting the next, the system continuously processes multiple color effects simultaneously, eliminating idle time between rendering operations.
Data Source
AI summary
A map rendering method comprises: obtaining map data, wherein the map data comprises more than one set of model data, and the model data comprises attribute data representing attributes for rendering map elements; organizing the attribute data corresponding to a same attribute across the more than one set of model data into a data group for storage, to obtain organized attribute data, and generating index data; generating a data filling instruction to transmit the organized attribute data and the corresponding index data to a graphics processing unit (GPU); and generating a map rendering instruction to trigger the GPU to render the map elements corresponding to the map data based on the organized attribute data and the corresponding index data.