Tile Rendering Cost Estimation for Load Balancing
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Solution Overview
Problem
Tiling rendering architectures face load imbalance issues in the back-end pipeline, where some tiles become excessively expensive to process, leading to idle cores and reduced rendering performance due to uneven distribution of work among cores.
Innovation Solution
A cost estimation model is implemented to predict the processing time of tiles, using per-tile counters and heuristics for splitting and ordering tasks, along with a special rasterizer to discard unnecessary triangles and fragments, ensuring even workload distribution among cores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the back-end processes each tile independently in parallel, then processing throughput is improved, but load balance deteriorates causing idle cores
Solution Approach 1:
The patent applies preliminary action by estimating the processing cost of each tile before dispatching work to cores. The front-end performs cost estimation for all tiles using geometric complexity metrics before the back-end processing begins, allowing expensive tiles to be identified and split in advance. This prevents load imbalance from occurring during actual rendering, as cores are dispatched with pre-calculated workload distribution.
Solution Approach 2:
The patent applies segmentation by dividing expensive tiles into smaller sub-tiles when cost estimation indicates they will cause load imbalance. The front-end identifies tiles exceeding a cost threshold and automatically splits them into multiple smaller tiles that can be distributed more evenly across available cores, transforming a single expensive task into multiple manageable tasks.
2Manufacturing precision
If expensive tiles are processed separately, then rendering accuracy is maintained, but processing time increases causing core idle time
Solution Approach 1:
The system performs preliminary cost estimation and tile splitting before rendering begins. By calculating geometric complexity metrics in the front-end and identifying expensive tiles in advance, the system can split complex tiles into smaller sub-tiles that will complete more quickly, ensuring all tiles finish within a predictable time window and minimizing core idle time while maintaining full rendering accuracy.
Solution Approach 2:
The patent applies dynamics by making the tile structure adaptive based on cost estimation. Tiles are not fixed in size but can be dynamically split into smaller sub-tiles based on their estimated processing cost. This dynamic adjustment allows the system to optimize processing time while maintaining the same rendering quality, as the split sub-tiles are processed with the same accuracy as original tiles.
3Ease of operation
If tile splitting is performed without cost estimation, then workload distribution may improve, but unnecessary splitting increases processing overhead
Solution Approach 1:
The patent applies parameter changes by using cost estimation metrics to dynamically determine when and how to split tiles. Instead of uniformly splitting all tiles or using fixed rules, the system calculates geometric complexity parameters for each tile and only splits tiles that exceed a cost threshold. This selective approach based on parameter evaluation improves workload distribution while minimizing unnecessary splitting overhead.
Solution Approach 2:
The patent applies local quality by treating each tile individually based on its specific geometric complexity characteristics. Rather than applying a global splitting strategy to all tiles, the front-end evaluates each tile's cost metric locally and applies splitting only where needed. This localized approach ensures optimal workload distribution without adding overhead to tiles that are already appropriately sized.
Data Source
AI summary
An analysis of the cost of processing tiles may be used to decide how to process the tiles. In one case two tiles may be merged. In another case a culling algorithm may be selected based on tile processing cost.