Redistributing Multi-Sample Workloads via Rasterized Coverage
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Solution Overview
Problem
The increased processing demand and bandwidth consumption associated with multi-sampling techniques in graphics processing lead to decreased performance and higher system costs, as each pixel sample requires separate processing, resulting in inefficiencies.
Innovation Solution
The redistribution of multi-sample processing workload between threads in a parallel thread group based on rasterized coverage information, allowing inactive threads to process covered samples, thereby reducing the number of processing passes and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-sampling techniques are used to produce anti-aliased images, then image quality is improved, but processing demand and bandwidth consumption increase
Solution Approach 1:
The patent merges the processing of multiple samples per pixel into a single processing pass by redistributing the workload across threads in a parallel thread group. Instead of processing each sample separately in multiple passes, threads are assigned to process different samples of the same pixel within one pass, combining what would have been separate operations into a unified workflow.
Solution Approach 2:
The patent implements dynamic workload redistribution based on rasterized coverage information. Threads are dynamically reassigned to process different samples based on which samples are actually covered by primitives, allowing the system to adapt to the specific rendering scenario and optimize processing efficiency in real-time.
2Measurement precision
If the number of samples per-pixel increases, then anti-aliasing quality is improved, but processing demand and bandwidth consumption increase
Solution Approach 1:
The patent extracts and processes only the covered samples that actually contribute to the final image, rather than processing all samples uniformly. By using rasterized coverage information to identify which samples are covered by primitives, the system extracts and processes only the necessary subset of samples, reducing redundant data transfer and processing.
3Ease of operation
If each processing thread processes one sample of a pixel, then sample processing is simplified, but the number of processing passes increases
Solution Approach 1:
The patent segments the processing workload by dividing it among multiple threads in a parallel thread group, where each thread handles specific samples of pixels. This segmentation allows multiple samples to be processed simultaneously within a single pass, maintaining the simplicity of individual thread operations while eliminating the need for multiple sequential passes.
Data Source
AI summary
A system, method, and computer program product are provided for redistributing multi-sample processing workloads between threads. A workload for a plurality of multi-sample pixels is received and each thread in a parallel thread group is associated with a corresponding multi-sample pixel of the plurality of pixels. The workload is redistributed between the threads in the parallel thread group based on a characteristic of the workload and the workload is processed by the parallel thread group. In one embodiment, the characteristic is rasterized coverage information for the plurality of multi-sample pixels.