GPU Sparse Volume Rendering via Ray Tile Segmentation
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Solution Overview
Problem
The SIMD architecture of GPUs, which excels in uniform processing, is inefficient for volume rendering in medical imaging due to the sparse and variable nature of the data, leading to performance degradation as some pixels require many depth samples while others require few or none, causing idle time among processing elements.
Innovation Solution
The process involves arranging rays into ray tiles based on predicted processing needs, using scatter-gather techniques to permute and process ray segments efficiently, matching the GPU's SIMD constraints, and transforming the intermediate image to form the final rendered image, thereby circumventing SIMD dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rays are processed in the original image order using standard SIMD architecture, then the GPU can process all pixels uniformly, but processing elements remain idle when some pixels require many depth samples while others require few or none
Solution Approach 1:
The patent segments the rays into different tiles based on their required processing depth. Rays are divided into groups where each tile contains rays with similar numbers of depth samples, allowing uniform processing within each tile while minimizing idle time across the GPU processing elements.
Solution Approach 2:
The patent dynamically reorders rays from the original image order into tiles based on their processing requirements. This dynamic reorganization allows the system to adapt to the variable depth sample requirements of different pixels, transforming the static processing sequence into a dynamic one that optimizes GPU utilization.
2Reliability
If the GPU processes all pixels uniformly regardless of depth sample requirements, then the SIMD architecture can maintain consistent processing, but the sparse and variable nature of medical imaging data causes performance degradation
Solution Approach 1:
The patent applies local quality by creating tiles with homogeneous processing characteristics. Within each tile, rays are selected to have similar depth sample requirements, ensuring consistent processing behavior locally while allowing different tiles to be processed in parallel with varying overall complexity.
Solution Approach 2:
The patent segments the heterogeneous set of rays into multiple homogeneous tiles. This segmentation allows the GPU to process each tile with consistent SIMD operations while the collection of tiles handles the overall variability in the sparse medical imaging data.
3Ease of manufacture
If rays are arranged in the original image order, then the rendering process is simple and straightforward, but the variable processing requirements cause some processing elements to remain idle
Solution Approach 1:
The patent performs preliminary sorting and grouping of rays into tiles before the actual rendering process. This preliminary action organizes the rays in advance based on their depth sample requirements, so that when rendering begins, the GPU can process each tile efficiently without idle time, while maintaining relative simplicity through automated sorting algorithms.
Data Source
AI summary
A computer automated process is presented for accelerating the rendering of sparse volume data on Graphics Processing Units (GPUs). GPUs are typically SIMD processors, and thus well suited to processing continuous data and not sparse data. The invention allows GPUs to process sparse data efficiently through the use of scatter-gather textures. The invention can be used to accelerate the rendering of sparse volume data in medical imaging or other fields.