Multi-GPU Volume Rendering via Spatial Partitioning
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Solution Overview
Problem
Current multi-GPU volume rendering technologies, such as SLI and CrossFire, face limitations in handling large volumetric datasets due to the need for duplicating textures on all GPUs, leading to reduced memory efficiency and performance issues, especially when one GPU's workload is not balanced with others.
Innovation Solution
A method utilizing multiple GPUs to build a block hierarchy with adaptive granularity and visibility order, partitioning the dataset into groups for each GPU to render in parallel, with atomic visibility order and alpha blending for final compositing, allowing for flexible load balancing and minimizing data transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If textures are duplicated on all GPUs for volume rendering, then each GPU can render independently in parallel, but the total available GPU memory is reduced by a factor of N (number of GPUs)
Solution Approach 1:
The patent divides the volumetric dataset into multiple sub-volumes or regions that can be rendered independently by different GPUs. Instead of duplicating complete textures on all GPUs, each GPU processes a specific spatial partition of the volume data, reducing memory requirements while maintaining parallel rendering capability
Solution Approach 2:
The patent transitions from duplicating data across multiple GPUs (horizontal scaling) to partitioning the volumetric space into different regions (spatial decomposition). This dimensional approach allows each GPU to handle a unique portion of the volume without requiring complete texture duplication, effectively trading spatial distribution for memory efficiency
2Ease of operation
If generic multi-GPU technologies (SLI/CrossFire) are used for volume rendering, then workload distribution is automatic and transparent, but performance severely degrades when application-specific limitations are violated
Solution Approach 1:
The patent implements application-specific optimization by tailoring the rendering pipeline to volume visualization requirements. This includes custom transfer functions, specialized ray marching algorithms, and optimized data structures that are locally adapted to the characteristics of volumetric data, achieving superior performance compared to generic multi-GPU approaches
Solution Approach 2:
The patent modifies rendering parameters and algorithms specifically for volume visualization, such as adjusting ray step sizes, optimizing transfer function evaluations, and adapting data layout to match GPU memory hierarchies. These parameter changes enable the system to achieve both automated workload distribution and high rendering performance
3Device complexity
If convex region assignment is enforced for load balancing, then workload distribution is simplified, but flexibility in load assignment and balancing is reduced
Solution Approach 1:
The patent implements dynamic load balancing that adapts to changing rendering conditions in real-time. The system can dynamically adjust block assignments between GPUs based on current workload, data access patterns, and performance metrics, allowing transition between convex and concave region assignments as needed without fixed constraints
Data Source
AI summary
A method for volume rendering a volumetric dataset with multiple graphics processing units (GPUs) coupled to a computer system, comprises building a block hierarchical structure of blocks for the volumetric dataset, the block hierarchy comprising a block sequence; partitioning the block sequence into a plurality of groups; determining a target work load for each GPU; allocating each group of the plurality to a respective GPU in accordance with the target load; rendering respective intermediate images by the respective GPUs; and compositing for a final image by blending the respective intermediate images.