Asset-Aware GPU LOD Management for Rendering Delays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computing system architectures face delays and inefficiencies when generating video frames for games due to the large size of assets, which can lead to stalling of the graphics pipeline, especially during scene transitions, as they require extensive loading and management of assets in system memory.
Innovation Solution
An asset-aware computing architecture that includes a central processing unit and graphics processing unit, configured to manage the loading and unloading of assets through an asset store, using bind-time triggers and asset-aware data to prioritize and load only the minimum required level of detail (LOD) for assets, allowing the graphics pipeline to execute without waiting for full asset loading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If assets are loaded into system memory for graphics processing, then rendering capability is improved, but system memory capacity is consumed and loading time increases
Solution Approach 1:
The patent applies preliminary action by pre-loading assets into system memory before they are actually needed for rendering. The asset manager proactively loads assets based on predicted usage patterns, scene transitions, and player behavior, so that when rendering is required, the assets are already available in memory. This eliminates loading delays during critical rendering moments while managing memory capacity through intelligent preload scheduling.
2Reliability
If all assets are loaded into system memory simultaneously, then asset availability is improved, but system memory capacity is exceeded
Solution Approach 1:
The patent segments assets into different categories and prioritizes their loading based on importance and usage frequency. Critical assets that are frequently accessed during rendering are loaded into system memory, while less critical assets are loaded from slower storage devices. The asset manager divides the asset library into priority levels and manages memory capacity by selectively loading only the necessary segments of assets at any given time.
Solution Approach 2:
The patent applies local quality by providing different levels of asset detail and quality based on local needs. High-quality assets are loaded into memory only when and where needed, while lower-quality or placeholder assets are used elsewhere. The asset manager adjusts asset quality dynamically based on rendering requirements, player proximity to assets, and available memory capacity, ensuring optimal balance between asset availability and memory usage.
3Adaptability or versatility
If scene complexity is increased to improve gaming experience, then immersion is improved, but asset management complexity increases
Solution Approach 1:
The patent implements self-service by enabling the asset manager to automatically handle asset loading, unloading, and memory management without manual intervention. The system autonomously monitors asset usage patterns, predicts when assets will be needed, and manages memory allocation automatically. This self-service approach allows developers to focus on creating complex immersive scenes while the asset manager handles the complexity of managing those assets efficiently.
4Quantity of substance
If minimum LOD is used for assets, then memory usage is reduced, but rendering quality may be compromised
Solution Approach 1:
The patent applies dynamics by making the LOD (level of detail) selection dynamic rather than static. The system automatically adjusts which LOD of an asset to load and use based on real-time factors such as player proximity to the asset, camera distance, rendering priorities, and available memory capacity. When memory is constrained, lower LODs are used; when memory is available and quality is prioritized, higher LODs are loaded and rendered. This dynamic adaptation balances memory usage and rendering quality continuously.
Data Source
AI summary
A method for executing a game by a computing system that uses a central processing unit (CPU) and graphics processing unit (GPU) for generating video frames. A draw call is generated for a video frame by the CPU. At bind time, i.e. writing of the GPU commands by the CPU using a GPU API, asset aware data (AAD) is written to the command buffer, and loading of one or more level of detail (LOD) data from an asset store to system memory is requested. The GPU executes the draw call for the frame using LOD data written to the system memory, the GPU using at least a minimum of LOD data based on the AAD. Additionally, the GPU uses information regarding the LOD load state when executing the draw call, in order to avoid access to LODs not yet loaded.