GPU Resource Allocation via Work Node Scheduling
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Solution Overview
Problem
Current graphics engines suffer from inefficient resource allocation, leading to memory fragmentation and resource wastage, particularly in non-use-case-specific allocations, which hampers performance in both rendering and computing tasks, and is unsuitable for safety-critical environments where resource balancing is essential.
Innovation Solution
A method for allocating resources in a GPU by creating a schedule for work nodes that allows simultaneous or separate use of resources by rendering and computing tasks, using a directed acyclic graph to define inputs, outputs, and system resource barriers, enabling optimized and balanced allocation of memory and processor capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pool memory is used for resource allocation in graphics engines, then resource allocation is simplified and use-case independent, but resource utilization efficiency deteriorates and memory fragmentation increases
Solution Approach 1:
The patent segments the resource allocation system into multiple independent resource pools, each dedicated to specific resource types (textures, vertex buffers, frame buffers, etc.). This segmentation allows each pool to be managed independently with optimized allocation strategies, improving overall resource utilization while maintaining allocation simplicity through standardized pool management interfaces.
Solution Approach 2:
The patent changes the allocation parameters by introducing use-case-specific allocation modes. Instead of a single pool memory approach, the system adjusts allocation parameters (allocation size, retention policy, sharing rules) based on the specific rendering scenario, thereby optimizing resource utilization for different workloads while maintaining manageable complexity.
2Reliability
If resources are permanently allocated for specific rendering operations, then resource availability for those operations is guaranteed, but resource wastage increases during portions when those operations are not needed
Solution Approach 1:
The patent implements dynamic resource allocation where resource pools can be activated, deactivated, or have their capacity adjusted based on real-time rendering needs. Resources are allocated to specific operations only when needed and can be released back to the pool when no longer required, providing both availability guarantees during active use and wastage prevention during idle periods.
Solution Approach 2:
The patent enables resources to be discarded (released) from permanent allocation and recovered back to the general pool when no longer needed by specific rendering operations. This recovery mechanism ensures that resources are available when needed while preventing wastage during periods when the specific operations are not active.
3Adaptability or versatility
If resources are redundantly allocated to avoid combinatorial explosion between algorithm configurations, then resource availability for different algorithms is ensured, but system memory resources are needlessly consumed
Solution Approach 1:
The patent creates universal resource pools that can serve multiple algorithm configurations. Instead of allocating dedicated resources for each possible algorithm combination, the system establishes resource pools with universal access rights, allowing the same physical resources to be shared across different rendering algorithms and configurations, thereby reducing total memory consumption while maintaining algorithmic flexibility.
Solution Approach 2:
The patent merges multiple potential resource allocations into single shared resource pools. By combining resources that could be allocated to different algorithm configurations into unified pools with shared access, the system reduces redundant memory consumption while maintaining the ability to support various algorithm combinations through dynamic resource sharing.
4Productivity
If rendering processes and computing processes share the same GPU resources, then resource utilization efficiency improves, but workload balancing deteriorates and performance bottlenecks arise
Solution Approach 1:
The patent segments GPU resources into separate pools dedicated to rendering operations and computing operations. This segmentation allows independent management and optimization of each resource type, improving workload balancing by preventing rendering and computing tasks from competing for the same resources, while maintaining high utilization through dedicated pool allocation.
5Device complexity
If resource allocation is hidden in current engines, then implementation complexity is reduced, but resource allocation transparency deteriorates making it hard to determine operation results
Solution Approach 1:
The patent introduces feedback mechanisms that provide visibility into resource allocation decisions and their outcomes. The system monitors and reports on resource pool states, allocation events, and usage patterns, enabling developers to determine the results of allocation operations while maintaining manageable implementation complexity through standardized monitoring interfaces.
Data Source
AI summary
Described herein is a method for allocating resources of a graphics processing unit (GPU). Methods as described herein can include assembling a plurality of work nodes having defined inputs and outputs, wherein at least one work node is a rendering work node and at least one work node is a compute work node. A schedule can be created for the plurality of work nodes based at least on the defined inputs and outputs, wherein the plurality of work nodes can be scheduled such that more than one GPU process can be carried out at a point in time. Additionally, the schedule can be created such that both render nodes and compute nodes can use the same GPU resources either simultaneously or at separate times. For example, the GPU does not need to be partitioned where certain resources are only for compute processes and others are reserved for rendering processes. A set of system resource barriers can be determined for processing the set of work nodes based on the created schedule.