Dynamic Graphics Processor Execution Resource Scaling
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Solution Overview
Problem
Existing mechanisms for scaling execution units in graphics processors often lead to increased power dissipation and maximum load current, while attempting to reduce power consumption, which can negatively impact overall system performance by reducing available execution resources.
Innovation Solution
The system dynamically scales execution units in a graphics processor based on utilization metrics, such as current execution unit utilization rates and anticipated high utilization periods indicated by new draw calls, to manage power without affecting performance during high graphics processor utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the number of active execution units is reduced during low utilization periods, then power dissipation and maximum load current are reduced, but execution resources available to graphics operations are reduced
Solution Approach 1:
The system dynamically adjusts the number of active execution units based on real-time workload detection. The graphics processor monitors workload characteristics and automatically scales execution unit activation, transitioning from a static configuration to a dynamic one that adapts to changing demands, thereby reducing power dissipation during low utilization while maintaining sufficient execution resources when needed.
Solution Approach 2:
The system changes the operational parameters of the graphics processor by adjusting the number of active execution units based on detected workload conditions. This parameter adjustment allows the system to optimize power dissipation by activating only the necessary number of execution units, while preserving the capability to quickly scale up resources when workload demands increase.
2Use of energy by stationary object
If existing mechanisms scale execution units, then power consumption is reduced, but overall system performance is harmed
Solution Approach 1:
The system performs preliminary detection of workload characteristics before making scaling decisions. By analyzing workload patterns and predicting future resource needs, the graphics processor can proactively adjust execution unit activation to match anticipated demands, preventing performance degradation that would occur with reactive scaling approaches.
Solution Approach 2:
The system implements a feedback mechanism where the graphics processor continuously monitors workload conditions and adjusts execution unit activation accordingly. This closed-loop control ensures that power consumption is optimized while maintaining system performance, as the feedback from workload monitoring guides the scaling decisions to prevent both over-provisioning and under-provisioning of execution resources.
Data Source
AI summary
In one embodiment execution units, graphics cores, or graphics sub-cores can be dynamically scaled across a frame of graphics operations. Available execution units within each graphics core may be scaled using utilization metrics such as the current utilization rate of the execution units and the submission of new draw calls. In one embodiment, one of more of the sub-cores within each graphics core may be enable or disabled based on current or past utilization of the sub-cores based on a set of current graphics operations.