Dynamic Performance Allocation Engine for GPU-CPU Power Optimization
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Solution Overview
Problem
Current computing systems face inefficiencies in power consumption due to imposed performance budgets on processors with multiple units, such as core and graphics processing units, which can lead to suboptimal resource allocation and increased energy usage.
Innovation Solution
A performance allocation engine is implemented within the graphics processing unit to monitor frame generation rates and utilization metrics, dynamically allocating resources between the GPU and CPU to optimize power consumption by adjusting performance resources based on workload and frame rate limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If performance budgets are imposed on processors to reduce power consumption, then power consumption is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent implements dynamic performance allocation between CPU and GPU based on real-time workload analysis. The system continuously monitors task characteristics and adjusts performance budgets dynamically, transitioning from static imposed budgets to adaptive resource distribution that responds to actual system demands, thereby maintaining efficiency while managing power consumption.
Solution Approach 2:
The system employs feedback mechanisms by monitoring workload metrics and performance outcomes to continuously optimize resource allocation. The performance allocation engine uses feedback from task execution results to adjust future resource distribution decisions, improving allocation efficiency while maintaining power consumption targets.
2Use of energy by stationary object
If static performance budgets are allocated to processors, then power consumption is controlled, but system performance optimization deteriorates
Solution Approach 1:
The patent transforms static performance budgets into dynamic allocation mechanisms that adapt to real-time system conditions. The performance allocation engine continuously adjusts resource distribution between CPU and GPU based on workload characteristics, task priorities, and performance goals, enabling both power consumption control and system performance optimization simultaneously.
Solution Approach 2:
The system changes performance parameters dynamically by adjusting clock frequencies, power states, and resource allocation ratios based on monitored workload conditions. This allows the system to optimize performance for different task types while maintaining overall power consumption within acceptable ranges.
3Productivity
If more performance resources are allocated to GPU, then graphics processing capability is improved, but overall power consumption increases
Solution Approach 1:
The patent applies local quality optimization by allocating performance resources specifically to the GPU only when and where needed based on task characteristics. Rather than uniformly boosting GPU performance, the system selectively enhances graphics processing capability for appropriate workloads while maintaining lower power states for other tasks, achieving localized performance improvement without proportional power increase.
Solution Approach 2:
The system dynamically adjusts GPU performance resources based on real-time workload analysis, transitioning between high-performance and low-power states according to actual graphics processing demands, thereby decoupling graphics capability from continuous high power consumption.
Data Source
AI summary
In accordance with some embodiments, a graphics process frame generation frame rate may be monitored in combination with a utilization or work load metric for the graphics process in order to allocate performance resources to the graphics process and in some cases, between the graphics process and a central processing unit.