Per-Process Power State Duration Prediction
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Solution Overview
Problem
Conventional processing devices face challenges in accurately predicting power management state durations for individual processes, leading to inefficient transitions between power states due to aliasing effects from multiple processes, which results in suboptimal power conservation and performance.
Innovation Solution
Implementing per-process prediction techniques that independently track and predict durations of power management states, allowing for more accurate decision-making on transitioning between states based on break-even analysis, thereby reducing unnecessary transitions and improving power management efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If per-process prediction techniques are implemented to accurately track and predict power management state durations, then power management efficiency and accuracy are improved, but device complexity increases due to the need for independent tracking and prediction mechanisms for each process
Solution Approach 1:
The system segments the prediction mechanism by implementing separate prediction tracks for each process. Each process maintains its own history of power management state durations, allowing independent prediction without interference from other processes. This segmentation resolves the contradiction by enabling accurate per-process predictions while keeping each individual track manageable and independent.
Solution Approach 2:
The patent applies local quality by tailoring prediction parameters and thresholds to each individual process based on its specific behavior patterns. Instead of using uniform global thresholds, the system adjusts prediction characteristics locally for each process, improving accuracy for each specific workload while maintaining overall system functionality.
2Speed
If transitions between power management states are made more frequent to respond to changing process needs, then responsiveness and performance are improved, but power consumption increases due to repeated entry and exit transitions
Solution Approach 1:
The system performs preliminary prediction of future power management state durations before making transition decisions. By anticipating whether a process will remain in a state long enough to justify the transition cost, the system can prepare appropriately and avoid premature or unnecessary transitions, thereby reducing energy consumption while maintaining responsiveness when needed.
Solution Approach 2:
The patent implements feedback mechanisms where actual process behavior is continuously monitored and fed back into the prediction models. This feedback loop allows the system to learn from real-world performance and adjust its transition decisions accordingly, optimizing the balance between responsiveness and power consumption based on actual workload patterns rather than static thresholds.
3Device complexity
If global time thresholds are used to manage power states uniformly across all processes, then device complexity is reduced, but power management efficiency deteriorates due to inability to account for process-specific behavior patterns
Solution Approach 1:
The system divides the unified power management approach into process-specific segments. Each process maintains its own duration history and prediction mechanisms, allowing tailored power management decisions for each workload type. This segmentation improves efficiency by accounting for process-specific behaviors while keeping each individual process management simple and independent.
Solution Approach 2:
The patent introduces dynamic adaptation where power management thresholds and parameters are adjusted based on observed process behavior over time. Rather than using static global thresholds, the system dynamically learns and adapts to each process's characteristic patterns, improving efficiency for diverse workloads while maintaining a consistent overall framework that manages complexity.
Data Source
AI summary
Durations of power management states are predicted on a per-process basis. Some embodiments include storing, in one or more data structures associated with one or more processes, information indicating previous durations of a power management state associated with the process(es). Some embodiments also include predicting a subsequent duration of the power management state for the process(es) using information stored in the data structure(s).