Processor Core Power State Prediction Using Kalman Filter
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Solution Overview
Problem
Current operating system (OS) C-state policies for power management in computer systems are inefficient, often leading to suboptimal power savings and performance degradation due to coarse CPU utilization sampling and failure to consider dynamic workloads and core interactions, resulting in missed transient idle periods and inappropriate deep sleep state entries.
Innovation Solution
A prediction mechanism using a Kalman filter algorithm to accurately predict CPU power state residency patterns, allowing for the selection of the most appropriate power state for processor cores, thereby maximizing power savings without performance degradation, by monitoring and analyzing core activity patterns and adjusting power states at a finer granularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the OS uses coarse-granularity CPU utilization sampling (e.g., 100ms intervals) to determine C-state, then the system complexity is reduced and ease of operation is improved, but transient idle periods lasting hundreds of microseconds are missed and power savings are suboptimal
Solution Approach 1:
The patent implements dynamic C-state selection by continuously monitoring CPU utilization at fine granularity and adapting the power state based on real-time workload patterns. The system transitions from static, coarse-granularity sampling to dynamic, fine-granularity monitoring that can detect transient idle periods and respond appropriately by selecting deeper C-states when idle periods are predicted to exceed thresholds.
2Loss of energy
If the OS enters deep sleep states (e.g., C3) to maximize power savings, then energy consumption is reduced, but the time to enter and exit these states increases significantly
Solution Approach 1:
The patent applies preliminary action by predicting future CPU utilization patterns before making C-state decisions. The system uses fine-granularity monitoring to detect early signs of extended idle periods and proactively transitions to deeper power states when prediction confidence is high, rather than waiting for coarse sampling intervals to confirm idle conditions. This allows the system to enter deep sleep states more aggressively while minimizing unnecessary wake-ups.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual CPU utilization patterns and comparing them against predictions. This feedback loop allows the system to learn from past behavior and refine its C-state selection strategy, adjusting the aggressiveness of deep sleep state entry based on observed workload characteristics and the accuracy of predictions.
3Device complexity
If the OS selects C-state based on historical CPU utilization, then the control logic is simplified, but the accuracy of power state selection deteriorates for dynamic workloads
Solution Approach 1:
The patent transforms the static, history-based C-state selection into a dynamic system that adapts to changing workload patterns. By implementing fine-granularity monitoring and prediction algorithms, the system can detect transient idle periods and predict future utilization patterns, enabling more accurate C-state selection for dynamic workloads while maintaining manageable control logic through systematic prediction and threshold-based decision-making.
4Measurement precision
If the OS monitors CPU utilization at fine granularity to capture transient idle periods, then power state selection accuracy is improved, but the computational overhead and system complexity increase
Solution Approach 1:
The patent introduces prediction algorithms as intermediaries between fine-granularity monitoring data and C-state selection decisions. Rather than directly reacting to every monitoring event, the system uses prediction mechanisms to filter and interpret the fine-granularity data, identifying meaningful patterns that indicate extended idle periods. This intermediary layer reduces the complexity of direct fine-granularity control while preserving the accuracy benefits of detailed monitoring.
Data Source
AI summary
In one embodiment, the present invention includes a method for providing power state change information from a plurality of cores of a processor to a predictor at a periodic interval and generating a prediction to indicate a predicted operation level of the cores during a next operating period. Other embodiments are described and claimed.