Processor Power Management via Workload Characteristic Clustering
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Solution Overview
Problem
Conventional CPU power management systems are inefficient as they solely rely on CPU percentage utilization to determine performance states, failing to optimize power consumption based on workload-specific performance characteristics.
Innovation Solution
A system that uses performance state control to determine CPU performance states by querying operating system and processor counters, identifying workload-specific characteristics like cache miss ratio and I/O throughput, and adjusting operating voltage and frequency to minimize power consumption while maintaining performance, employing ACPI specifications and dynamic power management policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If CPU performance state is determined solely based on percentage utilization, then the control system is simple, but power consumption cannot be optimized for different workload types
Solution Approach 1:
The system changes from using a single parameter (CPU utilization percentage) to multiple parameters (cache miss ratio, I/O throughput, computational intensity) to determine performance state. This allows differentiation between workload types with similar utilization but different power characteristics, enabling optimized power consumption without excessive complexity increase.
Solution Approach 2:
The patent segments workloads into different categories based on their characteristics (cache-bound, I/O-bound, compute-bound). By segmenting the control approach according to workload type, the system can apply appropriate performance state strategies for each segment, improving overall power efficiency while maintaining manageable complexity through structured classification.
2Speed
If CPU operates at higher frequency and voltage for better performance, then processing speed improves, but power consumption increases
Solution Approach 1:
The system dynamically adjusts CPU frequency and voltage based on real-time workload characteristics rather than using static performance states. By continuously monitoring parameters like cache miss ratio and I/O throughput, the system adapts the performance state to match actual computational needs, achieving optimal speed-power balance dynamically.
Solution Approach 2:
The patent changes operational parameters (frequency, voltage) based on workload analysis. By identifying workload characteristics and mapping them to appropriate performance states with specific frequency-voltage combinations, the system achieves high processing speed when needed while reducing power consumption during less demanding tasks.
3Use of energy by moving object
If CPU is controlled to operate in low performance state to save power, then power consumption decreases, but processing capability is reduced
Solution Approach 1:
The system uses feedback from performance counters and monitoring mechanisms to continuously assess actual workload characteristics. This feedback loop ensures that the CPU operates at the minimum necessary performance state to handle the current workload effectively, avoiding both over-provisioning (wasting power) and under-provisioning (reducing productivity).
Solution Approach 2:
The patent implements dynamic performance state adjustment based on real-time workload assessment. Rather than maintaining a fixed low performance state, the system continuously adapts the CPU operating state to match actual computational demands, ensuring adequate processing capability is maintained while minimizing power consumption.
Data Source
AI summary
Some embodiments provide determination of a processor performance characteristic associated with a first workload, and determination of a processor performance state for the first workload based on the performance characteristic. Further aspects may include determination of a second processor performance characteristic associated with a second workload, determination of a second processor performance state for the second workload based on the performance characteristic, determination of a similarity between the first performance characteristics and the second performance characteristics, determination of a cluster comprising the first workload and the second workload, and association of a third processor performance state with the cluster, wherein the third processor performance state is identical to the first processor performance state and to the second processor performance state.