Power Estimation Module for Computing Components
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Solution Overview
Problem
The varying power consumption of computing components in modern computer systems, due to their sophisticated designs and workload-dependent performance, poses challenges in optimizing power delivery, making it difficult to accurately estimate and manage power usage.
Innovation Solution
A power estimation module selects calibration datasets from a repository to estimate power consumption by measuring current power usage and applying these datasets to predict consumption at different operating points, allowing for informed power administration within the system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing components are designed with higher performance capabilities, then processing speed and functionality are improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts computing component operating points based on current workload requirements. The power estimation module continuously monitors workload characteristics and selects optimal operating points from calibration datasets, allowing the system to adapt power consumption to actual performance needs rather than operating at fixed high-performance settings
Solution Approach 2:
The system changes operational parameters (operating points) of computing components based on workload analysis. By selecting from multiple calibrated operating points with different power-performance characteristics, the system can optimize the balance between processing speed and power consumption for each specific workload scenario
2Loss of energy
If power consumption is reduced by lowering operating points, then energy efficiency improves, but measurement precision of power estimation becomes more challenging
Solution Approach 1:
The system performs preliminary calibration by measuring power consumption at multiple operating points before actual operation. These calibration datasets are stored and used to predict power consumption during runtime, eliminating the need for real-time measurements and providing accurate estimates even at low operating points where direct measurement would be challenging
Solution Approach 2:
The system creates a model (calibration dataset) that copies the relationship between operating points and power consumption. This model is then used to estimate power consumption for new workloads without requiring direct measurement, thereby maintaining measurement precision while enabling energy-efficient operation
Data Source
AI summary
Methods, apparatus, and products as disclosed for estimating power consumption of computing components configured in a computing system that include: selecting, by a power estimation module, a plurality of calibration datasets from a calibration dataset repository, each calibration dataset specifying calibration power consumption by one or more computing components in the computing system for a calibration workload at a plurality of calibration operating points; measuring, by the power estimation module, a current power consumption by one or more measured computing components in the computing system for a current workload at a current operating point; determining, by the power estimation module, an estimated power consumption for the measured computing components at a proposed operating point in dependence upon the selected calibration datasets and the current power consumption for the current workload at the current operating point; and administering the computing system in dependence upon the estimated power consumption.