Computational Configuration Modeling for Energy-Aware Device Tuning
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Solution Overview
Problem
Current computing systems face challenges in optimizing energy consumption and computational performance due to the lack of a reliable model for device-application combinations, as existing methods like race-to-idle and never-idle heuristics are not consistently optimal and require extensive testing for each configuration, which is resource-intensive.
Innovation Solution
A server-based method that compares performance data of a device executing an application with a database of other devices and applications to determine a performance model, enabling the device to optimize its computational configuration for energy efficiency and performance using machine learning and control theory, specifically employing hierarchical Bayesian models and proportional-integral control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive testing is performed for each device-application configuration to determine optimal settings, then accuracy of performance modeling is improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The system performs preliminary characterization of device hardware components (CPU, GPU, memory, storage) by running standardized benchmarks and collecting performance data in advance. This pre-characterization creates a database of device properties that can be reused across multiple applications, eliminating the need to re-test the same hardware configurations repeatedly. The preliminary action stores device fingerprints and performance profiles that serve as the foundation for rapid configuration recommendations without requiring exhaustive testing for each new application.
Solution Approach 2:
The system creates simplified copies or representations of device configurations through performance profiles and fingerprints. Instead of testing actual device-application combinations exhaustively, the system uses these copied performance characteristics to predict optimal configurations. The performance model copies patterns from characterized devices and applies them to recommend configurations for similar devices, avoiding the need for direct exhaustive testing while maintaining reasonable accuracy.
2Measurement precision
If device-specific performance models are created for each application-device combination, then optimization accuracy is improved, but system complexity and data requirements increase
Solution Approach 1:
The system segments the performance modeling task into distinct components: device characterization (hardware fingerprinting), application profiling (workload analysis), and configuration recommendation (optimization engine). Each segment handles a specific aspect of the problem independently. The device is segmented into its constituent components (CPU, GPU, memory, storage) with separate performance metrics for each. This segmentation reduces overall system complexity by breaking down the monolithic device-application modeling problem into manageable, reusable modules.
Solution Approach 2:
The system creates universal device fingerprints and performance profiles that can be applied across multiple applications and devices. Instead of creating entirely new models for each device-application pair, the same device characterization framework and performance metrics serve multiple purposes. The performance model is universal in that it can recommend configurations for any application on any characterized device by combining device properties with application requirements, eliminating the need for separate specialized models for each combination.
3Measurement precision
If computational resources are allocated to exhaustive configuration testing, then model accuracy is improved, but energy consumption and operational costs increase
Solution Approach 1:
The system performs energy-intensive device characterization and benchmarking as a preliminary action that occurs once per device, rather than repeatedly for each application. The initial characterization captures essential performance properties that remain valid across multiple uses. By front-loading the energy consumption into this one-time preliminary characterization, the system avoids repeated energy expenditure while maintaining model accuracy for subsequent configuration recommendations.
Solution Approach 2:
The system uses the device's own performance characteristics and hardware fingerprints to generate configuration recommendations, rather than relying on external exhaustive testing. The device essentially characterizes itself through benchmarking, and this self-generated information serves as the basis for optimization. This self-service approach eliminates the need for external testing infrastructure and reduces overall energy consumption by leveraging the device's inherent properties.
Data Source
AI summary
An apparatus and method are provided to perform constrained optimization of a constrained property of an apparatus, which is complex due to having several components, and these components are configurable in real-time. The optimization is achieved by detecting values of the constrained property and a plurality of other properties of the apparatus when the apparatus is configured in a first subset of the plurality of configurations. A model is learned using the detected values of the constrained property. The model represents the constrained property and can also represent other properties as a function of the configurations. The model can also include estimated uncertainties of the constrained property in the model. Then, using the d model and the estimated uncertainties, the optimal configuration can be selected to minimize an error value (e.g., the difference between a desired value and an observed value of the at least one constrained property).