Proactive Work-Stealing via Thermal Priority Balancing
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Solution Overview
Problem
Conventional work-stealing techniques in multi-processor systems fail to effectively balance workloads due to reactive approaches that do not consider dynamic power and thermal states of processing units, leading to suboptimal performance and potential thermal issues.
Innovation Solution
A method that calculates priority values for processing units based on static and dynamic characteristics such as temperature, power consumption, and frequency, allowing for proactive workload balancing by transferring work items from a victim processor to a winning work-ready processor, using equations like Pi=αi*fi*Tempi+βi*gi*Poweri+γi*hi*Freqi to determine the most suitable processor for workload redistribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reactive work-stealing techniques are used, then workload can be redistributed among processing units, but thermal issues and power consumption are not effectively managed
Solution Approach 1:
The system proactively monitors thermal states and power consumption metrics before critical thresholds are reached. By calculating priority values based on current thermal and power conditions, the system redistributes workloads in advance to prevent thermal issues and excessive power consumption, rather than reacting after problems occur.
Solution Approach 2:
The system continuously monitors dynamic characteristics including thermal states, power consumption, and frequency of processing units. This feedback is used to recalculate priority values and dynamically adjust workload distribution, creating a closed-loop control system that adapts to changing thermal and power conditions.
2Device complexity
If workloads are redistributed without considering dynamic characteristics, then redistribution is simpler, but system performance and efficiency are suboptimal
Solution Approach 1:
The system changes the parameters used for workload distribution decisions by incorporating dynamic characteristics such as thermal states, power consumption, and frequency into priority calculations. This allows the system to optimize performance for current operating conditions while managing complexity through standardized parameter collection and processing.
3Loss of energy
If proactive workload balancing based on multiple characteristics is implemented, then thermal and power issues are reduced, but calculation and data collection complexity increases
Solution Approach 1:
The system uses a universal priority calculation framework that can accommodate multiple different characteristics (thermal state, power consumption, frequency, workload amount) through a single standardized formula. This multi-functional approach allows comprehensive energy management while maintaining relatively simple and consistent data collection and processing procedures.
Data Source
AI summary
Various embodiments proactively balance workloads between a plurality of processing units of a multi-processor computing device by making work-stealing determinations based on operating state data. An embodiment method includes obtaining static characteristics data associated with each of a victim processor and one or more of a plurality of processing units that are ready to steal work items from the victim processor (work-ready processors), obtaining dynamic characteristics data for each of the processors, calculating priority values for each of the processors based on the obtained data, and transferring a number of work items assigned to the victim processor to a winning work-ready processor based on the calculated priority values. In some embodiments, the method may include acquiring control over a probabilistic lock for a shared data structure and updating the shared data structure to indicate the number of work items transferred to the winning work-ready processor.