Processor Performance Control via Queue Telemetry
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Solution Overview
Problem
In high-performance computing environments, the bursty nature of work item input into processor queues poses challenges in managing processor performance levels effectively, leading to inefficiencies in energy consumption and processing capacity.
Innovation Solution
Implementing a telemetry-based system that polls the input queue at regular intervals to assess its busy or idle state, adjusting the processor's performance level based on thresholds to match the workload, ensuring optimal balance between performance and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the processor operates at high performance level continuously, then processing capacity is improved, but energy consumption increases
Solution Approach 1:
The processor performance level is made dynamic rather than static. The system continuously monitors queue depth metrics and adjusts the processor performance level accordingly, transitioning between low, medium, and high performance states based on real-time workload conditions. This dynamic adjustment allows the processor to operate at high performance only when necessary (when queue depth exceeds threshold) and conserve energy when workload is light.
Solution Approach 2:
The system implements a feedback mechanism where the processor performance level is continuously adjusted based on monitored queue depth metrics. The telemetry data about queue depth feeds back to the performance management system, which then adjusts the processor performance level in response to changing workload conditions, creating a closed-loop control system that optimizes both performance and energy consumption.
2Use of energy by moving object
If the processor operates at low performance level to conserve energy, then energy consumption is reduced, but processing capacity decreases
Solution Approach 1:
The processor performance level transitions from a static state to a dynamic one that adapts to workload conditions. By monitoring queue depth metrics in real-time, the system can scale processor performance up or down as needed, ensuring adequate processing capacity is available when workload increases while conserving energy during low-utilization periods.
Solution Approach 2:
The system changes the performance parameter of the processor based on monitored queue depth metrics. When the queue depth exceeds a configured threshold, the performance level parameter is increased to handle the backlog; when queue depth is low, the performance level parameter is decreased to conserve energy. This parameter adjustment resolves the contradiction by making processing capacity adaptive rather than fixed.
3Measurement precision
If telemetry polling frequency is increased to improve performance monitoring accuracy, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system performs telemetry polling at regular intervals rather than continuously, using partial action to achieve sufficient monitoring accuracy. By polling at strategically chosen intervals and using configured thresholds to trigger performance adjustments, the system achieves adequate measurement precision without the excessive energy consumption of continuous monitoring.
Data Source
AI summary
A method is described. The method includes polling a queue a plurality of times over a plurality of intervals, where, the queue feeds work items to a processor. The method includes determining, from the polling, respective work item flow metrics for the plurality of intervals. The method includes determining a processor's performance setting based on the plurality of respective work item flow metrics.