Dynamic Peak Power Limiting for Processing Nodes
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Solution Overview
Problem
Information handling systems face challenges in seamlessly managing power demands and interruptions, leading to potential data loss and system unreliability due to variations in power consumption and availability.
Innovation Solution
A computer-implemented method and system that dynamically limits peak power consumption in processing nodes by receiving power-usage and workload data, identifying node peak power thresholds, and adjusting device performance metrics such as operating frequency and data throughput for variable performance devices like CPUs, NVMe, MIC, and GPUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processing nodes operate at high power consumption to maintain high performance, then productivity is improved, but reliability deteriorates due to power interruptions and inability to manage power fluctuations
Solution Approach 1:
The patent implements dynamic power management by continuously monitoring power consumption and workload data, then adjusting peak power limits in real-time based on current system conditions. This allows the system to adapt power allocation dynamically, maintaining high performance when power is available while ensuring reliability during power fluctuations or interruptions.
Solution Approach 2:
The system employs feedback mechanisms where power consumption data and workload data from processing nodes are continuously collected and fed back to the power management controller. This feedback loop enables the system to make informed decisions about power allocation, adjusting peak power limits based on actual system state to balance performance and reliability.
2Productivity
If processing nodes are allocated high peak power to handle workload spikes, then productivity is improved, but use of energy worsens due to increased overall power consumption
Solution Approach 1:
The patent applies partial action by allocating peak power selectively and temporarily only to processing nodes that require it for handling workload spikes, rather than providing continuous high power to all nodes. This allows the system to maintain productivity during critical periods while minimizing overall energy consumption during normal operation.
Solution Approach 2:
The system dynamically changes power consumption parameters by adjusting peak power limits based on workload data and current power consumption levels. This allows flexible optimization of energy usage, enabling high power consumption only when necessary for productivity while reducing consumption during low-demand periods.
3Reliability
If dynamic power management is implemented to improve reliability, then system lifespan is extended, but device complexity increases due to additional monitoring and control mechanisms
Solution Approach 1:
The power management controller is designed to perform multiple functions: monitoring power consumption, collecting workload data, determining peak power limits, and communicating with processing nodes. This multi-functionality reduces the need for separate dedicated components for each task, thereby limiting the increase in device complexity while maintaining reliability improvements.
Solution Approach 2:
The system implements self-service mechanisms where processing nodes autonomously adjust their operation based on received peak power limit instructions, and the power management controller automatically monitors and adjusts power allocation without external intervention. This automation reduces operational complexity while enhancing reliability through continuous adaptive management.
Data Source
AI summary
A computer-implemented method dynamically limits peak power consumption in processing nodes of an IHS. A power management micro-controller receives processing node-level power-usage and workload data from several node controllers, including current power consumption and a current workload, for each processing node within the IHS. A total available system power of the IHS is identified including a peak power output capacity and a sustained output power capacity. At least one node peak power threshold is determined based on the power-usage and workload data for each of the processing nodes. The node controllers are triggered to determine and set a device peak power limit for each of several variable performance devices within each of the processing nodes based on the node peak power threshold, wherein each of the variable performance devices dynamically adjusts a value of a corresponding device performance metric based on the device peak power limit.