Temporal Power Steering for Multi-Phase HPC Energy Budgets
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Solution Overview
Problem
Current power management techniques in computer systems, particularly in high-performance computing (HPC) systems, are inefficient as they statically assign power to domains based on average power consumption, leading to stranded power and sub-optimal performance due to phases with different operational behaviors, whereas dynamic approaches fail to improve power usage in applications with multiple phases.
Innovation Solution
Dynamic temporal power steering dynamically allocates power among phases by identifying opportunities to redistribute power based on the performance scaling of each phase, intentionally 'damaging' some phases to boost others, while ensuring the overall energy budget is preserved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static power assignment based on average power consumption is used, then power allocation is simple and stable, but power usage efficiency deteriorates due to stranded power in multi-phase applications
Solution Approach 1:
The patent implements dynamic power allocation that adapts to different application phases. The system transitions from static power assignment to dynamic power distribution based on real-time phase detection and power scaling analysis, allowing power to be reallocated from low-utilization phases to high-utilization phases, thereby eliminating stranded power while maintaining manageable complexity through automated control.
Solution Approach 2:
The system changes power allocation parameters dynamically based on application phase characteristics. By detecting phase transitions and analyzing power scaling relationships, the system adjusts power distribution parameters in real-time, transforming the fixed power allocation model into an adaptive one that optimizes power usage across different operational phases.
2Loss of energy
If dynamic power allocation is implemented, then power usage efficiency improves, but system complexity increases due to phase detection and power redistribution mechanisms
Solution Approach 1:
The patent enables the power management system to automatically detect application phases, analyze power scaling characteristics, and perform power redistribution without external intervention. The system self-adjusts power allocation based on real-time monitoring of application behavior, reducing the need for complex external control mechanisms while improving power efficiency through autonomous decision-making.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor application phase transitions and power consumption patterns. By using this feedback information, the system dynamically adjusts power allocation to optimize efficiency, managing complexity through closed-loop control that automatically responds to changing conditions rather than requiring pre-configured complex rules.
3Productivity
If power is redistributed among phases, then runtime performance improves, but power management complexity increases due to temporal power allocation
Solution Approach 1:
The patent segments the application execution into distinct phases and applies differentiated power allocation to each segment. By dividing the application timeline into identifiable phases with characteristic power scaling behaviors, the system can optimize power distribution for each segment independently, improving overall runtime performance while managing complexity through structured phase-based control.
Solution Approach 2:
The system dynamically adjusts power allocation in temporal response to detected phase transitions. Rather than using static or pre-configured power settings, the system adapts power distribution in real-time based on actual application behavior, improving runtime performance through responsive power management while keeping complexity manageable through automated dynamic adjustment.
Data Source
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AI summary
Apparatus, systems, and methods provide dynamic power steering that includes determining a sequence of phases of an application in a node. The sequence corresponds to a time interval associated with an energy budget. For each phase, the dynamic power steering includes determining a power scaling comprising a measured response to an increase or decrease in power distributed to a plurality of power domains in the node, and based on the power scaling for each phase, determining a temporal power distribution between the phases in the sequence to satisfy the energy budget.