Job Scheduling via Frequency Bin Sorting for Power Constraints
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Solution Overview
Problem
High performance computing (HPC) data centers face challenges in balancing performance increase with power constraint compliance, as existing job scheduling algorithms only consider nodal attributes like RAM and idle status, neglecting performance capability and frequency scaling characteristics of applications.
Innovation Solution
A method that sorts nodes into frequency bins based on performance and sorts applications into corresponding bins based on frequency scaling characteristics, selecting nodes to process applications while ensuring power compliance by balancing performance increase with power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If nodes are selected based on traditional scheduling algorithms considering only nodal attributes like RAM and idle status, then resource allocation is simple, but performance capability and frequency scaling characteristics are neglected leading to suboptimal performance
Solution Approach 1:
The patent segments nodes into different frequency bins based on their performance characteristics and frequency scaling capabilities. This segmentation allows the scheduler to match applications with appropriate node groups, improving performance without requiring complex individual node evaluations. Nodes are categorized into bins representing different performance tiers, simplifying the selection process while maintaining optimization.
Solution Approach 2:
The patent introduces frequency scaling characteristics as a new parameter for node selection, moving beyond traditional attributes like RAM and idle status. By incorporating frequency bins that represent different performance levels, the system changes the parameters used in scheduling decisions to include performance capability and frequency scaling behavior, thereby improving productivity without excessive complexity.
2Speed
If nodes operate at higher frequencies to increase performance, then processing speed improves, but power consumption increases exceeding TDP ratings
Solution Approach 1:
The patent applies local quality by assigning different frequency operating points to different nodes based on their individual characteristics and the specific application requirements. Instead of uniformly operating all nodes at maximum frequency, the system selectively operates nodes at appropriate frequency levels, matching performance needs with power consumption capabilities. This allows high-speed processing where needed while conserving power where sufficient performance is achieved at lower frequencies.
Solution Approach 2:
The patent implements dynamic frequency scaling by allowing nodes to operate at different frequency bins depending on workload requirements and power availability. The system dynamically adjusts the operating frequency of nodes based on real-time conditions, transitioning between frequency bins to balance performance and power consumption. This dynamic approach enables the system to adapt to changing demands while maintaining TDP compliance.
3Reliability
If the system enforces strict power constraints to maintain TDP compliance, then power consumption is controlled, but performance optimization is limited
Solution Approach 1:
The patent performs preliminary classification of nodes into frequency bins based on their performance characteristics and power consumption profiles before job scheduling. By pre-categorizing nodes according to their frequency scaling behaviors and performance capabilities, the system prepares a structured framework that enables both TDP compliance and performance optimization during actual job allocation. This preliminary action allows the scheduler to make informed decisions that satisfy power constraints while maximizing performance.
Solution Approach 2:
The patent creates a universal scheduling framework that handles both power constraint enforcement and performance optimization through a single bin-based selection mechanism. The frequency bin structure serves multiple functions: it categorizes nodes by performance level, predicts power consumption, enables TDP compliance checking, and facilitates performance optimization simultaneously. This multi-functional approach resolves the contradiction by making the same mechanism serve both reliability and productivity goals.
Data Source
AI summary
A method, system, and computer program product for processing an application in a high performance computing (HPC) data center are described. The method includes analyzing and sorting a plurality of nodes in a cluster of the HPC data center into a plurality of frequency bins, each of the plurality of nodes being sorted into more than one of the plurality of frequency bins based on performance. The method also includes analyzing and sorting the application into an application bin among a plurality of application bins based on a frequency scaling characteristic of the application. The method further includes selecting a first set of nodes among the plurality of nodes to process the application, the first set of nodes being sorted into a first frequency bin among the plurality of frequency bins corresponding with the application bin of the application.