GPU Power Balancing With Fused Coefficients for Rack Allocation
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Solution Overview
Problem
Data centers face challenges in efficiently assigning processing jobs to racks due to variable operating conditions and differing constraints among racks, making it difficult to optimize power consumption and performance.
Innovation Solution
Implementing a system that utilizes fused power coefficients for computing units to calculate and adjust operating frequencies, balancing power consumption across baseboards and racks, and optimizing rack configurations based on task parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If processing jobs are assigned to data center racks, then productivity is improved, but power consumption increases and becomes difficult to balance due to variable rack configurations
Solution Approach 1:
The system dynamically changes operating parameters (frequency, voltage) of computing units based on their power coefficients and task requirements. By adjusting these parameters in real-time, the system optimizes the balance between productivity and power consumption, allowing racks to operate at efficient power levels while maintaining required processing performance.
Solution Approach 2:
The patent implements dynamic frequency adjustment where computing units can change their operating frequency based on current workload and power budget constraints. This dynamic behavior allows the system to adapt to varying job requirements and power conditions, improving overall efficiency by preventing both under-utilization and over-consumption.
2Adaptability or versatility
If racks are configured with different sub-systems to meet diverse task requirements, then adaptability is improved, but power balancing becomes more difficult due to variable operating conditions
Solution Approach 1:
The system uses power coefficients as standardized parameters that capture the power characteristics of different computing units regardless of their specific configuration. By working with these standardized parameters rather than individual hardware details, the system simplifies power management while maintaining support for diverse rack configurations and sub-systems.
Solution Approach 2:
The patent divides the data center into manageable units (racks, baseboards, computing units) with hierarchical power management. Each level has its own power budget and can be independently optimized. This segmentation allows complex multi-rack systems to be managed through modular, independent control while maintaining overall system adaptability.
3Productivity
If operating frequencies are increased to improve performance, then productivity is improved, but power consumption increases beyond budget constraints
Solution Approach 1:
The system dynamically adjusts operating frequencies based on real-time power budget availability and task requirements. Rather than using fixed high frequencies, the system optimizes frequency selection to achieve required performance while staying within power constraints, allowing flexible adaptation to changing conditions without exceeding power budgets.
Solution Approach 2:
The patent implements feedback mechanisms where power consumption is continuously monitored and used to adjust operating frequencies. The system receives feedback on actual power usage and task performance, then modifies frequency settings accordingly to maintain both productivity and power budget compliance in an iterative optimization process.
Data Source
AI summary
A system to select graphics processing units (GPUs) to execute a task is disclosed. In at least one embodiment, GPUs are selected based on one or more task parameters and one or more fused parameters.