Dynamic Power Range Configuration for CPU GPU Workloads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional CPU/GPU concurrency systems offer limited control over concurrent operations, resulting in suboptimal performance for various applications as they often provide a static configuration that does not cater to specific workload requirements, leading to inefficient resource utilization.
Innovation Solution
An Information Handling System (IHS) with a processing system concurrency optimization engine that determines workloads and configures power systems to optimal power ranges for CPU and GPU subsystems based on predefined profiles, allowing dynamic adjustment of CPU/GPU settings to match specific application needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static CPU/GPU concurrency configuration is provided to satisfy the largest number of use cases, then broad compatibility is improved, but performance for specific use cases deteriorates
Solution Approach 1:
The patent implements dynamic CPU/GPU concurrency configuration that automatically adjusts power distribution between processing subsystems based on detected workload characteristics. Instead of a fixed static configuration, the system continuously monitors application behavior and modifies power allocation in real-time, transforming the system from static to dynamic operation to optimize performance for each specific workload while maintaining broad compatibility across different use cases
Solution Approach 2:
The system changes the power allocation parameters between CPU and GPU subsystems based on workload analysis. By detecting characteristics of the running application and adjusting power distribution parameters dynamically, the system adapts to different workload requirements without requiring manual configuration, thereby resolving the contradiction between broad compatibility and optimized specific performance
2Device complexity
If CPU and GPU operate with equal or static power distribution, then system simplicity is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating power allocation to different processing subsystems (CPU and GPU) based on their specific workload requirements. Instead of uniform power distribution, the system analyzes which subsystem needs more computational resources at any given moment and allocates power accordingly, allowing each component to operate at its optimal performance level for the current task
Solution Approach 2:
The system performs self-service by automatically detecting workload characteristics and adjusting its own power distribution without external intervention. The processing system monitors its own operational state and autonomously optimizes power allocation between CPU and GPU, eliminating the need for complex manual configuration while maximizing resource utilization efficiency
Data Source
AI summary
A processing system concurrency optimization system includes a processing system having first and second processing subsystems, a power system that is coupled to the first and second processing subsystems, a processing system concurrency optimization database, and a processing system concurrency optimization subsystem that is coupled to the power system and the processing system concurrency optimization database. The processing system concurrency optimization subsystem determines that a first workload has been provided for performance by the processing system, and identifies a first processing system concurrency optimization profile that is associated with the first workload in the processing system concurrency optimization database. Based on the first processing system concurrency optimization profile, the processing system concurrency optimization subsystem configures the power system to provide first power in a first power range to the first processing subsystem and second power in a second power range to the second processing subsystem.