Configuration Clusters for IHS Performance Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Information Handling Systems (IHS) face challenges in coordinating machine learning optimization across various resources due to numerous interdependent configuration settings, leading to suboptimal performance, especially when resources like CPUs and GPUs share thermal and power systems without considering concurrency and thermal design points.
Innovation Solution
Implementing configuration cluster-based performance optimization by measuring and selecting the optimal configuration cluster for each resource, using machine learning to fine-tune settings and store these for future use, thereby simplifying system-wide optimization and enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning optimization schemes are applied to enhance resource performance, then performance is improved, but coordination complexity increases due to numerous interdependent configuration settings
Solution Approach 1:
The patent segments the configuration settings into distinct clusters grouped by resource type (CPU, GPU, storage, network, platform). Each cluster contains related settings that can be optimized independently, reducing the coordination complexity while maintaining overall system performance optimization.
2Adaptability or versatility
If numerous configuration settings are used to optimize resource performance, then performance optimization capability is improved, but the difficulty of coordinating optimization across resources increases
Solution Approach 1:
Configuration settings are segmented into resource-type-specific clusters, making it easier to detect and measure the impact of each setting group. This segmentation reduces coordination difficulty by isolating interdependent settings within manageable clusters rather than treating all settings uniformly.
Solution Approach 2:
Each configuration cluster is optimized with local quality principles, where settings within a specific resource cluster are tuned according to that resource's characteristics and constraints. This allows targeted optimization without requiring system-wide coordination of all configuration settings.
3Device complexity
If resources share thermal and power systems, then device integration is improved, but performance optimization is hindered due to concurrency and thermal design point constraints
Solution Approach 1:
The patent applies local quality by creating separate configuration clusters for different resource types (CPU, GPU, storage, network, platform), allowing each resource to be optimized independently according to its specific thermal and power constraints while sharing the overall thermal and power system infrastructure.
Data Source
AI summary
Embodiments of systems and methods for managing performance optimization of a target application executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, an IHS may includes executable code to measure a performance of a target application at each of multiple configuration clusters that are applied to an IHS in which each configuration cluster includes multiple configuration settings of one or more resources that are used to execute the target application on the IHS. Using the measured performance values, the instructions may then select one of the configuration clusters that causes the target application to operate at an optimum performance level, and modify the IHS to operate with the one selected configuration cluster.