Dynamic Workload Management for Processor Core Clusters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current workload scheduling technologies face challenges in dynamically adapting to varying performance requirements, as they often rely on static topologies and external data, leading to inefficiencies in resource allocation and performance penalties when switching between processor core configurations.
Innovation Solution
The implementation of a dynamic workload management system that partitions CPU cores into clusters, allowing for intelligent selection of processor core configurations based on cost analysis and performance metrics, enabling efficient distribution of workloads across processor cores without performance defects, even when split into multiple cores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static topology and external data are used for workload scheduling, then implementation complexity is reduced, but adaptability to varying performance requirements deteriorates
Solution Approach 1:
The patent implements dynamic topology generation that adapts to varying workload characteristics. The system generates processor topologies dynamically based on workload analysis rather than using fixed static topologies, allowing the scheduling system to adapt to different performance requirements while maintaining manageable complexity through automated topology generation algorithms.
2Productivity
If processor cores are split into multiple cores, then resource utilization is improved, but performance penalties occur during configuration switching
Solution Approach 1:
The system performs preliminary topology generation and evaluation before actual workload execution. By pre-generating multiple candidate topologies and evaluating their suitability, the system minimizes switching penalties when configuration changes are needed, as the next configuration is already prepared and validated.
Solution Approach 2:
The patent employs cost functions that evaluate multiple parameters when determining topology switching decisions. By analyzing cost metrics related to performance, resource utilization, and switching overhead, the system intelligently determines when configuration changes are beneficial versus when to maintain current configurations to avoid unnecessary switching penalties.
3Adaptability or versatility
If dynamic topology generation is implemented, then adaptability to workload requirements is improved, but computational cost increases
Solution Approach 1:
The system uses cost functions to evaluate topology configurations based on multiple parameters including performance metrics and resource utilization. This parameter-based evaluation allows the system to identify optimal topologies efficiently without exhaustive search, balancing adaptability with computational cost by focusing evaluation on relevant performance parameters.
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to manage workloads for an operating system wherein it causes programmable circuitry to cause a task of a workload to be executed with a first processor core configuration; cause the task to be executed with a second processor core configuration; compare a first performance metric of the execution of the task with the first processor core configuration to a second performance metric of the execution with the second processor core configuration; and cause to be used one of the first processor core configuration or the second processor core configuration based on the comparison.