Platform Resource Allocation Using Real-Time Utilization Feedback
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Solution Overview
Problem
Computing systems lack insight into the real-time utilization and capabilities of processing resources, leading to suboptimal workload assignment and inefficient use of platform resources, particularly in tasks like upscaling operations, which can impact processing efficiency and power consumption.
Innovation Solution
A platform resource recommendation service that dynamically identifies and provides platform resource candidates based on performance capabilities, utilization metrics, and non-discoverable limitations, using a resource manager to recommend optimal resources for workload execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If applications query the operating system for available processing resources, then applications can select a processing resource to execute a workload, but the system lacks real-time insight into utilization and capabilities leading to suboptimal workload assignment
Solution Approach 1:
A resource manager acts as an intermediary between applications and processing resources. The resource manager collects real-time utilization metrics and capability information from multiple processing resources (GPUs, CPUs, NPUs) and provides this information to applications through a standardized interface, enabling informed workload assignment decisions without applications needing to directly query each resource
Solution Approach 2:
The system implements feedback mechanisms where the resource manager continuously monitors utilization metrics from processing resources and uses this feedback to dynamically update resource availability and performance information. This allows the system to adapt workload assignments based on real-time system state, improving overall productivity
2Productivity
If the system provides comprehensive resource information to applications, then workload assignment can be optimized, but system complexity increases
Solution Approach 1:
The resource management system is segmented into modular components: a resource manager that collects and processes information, a database that stores resource capabilities and utilization metrics, and application interfaces that provide standardized query mechanisms. This segmentation reduces overall system complexity by distributing functionality across independent modules
Solution Approach 2:
The resource manager provides multiple functions through a single unified interface: collecting utilization metrics, storing resource capabilities, querying available resources, and recommending optimal workload assignments. This multi-functionality reduces the number of separate components needed, simplifying the overall system architecture
3Loss of energy
If processing resources are dynamically allocated based on real-time metrics, then power consumption is reduced, but the requirement for continuous monitoring and management increases
Solution Approach 1:
Processing resources automatically report their utilization metrics and capability information to the resource manager without requiring manual intervention. The resource manager autonomously processes this information and makes allocation decisions, reducing the need for continuous human monitoring and management while enabling dynamic power optimization
Data Source
AI summary
A dynamic allocator for providing platform resource candidates is disclosed. In an implementation, a platform resource allocator receives a request from a workload initiator such as, an application, for a platform resource recommendation. The platform resource allocator analyzes performance capabilities and utilization metrics of a plurality of platform resources for each of a plurality of resource. The plurality of platform resources includes one or more graphics processor units (GPUs) and one or more accelerated processing units (APUs). The platform resource allocator dynamically provides the platform resource recommendation to the workload initiator to select one or more of the plurality of platform resources to execute a workload based on the performance capabilities and utilization metrics.