ML Workload Orchestration Without Direct Hardware Communication
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Solution Overview
Problem
Conventional Information Handling Systems (IHSs) face inefficiencies in managing and orchestrating Machine Learning (ML) workloads due to the need for software applications to directly communicate with specific hardware endpoints, leading to burdens on software developers and parallel execution inefficiencies.
Innovation Solution
A platform framework is introduced that enables comprehensive system management and orchestration of ML workloads by discovering and matching ML workload requirements with available resources, applying contextual rules, and prioritizing execution based on location, user proximity, power state, and network connection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software applications directly communicate with specific hardware endpoints to execute ML workloads, then execution control and hardware utilization are improved, but device complexity and software development burden increase
Solution Approach 1:
The patent introduces an operating system intermediary layer that sits between software applications and hardware endpoints. This intermediary discovers available ML resources, manages their capabilities, and handles the complexity of direct hardware communication, allowing applications to execute ML workloads without directly interfacing with hardware endpoints, thus reducing software development burden while maintaining execution control
2Productivity
If multiple ML workloads are executed in parallel on heterogeneous hardware resources, then system throughput and resource utilization are improved, but timing coordination and resource management complexity increase
Solution Approach 1:
The patent implements a feedback mechanism where the operating system continuously monitors the state of heterogeneous ML resources, tracks workload execution progress, and dynamically adjusts resource allocation and timing coordination based on real-time system state, enabling efficient parallel execution while managing complexity through adaptive control
Solution Approach 2:
The operating system acts as an intermediary that manages parallel workload execution across heterogeneous resources, handling timing coordination and resource allocation centrally, thereby enabling high throughput without requiring complex distributed coordination logic in individual applications
3Adaptability or versatility
If the system discovers and adapts to available ML resources dynamically, then system adaptability and resource flexibility are improved, but overhead and execution time increase
Solution Approach 1:
The patent implements preliminary discovery actions during system initialization where the operating system proactively identifies and catalogs available ML resources and their capabilities before workloads are submitted. This advance preparation stores resource information in a readily accessible format, enabling rapid workload-to-resource matching without repeated discovery overhead during runtime, thus maintaining high adaptability while minimizing time loss
Data Source
AI summary
Embodiments of systems and methods for orchestrating the execution of Machine Learning (ML) workloads are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: receive an indication of an ML workload to be executed by the IHS; and orchestrate execution of the ML workload with respect to a plurality of ML resources coupled to the IHS.