ML Configuration Engine for Heterogeneous Computing Task Assignment
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Solution Overview
Problem
In heterogeneous computing environments, optimizing the execution of applications across diverse processor types and memory types is challenging due to the complexity of determining the best processor for each task, especially when not all application portions are ported, and the notion of 'optimal' performance varies across applications and environments, leading to inefficiencies in throughput, latency, and resource utilization.
Innovation Solution
A machine learning-based configuration engine is trained using data from existing heterogeneous environments to generate configuration parameters that optimize the execution of applications across various processor and memory types, dynamically determining the best execution locations and resource allocations based on system requirements, and providing feedback for continuous optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration methods are used to assign tasks to processors in heterogeneous computing environments, then flexibility in task assignment is maintained, but the complexity of optimizing performance across diverse processor types increases significantly
Solution Approach 1:
The system employs machine learning models that automatically analyze application characteristics and processor capabilities to generate optimal configuration assignments without requiring manual intervention. The configuration engine self-adjusts task-to-processor mappings based on learned patterns from performance data, eliminating the need for complex manual optimization while maintaining adaptability across heterogeneous processor types.
2Productivity
If the number of processor types in a heterogeneous computing environment increases to handle diverse computational tasks, then application performance optimization improves, but the number of configuration options and decision complexity increases
Solution Approach 1:
The patent replaces manual configuration mechanisms with machine learning-based automated configuration systems. The ML models process multiple processor type characteristics and application requirements simultaneously, generating optimized task assignments that would be impractical to determine through manual analysis. This substitution handles the complexity of numerous processor types and configuration options while maintaining clear, actionable assignment decisions.
3Loss of time
If automated configuration systems are implemented to optimize task assignment across heterogeneous processors, then configuration time is reduced, but the precision of performance optimization may be compromised without extensive training data
Solution Approach 1:
The system performs preliminary training of machine learning models using extensive performance data from heterogeneous computing environments before deployment. This pre-training phase establishes accurate performance predictions and optimal configuration patterns, enabling the automated configuration system to make precise task assignment decisions quickly during runtime without requiring extensive real-time data collection or iteration.
Data Source
AI summary
In one embodiment, a device receives data regarding a plurality of heterogeneous computing environments. The received data comprises measured application metrics for applications executed in the computing environments and indications of processing capabilities of the computing environments. The device generates a training dataset by applying a machine learning-based classifier to the received data regarding the plurality of existing heterogeneous environments. The device trains a machine learning-based configuration engine using the training dataset. The device uses the configuration engine to generate configuration parameters for a particular heterogeneous computing environment based on one or more system requirements of the particular heterogeneous computing environment. The device provides the configuration parameters to the particular heterogeneous computing environment.