GPU Resource Allocation via ML-Predicted Virtualized Research Data
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Solution Overview
Problem
Existing schemes for allocating GPU resources in machine learning tasks are inefficient, leading to high idle rates and resource waste due to inaccurate predictions of resource needs, resulting in suboptimal utilization of expensive GPU cards.
Innovation Solution
A method that utilizes a research workspace with virtualized resource units to gather statistics on data analysis tasks, which are then used to optimize resource allocation in a production workspace, ensuring low idle rates and high utilization of GPU cards by calculating the precise allocation of physical GPU resources based on historical data and desired completion times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing schemes allocate GPU resources without optimization, then device complexity is reduced and ease of operation is maintained, but resource utilization rate deteriorates and idle rate increases
Solution Approach 1:
The system performs preliminary actions by training machine learning models in a research workspace to predict resource allocation parameters before actual production tasks. Statistics from these preliminary experiments (including idle rates and execution times) are used to pre-determine optimal allocation strategies, which are then applied in the production workspace to improve utilization without adding operational complexity.
2Productivity
If GPU resources are allocated to multiple tasks simultaneously, then productivity increases, but resource contention increases and idle rate worsens due to inaccurate predictions
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual resource usage and task execution results in the research workspace. These feedback statistics (including actual vs. predicted resource consumption, idle rates, and completion times) are used to refine and update the machine learning models, improving prediction accuracy for subsequent production task allocations.
Solution Approach 2:
The patent replaces manual or rule-based resource allocation mechanisms with machine learning-based predictive systems. The ML models automatically predict optimal GPU allocation parameters by learning from historical data, substituting complex manual scheduling decisions with data-driven automated predictions that improve both accuracy and throughput.
3Productivity
If GPU cards are allocated to tasks with inaccurate resource predictions, then ease of operation is maintained, but resource waste increases and utilization rate deteriorates
Solution Approach 1:
The system enables self-service by allowing the machine learning models to automatically determine optimal resource allocation parameters without manual intervention. The models self-adjust by learning from accumulated statistics in the research workspace, automatically predicting the right amount of GPU resources needed for each production task, thereby improving utilization while maintaining operational simplicity.
Data Source
AI summary
A first machine learning task having a first data size is executed via virtualized computing resource units in a research workspace. The first machine learning task is associated with the virtualized computing resource units and with an amount of execution time. A second machine learning task is executed in a production workspace having a plurality of physical computing resource units. The second machine learning task has a same algorithm as the first machine learning task and a second data size greater than the first data size. A subset of the physical computing resource units is allocated for the execution of the second machine learning task in the production workspace. The allocating is based on the virtualized computing resource units used during an execution of the first machine learning task in the research workspace and the amount of execution time of the first machine learning task in the research workspace.