Predictive GPU Malfunction Detection via Daemon Monitoring
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Solution Overview
Problem
Current GPU monitoring technologies only alert users of malfunctions after they occur, disrupting normal business operations by requiring immediate GPU replacement or migration of programs, which can be disruptive and inefficient.
Innovation Solution
A method and apparatus that predict GPU malfunctions by installing a daemon program to collect and compare GPU status parameters with pre-configured mean status fault parameters using statistical models, allowing for proactive replacement or migration of programs before a malfunction occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current GPU monitoring technology is used to detect faults, then fault detection capability is achieved, but business operations are disrupted due to immediate replacement or migration required after malfunction
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing GPU status parameters (temperature, power consumption, usage duration) before actual malfunction occurs. The system builds prediction models using historical data from normal and faulty GPUs, enabling proactive identification of GPUs at risk of failure. This allows replacement or migration to be performed in advance, avoiding disruption to business operations while maintaining reliable fault detection capability
2Reliability
If GPU replacement or program migration is performed immediately after malfunction detection, then system reliability is maintained, but operational efficiency decreases due to disruption
Solution Approach 1:
The system performs preliminary actions by predicting GPU failures before they occur using statistical models that analyze temperature, power consumption, and usage duration patterns. By identifying at-risk GPUs in advance, the system enables scheduled replacement or migration during maintenance windows rather than forcing immediate disruptive actions, thereby maintaining system reliability while minimizing operational disruption time
3Productivity
If predictive maintenance is implemented using statistical models, then operational efficiency is improved by avoiding disruption, but system complexity increases due to data collection and analysis requirements
Solution Approach 1:
The patent implements predictive maintenance by collecting GPU status parameters (temperature, power consumption, usage duration) and using statistical models to predict failures before they occur. This preliminary analysis enables proactive maintenance scheduling, improving operational efficiency by avoiding disruptive emergency replacements. The complexity is managed through systematic data collection frameworks and statistical prediction models that process multiple parameters to generate reliable failure predictions
Data Source
AI summary
A method of predicting GPU malfunctions includes installing a daemon program at a GPU node, the daemon program periodically collecting GPU status parameters corresponding to the GPU node at a pre-determined time period. The method also includes obtaining the GPU status parameters from the GPU node and comparing the obtained GPU status parameters with mean status fault parameters to determine whether the GPU is to malfunction, where the mean status fault parameters are obtained by use of a pre-configured statistical model. Prior to a GPU enters a malfunction state, the GPU can be replaced, or the programs executing on the GPU can be migrated to other GPUs for execution, without affecting the normal business operations.