Which metric should be prioritized to predict potential GPU failures in a health monitoring system?

Prioritizing error rates, such as ECC (Error-Correcting Code) errors, is crucial in predicting potential GPU failures because these metrics directly indicate the reliability and integrity of the computations being performed by the GPU. ECC errors are designed to detect and correct internal data corruption, which can be symptomatic of underlying hardware issues. An increase in error rates can signal that the GPU is experiencing problems that could lead to failure, making it an essential metric for health monitoring systems.

In contrast, while GPU temperature is also important, as high temperatures can lead to thermal throttling or damage, it does not provide a direct indication of the GPU's operational integrity. CPU utilization does not reflect the performance or health of the GPU directly; rather, it measures how much work the CPU is doing, which does not correlate with GPU reliability. Lastly, GPU clock speed can indicate performance but does not directly reveal any underlying issues that might lead to failures, as a change in clock speed can occur for various reasons not related to hardware health. Therefore, focusing on error rates is the most effective way to predict and prevent potential GPU failures.