Which log file is most useful for diagnosing GPU performance issues in an AI infrastructure?

NVIDIA GPU utilization logs are specifically designed to provide insights into the performance and usage of GPU resources in an AI infrastructure. This log file captures vital metrics such as GPU memory usage, utilization percentages, temperature, and clock speeds, all of which are essential for diagnosing performance issues. By examining these logs, an operator can identify whether a GPU is underutilized, overloaded, or operating within expected parameters, enabling targeted troubleshooting and optimization.

Other log files such as network traffic logs, application error logs, and system kernel logs serve different purposes. Network traffic logs focus on monitoring data transfer over the network, which can be important for performance issues related to data transmission but do not directly inform on GPU performance. Application error logs report issues related to the application software that may occur irrespective of GPU performance. System kernel logs reflect the interactions between hardware and software at a low level but may not provide insights specifically about GPU utilization or performance metrics. Therefore, the NVIDIA GPU utilization logs are uniquely suited for diagnosing GPU performance issues in an AI infrastructure context.