What type of visualization would best illustrate GPU resource utilization during AI model training?

A heatmap is the most effective visualization for illustrating GPU resource utilization during AI model training because it provides a clear, intuitive way to show how GPU utilization changes over time. In this context, training AI models can involve significant fluctuations in resource usage depending on the phases of training, the size of the data being processed, and the complexity of the models. A heatmap allows observers to quickly grasp these changes by displaying color variations that represent different levels of utilization.

This type of visualization makes it easy to pinpoint specific times where GPUs may be under-utilized or over-utilized, enabling more informed decisions about resource allocation. It can also facilitate the identification of patterns or anomalies in resource usage that are critical for optimizing AI model training processes. By visualizing this temporal aspect effectively, stakeholders can better understand performance bottlenecks and dynamically adjust resources as needed.

In contrast, other types of visualizations such as stacked bar charts, box plots, or pie charts would not provide the same level of insight into time-dependence and variability of GPU usage. For instance, stacked bar charts can illustrate cumulative usage but may obscure details regarding specific time intervals. Box plots give a summary of distribution but lack the temporal dimension, and pie charts are limited in conveying time-related information.