Which type of visualization best helps identify outliers in inference times across multiple GPUs?

The box plot is particularly effective for identifying outliers in inference times across multiple GPUs because it provides a clear visual summary of the distribution of data. It displays the median, quartiles, and any potential outliers, which enables users to quickly assess the spread and identify values that fall outside the typical range of inference times.

In a box plot, the central box represents the interquartile range, which contains the middle 50% of the data, while the "whiskers" extend to show variability outside this range. Any points that lie beyond the whiskers are considered outliers, making it easy to spot anomalies in inference times that could indicate performance issues or hardware inconsistencies. This visualization allows for a more immediate understanding of how each GPU is performing relative to others, particularly in pinpointing GPUs that may be underperforming or excessively deviating from the average inference time.

Other visualization types, while beneficial for different analytical objectives, do not provide the same level of clarity regarding outliers. For example, a line chart typically emphasizes trends over time rather than distribution, while a heatmap focuses on intensity and may obscure individual values. A scatter plot can show correlation but may not differentiate well between normal data and outliers without added context. Thus,