A scatter plot with GPU memory usage on one axis (e.g., x-axis) and training time on the other (e.g., y-axis) is the most effective visualization for conveying the relationship between these two variables across different training sessions. This type of plot allows you to plot individual data points for each session, revealing correlations, trends, or outliers (e.g., high memory usage leading to longer training times due to swapping).
NVIDIA's "AI Infrastructure and Operations Fundamentals" course and "NVIDIA DCGM" documentation encourage such visualizations for performance analysis, as they provide actionable insights into resource impacts on training efficiency.
A bar chart (A) shows averages but obscures session-specific relationships. A histogram (B) displays distribution, not pairwise relationships. A line chart (C) implies temporal continuity, which doesn't fit this use case. The scatter plot aligns with NVIDIA's best practices for GPU performance analysis.