OSS + ELC EU 2026

Memory efficiency problems are notoriously hard to see. DAMON was built to change that — but many users collect data incorrectly, visualize it poorly, and walk away with wrong conclusions. The tool gets blamed. The real problem goes unfixed.

This talk shows you the right ways to do it.

We cover data collection first: configuring sampling and aggregation parameters, the tradeoffs, and setup mistakes that corrupt your data before analysis begins.

We then walk through the visualization features in `damo` — heatmaps of access frequency across time and address space, working set size plots, and more. There is no single right visualization; we cover when to use each and how to read what it shows.

We also highlight mistakes that lead users astray: misreading sampling artifacts as real patterns, choosing granularities that hide behavior, observing too briefly to catch periodic patterns, and more. For each, we show what misleading output looks like and how to fix it.

By the end, you will know how to collect trustworthy DAMON data, pick the right visualization, and tell when your results are real versus when your setup is lying to you.