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AI Literacy & Safety Events at Chicago Public Library
Chicago Public Library is excited to share our full lineup of Artificial Intelligence literacy, safety, and community learning programs…
![[Notecard] How to Read Performance Graphs
Being able to read and interpret performance graphs can help detect leaks & deadlocks in concurrency, prevent false performance conclusions (i.e., a spike in 100% CPU is not inherently a defect), and validate E2E optimizations. Overall, it's about reading in context, and deciding for yourself using other graphs and measurements if performance should be doing what it's doing.
Memory Management
Healthy memory - Graph shaped like a rollercoaster
Healthy memory looks like a rollercoaster: memory is allocated and released correctly
Unhealthy memory - Graph shaped like stairs
Unhealthy memory looks like stairs: memory is alocateed but is never eleased (leaks!)
High CPU vs. Dragged Duration
High CPU is not automatically bad: A program with high CPU (i.e. 500%+ across multiple cores) simply means it is utilizing available hardware parallelization to compute tokens as fast as possible.
Evaluate CPU alongside Duration: The true measure of compute efficiency is total work over time (Total Energy = Power x Time). A brief burst of high CPU that completes work in 51s is better to a lower CPU draw that drags on for 114s.
Why lower peak CPU with faster completion is ideal: If normalized system-wide CPU drops (i.e. 94% down to 68%) while runtime gets cut in half (114.2s down to 51.1s), it proves wasted background churn, polling overhead, or thread spin-locks were eliminated—leaving the processor free to do pure inference work.
Shallow floors vs. high baselines: Watch the bottom of the graph, not just the top. If the CPU line never returns to near-zero during pauses between tasks, background worker threads or zombie routines are still consuming cycles.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:j35lllc4rqjaisq4zdhqrwd6/bafkreigt6ftxnyfmickbb6nsql7njkalr6bt4lwvforxg7trcd4i3vyn4e)
