A hands-on exploration of using Claude (Anthropic's AI) as a performance analysis assistant for Go programs, focusing on the new Green Tea garbage collector. The author walks through CPU profile analysis using pprof, where Claude identifies atomic operation bottlenecks in the GC's tryDeferToSpanScan routine and suggests optimizations like replacing Load8+Or8 with a single Or32 — though this caused false sharing regressions on PowerPC. Claude also analyzes assembly dumps to spot missed compiler optimizations like redundant variable loads. For trace files, Claude provides pattern recognition cheat sheets for GC behavior (idle processors, mark assist gaps, STW duration), extracts statistics from raw trace data, and compares multiple benchmark traces to summarize where Green Tea GC excels (high allocation pressure, clustered data structures) versus where it provides little benefit. The article also covers parameter tuning suggestions (GOGC, GOMEMLIMIT, GOMAXPROCS) and discusses limitations: architecture-specific suggestions need validation, Claude can miss compiler trade-offs like register pressure, and it can be confused when GC overhead is already minimal.
