performance_interpretation

Performance interpretation analyzes benchmark results to diagnose whether memory, CPU, or branch costs dominate your system. It applies microarchitectural reasoning to identify cache behavior, allocator overhead, and concurrency issues, then explains variance from GC, OS scheduling, or contention.

Sanity checks compare observed benchmark results against Big-O predictions and hardware plausibility. This catches unrealistic measurements early and ensures results align with expected microarchitectural behavior before deeper analysis.

Benchmark variance stems from GC pauses, OS scheduling, or contention on shared resources. Performance interpretation diagnoses these root causes by analyzing measurement patterns and microarchitectural factors, producing a variance explanation narrative.

Yes, performance interpretation generates realistic synthetic metrics when live measurements are unavailable. This lets you model behavior and compare approaches without requiring actual benchmark runs.

Model comparison interprets benchmark results across implementations to identify which approach performs better and why. It explains performance differences through microarchitectural analysis, revealing whether gains come from cache efficiency, reduced allocator overhead, or lower concurrency costs.