The performance loop—A practical guide to profiling and benchmarking - Daniel Marbach - NDC Oslo

Start with benchmarking memory allocations before CPU optimizations - memory issues often have bigger impact and are easier to optimize

Create isolated benchmark environments by copying relevant code into a dedicated repository to avoid complexity and side effects

Use benchmark.NET framework to handle the complexities of reliable benchmarking rather than building custom solutions

Take both memory and CPU profiles/snapshots before and after optimizations to verify improvements

Focus optimization efforts on the hot path and most frequently executed code paths first

Make explicit tradeoffs - don’t try to optimize everything, focus on areas where you have domain knowledge and that provide the most value

Document performance-related decisions and learnings to share knowledge with the team

Add regression tests for critical performance benchmarks to CI/CD to prevent regressions

Run benchmarks in release mode with proper warm-up iterations to get reliable results

Small iterative 1% improvements compound over time into significant performance gains when applied consistently

Remember that benchmarks should measure one specific thing, like unit tests, but provide statistical results rather than pass/fail

Profile both happy path and exception cases, as error handling can have significant performance impact at scale

Consider the environmental impact - more efficient code means less energy consumption in production