This article is about that critical 3%, where we’ll explore how to estimate performance impact, when to measure, what to look for, and practical techniques that work across different programming languages.

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Performance optimization requires developing intuition about where it matters most. Start by estimating operation costs across different speed tiers (cache, RAM, disk, network) to make informed architectural decisions. Always measure with profiling tools before optimizing, as intuition about bottlenecks is often wrong. The biggest wins come from better algorithms and data structures (O(N) vs O(N²)). Memory layout matters significantly due to CPU cache behavior—prefer contiguous storage and sequential access. Reduce allocations through pre-sizing containers, reusing objects, and moving instead of copying. Avoid unnecessary work through fast paths for common cases, caching, lazy evaluation, and early bailouts. Focus optimization efforts on the critical 20% of code that accounts for 80% of runtime, balancing performance gains against code complexity and maintainability.

How to Write High-Performance Code

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