Overview
Performance optimization is the practice of improving software speed, memory efficiency, throughput, or resource consumption without changing observable behavior. It spans from algorithmic improvements (reducing O(n²) to O(n log n)) to low-level techniques (cache locality, SIMD instructions).
Key Dimensions
- Latency: Time to complete a single operation
- Throughput: Operations completed per unit time
- Memory: Peak and average heap/stack usage
- I/O: Disk and network bandwidth utilization
- CPU: Core utilization and instruction efficiency
Fundamental Principles
- Measure first: Profile before optimizing — Knuth’s rule: “premature optimization is the root of all evil”
- Find the bottleneck: 90% of time is usually spent in 10% of code (Amdahl’s Law)
- Choose the right algorithm: Algorithmic complexity dominates at scale
- Cache behavior: CPU cache misses are expensive; data locality matters more than raw operations
Common Techniques
- Algorithmic: Better time/space complexity (sorting, searching, graph traversal)
- Concurrency: Parallelism via threads, async I/O, work-stealing queues
- Memory pools: Reduce allocation overhead by reusing memory
- Lazy evaluation: Defer computation until results are needed
- Memoization: Cache results of pure function calls
- Profiling: CPU profilers (flamegraphs), memory profilers, latency traces
Related
- Programming and Software Development — Broader software development context
- Rust — Language designed for zero-cost abstractions and performance