I jumped straight to 'profile it and look for hotspots' which felt right but I skipped defining what 'fast enough' even means before touching anything.
Start by clarifying the performance requirements and constraints, then systematically profile the program to identify bottlenecks before making any changes. Prioritize optimizations based on impact and risk, and validate each change with benchmarks to ensure correctness and measurable improvement.
Pro tip: Always measure before optimizing; premature optimization can lead to complexity without real gains. Also, consider the entire system including hardware, OS, and libraries, as bottlenecks often lie outside the application code.
Clarify what 'too slow' means: target latency, throughput, or resource usage. Understand constraints like hardware, budget, and deadlines.
Use profiling tools to identify hotspots (CPU, memory, I/O). Establish a baseline with representative workloads and metrics.
Determine root causes of bottlenecks (e.g., algorithmic complexity, memory leaks, lock contention). Prioritize fixes by potential impact and effort.
Apply targeted optimizations one at a time, ensuring correctness. Measure after each change to confirm improvement and avoid regressions.
Repeat profiling and optimization until goals are met. Set up continuous monitoring to catch future performance issues.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.