Performance Engineering · Cluster
Optimization Practice
Prioritizing work by measured impact.
- 01 Amdahl's Law as a Ceiling on Optimization Returns How the fraction of time a change addresses bounds its total payoff, why the bound is a filter rather than a forecast, and where the fixed-workload assumption breaks.
- 02 Ranking Optimization Candidates by Expected Payoff How to combine a measured cost share, a realistic achievable factor, and the probability that a change survives review into one comparable score per candidate.
- 03 Why the Busiest Stage Is Not Always the Critical Path Pipeline stages, parallel branches, and slack explain why time spent in the hottest function may not extend total latency, and how to identify the segment that actually does.
- 04 Removing Work Before Making It Faster Why eliminating an operation dominates optimizing it, how redundant computation and over-fetching hide in ordinary code, and what changes when work is removed rather than accelerated.
- 05 Measuring What a Component Costs by Taking It Away Ablation measures a component by disabling it and observing the change in a metric, which answers a different question than profiling and is easy to apply incorrectly.
- 06 The Break-Even Hit Rate for Adding a Cache A cache costs a lookup on every access and pays back only on hits, which gives a hit-rate threshold below which the cache makes the system slower.
- 07 Optimizations That Move Cost Instead of Removing It Batching, precomputation, and compression reduce one metric by increasing another, so measuring only the targeted metric counts a trade as a win.
- 08 Choosing an Objective Function for a Mixed Workload Means, percentiles, and goodput weight a workload mix differently, so the objective determines which optimization looks best before any measurement is taken.
- 09 Pareto Frontiers When No Single Setting Wins Configuration spaces have several objectives that trade against each other; dominance analysis reduces them to a frontier and explains why the frontier, not a score, is the deliverable.
- 10 Recognizing the Point Where Further Optimization Stops Paying When remaining candidates fall inside the measurement noise floor or the untouchable remainder dominates, the correct action changes from optimizing to sizing or redesigning.