Algorithm Performance Engineering: What Really Works in Practice

When we talk about algorithm performance, the discussion often drifts toward Big-O notation. While asymptotics are useful for comparing approaches, they rarely tell the full story. In practice, the difference between a sluggish algorithm and a blazing-fast one usually comes down to implementation details: caching, data structures, language quirks, and profiling.

Here are some field-tested lessons from building optimization software that apply far beyond operations research.

1. Caching vs. Recomputing: When to Store, When to Calculate

Caching is one of the most powerful tools for speeding up algorithms, but it comes with a trade-off: memory.

Take the Traveling Salesman Problem (TSP):

Without caching: each time you need the distance between two cities, you recalculate it.
With caching: you precompute all pairwise distances in a matrix. Lookups drop from $O (1)$ costly calculations to near-instant array access - but memory usage grows quadratically with the number of cities.

The rule of thumb:

Cache results if they're accessed repeatedly and costly to compute.
Recompute if memory is tight or if the data changes frequently.

2. Precomputation as a Performance Booster

Related to caching is precomputation - doing heavy work upfront to speed up the inner loop.

Examples:

Precomputing neighbor lists in local search heuristics. Instead of scanning all items each time, you maintain a list of candidates.
Creating lookup tables for cost functions in knapsack problems.

In optimization software, where the main loop can run millions of iterations, shaving off even a few operations per iteration compounds into massive speedups.

3. Custom Data Structures for Speed: When Lists Aren't Enough

Built-in data structures are versatile but not always optimal. For example, a standard list works well for appending and iterating, but falls short in specialized tasks like:

Managing timelines in scheduling problems.
Handling dynamic neighborhoods in metaheuristics.

In one project, replacing a native list with a custom timeline structure reduced insertion and lookup times from $O (n)$ to near $O (\log n)$ . The difference: solutions that previously took minutes to evaluate ran in seconds.

Lesson: if you're hitting performance walls, consider whether your data structure truly matches your access pattern.

4. Practical Language-Specific Quirks

Every language comes with hidden performance details that matter in tight loops:

List<T> in VB.NET / C#: Lists double their capacity when full, causing occasional costly reallocations. If you know the final size, preallocate with new List<T>(capacity) to avoid this.
Value types vs. reference types in .NET: Value types (structs) can be faster and more memory-efficient in some contexts, but copying large structs can hurt performance. Reference types avoid copying but add pointer indirection. Choosing the right one depends on your workload.
Garbage collection (GC): Frequent allocations (e.g., creating new objects in every iteration) can trigger GC pauses. Object pooling or reusing mutable objects can mitigate this.

Small quirks like these rarely show up in textbooks, but they make the difference in production code.

5. Profiling to Find the True Bottlenecks

Perhaps the most important rule: don't guess where the bottleneck is.

In practice, intuition is often wrong. I've seen cases where:

70% of runtime was spent generating random numbers.
String concatenation dominated an algorithm that was “about” combinatorics.
Data conversion between layers was slower than the optimization itself.

Tools like cProfile (Python), dotTrace (C#), or Visual Studio's profiler reveal the true hotspots. Only then does it make sense to decide: optimize, cache, or rewrite.

Conclusion

Algorithm performance engineering isn't about clever tricks - it's about understanding trade-offs and focusing effort where it matters most.

Cache wisely, but don't blow up memory.
Precompute to speed up inner loops.
Match your data structures to your access patterns.
Know your language's quirks and runtime constraints.
Always measure before optimizing.

Performance gains rarely come from theory alone. They come from careful engineering, a deep understanding of your tools, and an eye for the real bottlenecks.