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notes for randomized inputs

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Damian Gryski 2018-04-15 15:09:47 -07:00
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@ -531,6 +531,15 @@ well-studied by researchers. Then you can apply the more general solution to
your specific problem. Mapping your problem into a domain that already has
well-researched implementations can be a significant win.
TODO:
improve worst-case behaviour at slight cost to average runtime
linear-time regexp matching
randomized algorithms
improve worse-case running time
skip-list, treap, randomized marking,
primality testing, randomized pivot for quicksort
### Benchmark Inputs
Know how big each of your input sizes is likely to be in production.
@ -556,6 +565,9 @@ keep them around than to recompute them. If your benchmark data consists of
only the same repeated request, your cache will give an inaccurate view of
the performance.
TODO: randomized inputs may have nice properties but not be representative of
real-world inputs (random graphs, random trees, etc)
Also note that some issues that are not apparent on your laptop might be
visible once you deploy to production and are hitting 250k reqs/second on
a 40 core server.