Editing Program optimization (section)

==Bottlenecks==
Optimization may include finding a [[Bottleneck (engineering)|bottleneck]] in a system{{snd}} a component that is the limiting factor on performance. In terms of code, this will often be a [[Hot spot (computer science)|hot spot]]{{snd}} a critical part of the code that is the primary consumer of the needed resource{{snd}} though it can be another factor, such as I/O latency or network bandwidth.

In computer science, resource consumption often follows a form of [[power law]] distribution, and the [[Pareto principle]] can be applied to resource optimization by observing that 80% of the resources are typically used by 20% of the operations.<ref>{{cite book  | last = Wescott | first = Bob | title = The Every Computer Performance Book, Chapter 3: Useful laws | publisher = [[CreateSpace]] | date = 2013  | isbn = 978-1482657753}}</ref> In software engineering, it is often a better approximation that 90% of the execution time of a computer program is spent executing 10% of the code (known as the 90/10 law in this context).

More complex algorithms and data structures perform well with many items, while simple algorithms are more suitable for small amounts of data — the setup, initialization time, and constant factors of the more complex algorithm can outweigh the benefit, and thus a [[hybrid algorithm]] or [[adaptive algorithm]] may be faster than any single algorithm.  A performance profiler can be used to narrow down decisions about which functionality fits which conditions.<ref>{{cite web |url=http://www.developforperformance.com/PerformanceProfilingWithAFocus.html#FittingTheSituation |title=Performance Profiling with a Focus |access-date=15 August 2017}}</ref>

In some cases, adding more [[main memory|memory]] can help to make a program run faster. For example, a filtering program will commonly read each line and filter and output that line immediately. This only uses enough memory for one line, but performance is typically poor, due to the latency of each disk read.  Caching the result is similarly effective, though also requiring larger memory use.