Program optimization: Difference between revisions

Revision as of 10:15, 14 November 2023

Revision as of 10:16, 14 November 2023

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Optimization can be automated by compilers or performed by programmers. Gains are usually limited for local optimization, and larger for global optimizations. Usually, the most powerful optimization is to find a superior [[algorithm]].

Optimizing a whole system is usually undertaken by programmers because it is too complex for automated optimizers. In this situation, programmers or [[Systemsystem administrator|system administrators]]s explicitly change code so that the overall system performs better. Although it can produce better efficiency, it is far more expensive than automated optimizations. Since many parameters influence the program performance, the program optimization space is large. Meta-heuristics and machine learning are used to address the complexity of program optimization.<ref>{{cite journal|last1=Memeti|first1=Suejb|last2=Pllana|first2=Sabri|last3=Binotto|first3=Alécio|last4=Kołodziej|first4=Joanna|last5=Brandic|author5-link= Ivona Brandić |first5=Ivona|title=Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review|journal=Computing|volume=101|issue=8|pages=893–936|date=26 April 2018|doi=10.1007/s00607-018-0614-9|publisher=Springer Vienna|arxiv=1801.09444|bibcode=2018arXiv180109444M|s2cid=13868111}}</ref>

Use a [[Profiler (computer science)|profiler]] (or [[Profiling (computer programming)|performance analyzer]]) to find the sections of the program that are taking the most resources{{snd}} the ''bottleneck''. Programmers sometimes believe they have a clear idea of where the bottleneck is, but intuition is frequently wrong.{{citation needed|date=May 2012}} Optimizing an unimportant piece of code will typically do little to help the overall performance.