在这个网站上已经有很多性能问题了,但是在我看来,几乎所有的问题都是非常具体的,而且相当狭窄。几乎所有人都重复了避免过早优化的建议。
我们假设:
代码已经正常工作了
所选择的算法对于问题的环境已经是最优的
对代码进行了测量,并隔离了有问题的例程
所有优化的尝试也将被衡量,以确保它们不会使事情变得更糟
我在这里寻找的是策略和技巧,在一个关键算法中,当没有其他事情可做,但无论如何都要挤出最后百分之几。
理想情况下,尽量让答案与语言无关,并在适用的情况下指出所建议的策略的任何缺点。
我将添加一个带有我自己最初建议的回复,并期待Stack Overflow社区能想到的任何其他东西。
虽然我喜欢Mike Dunlavey的回答,但事实上这是一个很好的答案,并且有支持的例子,我认为它可以简单地表达出来:
首先找出哪些事情最耗费时间,并了解原因。
它是时间消耗的识别过程,可以帮助您了解必须在哪里改进算法。这是我能找到的唯一一个全面的语言不可知论答案,这个问题已经被认为是完全优化的。同时假设您希望在追求速度的过程中独立于体系结构。
因此,虽然算法可能被优化了,但它的实现可能没有。标识可以让您知道哪个部分是哪个部分:算法或实现。所以,占用时间最多的就是你审查的首选对象。但是既然你说你想把最后的%挤出来,你可能还想检查一下较小的部分,那些你一开始没有仔细检查过的部分。
最后,对实现相同解决方案的不同方法的性能数据进行一些尝试和错误,或者可能的不同算法,可以带来有助于识别浪费时间和节省时间的见解。
HPH,
asoudmove。
建议:
Pre-compute rather than re-calculate: any loops or repeated calls that contain calculations that have a relatively limited range of inputs, consider making a lookup (array or dictionary) that contains the result of that calculation for all values in the valid range of inputs. Then use a simple lookup inside the algorithm instead.
Down-sides: if few of the pre-computed values are actually used this may make matters worse, also the lookup may take significant memory.
Don't use library methods: most libraries need to be written to operate correctly under a broad range of scenarios, and perform null checks on parameters, etc. By re-implementing a method you may be able to strip out a lot of logic that does not apply in the exact circumstance you are using it.
Down-sides: writing additional code means more surface area for bugs.
Do use library methods: to contradict myself, language libraries get written by people that are a lot smarter than you or me; odds are they did it better and faster. Do not implement it yourself unless you can actually make it faster (i.e.: always measure!)
Cheat: in some cases although an exact calculation may exist for your problem, you may not need 'exact', sometimes an approximation may be 'good enough' and a lot faster in the deal. Ask yourself, does it really matter if the answer is out by 1%? 5%? even 10%?
Down-sides: Well... the answer won't be exact.