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。


当前回答

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.

其他回答

如果更好的硬件是一个选择,那么一定要去做。否则

Check you are using the best compiler and linker options. If hotspot routine in different library to frequent caller, consider moving or cloning it to the callers module. Eliminates some of the call overhead and may improve cache hits (cf how AIX links strcpy() statically into separately linked shared objects). This could of course decrease cache hits also, which is why one measure. See if there is any possibility of using a specialized version of the hotspot routine. Downside is more than one version to maintain. Look at the assembler. If you think it could be better, consider why the compiler did not figure this out, and how you could help the compiler. Consider: are you really using the best algorithm? Is it the best algorithm for your input size?

在带有模板的语言(c++ /D)中,您可以尝试通过模板参数传播常量值。你甚至可以用开关来处理小的非常值集合。

Foo(i, j); // i always in 0-4.

就变成了

switch(i)
{
    case 0: Foo<0>(j); break;
    case 1: Foo<1>(j); break;
    case 2: Foo<2>(j); break;
    case 3: Foo<3>(j); break;
    case 4: Foo<4>(j); break;
}

缺点是缓存压力,因此这只会在深度或长期运行的调用树中获得,其中值在持续时间内是恒定的。

通过引用而不是通过值传递

以下是我使用的一些快速而粗糙的优化技术。我认为这是“第一关”优化。

了解时间都花在了什么地方。是文件IO吗?是CPU时间吗?是因为网络吗?是数据库吗?如果IO不是瓶颈,优化IO是没有用的。

了解您的环境了解在哪里进行优化通常取决于开发环境。例如,在VB6中,通过引用传递比通过值传递慢,但是在C和c++中,通过引用传递要快得多。在C语言中,如果返回代码表明失败,尝试一些东西并做一些不同的事情是合理的,而在Dot Net中,捕获异常比尝试前检查有效条件要慢得多。

在频繁查询的数据库字段上构建索引。你几乎总是可以用空间来换取速度。

在要优化的循环内部,我避免了必须进行任何查找。找到循环外的偏移量和/或索引,并重用循环内的数据。

最小化IO尝试以一种减少必须读或写的次数的方式进行设计,特别是在网络连接上

减少抽象代码必须通过的抽象层越多,它就越慢。在关键循环内部,减少抽象(例如,揭示避免额外代码的低级方法)

对于带有用户界面的项目,生成一个新线程来执行较慢的任务使应用程序感觉反应更快,尽管不是。

你通常可以用空间来换取速度。如果有计算或其他密集的操作,看看是否可以在进入关键循环之前预先计算一些信息。

不像之前的答案那么深入或复杂,但下面是: (这些更多是初级/中级水平)

明显:干 向后运行循环,所以总是与0比较,而不是与变量比较 尽可能使用位操作符 将重复的代码分解为模块/函数 缓存对象 局部变量具有轻微的性能优势 尽可能限制字符串操作