有时改变数据的布局会有所帮助。在C语言中，可以从数组或结构切换到数组结构，反之亦然。

When you get to the point that you're using efficient algorithms its a question of what you need more speed or memory. Use caching to "pay" in memory for more speed or use calculations to reduce the memory footprint. If possible (and more cost effective) throw hardware at the problem - faster CPU, more memory or HD could solve the problem faster then trying to code it. Use parallelization if possible - run part of the code on multiple threads. Use the right tool for the job. some programing languages create more efficient code, using managed code (i.e. Java/.NET) speed up development but native programing languages creates faster running code. Micro optimize. Only were applicable you can use optimized assembly to speed small pieces of code, using SSE/vector optimizations in the right places can greatly increase performance.

当你不能再提高表现时，看看你是否可以提高感知的表现。

您可能无法使您的fooCalc算法更快，但通常有一些方法可以使您的应用程序对用户的响应更快。

举几个例子:

预测用户将要做什么 请求并开始着手这项工作 在那之前 将结果显示为 它们是进来的，而不是同时出现的 在最后 精确的进度计

这些不会让你的程序更快，但可能会让你的用户对你的速度更满意。

你在什么硬件上运行?您是否可以使用特定于平台化的优化(如向量化)? 你能找到更好的编译器吗?比如从GCC换成Intel? 你能让你的算法并行运行吗? 可以通过重新组织数据来减少缓存丢失吗? 可以禁用断言吗? 对编译器和平台进行微优化。在if/else语句中，把最常见的语句放在前面

通过引用而不是通过值传递

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.