更多的建议:

Avoid I/O: Any I/O (disk, network, ports, etc.) is always going to be far slower than any code that is performing calculations, so get rid of any I/O that you do not strictly need. Move I/O up-front: Load up all the data you are going to need for a calculation up-front, so that you do not have repeated I/O waits within the core of a critical algorithm (and maybe as a result repeated disk seeks, when loading all the data in one hit may avoid seeking). Delay I/O: Do not write out your results until the calculation is over, store them in a data structure and then dump that out in one go at the end when the hard work is done. Threaded I/O: For those daring enough, combine 'I/O up-front' or 'Delay I/O' with the actual calculation by moving the loading into a parallel thread, so that while you are loading more data you can work on a calculation on the data you already have, or while you calculate the next batch of data you can simultaneously write out the results from the last batch.

我想这已经用不同的方式说过了。但是当你在处理一个处理器密集型算法时，你应该以牺牲其他所有东西为代价来简化最内部循环中的所有东西。

That may seem obvious to some, but it's something I try to focus on regardless of the language I'm working with. If you're dealing with nested loops, for example, and you find an opportunity to take some code down a level, you can in some cases drastically speed up your code. As another example, there are the little things to think about like working with integers instead of floating point variables whenever you can, and using multiplication instead of division whenever you can. Again, these are things that should be considered for your most inner loop.

有时，您可能会发现在内循环中对整数执行数学运算的好处，然后将其缩小为随后可以使用的浮点变量。这是一个牺牲一个部分的速度来提高另一个部分的速度的例子，但在某些情况下，这样做是值得的。

向它扔更多的硬件!

很难对这个问题给出一般的答案。这实际上取决于你的问题领域和技术实现。一种与语言无关的通用技术:识别无法消除的代码热点，并手工优化汇编代码。

您可能应该考虑“谷歌视角”，即确定您的应用程序如何在很大程度上实现并行和并发，这也不可避免地意味着在某种程度上考虑将您的应用程序分布在不同的机器和网络上，这样它就可以理想地与您投入的硬件几乎线性扩展。

另一方面，谷歌人员也以投入大量人力和资源来解决他们正在使用的项目、工具和基础设施中的一些问题而闻名，例如，通过拥有一个专门的工程师团队来破解gcc内部，以便为Google典型的用例场景做好准备，从而对gcc进行整个程序优化。

类似地，分析应用程序不再仅仅意味着分析程序代码，还包括它周围的所有系统和基础设施(想想网络、交换机、服务器、RAID阵列)，以便从系统的角度识别冗余和优化潜力。

如果你有很多高度并行的浮点运算——尤其是单精度运算——尝试使用OpenCL或(对于NVidia芯片)CUDA将其卸载到图形处理器上(如果有的话)。gpu在着色器中拥有强大的浮点计算能力，这比CPU要大得多。