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。

我花了一些时间优化在低带宽和长延迟网络(例如卫星、远程、离岸)上运行的客户端/服务器业务系统，并能够通过相当可重复的过程实现一些显著的性能改进。

Measure: Start by understanding the network's underlying capacity and topology. Talking to the relevant networking people in the business, and make use of basic tools such as ping and traceroute to establish (at a minimum) the network latency from each client location, during typical operational periods. Next, take accurate time measurements of specific end user functions that display the problematic symptoms. Record all of these measurements, along with their locations, dates and times. Consider building end-user "network performance testing" functionality into your client application, allowing your power users to participate in the process of improvement; empowering them like this can have a huge psychological impact when you're dealing with users frustrated by a poorly performing system. Analyze: Using any and all logging methods available to establish exactly what data is being transmitted and received during the execution of the affected operations. Ideally, your application can capture data transmitted and received by both the client and the server. If these include timestamps as well, even better. If sufficient logging isn't available (e.g. closed system, or inability to deploy modifications into a production environment), use a network sniffer and make sure you really understand what's going on at the network level. Cache: Look for cases where static or infrequently changed data is being transmitted repetitively and consider an appropriate caching strategy. Typical examples include "pick list" values or other "reference entities", which can be surprisingly large in some business applications. In many cases, users can accept that they must restart or refresh the application to update infrequently updated data, especially if it can shave significant time from the display of commonly used user interface elements. Make sure you understand the real behaviour of the caching elements already deployed - many common caching methods (e.g. HTTP ETag) still require a network round-trip to ensure consistency, and where network latency is expensive, you may be able to avoid it altogether with a different caching approach. Parallelise: Look for sequential transactions that don't logically need to be issued strictly sequentially, and rework the system to issue them in parallel. I dealt with one case where an end-to-end request had an inherent network delay of ~2s, which was not a problem for a single transaction, but when 6 sequential 2s round trips were required before the user regained control of the client application, it became a huge source of frustration. Discovering that these transactions were in fact independent allowed them to be executed in parallel, reducing the end-user delay to very close to the cost of a single round trip. Combine: Where sequential requests must be executed sequentially, look for opportunities to combine them into a single more comprehensive request. Typical examples include creation of new entities, followed by requests to relate those entities to other existing entities. Compress: Look for opportunities to leverage compression of the payload, either by replacing a textual form with a binary one, or using actual compression technology. Many modern (i.e. within a decade) technology stacks support this almost transparently, so make sure it's configured. I have often been surprised by the significant impact of compression where it seemed clear that the problem was fundamentally latency rather than bandwidth, discovering after the fact that it allowed the transaction to fit within a single packet or otherwise avoid packet loss and therefore have an outsize impact on performance. Repeat: Go back to the beginning and re-measure your operations (at the same locations and times) with the improvements in place, record and report your results. As with all optimisation, some problems may have been solved exposing others that now dominate.

In the steps above, I focus on the application related optimisation process, but of course you must ensure the underlying network itself is configured in the most efficient manner to support your application too. Engage the networking specialists in the business and determine if they're able to apply capacity improvements, QoS, network compression, or other techniques to address the problem. Usually, they will not understand your application's needs, so it's important that you're equipped (after the Analyse step) to discuss it with them, and also to make the business case for any costs you're going to be asking them to incur. I've encountered cases where erroneous network configuration caused the applications data to be transmitted over a slow satellite link rather than an overland link, simply because it was using a TCP port that was not "well known" by the networking specialists; obviously rectifying a problem like this can have a dramatic impact on performance, with no software code or configuration changes necessary at all.

不好说。这取决于代码的样子。如果我们可以假设代码已经存在，那么我们可以简单地查看它并从中找出如何优化它。

更好的缓存位置，循环展开，尽量消除长依赖链，以获得更好的指令级并行性。尽可能选择有条件的移动而不是分支。尽可能利用SIMD指令。

理解你的代码在做什么，理解它运行在什么硬件上。然后，决定需要做什么来提高代码的性能就变得相当简单了。这是我能想到的唯一一个真正具有普遍性的建议。

好吧，还有“在SO上显示代码，并为特定的代码段寻求优化建议”。

你知道吗，一根CAT6电缆能够比缺省的Cat5e UTP电缆更好地屏蔽外部干扰10倍?

对于任何非离线项目，尽管拥有最好的软件和硬件，但如果你的throughoutput很弱，那么这条细线就会挤压数据并给你带来延迟，尽管只有几毫秒……

此外，CAT6电缆的最大吞吐量更高，因为您实际上更有可能收到铜芯电缆，而不是CCA，铜芯包覆铝，这通常出现在所有标准CAT5e电缆中。

如果您面临丢包，丢包，那么提高24/7操作的吞吐量可靠性可以使您所寻找的不同。

对于那些追求家庭/办公室连接可靠性的人来说(并且愿意对今年的快餐店说不，在年底你可以在那里)，以知名品牌的CAT7电缆的形式为自己提供LAN连接的顶峰。

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?

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。