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
目前最重要的限制因素是有限的内存带宽。多核只会让情况变得更糟，因为带宽是在核之间共享的。此外，用于实现缓存的有限芯片区域也分配给了内核和线程，这进一步恶化了这个问题。最后，保持不同缓存一致性所需的芯片间信号也会随着核数的增加而增加。这也增加了一个惩罚。
这些是您需要管理的影响。有时是通过对代码的微观管理，但有时是通过仔细考虑和重构。
很多注释已经提到了缓存友好的代码。至少有两种不同的风格:
避免内存读取延迟。 降低内存总线压力(带宽)。
第一个问题与如何使数据访问模式更规则有关，从而使硬件预取器更有效地工作。避免动态内存分配，这会将数据对象分散在内存中。使用线性容器代替链表、散列和树。
第二个问题与提高数据重用有关。修改算法以处理适合可用缓存的数据子集，并在数据仍在缓存中时尽可能多地重用这些数据。
更紧密地封装数据并确保在热循环中使用缓存线路中的所有数据，将有助于避免这些其他影响，并允许在缓存中安装更多有用的数据。
其他回答
首先，正如前面几个回答中提到的，了解是什么影响了您的性能——是内存、处理器、网络、数据库还是其他东西。这取决于…
...if it's memory - find one of the books written long time ago by Knuth, one of "The Art of Computer Programming" series. Most likely it's one about sorting and search - if my memory is wrong then you'll have to find out in which he talks about how to deal with slow tape data storage. Mentally transform his memory/tape pair into your pair of cache/main memory (or in pair of L1/L2 cache) respectively. Study all the tricks he describes - if you don's find something that solves your problem, then hire professional computer scientist to conduct a professional research. If your memory issue is by chance with FFT (cache misses at bit-reversed indexes when doing radix-2 butterflies) then don't hire a scientist - instead, manually optimize passes one-by-one until you're either win or get to dead end. You mentioned squeeze out up to the last few percent right? If it's few indeed you'll most likely win. ...if it's processor - switch to assembly language. Study processor specification - what takes ticks, VLIW, SIMD. Function calls are most likely replaceable tick-eaters. Learn loop transformations - pipeline, unroll. Multiplies and divisions might be replaceable / interpolated with bit shifts (multiplies by small integers might be replaceable with additions). Try tricks with shorter data - if you're lucky one instruction with 64 bits might turn out replaceable with two on 32 or even 4 on 16 or 8 on 8 bits go figure. Try also longer data - eg your float calculations might turn out slower than double ones at particular processor. If you have trigonometric stuff, fight it with pre-calculated tables; also keep in mind that sine of small value might be replaced with that value if loss of precision is within allowed limits. ...if it's network - think of compressing data you pass over it. Replace XML transfer with binary. Study protocols. Try UDP instead of TCP if you can somehow handle data loss. ...if it's database, well, go to any database forum and ask for advice. In-memory data-grid, optimizing query plan etc etc etc.
HTH:)
通过引用而不是通过值传递
最后几个%是一个非常CPU和应用程序依赖的东西....
缓存架构不同，有些芯片有片上内存 你可以直接映射，ARM的(有时)有一个矢量 单位，SH4是一个有用的矩阵操作码。有GPU吗 也许一个着色器是可行的。TMS320非常 对循环中的分支敏感(因此分离循环和 如果可能的话，将条件移到室外)。
名单在....上但这类事情真的是 最后的手段……
编译x86，并运行Valgrind/Cachegrind对代码 进行适当的性能分析。或者德州仪器的 CCStudio有一个贴心的侧写器。然后你就知道在哪里了 关注……
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.
谷歌方法是一个选项“缓存它..”只要可能，不要碰磁盘。”
