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:)

OK, you're defining the problem to where it would seem there is not much room for improvement. That is fairly rare, in my experience. I tried to explain this in a Dr. Dobbs article in November 1993, by starting from a conventionally well-designed non-trivial program with no obvious waste and taking it through a series of optimizations until its wall-clock time was reduced from 48 seconds to 1.1 seconds, and the source code size was reduced by a factor of 4. My diagnostic tool was this. The sequence of changes was this:

The first problem found was use of list clusters (now called "iterators" and "container classes") accounting for over half the time. Those were replaced with fairly simple code, bringing the time down to 20 seconds. Now the largest time-taker is more list-building. As a percentage, it was not so big before, but now it is because the bigger problem was removed. I find a way to speed it up, and the time drops to 17 seconds. Now it is harder to find obvious culprits, but there are a few smaller ones that I can do something about, and the time drops to 13 sec.

现在我似乎遇到了瓶颈。样本告诉我它到底在做什么，但我似乎找不到任何可以改进的地方。然后，我考虑了程序的基本设计及其事务驱动结构，并询问它所做的所有列表搜索实际上是否都是由问题的需求强制执行的。

然后我偶然发现了一种重新设计，在这种设计中，程序代码实际上是从一组较小的源代码中生成的(通过预处理器宏)，在这种设计中，程序不会不断地找出程序员知道的相当可预测的事情。换句话说，不要“解释”要做的事情的顺序，要“编译”它。

重新设计完成了，源代码缩减了1 / 4，时间减少到10秒。

现在，因为它变得如此之快，很难进行抽样，所以我给它10倍的工作，但下面的时间是基于原始工作负载的。

进一步的诊断表明，它是在队列管理上花费时间的。内联这些将时间缩短到7秒。 现在一个很大的时间消耗是我一直在做的诊断打印。冲水- 4秒 现在最浪费时间的是调用malloc和free。回收对象- 2.6秒。 继续进行抽样，我仍然发现了严格意义上没有必要的操作——1.1秒。

总加速系数:43.6

Now no two programs are alike, but in non-toy software I've always seen a progression like this. First you get the easy stuff, and then the more difficult, until you get to a point of diminishing returns. Then the insight you gain may well lead to a redesign, starting a new round of speedups, until you again hit diminishing returns. Now this is the point at which it might make sense to wonder whether ++i or i++ or for(;;) or while(1) are faster: the kinds of questions I see so often on Stack Overflow.

附注:可能有人想知道我为什么不用侧写器。答案是，几乎所有这些“问题”都是函数调用站点，堆栈样本可以精确定位。即使在今天，分析人员也只是勉强接受这样一个观点:语句和调用指令比整个函数更重要，更容易定位，也更容易修复。

我实际上构建了一个剖析器来做这件事，但是要真正了解代码正在做什么，没有什么可以替代您的手指。样本数量少并不是问题，因为被发现的问题没有一个小到容易被忽略的程度。

添加:jerryjvl要求一些例子。这是第一个问题。它由少量独立的代码行组成，加在一起占用了一半的时间:

 /* IF ALL TASKS DONE, SEND ITC_ACKOP, AND DELETE OP */
if (ptop->current_task >= ILST_LENGTH(ptop->tasklist){
. . .
/* FOR EACH OPERATION REQUEST */
for ( ptop = ILST_FIRST(oplist); ptop != NULL; ptop = ILST_NEXT(oplist, ptop)){
. . .
/* GET CURRENT TASK */
ptask = ILST_NTH(ptop->tasklist, ptop->current_task)

These were using the list cluster ILST (similar to a list class). They are implemented in the usual way, with "information hiding" meaning that the users of the class were not supposed to have to care how they were implemented. When these lines were written (out of roughly 800 lines of code) thought was not given to the idea that these could be a "bottleneck" (I hate that word). They are simply the recommended way to do things. It is easy to say in hindsight that these should have been avoided, but in my experience all performance problems are like that. In general, it is good to try to avoid creating performance problems. It is even better to find and fix the ones that are created, even though they "should have been avoided" (in hindsight). I hope that gives a bit of the flavor.

下面是第二个问题，分两行:

 /* ADD TASK TO TASK LIST */
ILST_APPEND(ptop->tasklist, ptask)
. . .
/* ADD TRANSACTION TO TRANSACTION QUEUE */
ILST_APPEND(trnque, ptrn)

它们通过在列表的末尾附加项目来构建列表。(解决方法是将项目收集到数组中，并一次性构建列表。)有趣的是，这些语句只花费了原始时间的3/48(即在调用堆栈上)，所以它们实际上在一开始并不是一个大问题。然而，在消除了第一个问题后，它们只花费了3/20的时间，所以现在是一条“大鱼”。总的来说，就是这样。

我可以补充说，这个项目是从我参与的一个真实项目中提炼出来的。在那个项目中，性能问题要严重得多(加速也是如此)，比如在内部循环中调用数据库访问例程来查看任务是否完成。

参考补充道: 源代码，无论是原始的还是重新设计的，都可以在www.ddj.com上找到，1993年，文件9311.zip, files slug。Asc和slug.zip。

编辑2011/11/26: 现在有一个SourceForge项目包含了Visual c++中的源代码，以及它是如何调优的详细描述。它只经历了上述场景的前半部分，并不完全遵循相同的顺序，但仍然获得了2-3个数量级的加速。

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。

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。

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。

向它扔更多的硬件!