I think the questioner probably wants to make a program run faster by having multiple cores work on it in parallel. That's what I would want anyway but all the answers leave me no wiser. However, I think I get this: You can't synchronize different threads down to instruction execution time accuracy. So you can't get 4 cores to do a multiply on four different array elements in parallel to speed up processing by 4:1. Rather, you have to look at your program as comprising major blocks that execute sequentially like

对一些数据做FFT吗 把结果放到一个矩阵中，然后找出它的特征值和特征向量 根据特征值对后者进行排序 用新的数据重复第一步

What you can do is run step 2 on the results of step 1 while running step one in a different core on new data, and running step 3 on the results of step2 in a different core while step 2 is running on the next data and step 1 is running on the data after that. You can do this in Compaq Visual Fortran and Intel Fortran which is an evolution of CVF by writing three separate programs/ subroutines for the three steps and instead of one "calling" the next it calls an API to start its thread. They can share data by using COMMON which will be COMMON data memory to all threads. You have to study the manual till your head hurts and experiment until you get it to work but I have succeeded once at least.

The main difference between a single- and a multi-threaded application is that the former has one stack and the latter has one for each thread. Code is generated somewhat differently since the compiler will assume that the data and stack segment registers (ds and ss) are not equal. This means that indirection through the ebp and esp registers that default to the ss register won't also default to ds (because ds!=ss). Conversely, indirection through the other registers which default to ds won't default to ss.

The threads share everything else including data and code areas. They also share lib routines so make sure that they are thread-safe. A procedure that sorts an area in RAM can be multi-threaded to speed things up. The threads will then be accessing, comparing and ordering data in the same physical memory area and executing the same code but using different local variables to control their respective part of the sort. This of course is because the threads have different stacks where the local variables are contained. This type of programming requires careful tuning of the code so that inter-core data collisions (in caches and RAM) are reduced which in turn results in a code which is faster with two or more threads than it is with just one. Of course, an un-tuned code will often be faster with one processor than with two or more. To debug is more challenging because the standard "int 3" breakpoint will not be applicable since you want to interrupt a specific thread and not all of them. Debug register breakpoints do not solve this problem either unless you can set them on the specific processor executing the specific thread you want to interrupt.

其他多线程代码可能涉及在程序的不同部分运行的不同线程。这种类型的编程不需要同样的调优，因此更容易学习。

非官方的SMP FAQ

曾经，为了编写x86汇编程序，例如，你会有这样的指令:“用值5加载EDX寄存器”，“增加EDX”寄存器，等等。对于拥有4核(甚至更多)的现代cpu，在机器代码级别上，它是否看起来就像有4个独立的cpu(即只有4个不同的“EDX”寄存器)?

完全正确。有4组寄存器，包括4个单独的指令指针。

如果是这样，当你说“增加EDX寄存器”时，是什么决定哪个CPU的EDX寄存器被增加?

当然是执行指令的CPU。可以把它想象成4个完全不同的微处理器共享相同的内存。

现在在x86汇编器中有“CPU上下文”或“线程”概念吗?

不。汇编程序只是像往常一样翻译指令。没有变化。

内核之间的通信/同步是如何工作的?

由于它们共享相同的内存，这主要是程序逻辑的问题。虽然现在有一个处理器间中断机制，但它不是必要的，最初也没有出现在第一个双cpu x86系统中。

如果您正在编写一个操作系统，通过硬件公开的什么机制允许您在不同的内核上调度执行?

The scheduler actually doesn't change, except that it is slightly more carefully about critical sections and the types of locks used. Before SMP, kernel code would eventually call the scheduler, which would look at the run queue and pick a process to run as the next thread. (Processes to the kernel look a lot like threads.) The SMP kernel runs the exact same code, one thread at a time, it's just that now critical section locking needs to be SMP-safe to be sure two cores can't accidentally pick the same PID.

是一些特殊的特权指令吗?

不。这些核心都运行在相同的内存中，使用相同的旧指令。

如果你正在为一个多核CPU编写一个优化编译器/字节码虚拟机，你需要特别了解什么，比如说，x86，以使它生成跨所有核高效运行的代码?

运行与之前相同的代码。需要改变的是Unix或Windows内核。

你可以把我的问题总结为“为了支持多核功能，x86机器码做了哪些改变?”

没有什么是必要的。第一个SMP系统使用与单处理器完全相同的指令集。现在，x86体系结构已经有了很大的改进，并且有了大量的新指令来让事情变得更快，但是对于SMP来说没有一个是必要的。

For more information, see the Intel Multiprocessor Specification. Update: all the follow-up questions can be answered by just completely accepting that an n-way multicore CPU is almost1 exactly the same thing as n separate processors that just share the same memory.2 There was an important question not asked: how is a program written to run on more than one core for more performance? And the answer is: it is written using a thread library like Pthreads. Some thread libraries use "green threads" that are not visible to the OS, and those won't get separate cores, but as long as the thread library uses kernel thread features then your threaded program will automatically be multicore. 1. For backwards compatibility, only the first core starts up at reset, and a few driver-type things need to be done to fire up the remaining ones.2. They also share all the peripherals, naturally.

汇编代码将转换为将在一个核心上执行的机器代码。如果你希望它是多线程的，你将不得不使用操作系统原语在不同的处理器上多次启动这段代码，或者在不同的核上启动不同的代码段——每个核将执行一个单独的线程。每个线程只能看到当前正在执行的一个内核。

每个核心从不同的内存区域执行。你的操作系统将把一个核心指向你的程序，这个核心将执行你的程序。你的程序不会知道有多个核或者它在哪个核上执行。

也没有仅用于操作系统的附加指令。这些核心与单核芯片是相同的。每个内核运行操作系统的一部分，该部分将处理与用于信息交换的公共内存区域的通信，以查找下一个要执行的内存区域。

这是一个简化，但它给了你基本的想法，它是如何做到的。更多关于多核和多处理器的信息在Embedded.com上有很多关于这个主题的信息…这个话题很快就变得复杂起来!

I think the questioner probably wants to make a program run faster by having multiple cores work on it in parallel. That's what I would want anyway but all the answers leave me no wiser. However, I think I get this: You can't synchronize different threads down to instruction execution time accuracy. So you can't get 4 cores to do a multiply on four different array elements in parallel to speed up processing by 4:1. Rather, you have to look at your program as comprising major blocks that execute sequentially like

对一些数据做FFT吗 把结果放到一个矩阵中，然后找出它的特征值和特征向量 根据特征值对后者进行排序 用新的数据重复第一步

What you can do is run step 2 on the results of step 1 while running step one in a different core on new data, and running step 3 on the results of step2 in a different core while step 2 is running on the next data and step 1 is running on the data after that. You can do this in Compaq Visual Fortran and Intel Fortran which is an evolution of CVF by writing three separate programs/ subroutines for the three steps and instead of one "calling" the next it calls an API to start its thread. They can share data by using COMMON which will be COMMON data memory to all threads. You have to study the manual till your head hurts and experiment until you get it to work but I have succeeded once at least.