As I learnd in university most of the answers above are right. In PRACTISE on different platforms (always using python) spawning multiple threads ends up like spawning one process. The difference is the multiple cores share the load instead of only 1 core processing everything at 100%. So if you spawn for example 10 threads on a 4 core pc, you will end up getting only the 25% of the cpus power!! And if u spawn 10 processes u will end up with the cpu processing at 100% (if u dont have other limitations). Im not a expert in all the new technologies. Im answering with own real experience background

As I learnd in university most of the answers above are right. In PRACTISE on different platforms (always using python) spawning multiple threads ends up like spawning one process. The difference is the multiple cores share the load instead of only 1 core processing everything at 100%. So if you spawn for example 10 threads on a 4 core pc, you will end up getting only the 25% of the cpus power!! And if u spawn 10 processes u will end up with the cpu processing at 100% (if u dont have other limitations). Im not a expert in all the new technologies. Im answering with own real experience background

另一件没有提到的事情是，它取决于你使用的是什么操作系统。在Windows中，进程是昂贵的，所以线程在Windows中会更好，但在unix中，进程比它们的Windows变体更快，所以在unix中使用进程要安全得多，而且生成速度快。

线程模块使用线程，多处理模块使用进程。不同之处在于线程在相同的内存空间中运行，而进程有单独的内存。这使得在多进程之间共享对象变得有点困难。由于线程使用相同的内存，必须采取预防措施，否则两个线程将同时写入同一内存。这就是全局解释器锁的作用。

生成进程比生成线程要慢一些。

以下是我想到的一些优点和缺点。

多处理

Pros

独立的内存空间 代码通常很简单 利用多个cpu和核 避免了cPython的GIL限制 消除了对同步原语的大部分需求，除非您使用共享内存(相反，它更像是IPC的通信模型) 子进程是可中断/可杀死的 Python多处理模块包含有用的抽象，其接口类似于线程。线程 必须使用cPython进行cpu绑定处理

Cons

IPC有点复杂，开销更大(通信模型vs.共享内存/对象) 更大的内存占用

线程

Pros

轻量级——低内存占用 共享内存-使访问状态从另一个上下文更容易 允许您轻松地创建响应式ui 正确释放GIL的cPython C扩展模块将并行运行 对于I/ o约束应用程序来说是一个很好的选择

Cons

cPython -服从GIL 不是可中断/ killable 如果不遵循命令队列/消息泵模型(使用queue模块)，则必须手动使用同步原语(需要对锁定的粒度进行决策) 代码通常更难理解和正确编写——竞争条件的可能性急剧增加

Threading's job is to enable applications to be responsive. Suppose you have a database connection and you need to respond to user input. Without threading, if the database connection is busy the application will not be able to respond to the user. By splitting off the database connection into a separate thread you can make the application more responsive. Also because both threads are in the same process, they can access the same data structures - good performance, plus a flexible software design.

注意，由于GIL，应用程序实际上并没有同时做两件事，但我们所做的是将数据库上的资源锁放在一个单独的线程中，这样CPU时间就可以在它和用户交互之间切换。CPU时间在线程之间分配。

Multiprocessing is for times when you really do want more than one thing to be done at any given time. Suppose your application needs to connect to 6 databases and perform a complex matrix transformation on each dataset. Putting each job in a separate thread might help a little because when one connection is idle another one could get some CPU time, but the processing would not be done in parallel because the GIL means that you're only ever using the resources of one CPU. By putting each job in a Multiprocessing process, each can run on it's own CPU and run at full efficiency.