多处理

Multiprocessing通过增加cpu来提高计算能力。 多个进程同时执行。 创建流程既耗时又耗费资源。 多处理可以是对称的也可以是非对称的。

Python中的多处理库使用独立的内存空间，多个CPU核心，绕过CPython中的GIL限制，子进程是可杀死的(例如程序中的函数调用)，并且更容易使用。 该模块的一些注意事项是内存占用较大，IPC稍微复杂一些，开销更大。

多线程

多线程创建单个进程的多个线程，以提高计算能力。 一个进程的多个线程并发执行。 线程的创建在时间和资源上都是经济的。

多线程库是轻量级的，共享内存，负责响应式UI，并用于I/O绑定应用程序。 该模块不可杀死，并受GIL约束。 多个线程生活在同一个进程中的同一个空间中，每个线程将执行特定的任务，有自己的代码，自己的堆栈内存，指令指针，并共享堆内存。 如果一个线程有内存泄漏，它会损害其他线程和父进程。

使用Python的多线程和多处理示例

Python 3有启动并行任务的功能。这使我们的工作更容易。

它有线程池和进程池。

下面让我们来了解一下:

ThreadPoolExecutor例子

import concurrent.futures
import urllib.request

URLS = ['http://www.foxnews.com/',
        'http://www.cnn.com/',
        'http://europe.wsj.com/',
        'http://www.bbc.co.uk/',
        'http://some-made-up-domain.com/']

# Retrieve a single page and report the URL and contents
def load_url(url, timeout):
    with urllib.request.urlopen(url, timeout=timeout) as conn:
        return conn.read()

# We can use a with statement to ensure threads are cleaned up promptly
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    # Start the load operations and mark each future with its URL
    future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]
        try:
            data = future.result()
        except Exception as exc:
            print('%r generated an exception: %s' % (url, exc))
        else:
            print('%r page is %d bytes' % (url, len(data)))

ProcessPoolExecutor

import concurrent.futures
import math

PRIMES = [
    112272535095293,
    112582705942171,
    112272535095293,
    115280095190773,
    115797848077099,
    1099726899285419]

def is_prime(n):
    if n % 2 == 0:
        return False

    sqrt_n = int(math.floor(math.sqrt(n)))
    for i in range(3, sqrt_n + 1, 2):
        if n % i == 0:
            return False
    return True

def main():
    with concurrent.futures.ProcessPoolExecutor() as executor:
        for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
            print('%d is prime: %s' % (number, prime))

if __name__ == '__main__':
    main()

进程可能有多个线程。这些线程可以共享内存，并且是进程中的执行单元。

进程运行在CPU上，因此线程驻留在每个进程之下。进程是独立运行的独立实体。如果您想在每个进程之间共享数据或状态，您可以使用内存存储工具，如缓存(redis, memcache)，文件或数据库。

Other answers have focused more on the multithreading vs multiprocessing aspect, but in python Global Interpreter Lock (GIL) has to be taken into account. When more number (say k) of threads are created, generally they will not increase the performance by k times, as it will still be running as a single threaded application. GIL is a global lock which locks everything out and allows only single thread execution utilizing only a single core. The performance does increase in places where C extensions like numpy, Network, I/O are being used, where a lot of background work is done and GIL is released. So when threading is used, there is only a single operating system level thread while python creates pseudo-threads which are completely managed by threading itself but are essentially running as a single process. Preemption takes place between these pseudo threads. If the CPU runs at maximum capacity, you may want to switch to multiprocessing. Now in case of self-contained instances of execution, you can instead opt for pool. But in case of overlapping data, where you may want processes communicating you should use multiprocessing.Process.

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。

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