下面是Python 2.7.9手册中对GIL的简单介绍：
The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.
However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.
Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.
个人理解：
- io是分为网络io和磁盘io，一般情况下，io有发送数据（output）和返回数据（input）两个过程。比如以浏览器为主体，浏览器发送请求给服务器（output），服务器再将请求结果返回给浏览器（input）。python在io阻塞的情况下，会释放GIL（global interpreter lock）锁，其他线程会在当前线程等待返回值（阻塞）的情况下继续执行发送请求（output），第三个线程又会在第二个线程等待返回值（阻塞）的情况下发送请求（output），即在同一时间片段，会有一个线程在等待数据，也会有一个线程在发数据。这就减少了io传输的时间。
- 但是，由于python在用cpu执行计算任务的时候，GIL锁不会被释放，python多线程其实还是使用的单核在进行cpu计算。一个cpu时间片只会分给一个线程，因此，cpu密集型的情况下，多线程并不会加快计算速度。
- 另，如果计算任务加锁了，cpu时间片调度机制会在一个cpu时间片（python默认是处理完1000个字节码）结束后，去释放GIL锁，并查看其他线程是否可以执行，由于任务被加锁，会在第二个cpu时间片继续把时间片分给第一个线程，这会让cpu调度时间白白浪费，反而导致多线程比单线程耗时更久。
转载自 https://blog.csdn.net/daijiguo/article/details/78042309