锁定与性能的 Python 成本(多线程有意义吗？)

Question

我正在从事一个项目，其中我的代码的吞吐量非常重要，经过一番考虑后我选择让我的程序线程化。

主线程和子线程都在两个共享字典中添加和删除。考虑到在 python 中锁定的性能，我一直在通过互联网查看一些输入，它是一个缓慢的操作等等。

所以我要说的是，因为 python 实际上根本不是线程化的(想想 GIL 只在一个内核上工作)，如果我的应用程序需要高性能，我有什么可以通过使其线程化来赢得的，除了用于处理 IO？

编辑

真正的问题是(在有见地的评论之后)

多线程在 python 中有意义吗，因为有 GIL？

Answer 1

IMO，锁解决方案对性能的影响很大，主要是当多个线程真正等待它时。

获取和释放无竞争 锁的成本应该是微不足道的。

This thread显示了对此的测试。

Ok, here is the cost of acquiring and releasing an uncontended lock under Linux, with Python 3.2:

$ python3 -m timeit \
  -s "from threading import Lock; l=Lock(); a=l.acquire; r=l.release" \
  "a(); r()"

10000000 loops, best of 3: 0.127 usec per loop

And here is the cost of calling a dummy Python function:

$ python3 -m timeit -s "def a(): pass" "a(); a()"

1000000 loops, best of 3: 0.221 usec per loop

And here is the cost of calling a trivial C function (which returns the False singleton):

$ python3 -m timeit -s "a=bool" "a(); a()"

10000000 loops, best of 3: 0.164 usec per loop

Also, note that using the lock as a context manager is actually slower, not faster as you might imagine:

$ python3 -m timeit -s "from threading import Lock; l=Lock()" \
  "with l: pass"

1000000 loops, best of 3: 0.242 usec per loop

At least under Linux, there doesn't seem to be a lot of room for improvement in lock performance, to say the least.

PS: RLock is now as fast as Lock:

$ python3 -m timeit \
  -s "from threading import RLock; l=RLock(); a=l.acquire; r=l.release" \
  "a(); r()"

10000000 loops, best of 3: 0.114 usec per loop