问题描述
下面的代码与 http://docs 上的 python 官方队列示例几乎相同.python.org/2/library/queue.html
from Queue import Queue
from threading import Thread
from time import time
import sys
num_worker_threads = int(sys.argv[1])
source = xrange(10000)
def do_work(item):
for i in xrange(100000):
pass
def worker():
while True:
item = q.get()
do_work(item)
q.task_done()
q = Queue()
for item in source:
q.put(item)
start = time()
for i in range(num_worker_threads):
t = Thread(target=worker)
t.daemon = True
t.start()
q.join()
end = time()
print(end - start)
这些是在 Xeon 12 核处理器上的结果:
These are the results on a Xeon 12-core processor:
$ ./speed.py 1
12.0873839855
$ ./speed.py 2
15.9101941586
$ ./speed.py 4
27.5713479519
我预计增加工作人员的数量会减少响应时间，但实际上它正在增加.我做了一次又一次的实验，但结果没有改变.
I expected that increasing the number of workers reduce the response time but instead, it is increasing. I did the experiment again and again but the result didn't change.
我是否遗漏了一些明显的东西?还是 python 队列/线程不能正常工作?
Am I missing something obvious? or the python queue/threading doesn't work well?
推荐答案
Python 在多线程方面表现相当差.由于全局锁，一次只有一个线程通常会取得进展.请参阅 http://wiki.python.org/moin/GlobalInterpreterLock
Python is rather poor at multi-threading. Due to a global lock only one thread normally makes progress at a time. See http://wiki.python.org/moin/GlobalInterpreterLock
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