Today small for everyone to share a python multithreading and efficient method to use shared variables, have a good reference value, we want to help. Come and see, to follow the small series together
python multithreading can make the tasks are executed concurrently, but sometimes when performing multiple tasks, a variable appears "accidental."
import threading,time
n=0
start=time.time()
def b1(num):
global n
n=n+num
n=n-num
def b2(num):
for i in range(1000000):
b1(num)
t1=threading.Thread(target=b2,args=(5,))
t2=threading.Thread(target=b2,args=(8,))
t1.start()
t2.start()
t1.join()
t2.join()
end=time.time()
print(n)
print(end-start)
Results of the:
18
0.7520430088043213
Visible variable n becomes 0 from 18, when 0.75 s is used, because the computer system calculates the similarity n = n + num is calculated in two steps, the first n + num calculated value into the memory and then calculated value assigned to n, it is this gap led to the emergence of the variable "accident."
This can be used when threading.Lock to lock the thread variable, used up and release!
import threading,time
n=0
lock=threading.Lock()
start=time.time()
def b1(num):
global n
n=n+num
n=n-num
def b2(num):
for i in range(1000000):
lock.acquire()#等待获取或获取修改变量的权限，并霸占它们
b1(num)
lock.release()#释放霸占的变量
t1=threading.Thread(target=b2,args=(5,))
t2=threading.Thread(target=b2,args=(8,))
t1.start()
t2.start()
t1.join()
t2.join()
end=time.time()
print(n)
print(end-start)
Results of the:
0
3.335190773010254
Although the value of the variable is correct, but a lot slower times, efficiency is greatly discounted, multi-threaded advantage did not stand out.
So try to use local variables instead of global variables in the thread, to avoid the problem of efficiency.
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