python 进程间通信 使用共享内存 脚本

"""
 Use multiprocess shared memory objects to communicate.
 Passed objects are shared, but globals are not on Windows.
 Last test here reflects common use case: distributing work.
 """
import os
 from multiprocessing import Process, Value, Array
procs = 3
 count = 0    # per-process globals, not shared
def showdata(label, val, arr):
 """
 print data values in this process
 """
 msg = '%-12s: pid:%4s, global:%s, value:%s, array:%s'
 print(msg % (label, os.getpid(), count, val.value, list(arr)))
def updater(val, arr):
 """
 communicate via shared memory
 """
 global count
 count += 1                         # global count not shared
 val.value += 1                     # passed in objects are
 for i in range(3): arr[i] += 1
if name == 'main':
 scalar = Value('i', 0)             # shared memory: process/thread safe
 vector = Array('d', procs)         # type codes from ctypes: int, double

# show start value in parent process
showdata('parent start', scalar, vector)

# spawn child, pass in shared memory
p = Process(target=showdata, args=('child ', scalar, vector))
p.start(); p.join()

# pass in shared memory updated in parent, wait for each to finish
# each child sees updates in parent so far for args (but not global)

print('\nloop1 (updates in parent, serial children)...')
for i in range(procs):
    count += 1
    scalar.value += 1
    vector[i] += 1
    p = Process(target=showdata, args=(('process %s' % i), scalar, vector))
    p.start(); p.join()

# same as prior, but allow children to run in parallel
# all see the last iteration's result because all share objects

print('\nloop2 (updates in parent, parallel children)...')
ps = []
for i in range(procs):
    count += 1
    scalar.value += 1
    vector[i] += 1
    p = Process(target=showdata, args=(('process %s' % i), scalar, vector))
    p.start()
    ps.append(p)
for p in ps: p.join()

# shared memory updated in spawned children, wait for each

print('\nloop3 (updates in serial children)...')
for i in range(procs):
    p = Process(target=updater, args=(scalar, vector))
    p.start()
    p.join()
showdata('parent temp', scalar, vector)

# same, but allow children to update in parallel

ps = []
print('\nloop4 (updates in parallel children)...')
for i in range(procs):
    p = Process(target=updater, args=(scalar, vector))
    p.start()
    ps.append(p)
for p in ps: p.join()
                                       # global count=6 in parent only
# show final results here              # scalar=12:  +6 parent, +6 in 6 children
showdata('parent end', scalar, vector) # array[i]=8: +2 parent, +6 in 6 children