您可以multiprocessing轻松地将共享内存与Numpy一起使用：
import multiprocessing
import ctypes
import numpy as np
shared_array_base = multiprocessing.Array(ctypes.c_double, 10*10)
shared_array = np.ctypeslib.as_array(shared_array_base.get_obj())
shared_array = shared_array.reshape(10, 10)
#-- edited 2015-05-01: the assert check below checks the wrong thing
# with recent versions of Numpy/multiprocessing. That no copy is made
# is indicated by the fact that the program prints the output shown below.
## No copy was made
##assert shared_array.base.base is shared_array_base.get_obj()
# Parallel processing
def my_func(i, def_param=shared_array):
shared_array[i,:] = i
if __name__ == '__main__':
pool = multiprocessing.Pool(processes=4)
pool.map(my_func, range(10))
print shared_array
哪个打印 ``
[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]
[ 3. 3. 3. 3. 3. 3. 3. 3. 3. 3.]
[ 4. 4. 4. 4. 4. 4. 4. 4. 4. 4.]
[ 5. 5. 5. 5. 5. 5. 5. 5. 5. 5.]
[ 6. 6. 6. 6. 6. 6. 6. 6. 6. 6.]
[ 7. 7. 7. 7. 7. 7. 7. 7. 7. 7.]
[ 8. 8. 8. 8. 8. 8. 8. 8. 8. 8.]
[ 9. 9. 9. 9. 9. 9. 9. 9. 9. 9.]]
但是，Linux在上具有写入时复制的语义fork()，因此，即使不使用multiprocessing.Array，也不会复制数据，除非将其写入。
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