添加到@unutbu的（不再可用）和@亨利戈默 solr 的答案。您可以在需要时使用shared_arr.get_lock()来同步访问：

shared_arr = mp.Array(ctypes.c_double, N)
# ...
def f(i): # could be anything numpy accepts as an index such another numpy array
    with shared_arr.get_lock(): # synchronize access
        arr = np.frombuffer(shared_arr.get_obj()) # no data copying
        arr[i] = -arr[i]

示例

import ctypes
import logging
import multiprocessing as mp

from contextlib import closing

import numpy as np

info = mp.get_logger().info

def main():
    logger = mp.log_to_stderr()
    logger.setLevel(logging.INFO)

    # create shared array
    N, M = 100, 11
    shared_arr = mp.Array(ctypes.c_double, N)
    arr = tonumpyarray(shared_arr)

    # fill with random values
    arr[:] = np.random.uniform(size=N)
    arr_orig = arr.copy()

    # write to arr from different processes
    with closing(mp.Pool(initializer=init, initargs=(shared_arr,))) as p:
        # many processes access the same slice
        stop_f = N // 10
        p.map_async(f, [slice(stop_f)]*M)

        # many processes access different slices of the same array
        assert M % 2 # odd
        step = N // 10
        p.map_async(g, [slice(i, i + step) for i in range(stop_f, N, step)])
    p.join()
    assert np.allclose(((-1)**M)*tonumpyarray(shared_arr), arr_orig)

def init(shared_arr_):
    global shared_arr
    shared_arr = shared_arr_ # must be inherited, not passed as an argument

def tonumpyarray(mp_arr):
    return np.frombuffer(mp_arr.get_obj())

def f(i):
    """synchronized."""
    with shared_arr.get_lock(): # synchronize access
        g(i)

def g(i):
    """no synchronization."""
    info("start %s" % (i,))
    arr = tonumpyarray(shared_arr)
    arr[i] = -1 * arr[i]
    info("end   %s" % (i,))

if __name__ == '__main__':
    mp.freeze_support()
    main()

如果你不需要同步访问或者你创建了自己的锁，那么mp.Array()是不必要的。在这种情况下，可以使用mp.sharedctypes.RawArray。

Array对象有一个与之关联的get_obj()方法，该方法返回ctypes数组，该数组表示缓冲区接口。我认为以下几点应该是可行的...

from multiprocessing import Process, Array
import scipy
import numpy

def f(a):
    a[0] = -a[0]

if __name__ == '__main__':
    # Create the array
    N = int(10)
    unshared_arr = scipy.rand(N)
    a = Array('d', unshared_arr)
    print "Originally, the first two elements of arr = %s"%(a[:2])

    # Create, start, and finish the child process
    p = Process(target=f, args=(a,))
    p.start()
    p.join()

    # Print out the changed values
    print "Now, the first two elements of arr = %s"%a[:2]

    b = numpy.frombuffer(a.get_obj())

    b[0] = 10.0
    print a[0]

运行时，打印出a的第一个元素，现在是10.0，显示a和b只是同一内存中的两个视图。
为了确保它仍然是多处理器安全的，我相信你必须使用Array对象a上存在的acquire和release方法，以及它的内置锁，以确保它的所有安全访问（尽管我不是多处理器模块的Maven）。

虽然已经给出的答案是好的，但如果满足两个条件，这个问题有一个更容易的解决方案：
1.您使用的是POSIX兼容操作系统（例如，Linux、Mac OSX）;和
1.您的子进程需要对共享数组进行只读访问。
在这种情况下，您不需要显式地使变量共享，因为子进程将使用fork创建。分叉的子节点自动共享父节点的内存空间。在Python多处理的上下文中，这意味着它共享所有 * 模块级 * 变量;请注意，对于显式传递给子进程或在multiprocessing.Pool上调用的函数的参数，这 * 不成立 *。
举个简单的例子：

import multiprocessing
import numpy as np

# will hold the (implicitly mem-shared) data
data_array = None

# child worker function
def job_handler(num):
    # built-in id() returns unique memory ID of a variable
    return id(data_array), np.sum(data_array)

def launch_jobs(data, num_jobs=5, num_worker=4):
    global data_array
    data_array = data

    pool = multiprocessing.Pool(num_worker)
    return pool.map(job_handler, range(num_jobs))

# create some random data and execute the child jobs
mem_ids, sumvals = zip(*launch_jobs(np.random.rand(10)))

# this will print 'True' on POSIX OS, since the data was shared
print(np.all(np.asarray(mem_ids) == id(data_array)))

我写了一个小的python模块，它使用POSIX共享内存在python解释器之间共享numpy数组。也许你会发现它很方便。
https://pypi.python.org/pypi/SharedArray
它的工作原理如下：

import numpy as np
import SharedArray as sa

# Create an array in shared memory
a = sa.create("test1", 10)

# Attach it as a different array. This can be done from another
# python interpreter as long as it runs on the same computer.
b = sa.attach("test1")

# See how they are actually sharing the same memory block
a[0] = 42
print(b[0])

# Destroying a does not affect b.
del a
print(b[0])

# See how "test1" is still present in shared memory even though we
# destroyed the array a.
sa.list()

# Now destroy the array "test1" from memory.
sa.delete("test1")

# The array b is not affected, but once you destroy it then the
# data are lost.
print(b[0])

您可以使用sharedmem模块：https://bitbucket.org/cleemesser/numpy-sharedmem
下面是你的原始代码，这次使用的是共享内存，其行为类似于NumPy数组（注意最后一个调用NumPy sum()函数的语句）：

from multiprocessing import Process
import sharedmem
import scipy

def f(a):
    a[0] = -a[0]

if __name__ == '__main__':
    # Create the array
    N = int(10)
    unshared_arr = scipy.rand(N)
    arr = sharedmem.empty(N)
    arr[:] = unshared_arr.copy()
    print "Originally, the first two elements of arr = %s"%(arr[:2])

    # Create, start, and finish the child process
    p = Process(target=f, args=(arr,))
    p.start()
    p.join()

    # Print out the changed values
    print "Now, the first two elements of arr = %s"%arr[:2]

    # Perform some NumPy operation
    print arr.sum()

在Python 3.8+中，你可以使用multiprocessing.shared_memory标准库模块来创建一个由共享内存支持的numpy数组。此共享内存可以由多个进程访问。下面是一个示例：

# np_sharing.py
from multiprocessing import Process
from multiprocessing.managers import SharedMemoryManager
from multiprocessing.shared_memory import SharedMemory
from typing import Tuple

import numpy as np

def create_np_array_from_shared_mem(
    shared_mem: SharedMemory, shared_data_dtype: np.dtype, shared_data_shape: Tuple[int, ...]
) -> np.ndarray:
    arr = np.frombuffer(shared_mem.buf, dtype=shared_data_dtype)
    arr = arr.reshape(shared_data_shape)
    return arr

def child_process(
    shared_mem: SharedMemory, shared_data_dtype: np.dtype, shared_data_shape: Tuple[int, ...]
):
    """Logic to be executed by the child process"""
    arr = create_np_array_from_shared_mem(shared_mem, shared_data_dtype, shared_data_shape)
    arr[0, 0] = -arr[0, 0]  # modify the array backed by shared memory

def main():
    """Logic to be executed by the parent process"""

    # Data to be shared:
    data_to_share = np.random.rand(10, 10)

    SHARED_DATA_DTYPE = data_to_share.dtype
    SHARED_DATA_SHAPE = data_to_share.shape
    SHARED_DATA_NBYTES = data_to_share.nbytes

    with SharedMemoryManager() as smm:
        shared_mem = smm.SharedMemory(size=SHARED_DATA_NBYTES)

        arr = create_np_array_from_shared_mem(shared_mem, SHARED_DATA_DTYPE, SHARED_DATA_SHAPE)
        arr[:] = data_to_share  # load the data into shared memory

        print(f"The [0,0] element of arr is {arr[0,0]}")  # before

        # Run child process:
        p = Process(target=child_process, args=(shared_mem, SHARED_DATA_DTYPE, SHARED_DATA_SHAPE))
        p.start()
        p.join()

        print(f"The [0,0] element of arr is {arr[0,0]}")  # after

        del arr  # delete np array so the shared memory can be deallocated

if __name__ == "__main__":
    main()

运行脚本：

$ python3.10 np_sharing.py
The [0,0] element of arr is 0.262091705529628
The [0,0] element of arr is -0.262091705529628

由于不同进程中的数组共享相同的底层内存缓冲区，因此标准警告r.e.竞争条件适用。