添加到
@unutbu（不再可用）和
@Henry Gomersall的答案中。你可以
shared_arr.get_lock()在需要时使用来同步访问：
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]
例
import ctypesimport loggingimport multiprocessing as mpfrom contextlib import closingimport numpy as npinfo = mp.get_logger().infodef 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 argumentdef 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()如果不需要同步访问或创建自己的锁，则mp.Array()没有必要。mp.sharedctypes.RawArray在这种情况下，你可以使用。
