This is the intended use case for Ray, which is a library for parallel and distributed Python. Under the hood, it serializes objects using the Apache Arrow data layout (which is a zero-copy format) and stores them in a shared-memory object store so they can be accessed by multiple processes without creating copies.
The code would look like the following.
import numpy as np
import ray
ray.init()
@ray.remote
def func(array, param):
# Do stuff.
return 1
array = np.ones(10**6)
# Store the array in the shared memory object store once
# so it is not copied multiple times.
array_id = ray.put(array)
result_ids = [func.remote(array_id, i) for i in range(4)]
output = ray.get(result_ids)
If you don't call ray.put then the array will still be stored in shared memory, but that will be done once per invocation of func, which is not what you want.
Note that this will work not only for arrays but also for objects that contain arrays, e.g., dictionaries mapping ints to arrays as below.
You can compare the performance of serialization in Ray versus pickle by running the following in IPython.
import numpy as np
import pickle
import ray
ray.init()
x = {i: np.ones(10**7) for i in range(20)}
# Time Ray.
%time x_id = ray.put(x) # 2.4s
%time new_x = ray.get(x_id) # 0.00073s
# Time pickle.
%time serialized = pickle.dumps(x) # 2.6s
%time deserialized = pickle.loads(serialized) # 1.9s
Serialization with Ray is only slightly faster than pickle, but deserialization is 1000x faster because of the use of shared memory (this number will of course depend on the object).
See the Ray documentation. You can read more about fast serialization using Ray and Arrow. Note I'm one of the Ray developers.