Matrix multiplication using multiprocessing without shared memory

multiprocessing-matmul-no-shm.py

import numpy as np
from typing import List
from time import time
from multiprocessing import Pool

print("--No shared memory---")

M = 100000
d = 32
N = 1000

matrix = np.random.randn(M, d)
query_vectors = [np.random.randn(1, d) for i in range(N)]


def get_numpy_cosine_for_query(query, np_matrix=matrix):
  return np.matmul(np_matrix, query.T)


def numpy_cosine_online_multiprocessing_nn(np_matrix: np.array, queries: List[np.array]):
  with Pool(4) as pool:
    result = list(pool.starmap(get_numpy_cosine_for_query, queries))


time_taken_list = []
for _ in range(10):

  start = time()
  numpy_cosine_online_multiprocessing_nn(matrix, query_vectors)
  end = time()
  time_taken_list.append(end-start)

print("Time taken:", np.mean(time_taken_list))