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How to Share Memory Between Processes in Python with multiprocessing.Value and Array
Share a numeric value and a list-like array across multiple Python processes using multiprocessing.Value and multiprocessing.Array, with each process modifying the same memory.
import multiprocessing
def worker(shared_value, shared_array, index):
shared_value.value += 10
shared_array[index] = shared_array[index] * 2
if __name__ == "__main__":
shared_value = multiprocessing.Value("i", 5)
shared_array = multiprocessing.Array("i", [1, 2, 3, 4, 5])
processes = []
for i…
How to Use ProcessPoolExecutor for CPU Parallel Map in Python
Run a function over a sequence of inputs in parallel across multiple CPU cores with ProcessPoolExecutor.map.
from concurrent.futures import ProcessPoolExecutor
import math
def compute_square(num):
return num * num
def is_prime(n):
if n < 2:
return False
for i in range(2, int(math.sqrt(n)) + 1):
if n % i == 0:
return False
return True
if __name__ == "__main__":
numbers = rang…
How to Use multiprocessing Pool map and starmap in Python
Parallelize functions over iterables with Pool.map, and unpack multiple arguments via Pool.starmap.
from multiprocessing import Pool
def square(x):
return x * x
def add_and_multiply(a, b, c):
return (a + b) * c
if __name__ == "__main__":
numbers = [1, 2, 3, 4, 5]
with Pool(processes=2) as pool:
squares = pool.map(square, numbers)
print(f"squares: {squares}")
starmap_arg…
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