July 2023
"""
Parallelization optimization is a form of optimization
that aims to improve program performance by parallelizing
it, i.e. by splitting the program into smaller parts that
can be executed at the same time.
"""
import multiprocessing as mp
import timeit
# timeit.template = """
# def inner(_it, _timer{init}):
# from tqdm import tqdm
# {setup}
# _t0 = _timer()
# for _i in tqdm(_it, total=_it.__length_hint__()):
# {stmt}
# _t1 = _timer()
# return _t1 - _t0
# """
def sum_of_squares(numbers):
return sum([num * num for num in numbers])
def sum_of_squares_parallel(numbers):
# use all available cores
num_processes = mp.cpu_count()
chunk_size = len(numbers) // num_processes
# create pool of processes
pool = mp.Pool(processes=num_processes)
# divide work into chunks
chunks = [numbers[i:i + chunk_size] for i in range(0, len(numbers), chunk_size)]
results = pool.map(sum_of_squares, chunks)
# close
pool.close()
# combine results
pool.join()
return sum(results)
if __name__ == '__main__':
assert sum_of_squares(list(range(1, 10000))) == sum_of_squares_parallel(list(range(1, 10000)))
# timeit
n = list(range(1, 10000000))
print(f"sum_of_squares: {timeit.timeit(lambda: sum_of_squares(n), number=10)}")
# sum_of_squares: 7.48474136996083
print(f"sum_of_squares_parallel: {timeit.timeit(lambda: sum_of_squares_parallel(n), number=10)}")
# sum_of_squares_parallel: 6.641842097043991
# CPU...