So using the multiprocess module it is easy to run a function in parallel with different arguments like this:
from multiprocessing import Pool
def f(x):
return x**2
p = Pool(2)
print(p.map(f, [1, 2]))
But I'm interested in executing a list of functions on the same argument. Suppose I have the following two functions:
def f(x):
return x**2
def g(x):
return x**3 + 2
How can I execute them in parallel for the same argument (e.g. x=1)?
You can use Pool.apply_async()
for that. You bundle up tasks in the form of (function, argument_tuple) and feed every task to apply_async()
.
from multiprocessing import Pool
from itertools import repeat
def f(x):
for _ in range(int(50e6)): # dummy computation
pass
return x ** 2
def g(x):
for _ in range(int(50e6)): # dummy computation
pass
return x ** 3
def parallelize(n_workers, functions, arguments):
# if you need this multiple times, instantiate the pool outside and
# pass it in as dependency to spare recreation all over again
with Pool(n_workers) as pool:
tasks = zip(functions, repeat(arguments))
futures = [pool.apply_async(*t) for t in tasks]
results = [fut.get() for fut in futures]
return results
if __name__ == '__main__':
N_WORKERS = 2
functions = f, g
results = parallelize(N_WORKERS, functions, arguments=(10,))
print(results)
Example Output:
[100, 1000]
Process finished with exit code 0