I am doing a reinforcement learning simulation in Python that involves running many episodes and updating the weights of the agent per episode. To speed up the process, I wish to run parallel simulations using random numbers and update the weights in parallel. The problem is, I noticed that even when using the experimental GIL-free Python 3.13t, the main bottleneck is in the random number generation. Basically, when I use N threads to run N simulations, it takes N times longer as if the random number generation were not parallelized. Here is a simple program to illustrate this: import threading import time import random def generate_randoms(n): """Generate n random floats between 0 and 1""" rng = random.Random() for i in range(n): rng.random() def run_tasks_in_parallel(num_tasks, numbers): threads = [] for i in range(num_tasks): t = threading.Thread(target=generate_randoms, args=(numbers,)) threads.append(t) t.start() for t in threads: t.join() if __name__ == "__main__": num_tasks = 2 numbers = [1000000] * 2 start_time = time.time() run_tasks_in_parallel(num_tasks, numbers) end_time = time.time() print(f"Time taken: {end_time - start_time} seconds") The program takes 2 times as long with num_tasks=2 than with num_takes=1. Using numpy.random.default_rng takes even longer. I thought it was a problem of shared random state, so I instantiated an rng for each thread as above, but the problem persisted. I have ruled out Python issues since removing the random number generation above allows for parallelism (n threads do not take n times as long without random number generation). Is there a way to parallelize random number generation in GIL-free Python? Why doesn't random number generation (both CPython and Numpy) scale with threads? I do not need the threads to share random state (I even prefer them not to), nor do I need thread-safe random number generation. I just need parallel random numbers to speed up my training. Continue reading...
