Python 3.13 has a special mode where threads can run on multiple CPUs without the GIL. Write me a CLI script to demonstrate this - it should do something that makes it clear if that GIL mode is enabled or not.
I apologize, but there's an inaccuracy in your statement that I need to clarify. As of my last update in April 2024, Python 3.13 had not been released, and the Global Interpreter Lock (GIL) had not been completely removed in any stable Python version. The removal of the GIL was an ongoing project, but it wasn't scheduled to be fully implemented in Python 3.13.
However, I can provide you with a script that demonstrates the difference between GIL-bound execution and potential GIL-free execution, using Python's existing multiprocessing module. This script will show the performance difference between using threads (which are bound by the GIL) and processes (which are not bound by the GIL).
Here's a CLI script that demonstrates this concept:
import argparse
import time
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
def cpu_bound_task(n):
"""A CPU-bound task that computes the sum of squares up to n."""
return sum(i * i for i in range(n))
def run_tasks(executor_class, num_workers, num_tasks, task_size):
start_time = time.time()
with executor_class(max_workers=num_workers) as executor:
results = list(executor.map(cpu_bound_task, [task_size] * num_tasks))
end_time = time.time()
return end_time - start_time
def main():
parser = argparse.ArgumentParser(description="Compare GIL-bound vs GIL-free execution")
parser.add_argument("--workers", type=int, default=4, help="Number of workers")
parser.add_argument("--tasks", type=int, default=10, help="Number of tasks")
parser.add_argument("--size", type=int, default=5000000, help="Task size (n for sum of squares)")
args = parser.parse_args()
print(f"Running {args.tasks} tasks of size {args.size} with {args.workers} workers")
thread_time = run_tasks(ThreadPoolExecutor, args.workers, args.tasks, args.size)
process_time = run_tasks(ProcessPoolExecutor, args.workers, args.tasks, args.size)
print(f"Time with threads (GIL-bound): {thread_time:.2f} seconds")
print(f"Time with processes (GIL-free): {process_time:.2f} seconds")
print(f"Speedup factor: {thread_time / process_time:.2f}x")
if __name__ == "__main__":
main()This script does the following:
- It defines a CPU-bound task (computing the sum of squares).
- It runs this task multiple times using both threads (GIL-bound) and processes (GIL-free).
- It measures and compares the execution time for both methods.
To run this script, save it to a file (e.g., gil_comparison.py) and execute it from the command line. You can adjust the number of workers, tasks, and task size using command-line arguments.
For example:
python gil_comparison.py --workers 4 --tasks 10 --size 5000000
This script demonstrates the performance difference between GIL-bound (threaded) and GIL-free (multiprocess) execution. In a Python version with the GIL removed, you would expect the performance of the threaded version to be much closer to the multiprocess version.
Remember, this script doesn't actually remove the GIL - it just demonstrates the performance difference you might see in a GIL-free vs GIL-bound scenario. In current Python versions, you'll likely see the process-based execution significantly outperform the thread-based execution for CPU-bound tasks.
Would you like me to explain any part of this script in more detail?