- 1 year ago
Basic parallel python program freezes on Windows
If your basic parallel Python program freezes on Windows, the issue might be related to how Python handles parallelism on the Windows platform. Windows has some limitations and considerations when it comes to running Python code in parallel.
One common cause of freezing is due to the Global Interpreter Lock (GIL), which prevents multiple native threads from executing Python bytecodes simultaneously. This means that Python threads do not run in parallel for CPU-bound tasks. However, they can run concurrently for I/O-bound tasks.
To achieve true parallelism on Windows for CPU-bound tasks, you can use the multiprocessing
module, which creates separate processes and bypasses the GIL. Here's an example of how you can modify your basic parallel Python program using the multiprocessing
module:
pythonimport multiprocessing
def worker_function(number):
result = number * 2
return result
if __name__ == "__main__":
numbers = [1, 2, 3, 4, 5]
# Create a multiprocessing pool
pool = multiprocessing.Pool()
# Map the worker function to the input data
results = pool.map(worker_function, numbers)
# Close the pool and wait for all processes to finish
pool.close()
pool.join()
# Print the results
print(results)
The multiprocessing
module creates a pool of worker processes, and the map
method applies the worker_function
to each element in the numbers
list, processing them concurrently.
Keep in mind that using multiprocessing
comes with some overhead due to the process creation, so it's best suited for CPU-bound tasks where true parallelism is required.
If your program is I/O-bound, using Python threads with the threading
module can be effective. In I/O-bound scenarios, the GIL does not hinder concurrency, and threads can perform well.
Always consider the nature of your task (CPU-bound vs. I/O-bound) and choose the appropriate parallelism approach accordingly. Additionally, be mindful of the number of processes or threads you create to avoid excessive resource usage.