Python provides two modules to run code in parallel:
1. Threading: It’s used for IO-bound tasks where you can execute multiple tasks simultaneously. However, due to global interpreter lock (GIL), at any given time, only one thread can execute in Python, which makes threading unsuitable for CPU-bound tasks.
1. Multiprocessing: For CPU-bound tasks where you need to leverage multiple CPUs or cores.
Here’s a simple example to run Python code in parallel using the multiprocessing module:
```
import multiprocessing
def worker(n): print(‘Worker:’, n)
if name == main
jobs = []
for i in range(5):
p = multiprocessing.Process(target=worker, args=(i,))
jobs.append(p)
p.start()
```
In the given code, we are creating 5 processes. Each one runs the “worker” function with argument ‘i’. Each process will run on a different CPU or core.
And here is a simple example using threading:
```
import threading
def worker(n): print(‘Worker:’, n)
threads = []
for i in range(5):
t = threading.Thread(target=worker, args=(i,))
threads.append(t)
t.start()
```
There are other methods and libraries to run Python code in parallel such as with `concurrent.futures` from the Python standard library or third-party libraries like Dask, Joblib, and PyZMQ. You would choose one based on your specific needs and the nature of your tasks. A good understanding of your tasks such as whether they are CPU-bound or IO-bound will help you to make the best choice.
