- 1 year ago
Python Multi-threading CPU workload
In Python, you can use the threading
module to perform multi-threading and distribute CPU workload across multiple threads. Here's a basic example to demonstrate how to perform CPU-bound work using multiple threads:
pythonimport threading
def cpu_work():
# Perform CPU-bound work here
# ...
# Create a list to store the threads
threads = []
# Define the number of threads
num_threads = 4
# Create and start the threads
for _ in range(num_threads):
thread = threading.Thread(target=cpu_work)
thread.start()
threads.append(thread)
# Wait for all threads to complete
for thread in threads:
thread.join()
# Continue with the rest of the program
# ...
In this example, the cpu_work
function represents the CPU-bound work that you want to distribute across multiple threads. The threading.Thread
class is used to create threads, and the start
method is called to start each thread. The threads are stored in a list for later joining.
After creating and starting the threads, the main program will continue execution while the threads are running in parallel. Finally, the join
method is called on each thread to wait for them to complete before continuing with the rest of the program.
It's important to note that the Global Interpreter Lock (GIL) in CPython restricts true parallel execution of threads. However, multi-threading can still be beneficial for CPU-bound tasks in certain cases, such as when performing I/O operations or waiting for external resources.
If you're looking to leverage multiple CPU cores for parallel processing in Python, you may consider using the multiprocessing
module instead, which enables the creation of multiple processes. Each process runs in a separate Python interpreter instance and can utilize multiple CPU cores effectively.