What are the best libraries for parallel computing in Python?

What are the best libraries for parallel computing in Python?

Some of the best libraries for parallel computing in Python include multiprocessing, Dask, and Joblib. Would you like to know more about any specific library?

Can you explain how the multiprocessing library works?

Sure! The multiprocessing library allows you to create multiple processes, each running in its own Python interpreter. This is useful for CPU-bound tasks as it bypasses the Global Interpreter Lock (GIL). You can use it to parallelize tasks easily.

What about Dask? How does it compare to multiprocessing?

Dask is more flexible than multiprocessing. It can handle larger-than-memory datasets and provides a parallel computing framework that integrates well with NumPy and Pandas. It's great for distributed computing as well.

Are there any performance benchmarks comparing these libraries?

Yes, there are several benchmarks available online that compare the performance of these libraries. Generally, Dask performs better for larger datasets, while multiprocessing is faster for smaller tasks due to lower overhead.