What are the best libraries for parallelism in Python?

What are the best libraries for parallelism in Python?

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

Can you explain how multiprocessing works?

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

What about Dask? How is it different?

Dask is designed for parallel computing with larger-than-memory datasets. It provides a flexible parallel computing framework and can scale from a single machine to a cluster. Unlike multiprocessing, Dask can handle complex workflows and lazy evaluation.

Are there any performance benchmarks comparing these libraries?

Yes, there are several benchmarks available online that compare the performance of these libraries based on different use cases. Generally, multiprocessing is great for CPU-bound tasks, while Dask excels in handling large datasets and complex computations.