For speeding specific activities, specialized parallel computer architectures are sometimes employed alongside regular CPUs. Numpy, pandas, sklearn, seaborn, and other Python libraries make data ...

From the outside, Dask looks a lot like Ray. It, too, is a library for distributed parallel computing in Python, with a built-in task scheduling system, awareness of Python data frameworks like ...

Multiprocessing enables the computer to utilize multiple cores of a CPU to run tasks/processes in parallel. This parallelization leads to significant speedup in tasks that involve a lot of computation ...

In this video from EuroPython 2019, Pierre Glaser from INRIA presents: Parallel computing in Python: Current state and recent advances. Modern hardware is multi-core. It is crucial for Python to ...

To support applications that stress performance, Python needs to access the full capabilities of modern CPUs. That means support for parallel multithreading. In this article, we describe PyOMP, a ...