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 ...

Python is powerful, versatile, and programmer-friendly, but it isn’t the fastest programming language around. Some of Python’s speed limitations are due to its default implementation, CPython, being ...

Asked on Twitter why a paper is coming out now, 15 years after NumPy's creation, Stefan van der Walt of the University of California at Berkeley's Institute for Data Science, one of the article's ...

Distributed computing is the simultaneous use of more than one computer to solve a problem. It is often used for problems that are so big that no individual computer can handle them. This method of ...

In this video from the ECSS Symposium, Abe Stern from NVIDIA presents: CUDA-Python and RAPIDS for blazing fast scientific computing. We will introduce Numba and RAPIDS for GPU programming in Python.

Tired of out-of-memory errors derailing your data analysis? There's a better way to handle huge arrays in Python.

So I got to work fairly quickly. Python was already installed on my Linux laptop. I added NumPy (think “numbers” and “Python” — a package for numerical/scientific computing in Python) plus ...