Part of the series: From Scripts to Software: Practical Python for Reproducible Research
Having learned how to write sustainable, object-oriented code and how to scale it across multiple processors, we will now explore how to dramatically accelerate your computations using Graphics Processing Units (GPUs).

Modern GPUs are massively parallel processors that can perform millions of identical operations at once. This makes them incredibly powerful for a wide range of scientific workloads, from numerical simulations to deep learning. We will discuss: (1) The difference between CPU and GPU architectures. (2) The CUDA ecosystem, a leading framework in GPU computing (3) JAX, a modern Python library that provides a high-level, Pythonic interface to GPUs.