2023-08-16 –, Aula
Python is slow. We feel the performance limitations when doing computationally intensive work. There are many libraries and methods to accelerate your computations, but which way to go? This talk serves as a navigation guide through the world of speeding up Python. At the end, you should have a high-level understanding of performance aspects and know which way to go when you want to speed up your code next time.
We start with the fundamental reasons why Python is slow by design - and why it's nevertheless often a good language choice. From there we'll cover basic Python programming paradigms, standard data libraries (NumPy, pandas), Just-in-time compilation (PyPy, numba), GPU-Acceleration, Multithreading, Multiprocessing, calling other languages (C/C++, Julia, Rust) as well as distributed computing. We'll discuss the benefits and costs of all these technologies, so that you know which way to go in different usage scenarios.
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Expected audience expertise: Domain:none
Abstract as a tweet:Learn which ways to go in the jungle of libraries and technologies that can speed up your python code.
Category [High Performance Computing]:Other
Tim Hoffmann is a physicist and software expert passionate to bring science and high-quality software together. He works as Simulation Architect at Carl Zeiss Semiconductor Manufacturing Technology, where he covers all aspects from coding, architecture and training up to software strategy. Tim is an active contributor to the Python open source community. In particular, he is core developer and API lead for the visualization library matplotlib.