This course explores how to use Numba--the just-in-time, type-specializing Python function compiler--to accelerate Python programs to run on massively parallel NVIDIA GPUs.
Participants will learn how to:
- Use Numba to compile CUDA kernels from NumPy universal functions (ufuncs)
- Use Numba to create and launch custom CUDA kernels
- Apply key GPU memory management techniques Upon completion, you'll be able to use Numba to compile and launch CUDA kernels to accelerate your Python applications on NVIDIA GPUs.
Course Prerequisites:
- Basic Python competency, including familiarity with variable types, loops, conditional statments, functions, and array manipulations
- NumPy competecy, including the use of ndarrays and ufuncs
- No previous knowledge of CUDA programming is required
Leitung: Prof. Dr. Karen Bradshaw (Rhodes University, South Africa) & Prof. Dr. Thomas Clemen
Prof Bradshaw obtained her PhD from Cambridge University through an 1851 Royal Exhibition Scholarship. She is also a Rhodes graduate with a Masters degree. Before returning to Rhodes, she lectured at the tertiary level within Southern Africa and has also worked in industry in both the UK and Zimbabwe. Her research interests are distributed and parallel processing including GPGPU, deep learning applications, computer simulation and modelling, and CS education.
Diese Veranstaltung wird vom Promotionsprogramm CEADS organisiert und in Kooperation mit dem CPS angeboten.
Sprache: Englisch
Geöffnet für Masterstudierende und anrechenbar als fachliches Angebot im PP CEADS
