Speeding up Python loops and NumPy functions with minimal code changes using JIT compilation decorators.

Discover how Numba — a powerful Just-in-Time (JIT) compiler for Python — can dramatically speed up your numerical and scientific computing workflows.

Through hands-on coding notebooks, you'll Learn:

CPU Parallelisation: Leverage Numba to execute Python code at near-native speeds.

GPU Acceleration: Harness the power of CUDA-enabled GPUs for massive parallelism.

Prerequisites

Basic experience with Python (Programming with Python).
Basic experience with bash or similar Unix shells. (Software Carpentry - The Unix Shell )
User experience on Gadi. (Seminar on Getting Started with Gadi)

Learning Outcomes

At the completion of this training, you will be able to

applying Numba’s features
optimising functions
integrating with libraries like NumPy
understand how to use Numba to improve code performance on both CPU and GPU

Contact the host of Simple Python Acceleration: Optimising Loops and Functions with Numba

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