Numba vs CuPy: Best Practices

What are the main differences between Numba and CuPy?

Numba is a just-in-time compiler for Python that translates a subset of Python and NumPy code into fast machine code. CuPy, on the other hand, is a library that implements NumPy-compatible multi-dimensional arrays on NVIDIA GPUs, allowing for GPU-accelerated computations.

When should I use Numba instead of CuPy?

Use Numba when you want to optimize existing Python code without changing much of your codebase, especially for CPU-bound tasks. CuPy is better for tasks that can benefit from GPU acceleration, particularly with large datasets.

Can you give me some best practices for using Numba?

Sure! Some best practices for Numba include: 1) Use the @jit decorator for functions you want to optimize, 2) Keep your functions simple and avoid complex Python features, and 3) Profile your code to identify bottlenecks before optimizing.

What about best practices for CuPy?

For CuPy, best practices include: 1) Use CuPy arrays instead of NumPy arrays for GPU operations, 2) Minimize data transfer between CPU and GPU, and 3) Leverage CuPy's built-in functions for better performance.