How can I optimize my code using Numba and CuPy?

How can I optimize my code using Numba and CuPy?

To optimize your code, you can use Numba for JIT compilation and CuPy for GPU acceleration. What specific tasks are you working on?

I'm working on matrix operations and want to speed them up.

For matrix operations, you can use CuPy's array functions, which are similar to NumPy but run on the GPU. Have you tried using CuPy's cupy.dot for matrix multiplication?

Not yet, how does it compare to Numba's JIT compilation?

CuPy can significantly speed up operations on large matrices by leveraging GPU parallelism, while Numba's JIT compilation is great for CPU-bound tasks. It depends on your hardware and the size of your data.

What are some common pitfalls to avoid when using these libraries?

Common pitfalls include not transferring data to the GPU before computation with CuPy and using Numba on small functions where the overhead outweighs the benefits. Always profile your code!