In simpler terms, CUDA allows software developers to use a CUDA-enabled graphics processing unit (GPU) for computing purposes in addition to the traditional graphics rendering purposes GPUs are typically used for.

PyCUDA provides a high-level interface to CUDA with the comfort of Python, which is generally more intuitive and easier to use than the native CUDA C/C++ bindings. With PyCUDA, you can perform complex data-intensive computations much faster by leveraging the power of the GPU.

Here are some of the features offered by PyCUDA:

1. Object cleanup tied to lifetime of objects: This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code.

2. Completeness: PyCUDA lets you access all of CUDA’s features. You can even seamlessly interleave low- and high-level code.

3. Automatic error checking: All CUDA errors are automatically translated into Python exceptions.

4. Speed: PyCUDA’s base layer is written in C++, so all the niceties above are virtually free.

5. Helpful Documentation: PyCUDA’s documentation is thorough and approachable.

However, while PyCUDA does abstract many of CUDA’s complexities, it’s still necessary to have an understanding of parallel programming concepts to use it effectively.

Also note that PyCUDA isn’t the only Python library for GPU-accelerated computing. Others include PyOpenCL (for using OpenCL, which is similar to CUDA but supports a wider range of hardware) and libraries like CuPy and Numba, which provide higher-level interfaces that can be easier to work with if you’re doing numerical computing.