Although Python's inheritance system is essential for code organization, its effect on performance is frequently disregarded. This study examines the cost of inheriting from many classes by quantifying the impact on attribute access time. Extensive testing reveals that there are some abnormalities in lookup performance and that the slowing is not exactly linear. Developers working on large-scale applications must be aware of these patterns because deep inheritance may cause unforeseen difficulties. Performance can be enhanced and these problems can be lessened using alternative strategies like composition and optimized attribute storage.

Python's dynamic inheritance allows for smooth CPU and GPU compatibility. Developers can simplify array handling by using tools like NumPy and CuPy as well as effective techniques like get_array_module. This method saves time and money by reducing complexity and guaranteeing excellent performance across hardware environments.