Performance refactoring with GenAI
Performance refactoring refers to changes made to the code that preserve its functionality while improving runtime or memory efficiency. One common approach is to use vectorized computations. These can reduce the runtime by benefiting from cache, lower overhead, and parallel computation.
For instance, in the GitHub Copilot implementation of calculate_distance example, this would mean replacing a nested for loop with a vectorized computation of the Euclidean distance. We will explore further runtime and space complexity in Chapter 14, including when and why to scale system capacity and the trade-offs involved. Until then, let us demonstrate how a simple optimization through vectorization might be applied.
Performance refactoring with GitHub Copilot
As with the CoT approach, we will leverage the context of existing code along with our desired structure. This time, we will specify the library we would like to implement a code block instead...
