Why is my exploded Python code actually running faster смотреть онлайн

Title: Why Is My "Exploded" Python Code Actually Running Faster?
Introduction:
Python is known for its simplicity and readability, but it's not typically celebrated for its raw performance. Many developers are often surprised to find that by restructuring their code to be more granular, or "exploded," it can sometimes run faster. This tutorial will explore why this happens and provide code examples to demonstrate the concept.
Before diving into the code example, it's important to understand why "exploded" code might run faster. Python's Global Interpreter Lock (GIL) can restrict the parallel execution of threads in multi-threaded programs, limiting performance gains. However, CPU-bound tasks can still benefit from the following factors:
The GIL is a mutex that allows only one thread to execute Python bytecode at a time. When your code is divided into smaller, more focused functions or methods, the GIL is released more frequently, reducing contention among threads.
When code is modular and functions are isolated, it's more likely that data required for a specific computation is close together in memory. This can lead to better CPU cache utilization, as the processor can cache data more efficiently.
In some cases, "exploded" code can lead to more straightforward parallelization. For instance, a list can be divided among multiple threads, each processing a portion independently.
Let's demonstrate the concept with a simple code example that reverses a list of numbers. We'll compare the performance of a single, non-modular function and an "exploded" version of the same code.
To measure the performance of both versions, we can use Python's timeit module or other profiling tools. Profiling is essential to quantitatively compare the execution times and understand the impact of code modularity.
Here's how to use timeit to profile the code: