Why is Python so fast?

Python is a language that doesn't allow concurrency through multithreading. Multithreading enables different program portions to run on separate CPU cores simultaneously, making the program run faster.

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Even though Python is a dynamic, easy-to-learn language with simple syntax, it is relatively slow when compared to languages like Java, Go, C and C++. A comparison of popular frameworks built with Python (Django) and languages like Go (Gin) shows that the Go framework runs more requests per second (114 963) than Django (8 945); this shows that Go is about 12 times faster than Python. The fundamental reason behind this slow execution of Python code is because it is a dynamically written language. Java, for example, is a statically typed language that runs all necessary checks and compiles the code before runtime; this optimizes the program and makes it run faster. On the other hand, Python is a language that is compiled at run time because, as a dynamically written language, any variable type or value can change while the program is running. For this reason, Python code cannot be compiled beforehand, and because of that, the code cannot be optimized at runtime as we have in low-level languages like Java or C Another reason behind the slow execution of Python compared to languages like Java or C is because Python is an interpreted language - while Java is a machine language. An interpreted language like Python needs to be converted to a machine language before its execution; this compilation process also explains another reason python is slow. Even if Python is slower, it has a simple syntax and a large number of libraries and contributors. This can partly explain why it is being used in many common and less common fields like GPU-dedicated tasks like machine learning and artificial intelligence.

So, is there no way to make Python faster?

There's a couple of ways you can go about making your Python program faster using:

Multi-processing module

Python is a language that doesn’t allow concurrency through multithreading. Multithreading enables different program portions to run on separate CPU cores simultaneously, making the program run faster. Python has a global interpreter lock which disallows it from multithreading; this is because python is a dynamic language that compiles at runtime; therefore, running multiple portions of a Python program at the same time will make the code encounter some issues. The Python multiprocessing module bypasses the global interpreter lock and allows you to have multiple interpreters running concurrently to make your Python program execution faster. However, you may run into issues with shared and locking memory.

C extensions

Other than the multiprocessing module, writing a C extension for your Python code can significantly improve the run time of your program.

The default Python implementation, CPython, is natively written in C.

Because of this, you can write C code as an extension to your python code. C is a fast low-level language that will help make your Python program run faster. But, both of these methods also have their downsides. Running the multiprocess module effectively is hard to achieve because of memory sharing and locking while using C extensions; you need to know the C programming language.

However, all these are about to change

The creator of the Python language, Guido Van Rossum, unveiled plans for making Python faster in contribution to the 2021 virtual Python Language Summit held in May. To improve the recently released Python 3.10, the plan to speed up Python to up to 2x will begin with Python 3.11. In the published presentation, Van Rossum explained that the Python performance improvement project is handled by a "small team funded by Microsoft" as part of Microsoft’s way of giving back to the Python community.

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