Python, compared to other programming languages such as Go, is a slower high-level language and makes it take longer to execute its programs. You should now understand why this is and how to speed it up. Also, what the core developers of Python are doing to speed up the runtime.
Python can be a dynamic and easy-to-learn programming language with simple syntax. However, it is slower than languages such as Java, Go, C, C++, and C++.
A comparison between popular frameworks built using Python (Django), and languages such as Go (Gin), shows that the Go framework handles more requests per second than Django (8 945); it is therefore 12 times faster than Python. Want to learn Python, but before that read this.
Python code execution is slow because it is dynamically written.
Java is an example of a statically-typed language. It runs all the necessary checks and compiles code before it runs. This optimizes the program and speeds it up.
Python, on the other hand is a language that's compiled at run-time because it is dynamically written and any value or variable can be changed while the program runs. Python code cannot therefore be compiled before execution. This means that the code can't be optimized at runtime like low-level languages such as Java and C.
Python's slow execution is also due to its interpreted nature, whereas Java and C are machine languages.
A machine language, such as Python, is an interpreted language that must be converted before it can be executed. This compilation process also helps explain why python runs slowly.
Although Python is slower than other languages, it still has a simple syntax as well as a large number libraries and contributors. This is partly why Python is used in many fields, both common and less well-known.
There are a few ways to make your Python program run faster:
Python doesn't support multithreading and concurrency. Multithreading allows different parts of a program to run simultaneously on different CPU cores, making it run faster.
Python has a global lock that prevents it from multithreading. This is due to Python being a dynamic language, which compiles at runtime. Therefore, multiple Python programs running at once will cause issues.
The Python multiprocessing module bypasses global interpreter locks and lets you have multiple interpreters running simultaneously to speed up your Python program execution. You may have issues with locking and shared memory.
Writing a C extension to your Python code will not only improve its run time, but also the multiprocessing module.
The default Python implementation, CPython is natively written using C.
This allows you to add C code to your Python code.
C is a low-level, fast language that can make Python programs run faster.
Both of these options have their pros and cons. It is difficult to run the multiprocess module efficiently due to memory sharing and locking when using C extensions. You need to be familiar with the C programming language.
Guido Van Rossum is the creator of Python and revealed plans to make Python more efficient in his contribution to the 2021 Python Language Summit in May.
The Python 3.11 release will improve the Python 3.10.
Van Rossum explained in the published presentation that the Python performance improvement project was handled by a small team funded by Microsoft as part of Microsoft’s giving back to the Python community.
He stated that maintenance and support would be taken care of by the team, and that the project would remain open-source.
Van Rossum stated that the plan would aim to speed up Python sequentially, with a 2x target in Python 3.11, and a 5x target in the subsequent Python releases.
Van Rossum said that although they don't know if they will make it to 2x, they believe they can be optimistic and curious.
He suggested some constraints to be aware of, as it might be difficult to achieve 2x speed with version 3.11.
He stated that any modifications or improvements to Python should not affect the stability of the application binary interface (ABI). It should not compromise the API compatibility. It should not slow down or break extreme cases. He said that modification must be "maintenable"
Rossum and the Python Improvement Project Team identified certain aspects that they could freely modify to achieve a 2x speed for version 3.11.
It is an excellent candidate for speed optimization, as both the bytecode interpreter and compiler are components that change with each release.
He suggested the introduction of an "Adaptive, specializing bytecode interlator" which will use bytecode instructions that are specific to a data type in order to execute a portion of the code. This will be an inline cache that will speed up execution.
Other speed optimizations for Python 3.11 include optimizing the frame stack and speed-proving function calls, changing the pyc format, and enacting more efficient exception handling.
Rossum stated that 5x speed could be achievable depending on how fast the team can run Python 3.11, but that it will require creativity.
Python 3.11, which will include improvements to make Python more efficient, won't be released until 2022. We need to find a way that will speed up Python programs before then.
We can speed up your Python programs by using the Python multiprocessing module or C code as an extension to Python, as described earlier.
If you are using NumPy, you can also use a JIT compiler such as Numba.
Numba a JIT compiler for just-in time that uses decorators to convert Python or NumPy codes into machine code. It compiles Python code and replaces the Python interpreter. Then, it makes Python functions compile to machine code directly, improving run-time speed.
Numba converts Python functions into optimized machine code during runtime by using the industry-standard LLVM compiler.
Numba doesn't require you to run separate compilation steps, replace the Python interpreter or have a C/C++ compiler installed. Numba will do the rest by simply applying one of the Numba decorators for your Python function.