How can Python optimize performance in embedded systems?

Python has plenty of libraries and one should know the basics to advanced level concepts for embedded systems. With the help of Python libraries like Numpy, pandas, matlib,etc one can easily write the most complex functionalities from scratch.

Python should be avoided in embedded system , Instead use C++/rust compiled code. Any kind of interpreted languages must be avoided. If last option, use optimized pythons distributions like ipython, cpython and optimized libs. Use dynamic loading of modules [e.g __import__, or imp.load_module to use dynamically loaded module to keep runtime memmory useage less. Don't bloat by keeping unnecessary array throughout the program run.

Python can optimize performance in embedded systems through the following methods: Using optimized libraries: Leveraging libraries like NumPy and Cython can speed up computations. Combining with C/C++: Using Python for high-level logic while relying on C/C++ for performance-critical parts can enhance efficiency. Interpreters and JIT compilation: Employing interpreters like MicroPython or JIT compilers like PyPy can improve execution speed. Efficient coding practices: Writing efficient Python code, such as avoiding unnecessary computations and using proper data structures, can boost performance. Asynchronous programming: Utilizing asynchronous programming techniques can optimize I/O-bound and high-latency operations.

Python itself is a good lang to prototype and deploy production ready code, with some extensions like mypy and numba python code become better readable and execute fast.

Stays with the built-in functions, use as much as possible lang specific things, like comprehension, context managers, and so on.

Python classes can use a special dunder, "__slots__", to reduce the memory use and increase the access speed of the attributes. That said using libs that are made in C, like numpy, to process arrays, as an example, can be a way to reduce your memory usage at all.

Python does have a good concurrency handling, bare with asyncio or using third party libs as trio. Python can also work with many processors at the same time using the multiprocessing lib.

Python does have a lot of bindings for a ton of languages. As an example using PyO3 let you write code in rust that compile to a performant code to be used in python.

Personally, I wouldn't invest too much effort in over-optimizing Python code. It's like overclocking a Pentium II in hopes of matching the performance of an i9 - it simply won't happen. If performance is critical, it's better to start with a more efficient language. While you can implement various optimizations in Python, it will never match the speed of languages like C, C++, or Rust. Although development time with these languages may be longer, the results will be significantly better, more robust, and scalable.

Consider that Python cannot optimise performance on embedded systems. It is a resource-intensive interpreted language.

Real-time systems require deterministic behavior and low-latency responses, which Python's interpreted nature and garbage collection cannot reliably match. The inherent overhead and unpredictability in Python's execution make it unsuitable for such applications. While the garbage collector and other parts of the language can be controlled, for real-time or critical systems, languages like C, C++, or Rust are much more appropriate choices, with many frameworks and real-time operating systems made specifically for this purpose.