This should not be used to conclude on the viability of using MicroPython for numeric type tasks. For that one should at least take into account the following:

Integers are much faster than floats (floats involve a pointer and a heap allocation, integers are stored in a single word/object).

array.array is preferred over list for something like sort. Continuous memory representation of numbers versus general purpose list of objects.

MicroPython has on-board "JIT" (native/viper emitters), have to explicitly annotate the function. Should give 4-10x improvements for this case.

MicroPython has an on-board assembler, so one can write ARM assembly and get that to a function.

MicroPython also has support for C modules, which expose a Python API. Including dynamic native modules which can be installed at runtime with the package manager.

Bubblesort is O(n*2), which hurts for even a few thousand numbers. Actual sorting on a microcontroller should be done with an O(n log n) algorithm.

Ok, you guys have successfully nerd sniped me this morning... Here some experiments showing the use of code emitter to speed this code up massively. Link to code: https://github.com/jonnor/embeddedml/tree/master/handson/mic... The results on ESP32S3 (M5Stick AtomS3U), running MicroPython 1.24.1. All times in seconds, for the 2000 number sort.

bubble.py 19.119

bubble_native.py 9.482

bubble_viper.py 0.904

heapsort_viper.py 0.02

So one can do 100x better without changing the algorithm, and 1000x by changing it :)

Microcontrollers are a constrained platform. They can be plenty fast - but it is much more important to use the tools available, compared to on PC. MicroPython has excellent tools available for this.

If anyone is interested in fast dynamic native C modules for MicroPython, you can check out emlearn-micropython - a machine learning and digital signal processing library. https://github.com/emlearn/emlearn-micropython Disclaimer: I am the maintainer