MicroPython uses several optimizations to save RAM but also ensure the efficient execution of programs. This chapter discusses some of these optimizations.
When MicroPython loads Python code from the filesystem, it first has to parse the file into
a temporary in-memory representation, and then generate bytecode for execution, both of which
are stored in the heap (in RAM). This can lead to significant amounts of memory being used.
The MicroPython cross compiler can be used to generate
.mpy file, containing the pre-compiled bytecode for a Python module. This will still
be loaded into RAM, but it avoids the additional overhead of the parsing stage.
As a further optimisation, the pre-compiled bytecode from a
.mpy file can be “frozen”
into the firmware image as part of the main firmware compilation process, which means that
the bytecode will be executed from ROM. This can lead to a significant memory saving, and
reduce heap fragmentation.
See MicroPython manifest files for more information.
MicroPython processes local and global variables differently. Global variables are stored and looked up from a global dictionary that is allocated on the heap (note that each module has its own separate dict, so separate namespace). Local variables on the other hand are are stored on the Python value stack, which may live on the C stack or on the heap. They are accessed directly by their offset within the Python stack, which is more efficient than a global lookup in a dict.
The length of global variable names also affects how much RAM is used as identifiers are stored in RAM. The shorter the identifier, the less memory is used.
The other aspect is that
const variables that start with an underscore are treated as
proper constants and are not allocated or added in a dictionary, hence saving some memory.
These variables use
const() from the MicroPython library. Therefore:
from micropython import const X = const(1) _Y = const(2) foo(X, _Y)
X = 1 foo(1, 2)
Allocation of memory¶
Most of the common MicroPython constructs are not allocated on the heap. However the following are:
Dynamic data structures like lists, mappings, etc;
Functions, classes and object instances;
First-time assignment of global variables (to create the slot in the global dict).
For a detailed discussion on a more user-centric perspective on optimization, see Maximising MicroPython speed