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This tutorial describes ways of improving the performance of MicroPython code. Optimisations involving other languages are covered elsewhere, namely the use of modules written in C and the MicroPython inline assembler.
The process of developing high performance code comprises the following stages which should be performed in the order listed.
Optimisation steps:
Performance issues should be considered at the outset. This involves taking a view on the sections of code which are most performance critical and devoting particular attention to their design. The process of optimisation begins when the code has been tested: if the design is correct at the outset optimisation will be straightforward and may actually be unnecessary.
The most important aspect of designing any routine for performance is ensuring that the best algorithm is employed. This is a topic for textbooks rather than for a MicroPython guide but spectacular performance gains can sometimes be achieved by adopting algorithms known for their efficiency.
To design efficient MicroPython code it is necessary to have an understanding of the way the interpreter allocates RAM. When an object is created or grows in size (for example where an item is appended to a list) the necessary RAM is allocated from a block known as the heap. This takes a significant amount of time; further it will on occasion trigger a process known as garbage collection which can take several milliseconds.
Consequently the performance of a function or method can be improved if an object is created once only and not permitted to grow in size. This implies that the object persists for the duration of its use: typically it will be instantiated in a class constructor and used in various methods.
This is covered in further detail :ref:`Controlling garbage collection <controlling_gc>` below.
An example of the above is the common case where a buffer is required, such as one used for communication with a device. A typical driver will create the buffer in the constructor and use it in its I/O methods which will be called repeatedly.
The MicroPython libraries typically provide support for pre-allocated buffers. For
example, objects which support stream interface (e.g., file or UART) provide read()
method which allocates new buffer for read data, but also a readinto() method
to read data into an existing buffer.
Some MicroPython ports allocate floating point numbers on heap. Some other ports may lack dedicated floating-point coprocessor, and perform arithmetic operations on them in "software" at considerably lower speed than on integers. Where performance is important, use integer operations and restrict the use of floating point to sections of the code where performance is not paramount. For example, capture ADC readings as integers values to an array in one quick go, and only then convert them to floating-point numbers for signal processing.
Consider the use of the various types of array classes as an alternative to lists. The array module supports various element types with 8-bit elements supported by Python's built in bytes and bytearray classes. These data structures all store elements in contiguous memory locations. Once again to avoid memory allocation in critical code these should be pre-allocated and passed as arguments or as bound objects.
When passing slices of objects such as bytearray instances, Python creates a copy which involves allocation of the size proportional to the size of slice. This can be alleviated using a memoryview object. memoryview itself is allocated on heap, but is a small, fixed-size object, regardless of the size of slice it points too.
ba = bytearray(10000) # big array
func(ba[30:2000]) # a copy is passed, ~2K new allocation
mv = memoryview(ba) # small object is allocated
func(mv[30:2000]) # a pointer to memory is passed
A memoryview can only be applied to objects supporting the buffer protocol - this includes arrays but not lists. Small caveat is that while memoryview object is live, it also keeps alive the original buffer object. So, a memoryview isn't a universal panacea. For instance, in the example above, if you are done with 10K buffer and just need those bytes 30:2000 from it, it may be better to make a slice, and let the 10K buffer go (be ready for garbage collection), instead of making a long-living memoryview and keeping 10K blocked for GC.
Nonetheless, memoryview is indispensable for advanced preallocated buffer
management. readinto() method discussed above puts data at the beginning
of buffer and fills in entire buffer. What if you need to put data in the
middle of existing buffer? Just create a memoryview into the needed section
of buffer and pass it to readinto().
This is a process known as profiling and is covered in textbooks and
(for standard Python) supported by various software tools. For the type of
smaller embedded application likely to be running on MicroPython platforms
the slowest function or method can usually be established by judicious use
of the timing ticks group of functions documented in utime.
Code execution time can be measured in ms, us, or CPU cycles.
The following enables any function or method to be timed by adding an
@timed_function decorator:
def timed_function(f, *args, **kwargs):
myname = str(f).split(' ')[1]
def new_func(*args, **kwargs):
t = utime.ticks_us()
result = f(*args, **kwargs)
delta = utime.ticks_diff(utime.ticks_us(), t)
print('Function {} Time = {:6.3f}ms'.format(myname, delta/1000))
return result
return new_func
MicroPython provides a const() declaration. This works in a similar way
to #define in C in that when the code is compiled to bytecode the compiler
substitutes the numeric value for the identifier. This avoids a dictionary
lookup at runtime. The argument to const() may be anything which, at
compile time, evaluates to an integer e.g. 0x100 or 1 << 8.
Where a function or method repeatedly accesses objects performance is improved by caching the object in a local variable:
class foo(object):
def __init__(self):
ba = bytearray(100)
def bar(self, obj_display):
ba_ref = self.ba
fb = obj_display.framebuffer
# iterative code using these two objects
This avoids the need repeatedly to look up self.ba and obj_display.framebuffer
in the body of the method bar().
When memory allocation is required, MicroPython attempts to locate an adequately sized block on the heap. This may fail, usually because the heap is cluttered with objects which are no longer referenced by code. If a failure occurs, the process known as garbage collection reclaims the memory used by these redundant objects and the allocation is then tried again - a process which can take several milliseconds.
There may be benefits in pre-empting this by periodically issuing gc.collect(). Firstly doing a collection before it is actually required is quicker - typically on the order of 1ms if done frequently. Secondly you can determine the point in code where this time is used rather than have a longer delay occur at random points, possibly in a speed critical section. Finally performing collections regularly can reduce fragmentation in the heap. Severe fragmentation can lead to non-recoverable allocation failures.
This causes the MicroPython compiler to emit native CPU opcodes rather than bytecode. It covers the bulk of the MicroPython functionality, so most functions will require no adaptation (but see below). It is invoked by means of a function decorator:
@micropython.native
def foo(self, arg):
buf = self.linebuf # Cached object
# code
There are certain limitations in the current implementation of the native code emitter.
with statement).raise is used an argument must be supplied.The trade-off for the improved performance (roughly twices as fast as bytecode) is an increase in compiled code size.
The optimisations discussed above involve standards-compliant Python code. The Viper code emitter is not fully compliant. It supports special Viper native data types in pursuit of performance. Integer processing is non-compliant because it uses machine words: arithmetic on 32 bit hardware is performed modulo 2**32.
Like the Native emitter Viper produces machine instructions but further optimisations are performed, substantially increasing performance especially for integer arithmetic and bit manipulations. It is invoked using a decorator:
@micropython.viper
def foo(self, arg: int) -> int:
# code
As the above fragment illustrates it is beneficial to use Python type hints to assist the Viper optimiser.
Type hints provide information on the data types of arguments and of the return value; these
are a standard Python language feature formally defined here
@micropython.viper
def foo(self, arg: int) -> int:
buf = ptr8(self.linebuf) # self.linebuf is a bytearray or bytes object
for x in range(20, 30):
bar = buf[x] # Access a data item through the pointer
# code omitted
In this instance the compiler "knows" that The following example illustrates the use of a A detailed technical description of the three code emitters may be found
on Kickstarter here Note 2 Note Code examples in this section are given for the Pyboard. The techniques
described however may be applied to other MicroPython ports too. This comes into the category of more advanced programming and involves some knowledge
of the target MCU. Consider the example of toggling an output pin on the Pyboard. The
standard approach would be to write This involves the overhead of two calls to the :class:`~machine.Pin` instance's :meth:`~machine.Pin.value()`
method. This overhead can be eliminated by performing a read/write to the relevant bit
of the chip's GPIO port output data register (odr). To facilitate this the buf is the address of an array of bytes;
it can emit code to rapidly compute the address of buf[x] at runtime. Where casts are
used to convert objects to Viper native types these should be performed at the start of
the function rather than in critical timing loops as the cast operation can take several
microseconds. The rules for casting are as follows:
int, bool, uint, ptr, ptr8, ptr16 and ptr32.uint to ptr8) so that you can then store/load
using this pointer.int or uint, then the Python object
must be of integral type and the value of that integral object is returned.ptr, ptr, ptr16 or ptr32,
then the Python object must either have the buffer protocol with read-write capabilities
(in which case a pointer to the start of the buffer is returned) or it must be of integral
type (in which case the value of that integral object is returned).ptr16 cast to toggle pin X1 n times:
BIT0 = const(1)
@micropython.viper
def toggle_n(n: int):
odr = ptr16(stm.GPIOA + stm.GPIO_ODR)
for _ in range(n):
odr[0] ^= BIT0
Accessing hardware directly
mypin.value(mypin.value() ^ 1) # mypin was instantiated as an output pin
stm
module provides a set of constants providing the addresses of the relevant registers.
A fast toggle of pin P4 (CPU pin A14) - corresponding to the green LED -
can be performed as follows:
import machine
import stm
BIT14 = const(1 << 14)
machine.mem16[stm.GPIOA + stm.GPIO_ODR] ^= BIT14
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