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| * [[http://www.raspberrypi.org/|Raspberry Pi]] * [[http://beagleboard.org/Products/BeagleBone%20Black|BeagleBone Black]] |
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| See also [[PythonForArmLinux]] and [[OpenEmbedded]]. | See also PythonForArmLinux and OpenEmbedded. |
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| == Work to improve CPython for embedded applications == | |
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| * File access overhead on startup: [[http://mail.python.org/pipermail/python-list/2008-October/564494.html|Improving interpreter startup speed]] | * General interpreter startup costs: SpeedUpInterpreterStartup * File access overhead on startup: [[http://mail.python.org/pipermail/python-list/2008-October/467718.html|Improving interpreter startup speed]], [[http://mail.python.org/pipermail/python-list/2005-May/339691.html|Tons of stats/opens to non-existing files increases Python's startup on loaded NFS servers]], [[http://mail.python.org/pipermail/python-dev/2003-May/035359.html|Startup time]] * Import-related costs: [[http://mail.python.org/pipermail/python-dev/2010-March/098042.html|__file__]] * Using a launcher process where "expensive" modules are required: [[http://blogs.gnome.org/johan/2007/01/18/introducing-python-launcher/|Introducing python-launcher]] |
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| * [[Zerynth]] formerly Viper | |
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| On yet another hand, one can start with a full build, and simply remove unneeded modules--Tkinter, ...--to realize a reduced-size Python with little effort. |
On the other hand, one can start with a full build, and simply remove unneeded modules, ''e.g.'', Tkinter, etc., to realize a reduced-size Python with little effort. |
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| Tools which support this kind of development include the following: | Tools that support this kind of development include the following: |
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| * [[http://myhdl.jandecaluwe.com/|MyHDL]] * [[http://www.sticlete.com/whatos/|WhatOS]] |
* [[https://myhdl.org/|MyHDL]] * [[https://sourceforge.net/projects/whatos/|WhatOS]] |
Embedded Python
Python can be used in embedded, small or minimal hardware devices, depending on how limiting the devices actually are.
Devices capable of running CPython
Some modern embedded devices have enough memory and a fast enough CPU to run a typical Linux-based environment, for example, and running CPython on such devices is mostly a matter of compilation (or cross-compilation) and tuning.
Devices which could be considered as "embedded" by modern standards and which can run tuned versions of CPython include the following:
FIC Neo1973 and Neo FreeRunner (Python on Openmoko)
Telit GSM/GPRS modules (also available as AarLogic family GPRS/GPS QUAD Band Modules)
See also PythonForArmLinux and OpenEmbedded.
Work to improve CPython for embedded applications
Various efforts have been made to make CPython more usable for embedded applications:
General interpreter startup costs: SpeedUpInterpreterStartup
File access overhead on startup: Improving interpreter startup speed, Tons of stats/opens to non-existing files increases Python's startup on loaded NFS servers, Startup time
Import-related costs: __file__
Using a launcher process where "expensive" modules are required: Introducing python-launcher
Reduced or reworked Python implementations
Some devices may be more restrictive in that the typical memory footprint of CPython may exceed the amount of memory available on the device. In such cases, a re-engineered or adapted version of CPython, perhaps even to the point where it can be considered a new implementation of Python, might be appropriate.
Examples of such implementations include the following:
Zerynth formerly Viper
On the other hand, one can start with a full build, and simply remove unneeded modules, e.g., Tkinter, etc., to realize a reduced-size Python with little effort.
Python-based tools for developing embedded applications
Sometimes the embedded environment is just too restrictive to support a Python virtual machine. In such cases, various Python tools can be employed for prototyping, with the eventual application or system code being generated and deployed on the device.
Tools that support this kind of development include the following: