This is a Python interpreter designed for low memory many core chips such as the Epiphany co-processor and supports the writing of parallel codes for these architectures. The interpreter and runtime resident in the memory of the actual core is only 24Kb, with the remainder of core memory available for the user's byte code and data. This 24Kb implementation is standalone which means that ePython works both with many core processors executing independently and those working as co-processors with some extra shared memory between the host. Hence ePython is specifically designed to be a very small, tight implementation of the imperative aspects of Python with extensions (via Python modules) for parallelism such as messaging, task farming, interoperability with a full Python interpreter (such as CPython) running on the host and many other features. ePython also supports full memory management and garbage collection.
ePython comes pre-installed with the latest Parallella Linux image so it should be all set up and ready to go as soon as you switch the machine on. If you do want to manually install ePython then execute make and then sudo make install at the command line. Importantly you will then need to start a new bash session (either log out and log back in, or execute bash at the command line.
Create a file called hello.py:
print "Hello world"
Now execute epython hello.py , each core will display the Hello world message on the screen. This is an example of running code directly on the Epiphany cores and more information can be found here
You can also use ePython to offload kernels to the Epiphany and use it as an accelerator. For instance create a file called hello2.py:
from epython import offload @offload def helloworld(): print "Hello World" helloworld()
Execute python hello2.py and again you will see the Hello world message on the screen. This is very different from the previous example, because the code is running via CPython on the host and simply offloading this function (helloworld) to each Epiphany core. If you comment out the offload directive and rerun you will see the host display the message instead. Take a look at this tutorial for more information and examples around offloading.
Often these are set by default, but if it complains that it can not find e-gcc or the libraries, then you will need to set these environment variables:
export PATH=/opt/adapteva/esdk/tools/e-gnu/bin:$PATH export EPIPHANY_HOME=/opt/adapteva/esdk
(you might want to place this in your .bashrc file)
More advanced installation
If you do not install ePython then you can still run epython from the current directory, as ./epython.sh but ensure that epython-device.elf is in the current directory when you run the interpreter. The epython.sh script will detect whether to run as sudo (earlier versions of the parallella OS) or not (later versions.)
In order to include files (required for parallel functions) you must either run your Python codes in the same directory as the executables (and the modules directory) and/or export the EPYTHONPATH environment variable to point to the modules directory. When including files, by default ePython will search in the current directory, any subdirectory called modules and then the EPYTHONPATH variable, which follows the same syntax as the PATH variable.
Issuing export EPYTHONPATH=$EPYTHONPATH:
pwd/modules in the epython directory will set this to point to the current directory. You can also modify your ~/.bashrc file to contain a similiar command. For offload support you will need to export PYTHONPATH=$PYTHONPATH:
Rebuilding the parser/lexer
To rebuild the parser and lexer too, then execute make full
SREC and ELF
The device executable is built in both SREC and ELF format, as of 2016 the loading of SREC on the Epiphany is deprecated and will be removed from later SDK releases. You can choose which to load via the -elf and -srec command line arguments. ELF is the default for ePython, apart from old Epiphany SDK versions which support SREC.