Turbine is a Dataflow graph generator. For any question or bug report please contact [email protected]. It can generate SDFGs, CSDFGs and PCGs with initilization phases. All the graphs can be saved in the SDF3 XML format (a particular notation is considered for threshold and initilization phases).
- Generate live SDFG/CSDFG/PCG up to 10,000 actors in ~20s.
- Normalize/denormalize dataflow graph.
- Compute initial marking (under period constraint and with linear or mixed interger programming).
- Compute symbolic execution of dataflow graphs.
- Compute period.
- Generate .sdf3 xml files (see SDF3 generator)
- Generate .tur files (10 times smaller than sdf3 files and easy to write by hand)
- python 2.7
- networkx-1.11 (does not work with networkx 2.0)
- swiglpk (python-glpk-0.4.43 still work but is obsolete)
- GLPK in any version schould work, also, v4.58 work great (GLPK 4.47 or 4.48 if running with python-glpk).
With Fedora 17+, you can install networkx by using yum install networkx -y.
Instructions to install swiglpk can be found here https://pypi.python.org/pypi/swiglpk.
GLPK is available at http://ftp.gnu.org/gnu/glpk/.
To test if the installation is ok run basic_test.py located on the root of turbine. Please, report any trouble at [email protected] .
Update note for Fedora 28, the two following command can help to prepare the system for Turbine:
- yum install python2-networkx.noarch glpk python2-pydot
- pip install swiglpk
There is no installation step, to use Turbine you just have to specify it in the PYTHONPATH environnement variable.
As an example, to run the examples/compute_initial_marking.py script, in the root directory of turbine :
$ PYTHONPATH=`pwd` ./Turbine/examples/compute_initial_marking.py
#################generated graph#################
###### Setup the SDF generation #####
###### Generate dataflow ############
name: SDF_of_test
task count: 10 arc count: 30
tot initial marking: 0
###### Compute the initial marking ##
name: SDF_of_test
task count: 10 arc count: 30
tot initial marking: 6714
....
import turbine
param= param.parameters.Parameters()
dataflow = generate(param)
write_sdf3_file(dataflow, filename = "fileName.sdf3")
Then a SDF3 XML file filename.sdf3 will be created in current directory.
param= param.parameters.Parameters()
param.set_lp_filename(test.lp)
generate(param)
Several examples are avalaible in 'turbine/examples'.