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- C++11
- MPI-3
- Boost 1.59 or higher
- Python 3.0 or higher
- Networkx 2.0
- CMake 3.0 or higher
- CCI 2.1 or higher (optional)
Retrieve the sources of Decaf (in the current directory, e.g.):
git clone https://bitbucket.org/tpeterka1/decaf.git .
Decaf is built using CMake. Assuming that you created a build directory, then:
cd path/to/decaf/build
cmake /path/to/decaf/source \
-DCMAKE_INSTALL_PREFIX=/path/to/decaf/install \
-Dtransport_mpi=on \
Then, make, install, and set environment variables:
make
make install
export DECAF_PREFIX=/path/to/decaf/install
export LD_LIBRARY_PATH=/path/to/decaf/install/lib:$LD_LIBRARY_PATH
(The syntax above is for Bash and Linux; other shells and unixes are similar;
eg., the dynamic library path variable is DYLD_LIBRARY_PATH on Mac OSX.
You may consider setting the environment variables in .bashrc or .profile)
Decaf provides several examples of simple workflows. Assuming Decaf was installed following the previous instructions, run the following commands:
cd /path/to/Decaf/install/examples/direct
python3 linear-2nodes.py
./linear2.sh
cd /path/to/Decaf/install/examples/direct
python3 cycle-4nodes.py
./cycle.sh
The workflow can be hand-coded in C++, but the python instructions below describe an easier way to define the network. The Python program creates a JSON configuration file that Decaf reads.
A short Python script (eg. example.py) can be used to set workflow parameters and run the workflow.
# a small 2-node example, just a producer and consumer
# --- include the following 4 lines each time ---
import networkx as nx
import os
import imp
wf = imp.load_source('workflow', os.environ['DECAF_PREFIX'] + '/python/decaf.py')
# --- set your options here ---
# path to .so module for dataflow callback functions
mod_path = os.environ['DECAF_PREFIX'] + '/examples/direct/mod_linear_2nodes.so'
# define workflow graph
# 2-node workflow
#
# prod (4 procs) -> con (2 procs)
#
# entire workflow takes 8 procs (2 dataflow procs between producer and consumer)
# dataflow can be overlapped, but currently all disjoint procs (simplest case)
# --- Graph definition ---
prod = wf.Node("prod", start_proc=0, nprocs=4, func='prod', cmdline='./linear_2nodes')
outPort = prod.addOutputPort("out")
con = wf.Node("con", start_proc=6, nprocs=2, func='con', cmdline='./linear_2nodes')
inPort = con.addInputPort("in")
link = wf.Edge(prod.getOutputPort("out"), con.getInputPort("in"), start_proc=4, nprocs=2, func='dflow',
path=mod_path, prod_dflow_redist='count', dflow_con_redist='count', cmdline='./linear_2nodes')
# --- convert the nx graph into a workflow data structure and run the workflow ---
wf.processGraph("linear2")
Decaf is public domain software.
See the documents here.