SUNNY-CP [5] is a parallel portfolio solver that allows one to solve constraint satisfaction/optimization problems defined in the MiniZinc language [6]. It implements the SUNNY algorithm described in [1][2][3] and extends its sequential version [4].
SUNNY-CP can use any CP solver supporting the MiniZinc language (more precisely, interpreting the FlatZinc language). In a nutshell, it relies on two steps:
-
PRE-SOLVING: the (parallel) execution of a (maybe empty) static schedule, and the neighborhood computation for the underlying k-NN algorithm;
-
SOLVING: the (parallel) cooperative execution of the solvers' schedule returned by the SUNNY algorithm.
SUNNY-CP won the gold medal in the open track of MiniZinc Challenges 2015, 2016, and 2017, and the silver medal in 2018 and 2019 [6].
bincontains the executables of SUNNY-CPkbcontains the utilities for the knowledge base of SUNNY-CPsrccontains the sources of SUNNY-CPsolverscontains the utilities for the constituent solvers of SUNNY-CPtestcontains some MiniZinc examples for testing SUNNY-CPdockercontains the dockerfile used to generate the image in the dockerhubtmppossibly contains the temporary files produced by SUNNY-CP
You can install SUNNY-CP on your local machine, or use it via Docker
To install SUNNY-CP locally, you need to install first:
- Python (version >= 3)
- MiniZinc (version >= 2.6.4)
mzn2featfeature extractor, available at https://github.com/CP-Unibo/mzn2feat.
Then, run the command sunny-cp/install.sh. This script performs pre-checks,
compiles the Python sources and adds the default solvers to the portfolio of
SUNNY-CP. The default portfolio includes the following MiniZinc solvers:
- Chuffed
- COIN-BC
- Gecode
However, the user can specify its own portfolio, provided that each of its
solvers is installed in the MiniZinc bundle, that is, it can be run via the
minizinc --solver <solver> command. For more details on how to build a
user-defined portfolio, please see the README file in the sunny-cp/solvers
folder.
After the installation, you can test SUNNY-CP by running the
sunny-cp/test/examples/run_examples script.
To install SUNNY-CP via Docker, you need to download the Docker image available in Docker Hub with the command:
sudo docker pull jacopomauro/sunny-cp
To execute SUNNY-CP from command line you can use the command
sudo docker run --rm -i -t jacopomauro/sunny-cp
You will get shell control inside the docker container and you can trigger
SUNNY-CP by invoking the sunny-cp command (sunny-cp --help for getting
information on its command line usage).
Note that SUNNY-CP will run inside the container. MiniZinc files can be
shared from the host computer to the Docker container by using Docker
volumes (see https://docs.docker.com/storage/volumes/
for more information). For example, assuming that the MiniZinc model test.mzn to
solve is in the folder /host_dir, you can run SUNNY-CP by first invoking the
command
sudo docker run --rm -it -v /host_dir:/cont_dir jacopomauro/sunny-cp
and then, after getting the shell, run the command
sunny-cp /cont_dir/test.mzn
During the presolving phase, SUNNY-CP possibly extracts a feature vector identifying the problem to solve. This is a necessary step for the k-nearest neighbours algorithm on which the SUNNY approach relies on.
By default, the feature vector is extracted by the mzn2feat tool.
However, the user can define its own extractor by implementing a corresponding
class in sunny-cp/src/features.py.
The SUNNY algorithm needs a knowledge base, stored by SUNNY-CP in a folder
containing all the relevant information for the schedule computation.
By default, the knowledge base of SUNNY-CP is empty. But the user can define its
own knowledge base. For more details, see the README file in the sunny-cp/kb folder.
SUNNY-CP is developed by Roberto Amadini (University of Bologna) and Jacopo Mauro (University of Southern Denmark). For any question or information, please contact us:
- roberto.amadini at unibo.it
- mauro.jacopo at gmail.com
Other contributors:
- Tong Liu
- Mario Sabatini
- Sebastian Kosch
We would like to thank the staff of the former Optimization Research Group of NICTA (National ICT of Australia) for granting us the computational resources needed for building and testing SUNNY-CP.
[1] R. Amadini, M. Gabbrielli, and J. Mauro. SUNNY: a Lazy Portfolio Approach for Constraint Solving 2013. In ICLP, 2014.
[2] R. Amadini, M. Gabbrielli, and J. Mauro. Portfolio Approaches for Constraint Optimization Problems. In LION, 2014.
[3] R. Amadini, and P.J. Stuckey. Sequential Time Splitting and Bounds Communication for a Portfolio of Optimization Solvers. In CP, 2014.
[4] R. Amadini, M. Gabbrielli, and J. Mauro. SUNNY-CP: a Sequential CP Portfolio Solver. In SAC, 2015.
[5] R. Amadini, M. Gabbrielli, and J. Mauro. A Multicore Tool for Constraint Solving. In IJCAI, 2015.
[6] MiniZinc Challenge webpage. http://www.minizinc.org/challenge.html