SUNNY-CP: a CP Portfolio Solver

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:

1. PRE-SOLVING: the (parallel) execution of a (maybe empty) static schedule, and the neighborhood computation for the underlying k-NN algorithm;

2. 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].

Contents

• bin contains the executables of SUNNY-CP
• kb contains the utilities for the knowledge base of SUNNY-CP
• src contains the sources of SUNNY-CP
• solvers contains the utilities for the constituent solvers of SUNNY-CP
• test contains some MiniZinc examples for testing SUNNY-CP
• docker contains the dockerfile used to generate the image in the dockerhub
• tmp possibly contains the temporary files produced by SUNNY-CP

Installation & Usage

You can install SUNNY-CP on your local machine, or use it via Docker

Local installation

To install SUNNY-CP locally, you need to install first:

• Python (version >= 3)
• MiniZinc (version >= 2.6.4)
• mzn2feat feature 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.

Docker installation

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

Features

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.

Knowledge Base

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.

Authors

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

Acknowledgement

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.

References

[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