16 research outputs found
A Multicore Tool for Constraint Solving
*** To appear in IJCAI 2015 proceedings *** In Constraint Programming (CP), a
portfolio solver uses a variety of different solvers for solving a given
Constraint Satisfaction / Optimization Problem. In this paper we introduce
sunny-cp2: the first parallel CP portfolio solver that enables a dynamic,
cooperative, and simultaneous execution of its solvers in a multicore setting.
It incorporates state-of-the-art solvers, providing also a usable and
configurable framework. Empirical results are very promising. sunny-cp2 can
even outperform the performance of the oracle solver which always selects the
best solver of the portfolio for a given problem
SUNNY-CP and the MiniZinc Challenge
In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of the portfolio solver sunny-cp, and we discuss its performance
in the MiniZinc Challenge---the annual international competition for CP
solvers---where it won two gold medals in 2015 and 2016. Under consideration in
Theory and Practice of Logic Programming (TPLP)Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems
In the context of Constraint Programming, a portfolio
approach exploits the complementary strengths of a portfolio of
different constraint solvers. The goal is to predict and run the best
solver(s) of the portfolio for solving a new, unseen problem. In
this work we reproduce, simulate, and evaluate the performance
of different portfolio approaches on extensive benchmarks of
Constraint Satisfaction Problems. Empirical results clearly show
the benefits of portfolio solvers in terms of both solved instances
and solving time
Automatic Deployment of Services in the Cloud with Aeolus Blender
International audienceWe present Aeolus Blender (Blender in the following), a software product for the automatic deployment and configuration of complex service-based, distributed software systems in the " cloud ". By relying on a configuration optimiser and a deployment planner, Blender fully automates the deployment of real-life applications on OpenStack cloud deployments , by exploiting a knowledge base of software services provided by the Mandriva Armonic tool suite. The final deployment is guaranteed to satisfy not only user requirements and relevant software dependencies , but also to be optimal with respect to the number of used virtual machines
SUNNY with Algorithm Configuration
International audienceThe SUNNY algorithm is a portfolio technique originally tailored for Constraint Satisfaction Problems (CSPs). SUNNY allows to select a set of solvers to be run on a given CSP, and was proven to be effective in the MiniZinc Challenge, i.e., the yearly international competition for CP solvers. In 2015, SUNNY was compared with other solver selectors in the first ICON Challenge on algorithm selection with less satisfactory performance. In this paper we briefly describe the new version of the SUNNY approach for algorithm selection, that was submitted to the first Open Algorithm Selection Challenge
SUNNY for Algorithm Selection: A Preliminary Study
National audienceGiven a collection of algorithms, the Algorithm Selection (AS) problem consists in identifying which of them is the best one for solving a given problem. In this paper we show how we adapted the algorithm selector SUNNY, originally tailored for constraint solving, to deal with general AS problems. Preliminary investigations based on the AS Library benchmarks already show some promising results: for some scenarios SUNNY is able to outperform AS state-of-the-art approaches
SUNNY-CP: a Portfolio Solver for Constraint Programming
In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the last MiniZinc Challenge —the annual international competition for CP solvers— where it won a gold medal