UG Framework to Parallelize MIP, MINLP, and ExactIP Solvers

Open House, ISM in Tachikawa, 2012.6.15統計数理研究所オープンハウス（立川）、H24.6.15ポスター発

Distributed Domain Propagation

Portfolio parallelization is an approach that runs several solver instances in parallel and terminates when one of them succeeds in solving the problem. Despite its simplicity, portfolio parallelization has been shown to perform well for modern mixed-integer programming (MIP) and boolean satisfiability problem (SAT) solvers. Domain propagation has also been shown to be a simple technique in modern MIP and SAT solvers that effectively finds additional domain reductions after the domain of a variable has been reduced. In this paper we introduce distributed domain propagation, a technique that shares bound tightenings across solvers to trigger further domain propagations. We investigate its impact in modern MIP solvers that employ portfolio parallelization. Computational experiments were conducted for two implementations of this parallelization approach. While both share global variable bounds and solutions, they communicate differently. In one implementation the communication is performed only at designated points in the solving process and in the other it is performed completely asynchronously. Computational experiments show a positive performance impact of communicating global variable bounds and provide valuable insights in communication strategies for parallel solvers

SCIP-Jack - A solver for STP and variants with parallelization extensions

The Steiner tree problem in graphs is a classical problem that commonly arises in practical applications as one of many variants. While often a strong relationship between different Steiner tree problem variants can be observed, solution approaches employed so far have been prevalently problem-specific. In contrast, this paper introduces a general-purpose solver that can be used to solve both the classical Steiner tree problem and many of its variants without modification. This versatility is achieved by transforming various problem variants into a general form and solving them by using a state-of-the-art MIP-framework. The result is a high-performance solver that can be employed in massively parallel environments and is capable of solving previously unsolved instances

制約整数計画ソルバSCIPの並列化

要旨あり最適化技術に基づく統計的推論原著論

Could we use a million cores to solve an integer program?

Abstract Given the steady increase in cores per CPU, it is only a matter of time before supercomputers will have a million or more cores. In this article, we investigate the opportunities and challenges that will arise when trying to utilize this vast computing power to solve a single integer linear optimization problem. We also raise the question of whether best practices in sequential solution of ILPs will be effective in massively parallel environments

Control Schemes in a Generalized Utility for Parallel Branch and Bound

Branch-and-bound algorithms are general methods applicable to various combinatorial optimization problems and parallelization is one of the most hopeful methods to improve these algorithms. Parallel branch-and-bound algorithm implementations can be divided in two types based on whether a central or a distributed control scheme is used. Central control schemes have reduced scalability because of bottleneck problems frequently encountered. In order to solve problem cases that cannot be solved with sequential branch-and-bound algorithm, distributed control schemes are necessary. However, compared to central control schemes, higher efficiency is not always achieved through the use of a distributed control scheme. In this paper, a mixed control scheme is proposed, changing between the two different types of control schemes during execution. In addition, a dynamic load balancing strategy is applied in the distributed control scheme. Performance evaluation for three different cases is carried out: central, distributed, and mixed control scheme. Several TSP instances from the TSPLIB are experimentally solved, using up to 101 workstations. The results of these experiments show that the mixed control scheme is one of the most promising control schemes and furthermore, the hybrid selection rule, which was introduced in our previous work, has an advantage in parallel branch-and-bound algorithms. 1