Multi-start local search algorithms on GPU

International audienceIn practice, combinatorial optimization problems are complex and computationally time-intensive. Even if local search (LS) algorithms allow to significantly reduce the computation time cost of the solution exploration space, the use of parallelism is required to accelerate the search process. Indeed, LSs are inherently parallel and three parallel models are often used to solve efficiently large combinatorial problems: algorithmic-level (multi-start model), iteration-level (parallel evaluation of the neighborhood), and the solution-level (parallel evaluation of a single solution). The main objective of this paper is to deal with the algorithmic-level on GPU architectures where many LSs are executed in parallel. More exactly, we propose to study different schemes of deployment for the design of multi-start LSs on GPU based on popular hill climbing (HC), simulated annealing (SA) and tabu search (TS)

TOWARDS A UNIFIED VIEW OF METAHEURISTICS

This talk provides a complete background on metaheuristics and presents in a unified view the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. The key search components of metaheuristics are considered as a toolbox for: - Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems. - Designing efficient metaheuristics for multi-objective optimization problems. - Designing hybrid, parallel and distributed metaheuristics. - Implementing metaheuristics on sequential and parallel machines

Parallelizing Tabu Search on a Cluster of HeterogeneousWorkstations

In this paper, we present the parallelization of tabu search on a network of workstations using PVM. Two parallelization strategies are integrated: functional decomposition strategy and multi-search threads strategy. In addition, domain decomposition strategy is implemented probabilistically. The performance of each strategy is observed and analyzed. The goal of parallelization is to speedup the search in finding better quality solutions. Observations support that both parallelization strategies are beneficial, with functional decomposition producing slightly better results. Experiments were conducted for the VLSI cell placement, an NP-hard problem, and the objective was to achieve the best possible solution in terms of interconnection length, timing performance (circuit speed), and area. The multiobjective nature of this problem is addressed using a fuzzy goal-based cost computation. Key Words: tabu search, parallel tabu search, metaheuristic, functional decomposition, multi-search threads, combinatorial optimization, VLSI, standard cell design, placement, fuzzy logi

Parallelizing Tabu Search on a Cluster of HeterogeneousWorkstations

In this paper, we present the parallelization of tabu search on a network of workstations using PVM. Two parallelization strategies are integrated: functional decomposition strategy and multi-search threads strategy. In addition, domain decomposition strategy is implemented probabilistically. The performance of each strategy is observed and analyzed. The goal of parallelization is to speedup the search in finding better quality solutions. Observations support that both parallelization strategies are beneficial, with functional decomposition producing slightly better results. Experiments were conducted for the VLSI cell placement, an NP-hard problem, and the objective was to achieve the best possible solution in terms of interconnection length, timing performance (circuit speed), and area. The multiobjective nature of this problem is addressed using a fuzzy goal-based cost computation. Key Words: tabu search, parallel tabu search, metaheuristic, functional decomposition, multi-search threads, combinatorial optimization, VLSI, standard cell design, placement, fuzzy logi

Parallelization of Iterative Heuristic for Performance-Driven Low-Power VLSI Standard Cell Placement.

The complexity involved in VLSI design and its sub-problems has always made them ideal application areas for non-deterministic iterative heuristics. However, the major drawback has been the large runtime involved in reaching acceptable solutions especially in the case of multi-objective optimization problems. Among the acceleration techniques proposed, parallelization of these heuristics is one promising alternate. The motivation for Parallel CAD include faster runtimes, handling of larger problem sizes, and exploration of larger search space. In this work, the development of parallel algorithms for Tabu Search, applied on multi-objective VLSI cell-placement problem is presented. In VLSI circuit design, placement is the process of arranging circuit blocks on a layout. In standard cell design, placement consists of determining optimum positions of all blocks on the layout to satisfy the constraint and improve a number of objectives. The placement objectives in our work are to reduce power dissipation and wire-length while improving performance (timing). The parallelization is achieved on a cluster of workstations interconnected by a low-latency network (ethernet), by using Message Passing Interface (MPI) communication libraries. Circuits from ISCAS-89 are used as benchmarks. Results for parallel Tabu Search are compared with its sequential counterpart as a reference point for both, the quality of solution as well as the execution time