Jean-Marc Geib

A parallel adaptive tabu search approach

Abstract This paper presents a new approach for parallel tabu search based on adaptive parallelism. Adaptive parallelism was used to dynamically adjust the parallelism degree of the application with respect to the system load. Adaptive parallelism demonstrates that high-performance computing using a hundred of heterogeneous workstations combined with massively parallel machines is feasible to solve large optimization problems. The parallel tabu search algorithm includes different tabu list sizes and new intensification/diversification mechanisms. Encouraging results have been obtained in solving the quadratic assignment problem. We have improved the best known solutions for some large real-world problems.

Building and scheduling parallel adaptive applications in heterogeneous environments

In this paper we present a dynamic approach for constructing and scheduling parallel adaptive applications in heterogeneous multi-user environments (networks of workstations). Parallel adaptive applications have the property of varying their parallelism degree following the load fluctuation of the underlying environment. Our tool provides a programming facility that allows the application construction to avoid managing these complex problems and an allocation module responsible for running and scheduling application tasks. The allocation module handles also all problems related to the dynamic character of the application so that the user may not know at any time whether his application executes on one or dozens of workstations. The allocation module is completed by a scheduler which tries to make good mapping decisions and to adjust the mapping when the application reconfigures dynamically. The scheduling approach based on the dependency graphs model tries to minimize the execution time of the application by decreasing the parallelism loss situations in which some nodes allocated to the application are waiting for the work availability which must be generated by some slow nodes. This can be achieved by analysing dynamically the dep-graph structure and using the heterogeneity aspect. Encouraging results were obtained from experiments conducted on a parallel version of the Gaussian elimination application which is not well adapted to our environment.

A fault-tolerant parallel heuristic for assignment problems

This paper presents a new approach for parallel heuristic algorithms based on adaptive parallelism. Adaptive parallelism was used to dvnamically adjust the parallelism degree of the application with respect to the system load. This approach demonstrates that high-performance computing using heterogeneous workstations combined with massively parallel machines is feasible to solve large assignment problems. The fault-tolerant algorithm allows a minimal loss of computation in case of failures. The proposed algorithm exploits the properties of this class of applications in order to reduce the complexity of the algorithm. The parallel heuristic algorithm combines different search strategies: simulated annealing and tabu search. Encouraging results have been obtained in solving the quadratic assignment problem. We have improved the best known solutions for some large real-world problems.

Parallel Tabu Search for Large Optimization Problems

This paper presents a parallel implementation of a tabu search algorithm based on adaptive parallelism. Adaptive parallelism demonstrates that massively parallel computing using a hundred of heterogeneous machines is feasible to solve large optimization problems. The parallel tabu search algorithm includes different tabu list sizes and new intensification/diversification mechanisms. Encouraging results have been obtained in solving the quadratic assignment problem. We have improved the best known solutions for some large real-world problems.