GPU-based Approaches for Multiobjective Local Search Algorithms. A Case Study: the Flowshop Scheduling Problem

International audienceMultiobjective local search algorithms are efficient methods to solve complex problems in science and industry. Even if these heuristics allow to significantly reduce the computational time of the solution search space exploration, this latter cost remains exorbitant when very large problem instances are to be solved. As a result, the use of GPU computing has been recently revealed as an efficient way to accelerate the search process. This paper presents a new methodology to design and implement efficiently GPU-based multiobjective local search algorithms. The experimental results show that the approach is promising especially for large problem instances

ParaDisEO-Based Design of Parallel and Distributed Evolutionary Algorithms

The original publication is available at www.springerlink.comInternational audienceParaDisEO is a framework dedicated to the design of parallel and distributed metaheuristics including local search methods and evolutionary algorithms. This paper focuses on the latter aspect. We present the three parallel and distributed models implemented in ParaDisEO and show how these can be exploited in a user-friendly, flexible and transparent way. These models can be deployed on distributed memory machines as well as on shared memory multi-processors, taking advantage of the shared memory in the latter case. In addition, we illustrate the instantiation of the models through two applications demonstrating the efficiency and robustness of the framework

Many-core Branch-and-Bound for GPU accelerators and MIC coprocessors

International audienceCoprocessors are increasingly becoming key building blocks of High Performance Computing platforms. These many-core energy-efficient devices boost the performance of traditional processors. On the other hand, Branch-and-Bound (B&B) algorithms are tree-based exact methods for solving to optimality combinatorial optimization problems (COPs). Solving large COPs results in the generation of a very large pool of subproblems and the evaluation of their associated lower bounds. Generating and evaluating those subproblems on coprocessors raises several issues including processor-coprocessor data transfer optimization, vectorization, thread divergence, and so on. In this paper, we investigate the offload-based parallel design and implementation of B&B algorithms for coprocessors addressing these issues. Two major many-core architectures are considered and compared: Nvidia GPU and Intel MIC. The proposed approaches have been experimented using the Flow-Shop scheduling problem and two hardware configurations equivalent in terms of energy consumption: Nvidia Tesla K40 and Intel Xeon Phi 5110P. The reported results show that the GPU-accelerated approach outperforms the MIC offload-based one even in its vectorized version. Moreover, vectorization improves the efficiency of the MIC offload-based approach with a factor of two

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

A new parallel adaptive block-based Gauss-Jordan algorithm

Parallelism in adaptive execution environments requires a parallel adaptive programming methodology. In this paper, we present this methodology on the block-based Gauss-Jordan algorithm used in numeric analysis to solve linear systems. The application includes a work scheduling strategy and is in some way fault tolerant. It is implemented and experimented with the MARS parallel adaptive programming environment. The results show that an absolute efficiency of 92% is possible on a farm of DEC/ALPHA processors interconnected by a Gigaswitch network, an absolute efficiency of 67% can be obtained on an Ethernet network of SUN-Sparc 4 workstations and on each of these networks perfect relative efficiency is often reached. Moreover, some experimentations done on a network of heterogeneous machines show that the overhead induced by the management of the adaptivity is not important. Keywords:Gauss-Jordan Method, Adaptive Parallelism, Networks of Workstations (NOWs), Heterogeneous Systems, Fau..

A grid-enabled branch and bound algorithm for solving challenging combinatorial optimization problems

Solving optimally large instances of combinatorial optimization problems requires a huge amount of computational resources. In this paper, we propose an adaptation of the parallel Branch and Bound algorithm for computational grids. Such gridification is based on new ways to efficiently deal with some crucial issues, mainly dynamic adaptive load balancing, fault tolerance, global information sharing and termination detection of the algorithm. A new efficient coding of the work units (search sub-trees) distributed during the exploration of the search tree is proposed to optimize the involved communications. The algorithm has been implemented following a large scale idle time stealing paradigm (Farmer-Worker). It has been experimented on a Flow-Shop problem instance ( ) that has never been optimally solved. The new algorithm allowed to realize a success story as the optimal solution has been found with proof of optimality, within days using about processors belonging to Nation-wide distinct clusters (administration domains). During the resolution, the worker processors were exploited with an average of while the farmer processor was exploited only of the time. These two rates are good indicators on the efficiency of the proposed approach and its scalability

A Grid-based Parallel Approach of the Multi-Objective Branch and Bound

International audienceThe branch and bound (B&B) algorithm is one of the most used methods to solve in an exact way combinatorial optimization problems. This article focuses on the multi-objective version of this algorithm, and proposes a new parallel approach adapted to grid computing systems. This approach addresses several issues related to the characteristics of the algorithm itself and the properties of grid computing systems. Validation is performed by experimenting the approach on a bi-objective flow-shop problem instance that has never been solved exactly. Solving this instance, after several days of computation on a grid of more than 1000 processors, belonging to 7 distinct clusters, the obtained results prove the efficiency of the proposed approac