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Dive into the research topics where Laurent Philippe is active.

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Featured researches published by Laurent Philippe.


european conference on parallel processing | 2014

A Survey on Parallel and Distributed Multi-Agent Systems

Alban Rousset; Bénédicte Herrmann; Christophe Lang; Laurent Philippe

Simulation has become an indispensable tool for researchers to explore systems without having recourse to real experiments. Depending on the characteristics of the modeled system, methods used to represent the system may vary. Multi-agent systems are, thus, often used to model and simulate complex systems. Whatever modeling type used, increasing the size and the precision of the model increases the amount of computation, requiring the use of parallel systems when it becomes too large. In this paper, we focus on parallel platforms that support multi-agent simulations. Our contribution is a survey on existing platforms and their evaluation in the context of high performance computing. We present a qualitative analysis, mainly based on platform properties, then a performance comparison using the same agent model implemented on each platform.


distributed computing and artificial intelligence | 2012

Using GPU for Multi-agent Multi-scale Simulations

Guillaume Laville; Kamel Mazouzi; Christophe Lang; Nicolas Marilleau; Laurent Philippe

Multi-Agent System (MAS) is an interesting way to create models and simulators and is widely used to model complex systems. As the complex system community tends to build up larger models to fully represent real systems, the need for computing power raise significantly. Thus MAS often lead to long computing intensive simulations. Parallelizing such a simulation is complex and it execution requires the access to large computing resources. In this paper, we present the adaptation of a MAS system, Sworm, to a Graphical Processing Unit.We show that such an adaptation can improve the performance of the simulator and advocate for a more wider use of the GPU in Agent Based Models in particular for simple agents.


Concurrency and Computation: Practice and Experience | 2007

DTM: a service for managing data persistency and data replication in network-enabled server environments

Bruno Del-Fabbro; David Laiymani; Jean-Marc Nicod; Laurent Philippe

Network‐enabled server (NES) environments are valuable candidates to provide simple computing Grid access. These environments allow transparent access to a set of computational servers via Remote Procedure Call mechanisms. In this context, a challenge is to increase performances by decreasing data traffic. This paper presents DTM (Data Tree Manager), a data management service for NES environments. Based on the notions of data persistency and data replication, DTM proposes a set of efficient policies which minimize computation times by decreasing data transfers between the clients and the platform. From the end‐user point of view, DTM is accessible through a simple and transparent API. We describe DTM and its implementation in the DIET (Distributed Interactive Engineering Toolbox) platform. We also present a set of experimental results which show the feasibility and the efficiency of our approach. Copyright


european conference on parallel processing | 2013

MCMAS: A Toolkit to Benefit from Many-Core Architecure in Agent-Based Simulation

Guillaume Laville; Kamel Mazouzi; Christophe Lang; Nicolas Marilleau; Bénédicte Herrmann; Laurent Philippe

Multi-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new architectures such as multi-cores and many-cores. Using them efficiently however remains a challenge in many models as it requires adaptations tailored to each program, using low-level code and libraries. In this paper we present MCMAS a generic toolkit allowing an efficient use of many-core architectures through already defined data structures and kernels. This toolkit promotes few famous algorithms (diffusion, path-finding, population dynamics) which are ready to be used in an Agent Based Model. For other needs, MCMAS is based on a flexible architecture and can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with two models and their performance analysis.


european conference on parallel processing | 2015

On the Heterogeneity Bias of Cost Matrices When Assessing Scheduling Algorithms

Louis-Claude Canon; Laurent Philippe

Assessing the performance of scheduling heuristics through simulation requires one to generate synthetic instances of tasks and machines with well-identified properties. Carefully controlling these properties is mandatory to avoid any bias. We consider the scheduling problem consisting of allocating independent sequential tasks on unrelated machines while minimizing the maximum execution time. In this problem, the instance is a cost matrix that specifies the execution cost of any task on any machine. This article proposes two measures for quantifying the heterogeneity properties of a cost matrix. An analysis of two classical methods used in the literature reveals a bias in previous studies. We propose new methods to generate instances with given heterogeneity properties and we show that heterogeneity has a significant impact on twelve heuristics.


Multiagent and Grid Systems | 2015

MCMAS: A toolkit for developing agent-based simulations on many-core architectures

Guillaume Laville; Christophe Lang; Bénédicte Herrmann; Laurent Philippe; Kamel Mazouzi; Nicolas Marilleau

Multi-agent models and simulations are used to describe complex systems in domains such as biological, geographical or ecological sciences. The increasing model complexity results in a growing need for computing resources and motivates the use of new architectures such as multi-cores and many-cores. Using them efficiently however remains a challenge in many models as it requires adaptations tailored to each program, using low-level code and libraries. In this paper we present MCMAS a generic toolkit allowing an efficient use of many-core architectures through already defined data structures and kernels. The toolkit provides few famous algorithms as diffusion, path-finding or population dynamics that are frequently used in an agent based models. For further needs, MCMAS is based on a flexible architecture that can easily be enriched by new algorithms thanks to development features. The use of the library is illustrated with three models and their performance analysis.


International Journal of Parallel, Emergent and Distributed Systems | 2012

Assessing new approaches to schedule a batch of identical intree-shaped workflows on a heterogeneous platform

Sékou Diakité; Jean-Marc Nicod; Laurent Philippe; Lamiel Toch

In this paper, we consider the makespan optimisation when scheduling a batch of identical workflows on a heterogeneous platform as a service-oriented grid or a micro-factory. A job is represented by a directed acyclic graph (DAG) with typed tasks and no fork nodes (in-tree precedence constraints). The processing resources are able to process a set of task types, each with unrelated processing cost. The objective function is to minimise the execution makespan of a batch of identical workflows while most of the works concentrate on the throughput in this case. Three algorithms are studied in this context: a classical list algorithm and two algorithms based on new approaches, a genetic algorithm and a steady-state algorithm. The contribution of this paper is both on the adaptation of these algorithms to the particular case of batches of identical workflows and on the performance analysis of these algorithms regarding the makespan. We show the benefits of their adaptation, and we show that the algorithm performance depends on the structure of the workflow, on the size of the batch and on the platform characteristics.


parallel computing | 2011

Mapping workflow applications with types on heterogeneous specialized platforms

Anne Benoit; Alexandru Dobrila; Jean-Marc Nicod; Laurent Philippe

In this paper, we study the problem of optimizing the throughput of coarse-grain workflow applications, for which each task of the workflow is of a given type, and subject to failures. The goal is to map such an application onto a heterogeneous specialized platform, which consists of a set of processors that can be specialized to process one type of tasks. The objective function is to maximize the throughput of the workflow, i.e., the rate at which the data sets can enter the system. If there is exactly one task per processor in the mapping, then we prove that the optimal solution can be computed in polynomial time. However, the problem becomes NP-hard if several tasks can be assigned to the same processor. Several polynomial time heuristics are presented for the most realistic specialized setting, in which tasks of the same type can be mapped onto the same processor, but a processor cannot process two tasks of different types. Also, we give an integer linear program formulation of this problem, which allows us to find the optimal solution (in exponential time) for small problem instances. Experimental results show that the best heuristics obtain a good throughput, much better than the throughput obtained with a random mapping. Moreover, we obtain a throughput close to the optimal solution in the particular cases on which the optimal throughput can be computed (small problem instances or particular mappings).


international conference on parallel processing | 2011

Workload balancing and throughput optimization for heterogeneous systems subject to failures

Anne Benoit; Alexandru Dobrila; Jean-Marc Nicod; Laurent Philippe

In this paper, we study the problem of optimizing the throughput of streaming applications for heterogeneous platforms subject to failures. The applications are linear graphs of tasks (pipelines), and a type is associated to each task. The challenge is to map tasks onto the machines of a target platform, but machines must be specialized to process only one task type, in order to avoid costly context or setup changes. The objective is to maximize the throughput, i.e., the rate at which jobs can be processed when accounting for failures. For identical machines, we prove that an optimal solution can be computed in polynomial time. However, the problem becomes NP-hard when two machines can compute the same task type at different speeds. Several polynomial time heuristics are designed, and simulation results demonstrate their efficiency.


parallel, distributed and network-based processing | 2006

An agent based framework for urban mobility simulation

Nicolas Marilleau; Christophe Lang; Pascal Chatonnay; Laurent Philippe

Mobility study is composed of many research areas like urban mobility. In the literature, urban mobilities are represented by analytical techniques like stochastic laws or they are defined by simulation tools like multi-agents systems (MAS). The goal of our work is to define citizen behaviour in order to observe population dynamics by a simulation. This strategy is facilitated by a meta-model and a toolkit which are used with a particular method. The latter begins by a conceptual representation of each mobile and finishes by a mobility simulator. This paper aims at describing the mobility simulation toolkit. Thanks to this framework, mobility simulator development is simplified. It allows us to create distributed applications which are based on MAS.

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Dive into the Laurent Philippe's collaboration.

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Bénédicte Herrmann

University of Franche-Comté

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Christophe Lang

University of Franche-Comté

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Jean-Marc Nicod

University of Franche-Comté

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Kamel Mazouzi

University of Franche-Comté

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Alexandru Dobrila

University of Franche-Comté

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Anne Benoit

École normale supérieure de Lyon

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Guillaume Laville

University of Franche-Comté

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Nicolas Marilleau

Institut de recherche pour le développement

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Alban Rousset

University of Franche-Comté

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Bruno Del-Fabbro

University of Franche-Comté

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