On the optimality of Allen and Kennedy's algorithm for parallelism extraction in nested loops

We explore the link between dependence abstractions and maximal parallelism extraction in nested loops. Our goal is to find, for each dependence abstraction, the minimal transformations needed for maximal parallelism extraction. The result of this paper is that Allen and Kennedy's algorithm is optimal when dependences are approximated by dependence levels. This means that even the most sophisticated algorithm cannot detect more parallelism than found by Allen and Kennedy's algorithm, as long as dependence level is the only information available. In other words, loop distribution is sufficient for detecting maximal parallelism in dependence graphs with levels.Nous étudions les relations entre représentations des dépendances et extraction maximale du parallélisme dans les nids de boucles. Nous recherchons, pour chaque représentation des dépendances, la plus petite transformation capable d'extraire le maximum de parallélisme. Nous prouvons dans cet article que l'algorithme d'Allen et Kennedy est optimal quand les dépendances sont approximées par des niveaux de dépendance: aucun algorithme, aussi sophistiqué soit-il, ne peut détecter plus de parallélisme que l'algorithme d'Allen et Kennedy, si la seule information disponible sur les dépendances est le niveau de la dépendance. Autrement dit, la distribution de boucles suffit à détecter le maximum de parallélisme dans les graphes de dépendance étiquetés par niveaux

On the optimality of Allen and Kennedy's algorithm for parallelism extraction in nested loops

Extended version of Europar'96no abstrac

On the optimality of Allen and Kennedy's algorithm for parallelism extraction in nested loops

SIGLEAvailable at INIST (FR), Document Supply Service, under shelf-number : RP 14224 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

On the optimality of Allen and Kennedy's algorithm for parallelism extraction in nested loops

Extended version of Europar'96no abstrac

The 1989 Goddard Conference on Space Applications of Artificial Intelligence

The following topics are addressed: mission operations support; planning and scheduling; fault isolation/diagnosis; image processing and machine vision; data management; and modeling and simulation

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808

Autonomous Navigation of Automated Guided Vehicle Using Monocular Camera

This paper presents a hybrid control algorithm for Automated Guided Vehicle (AGV) consisting of two independent control loops: Position Based Control (PBC) for global navigation within manufacturing environment and Image Based Visual Servoing (IBVS) for fine motions needed for accurate steering towards loading/unloading point. The proposed hybrid control separates the initial transportation task into global navigation towards the goal point, and fine motion from the goal point to the loading/unloading point. In this manner, the need for artificial landmarks or accurate map of the environment is bypassed. Initial experimental results show the usefulness of the proposed approach.COBISS.SR-ID 27383808