Optimal Stochastic Scaling of CAE Parallel Computations

Flasiński, Mariusz; Schaefer, Robert; Toporkiewicz, W.

doi:10.1007/3-540-69115-4_77

Cited by 6 publications

(1 citation statement)

References 7 publications

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“…A formal automaton (syntax analyzer, parser) constructed for the grammar is then used as a recognition algorithm. The ETPL(k) graph grammars (Embedding Transformationpreserving Production-ordered k-Left nodes unambiguous grammars) [2,4,7,10] have been used as a syntactic pattern recognition model for a variety of applications such, as: a pattern recognition in the industrial robot control system [4], an analysis of a distributed environment configuration in the software allocation system [3], a solid modelling and analysis in the CAD/CAM (Computer Aided Design / Computer Aided Manufacturing) integration system [5], a solid representation for an optimal scaling in the CAE (Computer Aided Engineering) parallel computation system [6], an analysis of a complex experimental physics equipment in the real-time expert control system [8,16]. Analogously like in the last mentioned area (real-time expert control system), we have applied a multiagent approach during our recent research concerning a real-time recognition of hand postures occurring in the Polish Sign Language.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent System for Recognition of Hand Postures

Flasiński

Jurek

Myśliński

2009

Lecture Notes in Computer Science

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Abstract.A multi-agent system for a recognition of hand postures of the Polish Sign Language is presented in the paper. The system is based on a syntactic pattern recognition approach, namely on parsable ETPL(k) graph grammars. An occurrence of a variety of styles of performing hand postures requires an introduction of many grammar productions that differ each from other slightly. This makes a construction of a grammar within a parsable class ETPL(k) dubious. Dividing a whole grammar into sub-grammars and distributing them to agents allows one to solve the problem.

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Section: Introductionmentioning

confidence: 99%

Multi-agent System for Recognition of Hand Postures

Flasiński

Jurek

Myśliński

2009

Lecture Notes in Computer Science

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The Dynamics of Computing Agent Systems

Smółka

Uhruski

Schaefer

et al. 2005

Lecture Notes in Computer Science

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Abstract. The paper presents the Multi Agent System (MAS) designed for the large scale parallel computations. The special kind of diffusionbased scheduling enables to decompose and allocate the migrable computing agents basing only of the local information. The paper introduces the formal model of the MAS under consideration in order to depict the roles of agent behavior and the whole system dynamics. The optimal scheduling problem for MAS as well as the way of its verification was presented in terms of such model. The brief report of the test results is stressed in the section 6. MotivationThe Multi-Agent System (MAS) governed by the local diffusion scheduling is a reasonable alternative for the centrally governed distributed computing systems. Although the idea of mobile task diffusion is well known since more then ten years (see. e.g. [6]) we propose the policy that consists in on-demand task partitioning and task remaping obtained by the dynamic agent creation and migration. It was implemented and initially tested for the regularly and irregularly concurrent computations (see [2,1,5,7]). The brief report of the test results is stressed in the section 6. The paper introduces the formal model of the MAS under consideration in order to depict the roles of agent behavior and the whole system dynamics. The optimal scheduling problem for MAS as well as the way of its verification was presented in terms of such model. Formal Description of the ArchitectureThe MAS under consideration that allows the diffusion governed scheduling is a collection of: a computational environment (MAS platform) and a computing application composed of mobile agents called Smart Solid Agents (SSA). The computational environment is a triple (N, B H , perf), where: N = {P 1 , . . . , P n } , where P i is the Virtual Computation Node (VCN). Each VCN can maintain more than one agent (the number of hardware processors usage is not relevant in our assumptions).

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The Autonomous Concurrent Strategy for Large Scale CAE Computation

Uhruski

Toporkiewicz

Schaefer

et al. 2006

Computational Science – ICCS 2006

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The paper presents the Agent-Oriented technology for running the parallel CAE computation. Fast and effective distributed diffusion scheduling is available that minimizes computation and communication time necessary for task governing and provides transparency in resource availability. Detailed evaluation of the diffusion rule parameters was obtained in the course of analysis of computational, memory and communicational complexity of CAE tasks.

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Optimal Stochastic Scaling of CAE Parallel Computations

Cited by 6 publications

References 7 publications

Multi-agent System for Recognition of Hand Postures

Multi-agent System for Recognition of Hand Postures

The Dynamics of Computing Agent Systems

The Autonomous Concurrent Strategy for Large Scale CAE Computation

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