Visual programming environments for multi-disciplinary distributed applications

A Problem Solving Environment is a complete, integrated computing environment for composing, compiling and running applications in a specific problem area or domain. A Visual Programming Environment is one possible front end to a problem solving environment. It applies the visual programming paradigms of "point and click" and "drag and drop", via a Graphical User Interface, to the various constituent components that are used to assemble an application. The aim of the problem solving environment presented here is to provide the ability to build up scientific applications by connecting, or plugging, software components together and to provide an intuitive way to construct scientific applications. Problem solving environments promise a totally new user environment for computational scientists and engineers. In this new paradigm, individual programs combined to solve a problem in their given area of expertise, are wrapped as components within an integrated system that is both powerful and easy to use. This thesis aims to address: problems in code reuse the combination of different codes in new ways and problems with underlying system familiarity and distribution. This is achieved by abstracting application composition using visual programming techniques. The work here focuses on a prototype environment using a number of demonstration problems from multi-disciplinary problem domains to illustrate some of the main difficulties in building problem solving environments and some possible solutions. A novel approach to code wrapping, component definition and application specification is shown, together with timing and usage comparisons that illustrate that this approach can be used successfully to help scientists and engineers in their daily work.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Visual programming environments for multi-disciplinary distributed applications

A Problem Solving Environment is a complete, integrated computing environment for composing, compiling and running applications in a specific problem area or domain. A Visual Programming Environment is one possible front end to a problem solving environment. It applies the visual programming paradigms of "point and click" and "drag and drop", via a Graphical User Interface, to the various constituent components that are used to assemble an application. The aim of the problem solving environment presented here is to provide the ability to build up scientific applications by connecting, or plugging, software components together and to provide an intuitive way to construct scientific applications. Problem solving environments promise a totally new user environment for computational scientists and engineers. In this new paradigm, individual programs combined to solve a problem in their given area of expertise, are wrapped as components within an integrated system that is both powerful and easy to use. This thesis aims to address: problems in code reuse the combination of different codes in new ways and problems with underlying system familiarity and distribution. This is achieved by abstracting application composition using visual programming techniques. The work here focuses on a prototype environment using a number of demonstration problems from multi-disciplinary problem domains to illustrate some of the main difficulties in building problem solving environments and some possible solutions. A novel approach to code wrapping, component definition and application specification is shown, together with timing and usage comparisons that illustrate that this approach can be used successfully to help scientists and engineers in their daily work

Leveraging legacy codes to distributed problem solving environments: A web service approach

This paper describes techniques used to leverage high performance legacy codes as CORBA components to a distributed problem solving environment. It first briefly introduces the software architecture adopted by the environment. Then it presents a CORBA oriented wrapper generator (COWG) which can be used to automatically wrap high performance legacy codes as CORBA components. Two legacy codes have been wrapped with COWG. One is an MPI-based molecular dynamic simulation (MDS) code, the other is a finite element based computational fluid dynamics (CFD) code for simulating incompressible Navier-Stokes flows. Performance comparisons between runs of the MDS CORBA component and the original MDS legacy code on a cluster of workstations and on a parallel computer are also presented. Wrapped as CORBA components, these legacy codes can be reused in a distributed computing environment. The first case shows that high performance can be maintained with the wrapped MDS component. The second case shows that a Web user can submit a task to the wrapped CFD component through a Web page without knowing the exact implementation of the component. In this way, a user’s desktop computing environment can be extended to a high performance computing environment using a cluster of workstations or a parallel computer

Can Computer Algebra be Liberated from its Algebraic Yoke ?

So far, the scope of computer algebra has been needlessly restricted to exact algebraic methods. Its possible extension to approximate analytical methods is discussed. The entangled roles of functional analysis and symbolic programming, especially the functional and transformational paradigms, are put forward. In the future, algebraic algorithms could constitute the core of extended symbolic manipulation systems including primitives for symbolic approximations.Comment: 8 pages, 2-column presentation, 2 figure

Paradigms of Intelligent Systems

This paper approaches the subject of paradigms for the categories of intelligent systems. First we can look at the term paradigm in its scientific meaning and then we make acquaintance with the main categories of intelligent systems (expert systems, intelligent systems based on genetic algorithms, artificial neuronal systems, fuzzy systems, hybrid intelligent systems). We will see that every system has one or more paradigms, but hybrid intelligent systems combine paradigms because they are made of different technologies. This research has been made under the guidance of Dr. Ioan AND ONE, Professor and Director of Research Laboratory.paradigm, intelligent systems, expert systems, genetic algorithms, fuzzy systems, artificial neuronal networks, hybrid intelligent systems