Problem solving environment

problem solving environment (PSE) is a completed, integrated and specialized computer software for solving a class of problems, involving automated problem-solving methods with human-oriented tools for guiding the problem. A PSE may also assist users in formulating problem resolution. A PSE may also assist users in formulating problems, selecting algorithms, simulating numerical value and viewing and analyzing results.

Purpose of PSE

Many PSEs were introduced in the 1990s. They use the language of the respective field and often employ modern graphical user interfaces . The goal is to make the software easy to use for specialists in fields other than computer science . PSEs are available for generic problems like data visualization gold broad systems of equations and for narrow fields of science or engineering like gas turbine design. [1]

History

The Problem Solving Environment (PSE) released for a few years after the release of Fortran and Algol 60, people thought that this system would bring about the elimination of professional programmers. However, surprisingly, PSE has been accepted and even though. [2]

The Problem Solving Environment for Parallel Scientific Computation was introduced in 1960, where this was the first Organized Collection was introduced in 1960, where this was the first Organized Collections with minor standardization. [2] In 1970, PSE was initially researched for providing high-class programming rather than Fortran, [3] also Libraries Plotting Packages advent. The development of Libraries was continued, and there were introduction of Emergence of Computational Packages and Graphical Systems which is visualization. By 1990s, Hypertext, Point and Click had moved towards inter-operability. Moving on, a “Software Parts” Industry finally existed. [2]

Throughout a few decades, there have been many applications, including general education, CSE software learning, job executing and Grid / Cloud computing. [3] [4]

Examples of PSE

Grid-Based Numerical Optimization

The shell software GOSPEL is an example of how a PSE can be designed for EHL modeling using a Grid resource. With the PSe, one can visualize the optimization progress, as well as interact with other simulations. [5]

The PSE parallels and integrates many individual numerical calculations in an individual numerical calculations in an industrial serial optimization code. It is built in NAG’s IRIS Explorer package to solve EHL and Parallelism problems and can use the gViz libraries, to run all the communication between the PSE and the simulation. Also use MPI, which is part of the NAG libraries. levels of continuation. [5]

Moreover, the system is designed to allow users to make simulations using visualized output. An example is using local minima, or else it can be used in other ways, and it can be used to simulate the simulation. [6]

Grid-based PSEs for mobile devices

PSEs are a large amount of resources that are most powerful computers of today. Translating PSEs into software that can be used for mobile devices in an important challenge that faces programmers today. [7]

Grid computing is seen as a solution to the rescue issues of PSEs for mobile devices. This is made possible through a Brokering Service. This service is required by PSE to resolve task. The brokering service then subtasks this subtasks that distributes the information to various subordinate devices that perform these subtasks. [7] The brokering necessitates an Active Repository Agent (AAR) and a Task Allocation Table (TAT) that both work to manage the subtasks. A keep-Alive Server is tapped to handle communication between the brokering service and the subordinate devices. The Keep-Alive server is connected to a lightweight client application installed in the participating mobile devices.

Security, transparency and dependability are issues that arise when using the grid for mobile-based PSEs. [7]

Education Support

There is a revolution for network-based learning and e-learning for education. TSUNA-TASTE, is developed by T. Teramoto, a PSE to support education and learning processes. This system can create a new idea of ​​the e-learning by supporting teachers and students in computer-related education. It consists of four parts, including agents of students, an education support server, a database system and a Web server. This system makes e-learning more convenient and informative. [4]

P-NCAS

A computer-assisted parallel program generation support (P-NCAS), is a PSE, creates a new way to reduce the programming hard task for computer programming. This program can be used to reduce the likelihood that this computer will be broken down. Moreover, partial differential equations (PDEs) can be solved by parallel programs which are generated by P-NCAS supports. P-NCAS employs the Single Program Multi Data (SPMD) and uses a decomposition method for parallelization. These enable users of PDES, algorithms and discretization schemes, etc., and to view and edit all details through the visualization and windows for edition. At last,[8]

Future Improvement

Firstly, it was difficult doing 2-D EHL problems because of the expense and computer power available. The development of parallel 2-D EHL codes and faster computers for 2-D EHL problem solving to be possible. Friction and lubricant data need a higher level of security given their sensitivity. Accounting for simulations can be difficult because they are done quickly. This can be solved by a registration system or a ‘directory’. Collaborative PSEs with multiple users, where differences were made. This may also be solved with a directory of changes made. [5]

Secondly, future improvement of the Grid-based PSEs for mobile devices, the group aims to generate new scenarios through manipulation of the control variables available. By changing those control variables, the simulation software is able to create scenarios from each other, allowing for greater scrutiny of the conditions in each scenario. It is expected that it will be possible to use different scenarios. [7]

The variables that we are interested in studying network stability and device mobility. We feel that these variables will hasten the greatest impact on grid performance. Our study will be able to measure the performance of the task. [7]

PSE PARK

As PES grow more complex, the need for computing resources has risen dramatically. Conversely, with PSE applications, venturing into fields and environments of growing complexity, the creation of PSEs have become tedious and difficult. [9]

Hirumichi Kobashi and his colleagues have designed PSE. This has been dubbed as a ‘meta PSE’ gold at PSEs. This was how PSE PSRk was born. [9]

The Framework

The architecture of PSE Park emphasizes flexibility and extensibility. These characteristics make it an attractive platform for varied levels of expertise, from entry-level users to developers. [9]

PSE Park provides these through its repository of functions. the repository contains modules required to build PSEs. Some of the most basic modules, called Cores, are used as the foundation of PSEs. More complex modules are available for use by programmers. Users access PSE Park through a console linked to the programmers. Once the user is register, he has evaluated the repository. A PIPE server is used as the mediator between the user and PSE Park. It grants access to modules and constructs the selected functions into PSE. [9]

Developers can develop functions, or even whole PSEs, for inclusion into the repository. Entry-level and expert users can access these pre-made PSEs for their own purposes. Given this architecture, PSE Park requires a cloud computing environment to support the enormous data sharing that occurs during PSe use and development. [9]

The PIPE Server

The PIPE Server differs from other servers in terms of how it handles intermediate results. Since the PIPE Server acts as a mediator in a meta-PSE, any results or variables generated by a core module are retrieved as global variables to be used by the next core. The sequence or hierarchy is defined by the user. The way, same name variables are revised to the new set of variables. [9]

Another important feature of the PIPE Server is that it executes each module or core independently. This means that the language of each module does not belong to the same as the others in the PSE. Modules are implemented according to the defined hierarchy. This feature is important for developers and users who have varied backgrounds in programming. The modular format also allows that existing PSEs can be extended and easily modified. [9]

Cores

In order to be registered, a core must be fully defined. The input and output definitions allow the PIPE to be compatible with other cores and modules. Any lack of definition is flagged by the PIPE server for incompatibility. [9]

Registration Engine and Console

The registration engine keeps track of all that can be used in PSE Park. A history of use is also created. A core map may be developed in a better context. The console is the users’ main interface with PSE Park. It is highly visual and diagrammatic, allowing users to understand the linkages between modules and cores for the PSEs that they are working on. [9]

See also

  • Virginia Tech
  • Prototype
  • Grid computing
  • cloud computing
  • Mathematical optimization

External links

  • PSE research

References

  1. Jump up^ Richard J. Fateman. “Problem solving environment and symbolic computing” (PDF) . University of California, Berkeley . Retrieved 2015-11-03 .
  2. ^ Jump up to:c Jack Dongarra. “Problem Solving Environments for Parallel Scientific Computation” (PDF) . University of Tenn./Oak Ridge National Lab . Retrieved 2015-11-03 .
  3. ^ Jump up to:b Ibrahim Haruna Umar. “Minimizing Error in Scientific Numerical Computation” . International Journal of Novel Research in Engineering and Science . Retrieved 2015-11-03 .
  4. ^ Jump up to:b Shigeo Kawata. “Review of PSE (Problem Solving Environment) Study” . Department of Advanced Interdisciplinary Sciences, Utsunomiya University . Retrieved 2015-11-03 .
  5. ^ Jump up to:c C.E. Goodyer; Mr. Berzins; PK Jimack; Chocks. “Grid-Based Numerical Optimization in a Problem Solving Environment” (PDF) . The University of Leeds . Retrieved 2015-11-03 .
  6. Jump up^ Mark Walkley; Jason Wood & Ken Brodlie. “Distributed Co-operative Problem Solving Environment” (PDF) . The University of Leeds . Retrieved 2015-11-03 .
  7. ^ Jump up to:e Stan Kurkovsky, Bhagyavati, Arris Ray. “Modeling a Grid-Based Problem-Solving Environment for Mobile Devices” . Columbus State University . Retrieved 2015-11-03 .
  8. Jump up^ Stan Kurkovsky, Bhagyavati, Arris Ray. “Modeling a Grid-Based Problem-Solving Environment for Mobile Devices”. arXiv : 1503.04501  .
  9. ^ Jump up to:i Kobashi H; et al. “PSE Park: framework for problem solving environments” (PDF) . J Convergence Info Tech . Retrieved 2015-11-03 .

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