Parallelising programs with algebraic programming tools

1995 ◽  
pp. 685-690
Author(s):  
Anatoly E. Doroshenko ◽  
Alexander B. Godlevsky
Keyword(s):  
Algebraic Programming ◽  
Programming Tools

Model-based programming tools for integrated monitoring, simulation, diagnosis and control

1993 ◽  
Author(s):  
Gabor Karsai ◽  
Samir Padalkar ◽  
Hubertus Franke ◽  
Janos Sztipanovits
Keyword(s):  
Integrated Monitoring ◽  
Model Based ◽  
Programming Tools ◽  
And Control

scholarly journals Middleware based on XML technologies for achieving true interoperability between PLC programming tools

2008 ◽  
Vol 41 (2) ◽  
pp. 8461-8466
Author(s):  
E. Estévez ◽  
M. Marcos ◽  
D. Orive ◽  
F. López ◽  
E. Irisarri ◽  
...  
Keyword(s):  
Programming Tools ◽  
Xml Technologies ◽  
Plc Programming

Using algebraic programming to teach general relativity

2001 ◽  
Vol 3 (2) ◽  
pp. 65-70 ◽  
Author(s):  
F.A. Ghergu ◽  
D.N. Vulcanov
Keyword(s):  
General Relativity ◽  
Algebraic Programming

Algebraic programming: Methods and tools

1993 ◽  
Vol 29 (3) ◽  
pp. 307-312 ◽  
Author(s):  
Yu. V. Kapitonova ◽  
A. A. Letichevskii
Keyword(s):  
Algebraic Programming

scholarly journals Parallel Object-Oriented Computation Applied to a Finite Element Problem

1993 ◽  
Vol 2 (4) ◽  
pp. 133-144 ◽  
Author(s):  
Jon B. Weissman ◽  
Andrew S. Grimshaw ◽  
R.D. Ferraro
Keyword(s):  
Finite Element ◽  
Message Passing ◽  
Large Scale ◽  
Processing System ◽  
Object Oriented ◽  
Data Parallel ◽  
Programming Tools ◽  
Comparable Performance ◽  
Oriented Parallel ◽  
Performance Results

The conventional wisdom in the scientific computing community is that the best way to solve large-scale numerically intensive scientific problems on today's parallel MIMD computers is to use Fortran or C programmed in a data-parallel style using low-level message-passing primitives. This approach inevitably leads to nonportable codes and extensive development time, and restricts parallel programming to the domain of the expert programmer. We believe that these problems are not inherent to parallel computing but are the result of the programming tools used. We will show that comparable performance can be achieved with little effort if better tools that present higher level abstractions are used. The vehicle for our demonstration is a 2D electromagnetic finite element scattering code we have implemented in Mentat, an object-oriented parallel processing system. We briefly describe the application. Mentat, the implementation, and present performance results for both a Mentat and a hand-coded parallel Fortran version.

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