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The proposed algorithm was coded using MATLAB language executed on a computer with a 1.00-GB RAM and an Intel, 2.80-GHz CPU.
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Firstly, distributed object technology facilitates the encapsulating of parallel computing resources into a uniform model despite their differences in implementations that are based on different languages executing on different platforms.
We implemented the algorithm using C++ Language and executed it on a desktop PC with Intel®; Core™ i7-3770K professor of 3.4 GHz and 16 GB memory installed.
For instance, when a multiplication program is run through a standard conventional computer, the steps of the program are translated into the computer's machine language and executed there, but at this level the operations apply to 1's and 0's with no obvious way to map them onto the original numbers to be multiplied or to the multiplication operation.
All algorithms used in MemBrain were implemented in the C programming language and executed in the Linux operating system.
Queries were written in the SPARQL language and executed using ARQ query engine.
A biosimulation model can be expressed in a biosimulation modelling language and executed within a simulation framework.
Routines for image analysis were coded in ImageJ macro language and executed on RGB images without further treatment.
We refine the notion of embedding in order to obtain a formal tool for the comparison of the relative expressive power of different languages, by taking into account also the intended architectures on which the software components described using those languages are executed.
The computer program developed in FORTRAN programming language could execute our suggested methodology automatically.
A compiler optimizes by analyzing source code and determining a representation of the source code in machine language that executes as effectively as possible.
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