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Ateji PX
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Ateji PX
Ateji PX is an object-oriented programming language extension for Java. It is intended to facilliate parallel computing on multi-core processors, GPU, Grid and Cloud. It appears to be no longer maintained.
Ateji PX can be integrated with the Eclipse IDE, requires minimal learning of the additional parallel constructs and does not alter the development process.
Each || symbol introduces a parallel branch. Running this program will print either
or
depending on how the parallel branches happen to be scheduled.
The quantification (int i : N) creates one parallel branch for each value of i. The effect of this code is to increment all elements of array in parallel. This code is equivalent to
More complex quantifications are possible. The following example quantifies over the upper left triangle of a square matrix:
Code that performs a similar and typically small operation on a large collection of elements is called data parallel, and appears often in high-performance scientific applications. A typical representative of data-parallel languages for the C/C++ or Fortran ecosystems is OpenMP.
Hub AI
Ateji PX AI simulator
(@Ateji PX_simulator)
Ateji PX
Ateji PX is an object-oriented programming language extension for Java. It is intended to facilliate parallel computing on multi-core processors, GPU, Grid and Cloud. It appears to be no longer maintained.
Ateji PX can be integrated with the Eclipse IDE, requires minimal learning of the additional parallel constructs and does not alter the development process.
Each || symbol introduces a parallel branch. Running this program will print either
or
depending on how the parallel branches happen to be scheduled.
The quantification (int i : N) creates one parallel branch for each value of i. The effect of this code is to increment all elements of array in parallel. This code is equivalent to
More complex quantifications are possible. The following example quantifies over the upper left triangle of a square matrix:
Code that performs a similar and typically small operation on a large collection of elements is called data parallel, and appears often in high-performance scientific applications. A typical representative of data-parallel languages for the C/C++ or Fortran ecosystems is OpenMP.