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An Experimentation Platform for the Automatic Parallelization of R Programs

An Experimentation Platform for the Automatic Parallelization of R Programs
Name:

Konferenzartikel 

Year:

2012 

Author:

Frank Padberg, Michael Mirold 

Links:PDF

Zusammenfassung

We present our ALCHEMY platform that supports the automatic parallelization of R programs during execution. Parallelization occurs fully transparent to the user. Different parallelization techniques can be implemented as modules, linked into the platform, and combined with each other. The parallelization analysis modules and code transformation modules use a new intermediate representation for sequential and parallelized R code. Successfully parallelized parts of the R program are executed on a multicore processor; the results and the remaining sequential parts are fed back into the standard R interpreter and evaluated to completion. This way, an R user can benefit from multiprocessor performance without writing a single line of parallel code. At this stage of the research project, the main goal is to enable ample experimentation with different approaches to the automatic parallelization of scripting languages such as R.

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Bibtex

Forschungsprojekt Apart - Entwurfsmustergestützte


@@conferece{,

  author={Frank Padberg, Michael Mirold},
  title={An Experimentation Platform for the Automatic Parallelization of R Programs},
  year=2012,
  month=12,
  booktitle={19th Asia-Pacific Conference on Software Engineering APSEC},
  url={https://ps.ipd.kit.edu/backend/tl_files/ipd/Dateien/cat_veroeffentlichungen/PadbergMirold_APSEC2012.pdf},
  abstract={We present our ALCHEMY platform that supports the automatic parallelization of R programs during execution. Parallelization occurs fully transparent to the user. Different parallelization techniques can be implemented as modules, linked into the platform, and combined with each other. The parallelization analysis modules and code transformation modules use a new intermediate representation for sequential and parallelized R code. Successfully parallelized parts of the R program are executed on a multicore processor; the results and the remaining sequential parts are fed back into the standard R interpreter and evaluated to completion. This way, an R user can benefit from multiprocessor performance without writing a single line of parallel code. At this stage of the research project, the main goal is to enable ample experimentation with different approaches to the automatic parallelization of scripting languages such as R.  },

}