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BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments

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  • Bischl, Bernd
  • Lang, Michel
  • Mersmann, Olaf
  • Rahnenführer, Jörg
  • Weihs, Claus

Abstract

Empirical analysis of statistical algorithms often demands time-consuming experiments. We present two R packages which greatly simplify working in batch computing environments. The package BatchJobs implements the basic objects and procedures to control any batch cluster from within R. It is structured around cluster versions of the well-known higher order functions Map, Reduce and Filter from functional programming. Computations are performed asynchronously and all job states are persistently stored in a database, which can be queried at any point in time. The second package, BatchExperiments, is tailored for the still very general scenario of analyzing arbitrary algorithms on problem instances. It extends package BatchJobs by letting the user define an array of jobs of the kind “apply algorithm A to problem instance P and store results”. It is possible to associate statistical designs with parameters of problems and algorithms and therefore to systematically study their influence on the results.The packages’ main features are: (a) Convenient usage: All relevant batch system operations are either handled internally or mapped to simple R functions. (b) Portability: Both packages use a clear and well-defined interface to the batch system which makes them applicable in most high-performance computing environments. (c) Reproducibility: Every computational part has an associated seed to ensure reproducibility even when the underlying batch system changes. (d) Abstraction and good software design: The code layers for algorithms, experiment definitions and execution are cleanly separated and enable the writing of readable and maintainable code.

Suggested Citation

  • Bischl, Bernd & Lang, Michel & Mersmann, Olaf & Rahnenführer, Jörg & Weihs, Claus, 2015. "BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i11).
  • Handle: RePEc:jss:jstsof:v:064:i11
    DOI: http://hdl.handle.net/10.18637/jss.v064.i11
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    References listed on IDEAS

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    1. Kane, Michael & Emerson, John W. & Weston, Stephen, 2013. "Scalable Strategies for Computing with Massive Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i14).
    2. Hoffmann, Thomas J., 2011. "Passing in Command Line Arguments and Parallel Cluster/Multicore Batching in R with batch," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(c01).
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    Cited by:

    1. Daniel Horn & Aydın Demircioğlu & Bernd Bischl & Tobias Glasmachers & Claus Weihs, 2018. "A comparative study on large scale kernelized support vector machines," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 867-883, December.
    2. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).

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