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

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 structu...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bernd Bischl [verfasserIn] Michel Lang [verfasserIn] Olaf Mersmann [verfasserIn] Jörg Rahnenführer [verfasserIn] Claus Weihs [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Übergeordnetes Werk:	In: Journal of Statistical Software - Foundation for Open Access Statistics, 2003, 64(2015), 1, Seite 25
Übergeordnetes Werk:	volume:64 ; year:2015 ; number:1 ; pages:25

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.18637/jss.v064.i11

Katalog-ID:	DOAJ005770521

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520			\|a 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.
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allfieldsSound	10.18637/jss.v064.i11 doi (DE-627)DOAJ005770521 (DE-599)DOAJ60e9cca19e5f45d1be88275346059ebd DE-627 ger DE-627 rakwb eng HA1-4737 Bernd Bischl verfasserin aut BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Statistics Michel Lang verfasserin aut Olaf Mersmann verfasserin aut Jörg Rahnenführer verfasserin aut Claus Weihs verfasserin aut In Journal of Statistical Software Foundation for Open Access Statistics, 2003 64(2015), 1, Seite 25 (DE-627)313105669 (DE-600)2010240-9 15487660 nnns volume:64 year:2015 number:1 pages:25 https://doi.org/10.18637/jss.v064.i11 kostenfrei https://doaj.org/article/60e9cca19e5f45d1be88275346059ebd kostenfrei http://www.jstatsoft.org/index.php/jss/article/view/2248 kostenfrei https://doaj.org/toc/1548-7660 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 64 2015 1 25
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author	Bernd Bischl
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topic_title	HA1-4737 BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments
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title	BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments
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title_auth	BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	BatchJobs and BatchExperiments: Abstraction Mechanisms for Using R in Batch Environments
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