batchtools

R package batchtools: Tools for Computation on Batch Systems. As a successor of the packages ’BatchJobs’ and ’BatchExperiments’, this package provides a parallel implementation of the Map function for high performance computing systems managed by schedulers ’IBM Spectrum LSF’ (<https://www.ibm.com/us-en/marketplace/hpc-workload-management>), ’OpenLava’ (<http://www.openlava.org/>), ’Univa Grid Engine’/’Oracle Grid Engine’ (<http://www.univa.com/>), ’Slurm’ (<http://slurm.schedmd.com/>), ’TORQUE/PBS’ (<http://www.adaptivecomputing.com/products/open-source/torque/>), or ’Docker Swarm’ (<https://docs.docker.com/swarm/>). A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.


References in zbMATH (referenced in 13 articles )

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  1. McDonald, Daniel J.; McBride, Michael; Gu, Yupeng; Raphael, Christopher: Markov-switching state space models for uncovering musical interpretation (2021)
  2. Abbas, Sermad; Fried, Roland: Robust control charts for the mean of a locally linear time series (2020)
  3. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  4. Homrighausen, Darren; McDonald, Daniel J.: Compressed and penalized linear regression (2020)
  5. Jonas Rieger: ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations (2020) not zbMATH
  6. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)
  7. George G Vega Yon; Paul Marjoram: sluRm: A lightweight wrapper for HPC with Slurm (2019) not zbMATH
  8. Michael Schubert: clustermq enables efficient parallelization of genomic analyses (2019) not zbMATH
  9. Probst, Philipp; Boulesteix, Anne-Laure; Bischl, Bernd: Tunability: importance of hyperparameters of machine learning algorithms (2019)
  10. Seibold, Heidi; Hothorn, Torsten; Zeileis, Achim: Generalised linear model trees with global additive effects (2019)
  11. Probst, Philipp; Boulesteix, Anne-Laure: To tune or not to tune the number of trees in random forest (2018)
  12. Nathan Sheffield, VP Nagraj, Vince Reuter: simpleCache: R caching for reproducible, distributed, large-scale projects (2017) not zbMATH
  13. Thomas, Janek; Hepp, Tobias; Mayr, Andreas; Bischl, Bernd: Probing for sparse and fast variable selection with model-based boosting (2017)