Slurm is an open source, fault-tolerant, and highly scalable cluster management and job scheduling system for large and small Linux clusters. Slurm requires no kernel modifications for its operation and is relatively self-contained. As a cluster workload manager, Slurm has three key functions. First, it allocates exclusive and/or non-exclusive access to resources (compute nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (normally a parallel job) on the set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending work.

References in zbMATH (referenced in 17 articles )

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  1. Kiselev, E. A.; Telegin, P. N.; Shabanov, B. M.: An energy-efficient scheduling algorithm for shared facility supercomputer centers (2021)
  2. Sakdhnagool, Putt; Kunaseth, Manaschai; Piyatumrong, Apivadee; Rungnim, Chompoonut; Jarerattanachat, Viwan; Udomsiripinij, Wirote; Tharatipayakul, Kittithorn; Suwan, Takdanai; Srichaikul, Piyawut: TARA: a year in review (2021)
  3. Victoria B. Stephens, David O. Lignell: One-dimensional turbulence (ODT): Computationally efficient modeling and simulation of turbulent flows (2021) not zbMATH
  4. Kiselev, E. A.; Kiselev, V. I.; Shabanov, B. M.; Aladyshev, O. S.; Baranov, A. V.: The energy efficiency evaluating method determining energy consumption of the parallel program according to its profile (2020)
  5. Max Langer, Yuhe Zhang, Diogo Figueirinhas, Jean-Baptiste Forien, Claire Mouton, Rajmund Mokso, Pablo Villanueva-Perez: PyPhase - a Python package for X-ray phase imaging (2020) arXiv
  6. Mortensen, J., Gjerding, M., Thygesen, K.: MyQueue: Task and work ow scheduling system (2020) not zbMATH
  7. Savin, G. I.; Shabanov, B. M.; Nikolaev, D. S.; Baranov, A. V.; Telegin, P. N.: Jobs runtime forecast for JSCC RAS supercomputers using machine learning methods (2020)
  8. George G Vega Yon; Paul Marjoram: sluRm: A lightweight wrapper for HPC with Slurm (2019) not zbMATH
  9. Savin, G. I.; Shabanov, B. M.; Telegin, P. N.; Baranov, A. V.: Joint supercomputer center of the Russian Academy of Sciences: present and future (2019)
  10. Tim Besard, Valentin Churavy, Alan Edelman, Bjorn De Sutter: Rapid software prototyping for heterogeneous and distributed platforms (2019) not zbMATH
  11. Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, Weinan E: DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models (2019) arXiv
  12. Alexander Foss; Marianthi Markatou: kamila: Clustering Mixed-Type Data in R and Hadoop (2018) not zbMATH
  13. Hiller, Benjamin; Koch, Thorsten; Schewe, Lars; Schwarz, Robert; Schweiger, Jonas: A system to evaluate gas network capacities: concepts and implementation (2018)
  14. Soner, Seren; Ă–zturan, Can: Integer programming based heterogeneous CPU-GPU cluster schedulers for SLURM resource manager (2015)
  15. Giorgino, Toni; Harvey, M. J.; de Fabritiis, Gianni: Distributed computing as a virtual supercomputer: Tools to run and manage large-scale BOINC simulations (2010) ioport
  16. He, Xubin; Ou, Li; Engelmann, Christian; Chen, Xin; Scott, Stephen L.: Symmetric active/active metadata service for high availability parallel file systems (2009) ioport
  17. Castain, Ralph H.; Squyres, Jeffrey M.: Creating a transparent, distributed, and resilient computing environment: the openRTE project (2007) ioport