Slurm Workload Manager
||This article includes a list of references, but its sources remain unclear because it has insufficient inline citations. (January 2014)|
|Type||Job Scheduler for Cluster and Supercomputers|
|License||GNU General Public License|
The Slurm Workload Manager (formally known as Simple Linux Utility for Resource Management (SLURM)), or Slurm for short, is a free and open-source job scheduler for the Linux kernel used by many of the world's supercomputers and computer clusters. It provides three key functions. First, it allocates exclusive and/or non-exclusive access to resources (computer nodes) to users for some duration of time so they can perform work. Second, it provides a framework for starting, executing, and monitoring work (typically a parallel job such as MPI) on a set of allocated nodes. Finally, it arbitrates contention for resources by managing a queue of pending jobs.
Slurm began development as a collaborative effort primarily by Lawrence Livermore National Laboratory, SchedMD, Linux NetworX, Hewlett-Packard, and Groupe Bull as a Free Software resource manager. It was inspired by the closed source Quadrics RMS and shares a similar syntax. Over 100 people around the world have contributed to the project. It has since evolved into a sophisticated batch scheduler capable of satisfying the requirements of many large computer centers.
As of June 2014[update], TOP500 list of most powerful computers in the world indicates that Slurm is the workload manager on six of the top ten systems. Some of the systems in the top ten running Slurm include Tianhe-2, a 33.86 PetaFlop system at NUDT, IBM Sequoia, an IBM Bluegene/Q with 1.57 million cores and 17.2 Petaflops at Lawrence Livermore National Laboratory; Piz Daint a 7.78 PetaFlop Cray computer at Swiss National Supercomputing Centre, Stampede, a 5.17 PetaFlop Dell computer at the Texas Advance Computing Center; and Vulcan, a 4.29 Petaflop IBM Bluegene/Q at Lawrence Livermore National Laboratory;.
Slurm's design is very modular with about 100 optional plugins. In its simplest configuration, it can be installed and configured in a couple of minutes. More sophisticated configurations provide database integration for accounting, management of resource limits and workload prioritization. Slurm also works with several meta-schedulers such as Moab Cluster Suite, Maui Cluster Scheduler, and Platform LSF.
Notable Slurm features include the following:
- No single point of failure, backup daemons, fault-tolerant job options
- Highly scalable (schedules up to 100,000 independent jobs on the 100,000 sockets of IBM Sequoia)
- High performance (up to 1000 job submissions per second and 600 job executions per second)
- Free and open-source software (GNU General Public License)
- Highly configurable with about 100 plugins
- Fair-share scheduling with hierarchical bank accounts
- Preemptive and gang scheduling (time-slicing of parallel jobs)
- Integrated with database for accounting and configuration
- Resource allocations optimized for network topology and on-node topology (sockets, cores and hyperthreads)
- Advanced reservation
- Idle nodes can be powered down
- Different operating systems can be booted for each job
- Scheduling for generic resources (e.g. Graphics processing unit)
- Real-time accounting down to the task level (identify specific tasks with high CPU or memory usage)
- Accounting for power usage by job
- Support of IBM Parallel Environment (PE/POE)
- Support for job arrays
- Job profiling (periodic sampling of each tasks CPU use, memory use, power consumption, network and file system use)
- Accounting for a job's power consumption
- Support for MapReduce+
The following features are announced for version 14.11 of Slurm, was released in November 2014:
- Improved job array data structure and scalability
- Support for heterogeneous generic resources
- Add user options to set the CPU governor
- Automatic job requeue policy based on exit value
- Report API use by user, type, count and time consumed
- Communication gateway nodes improve scalability
While Slurm was originally written for the Linux kernel, the latest version supports many other operating systems, including AIX, BSDs (FreeBSD, NetBSD and OpenBSD), Linux, Mac OS X, and Solaris. Slurm also supports several unique computer architectures, including:
- IBM BlueGene L, P and Q models including the 20 petaflop IBM Sequoia
- Cray XT, XE and Cascade
- Tianhe-2 a 33.9 petaflop system with 32,000 Intel Ivy Bridge chips and 48,000 Intel Xeon Phi chips with a total of 3.1 million cores
- IBM Parallel Environment
Slurm is available under the GNU General Public License V2.
In 2010, the developers of Slurm founded SchedMD, which maintains the canonical source, provides development, level 3 commercial support and training services. Commercial support is also available from Bright Computing, Bull. Cray, and Science + Computing
- Pascual, Jose Antonio; Navaridas, Javier; Miguel-Alonso, Jose (2009). "Job Scheduling Strategies for Parallel Processing". Lecture Notes in Computer Science 5798. pp. 138–144. doi:10.1007/978-3-642-04633-9_8. ISBN 978-3-642-04632-2.
- "Slurm Commercial Support, Development, and Installation". SchedMD. Retrieved 2014-02-23.
- "Texas Advanced Computing Center - Home". Tacc.utexas.edu. Retrieved 2014-02-23.
- Donald B Johnston (2010-10-01). "Lawrence Livermore's Vulcan brings 5 petaflops computing power to collaborations with industry and academia to advance science and technology". Llnl.gov. Retrieved 2014-02-23.
- "Slurm - What's New". SchedMD. Retrieved 2014-08-29.
- SLURM Platforms
- Balle, Susanne M.; Palermo, Daniel J. (2008). "Job Scheduling Strategies for Parallel Processing". Lecture Notes in Computer Science 4942. p. 37. doi:10.1007/978-3-540-78699-3_3. ISBN 978-3-540-78698-6.
- Jette, M.; Grondona, M. (June 2003). "SLURM: Simple Linux Utility for Resource Management" (PDF). Proceedings of ClusterWorld Conference and Expo (San Jose, California).
- Layton, Jeffrey B. (5 February 2009). "Caos NSA and Perceus: All-in-one Cluster Software Stack". Linux Magazine.
- Yoo, Andy B.; Jette, Morris A.; Grondona, Mark (2003). "Job Scheduling Strategies for Parallel Processing". Lecture Notes in Computer Science 2862. p. 44. doi:10.1007/10968987_3. ISBN 978-3-540-20405-3.