Slurm Workload Manager

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. The first release happened in 2002. It was inspired by the closed source Quadrics RMS and shares a similar syntax. The name is a reference to the soda in Futurama. Over 250 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. 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 Bull, Cray, and Science + Computing (subsidiary of Atos). , Slurm is the workload manager on about 60% of the TOP500 supercomputers. ==Structure==

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. ==Features==

Slurm features include: • 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) • Resource limits by user or bank account • Accounting for power consumption by job • Support of IBM Parallel Environment (PE/POE) • Support for job arrays • Job profiling (periodic sampling of each task's CPU use, memory use, power consumption, network and file system use) • Sophisticated multifactor job prioritization algorithms • Support for MapReduce+ • Support for burst buffer that accelerates scientific data movement • Support for heterogeneous generic resources • Automatic job requeue policy based on exit value ==Supported platforms==

Recent Slurm releases run only on Linux. Older versions had been ported to a few other POSIX-based operating systems, including BSDs (FreeBSD, NetBSD and OpenBSD), but this is no longer feasible as Slurm now requires cgroups for core operations. Clusters running operating systems other than Linux will need to use a different batch system, such as LPJS. Slurm also supports several unique computer architectures, including: • IBM BlueGene/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 • Anton == Usage ==

The slurm system has three main parts: • slurmctld, a central control daemon running on a single control node (optionally with failover backups); • Many computing nodes, each with one or more slurmd daemons; • Clients that connect to the manager node, often with ssh. • The clients can issue commands to the control daemon, which would accept and divide the workload to the computing daemons. For clients, the main commands are srun (queue up an interactive job), sbatch (queue up a job), squeue (print the job queue) and scancel (remove a job from the queue). Jobs can be run in batch mode or interactive mode. For interactive mode, a compute node would start a shell, connects the client into it, and run the job. From there the user may observe and interact with the job while it is running. Usually, interactive jobs are used for initial debugging, and after debugging, the same job would be submitted by sbatch. For a batch mode job, its stdout and stderr outputs are typically directed to text files for later inspection. ==See also==