Batch computing allows you to queue up jobs and have them executed by the batch system, rather than you having to start an interactive session on a high-performance system and performing tasks one by one. Using the batch system allows you to queue up thousands of jobs- something impractical to impossible when using an interactive session. There are benefits when you have a smaller volume of jobs as well- interactive jobs are dependent on the shell from which they are launched- if your laptop should be disconnected for any reason the job will be terminated.
The batch system used at the Hutch is Slurm. Slurm provides a set of commands for submitting and managing jobs on the gizmo and beagle clusters as well as providing information on the state (success or failure) and metrics (memory and compute usage) of completed jobs. For more detailed information about Slurm see the section below on Using Slurm on Fred Hutch Systems, which also links to a variety of detailed how-to’s and examples to get you started using the on-premise HPC resources available
Using Slurm on Fred Hutch Systems
This section is intended to be a basic introduction to using the workload manager for Fred Hutch managed clusters for high performance computing. Slurm is the workload manager that manages both your jobs and the resources available in the clusters available. There are two main clusters in use today that rely on Slurm - the on-campus
Gizmo cluster and the cloud-based
Beagle cluster (see our Technology page for more information about those resources. Commands work the same in either environment.
Examples of Use
A GitHub repository has been created that is an evolving resource for the community containing working examples of using Slurm at Fred Hutch. Please see the Slurm Examples repo for more specific guidance on using Slurm in variety of settings. This is an evolving example repo that new users can refer to to begin to get into parallel computing and more adept use of Slurm. If you are a Fred Hutch user and would like to contribute to the documentation or the examples there, to share with the community how you structure your interactions with Slurm, submit a pull request there.
Basic Slurm Terminology
A cluster is a collection of compute resources (nodes) under the control of the workload manager (Slurm in our case). At the Hutch we have two clusters,
Gizmo. From most hosts the default cluster will be gizmo- selection of the target cluster is done via an argument to Slurm commands (see Multi-Cluster Operation below)
A partition is a collection of resources (nodes) inside of a cluster. There are defaults, so specifying a partition name is not required. While the different clusters may have different partitions, there are two partitions- a default partition with smaller nodes named campus and a partition with more capable nodes (more memory and CPUs) named largenode.
A node is the basic computing unit that shares processors, memory, and some (limited) local disk. As a rule, you don’t want to worry about choosing a node for your jobs.
A job is a collection of tasks, typically implemented as a shell script. Jobs have an ID (just a number) and a name. The ID is automatically assigned, but you can assign a name to your job.
When we refer to an “account” in the context of Slurm, we are referring to the PI account used to enforce limits and priority and not your HutchNet ID. Your HutchNet ID is associated with an account.
Commands for Managing Jobs
squeue command allows you to see the jobs running and waiting in the job queue.
squeue takes many options to help you select the jobs displayed by the command
|-M cluster||Only show jobs running on the indicated cluster|
|-u username||Limit jobs displayed to those owned by a user|
|-A account||Limit jobs displayed to those owned by an account|
|-p partition||Only show jobs in the indicated partition|
rhino[~]: squeue -u edgar JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON) 31071290 campus wrap edgar R 19:03:13 1 gizmof404
NODELIST(REASON) will show either the name of the node(s) allocated for running jobs or the reason a job isn’t running.
There are many ways to alter which jobs are shown and how the output is formatted- refer to the
squeue manpage for more details on using this command.
scancel allows you to signal jobs- most commonly this command is used to stop execution of a running job or remove a pending job from the job queue. A job ID is the common argument though
scancel will take many other arguments that allow bulk management of jobs- it shares many of the same arguments as
squeue. For example, the following command will cancel all jobs (pending or running) owned by the user
rhino[~]: scancel -u edgar
Obtain a Slurm job allocation (a set of nodes), execute a command, and then release the allocation when the command is finished.
hitparade (Fred Hutch homebrew)
hitparade command will show a summary of all jobs running and queued on the cluster broken down by user and account. Note that this isn’t a Slurm command, rather something built in-house at Fred Hutch.
hitparade takes the
-M argument to select a cluster about which to generate the output.
rhino[~]: hitparade -M beagle loading Python/3.6.4-foss-2016b-fh2... === Queue: campus ======= (R / PD) ====== poe_e (edgar) 300 / 72 === Queue: largenode ======= (R / PD) === schulz_cm (snoopy) 273 / 0
sbatch is used to submit a job script to the cluster. These run jobs without your intervention or input (i.e. non-interactively). Common arguments are:
srun is used to run a task on the cluster. This is an interactive session,
where you can directly view output as it’s produced or provide input (if needed
by the task you are running).
These two take many of the same options:
-Mselect the cluster on which the job will run
-pchange the partition
-trequest a certain amount of time for the job.
-nrequest a number of tasks (default 1)
-crequest a number of processors per task (default 1)
-Jname a job
Output (stdout and stderr) from your job script, steps, tasks, and processes is
captured by Slurm and written to a file named _slurm-
-o will redirect this output (errors as well) to the file indicated as the argument to this option. For example,
-o myjob.out redirects to myjob.out in the submission directory. Adding
%j in this file name will include the job ID.
-o myjob-%j.out would create a file like myjob-12345.out.
Memory is requested with the
--mem option. This option takes an argument: a
number indicating the amount of memory required on the node. The default unit
is megabytes- to specify the unit, append
T for kilobytes,
megabytes, gigabytes, or terabytes.
A memory request is required for the largenode partition. Note that adding a memory request only ensures that sufficient memory is configured on the node and that your job will not exceed the requested memory
GPUs are available on some nodes- these are requested using the option
Submit a batch job (
sbatch), that will run in one day, six hours (with the flag
-t 1-6) in the largenode partition (with the flag
-p largenode) in Beagle (with the flag
-M beagle). This will run one instance of the job with one processor (because no flags were provided to tell it to ask for more than the default). Name the job “quoth-the-raven” (with the
-J flag) and list the script to use in the job
sbatch -M beagle -p largenode -t 1-6 -J quoth-the-raven myscript.sh
Submit a job using 6 cores (with the flag
-c 6) and redirect output to a file named “my-output”:
sbatch -c 6 myscript.sh my-output
Most Slurm commands can operate against remote clusters (i.e.
gizmo). Typically the only change required is to add the argument
-M <cluster name>.
sbatch -M beagle -c 6 myscript.sh my-output scancel -M beagle 12345
hitparade also supports
-M and can be used to show the queue on the different clusters. At this time, multi-cluster operations using the commands
salloc will not work. If use of those commands is necessary, please contact SciComp.
External Slurm and HPC Reference and Learning Resources
For more information and education on how to use HPC resources from external sources see the following sites: