BwUniCluster3.0/Running Jobs
Purpose and function of a queuing system
Job submission process
Terms and definitions
Partitions
Slurm manages job queues for different partitions. Partitions are used to group similar node types (e.g. nodes with and without accelerators) and to enforce different access policies and resource limits.
On bwUniCluster 3.0 there are different partitions:
- CPU-only nodes
- 2-socket nodes, consisting of 2 Intel Ice Lake processors with 32 cores each or 2 AMD processors with 48 cores each
- 2-socket nodes with very high RAM capacity, consisting of 2 AMD processors with 48 cores each
- GPU-accelerated nodes
- 2-socket nodes with 4x NVIDIA A100 or 4x NVIDIA H100 GPUs
- 4-socket node with 4x AMD Instinct accelerator
Queues
Job queues are used to manage jobs that request access to shared but limited computing resources of a certain kind (partition).
On bwUniCluster 3.0 there are different main types of queues:
- Regular queues
- cpu: Jobs that request CPU-only nodes.
- gpu: Jobs that request GPU-accelerated nodes.
- Development queues (dev)
- Short, usually interactive jobs that are used for developing, compiling and testing code and workflows. The intention behind development queues is to provide users with immediate access to computer resources without having to wait. This is the place to realize instantaneous heavy compute without affecting other users, as would be the case on the login nodes.
Requested compute resources such as (wall-)time, number of nodes and amount of memory are restricted and must fit into the boundaries imposed by the queues. The request for compute resources on the bwUniCluster 3.0 requires at least the specification of the queue and the time.
Jobs
Jobs can be run non-interactively as batch jobs or as interactive jobs. Submitting a batch job means, that all steps of a compute project are defined in a Bash script. This Bash script is queued and executed as soon as the compute resources are available and allocated. Jobs are enqueued with the sbatch command.
For interactive jobs, the resources are requested with the salloc command. As soon as the computing resources are available and allocated, a command line prompt is returned on a computing node and the user can freely dispose of the resources now available to him.
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Please remember:
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Slurm HPC Workload Manager
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.
Any kind of calculation on the compute nodes of bwUniCluster 3.0 requires the user to define calculations as a sequence of commands or single command together with required run time, number of CPU cores and main memory and submit all, i.e., the batch job, to a resource and workload managing software. bwUniCluster 3.0 has installed the workload managing software Slurm. Therefore any job submission by the user is to be executed by commands of the Slurm software. Slurm queues and runs user jobs based on fair sharing policies.
Queues
Regular Queues
| Queue | Node-Type | Default Resources | Minimal Resources | Maximum Resources |
|---|---|---|---|---|
cpu_il
|
CPU nodes Ice Lake |
mem-per-cpu=1950mb | time=72:00:00, nodes=80, mem=249600mb, ntasks-per-node=64, (threads-per-core=2) | |
cpu
|
CPU nodes Standard |
mem-per-cpu=1125mb | time=72:00:00, nodes=70, mem=380000mb, ntasks-per-node=96, (threads-per-core=2) | |
highmem
|
CPU nodes High Memory |
mem-per-cpu=1125mb | time=72:00:00, nodes=4, mem=2300000mb, ntasks-per-node=96, (threads-per-core=2) | |
gpu_h100
|
GPU nodes NVIDIA GPU x4 |
mem-per-cpu=1125mb | time=72:00:00, nodes=12, mem=760000mb, ntasks-per-node=96, (threads-per-core=2) | |
gpu_mi300
|
GPU node AMD GPU x4 |
mem-per-cpu=1125mb cpus-per-gpu=24 |
time=72:00:00, nodes=1, mem=510000mb, ntasks-per-node=40, (threads-per-core=2) | |
gpu_a100_il/gpu_h100_il
|
GPU nodes Ice Lake NVIDIA GPU x4 |
mem-per-gpu=127500mb cpus-per-gpu=16 |
time=72:00:00, nodes=9, mem=510000mb, ntasks-per-node=64, (threads-per-core=2) |
Table 1: Regular Queues
Development Queues
Only for development, i.e. debugging or performance optimization ...
| Queue | Node Type | Default Resources | Minimal Resources | Maximum Resources |
|---|---|---|---|---|
dev_cpu_il
|
CPU nodes Ice Lake |
mem-per-cpu=1950mb | time=30, nodes=8, mem=249600mb, ntasks-per-node=64, (threads-per-core=2) | |
dev_cpu
|
CPU nodes Standard |
mem-per-cpu=1125mb | time=30, nodes=1, mem=180000mb, ntasks-per-node=40, (threads-per-core=2) | |
dev_highmem
|
CPU nodes High Memory |
mem-per-cpu=1125mb | time=30, nodes=1, mem=180000mb, ntasks-per-node=40, (threads-per-core=2) | |
dev_gpu_h100
|
GPU nodes NVIDIA GPU x4 |
mem-per-cpu=1125mb | time=30, nodes=1, mem=180000mb, ntasks-per-node=40, (threads-per-core=2) | |
dev_gpu_a100_il
|
GPU nodes NVIDIA GPU x4 |
mem-per-gpu=127500mb cpus-per-gpu=16 |
time=30, nodes=1, mem=510000mb, ntasks-per-node=64, (threads-per-core=2) |
Table 2: Development Queues
Default resources of a queue class defines time, #tasks and memory if not explicitly given with sbatch command. Resource list acronyms --time, --ntasks, --nodes, --mem and --mem-per-cpu are described here.
Check available resources
Running Jobs
Batch Jobs: sbatch
To run your batch job on one of the thin nodes, please use:
$ sbatch --partition=dev_multiple
or
$ sbatch -p dev_multiple
Interactive Jobs: salloc
On bwUniCluster 3.0 you are only allowed to run short jobs (<< 1 hour) with little memory requirements (<< 8 GByte) on the logins nodes. If you want to run longer jobs and/or jobs with a request of more than 8 GByte of memory, you must allocate resources for so-called interactive jobs by usage of the command salloc on a login node. Considering a serial application running on a compute node that requires 5000 MByte of memory and limiting the interactive run to 2 hours the following command has to be executed:
$ salloc -p single -n 1 -t 120 --mem=5000
Then you will get one core on a compute node within the partition "single". After execution of this command DO NOT CLOSE your current terminal session but wait until the queueing system Slurm has granted you the requested resources on the compute system. You will be logged in automatically on the granted core! To run a serial program on the granted core you only have to type the name of the executable.
$ ./<my_serial_program>
Please be aware that your serial job must run less than 2 hours in this example, else the job will be killed during runtime by the system.
You can also start now a graphical X11-terminal connecting you to the dedicated resource that is available for 2 hours. You can start it by the command:
$ xterm
Note that, once the walltime limit has been reached the resources - i.e. the compute node - will automatically be revoked.
An interactive parallel application running on one compute node or on many compute nodes (e.g. here 5 nodes) with 40 cores each requires usually an amount of memory in GByte (e.g. 50 GByte) and a maximum time (e.g. 1 hour). E.g. 5 nodes can be allocated by the following command:
$ salloc -p multiple -N 5 --ntasks-per-node=40 -t 01:00:00 --mem=50gb
Now you can run parallel jobs on 200 cores requiring 50 GByte of memory per node. Please be aware that you will be logged in on core 0 of the first node. If you want to have access to another node you have to open a new terminal, connect it also to bwUniCluster 3.0 and type the following commands to connect to the running interactive job and then to a specific node:
$ srun --jobid=XXXXXXXX --pty /bin/bash $ srun --nodelist=uc2nXXX --pty /bin/bash
With the command:
$ squeue
the jobid and the nodelist can be shown.
If you want to run MPI-programs, you can do it by simply typing mpirun <program_name>. Then your program will be run on 200 cores. A very simple example for starting a parallel job can be:
$ mpirun <my_mpi_program>
You can also start the debugger ddt by the commands:
$ module add devel/ddt $ ddt <my_mpi_program>
The above commands will execute the parallel program <my_mpi_program> on all available cores. You can also start parallel programs on a subset of cores; an example for this can be:
$ mpirun -n 50 <my_mpi_program>
If you are using Intel MPI you must start <my_mpi_program> by the command mpiexec.hydra (instead of mpirun).
Interactive usage with Jupyter
Monitor and manage jobs
Slurm Options
Slurm Commands (excerpt)
Important Slurm commands for non-administrators working on bwUniCluster 3.0.
| Slurm commands | Brief explanation |
|---|---|
| sbatch | Submits a job and puts it into the queue [sbatch] |
| salloc | Requests resources for an interactive Job [salloc] |
| scontrol show job | Displays detailed job state information [scontrol] |
| squeue | Displays information about active, eligible, blocked, and/or recently completed jobs [squeue] |
| squeue --start | Returns start time of submitted job [squeue] |
| sinfo_t_idle | Shows what resources are available for immediate use [sinfo] |
| scancel | Cancels a job [scancel] |
Job submission : sbatch
Batch jobs are submitted by using the command sbatch. The main purpose of the sbatch command is to specify the resources that are needed to run the job. sbatch will then queue the batch job. However, starting of batch job depends on the availability of the requested resources and the fair sharing value.
Command parameters sbatch
The syntax and use of sbatch can be displayed via:
$ man sbatch
sbatch options can be used from the command line or in your job script. Different defaults for some of these options are set based on the queue and can be found here
| sbatch Options | ||
|---|---|---|
| Command line | Script | Purpose |
| -t, --time=time | #SBATCH --time=time | Wall clock time limit. |
| -N, --nodes=count | #SBATCH --nodes=count | Number of nodes to be used. |
| -n, --ntasks=count | #SBATCH --ntasks=count | Number of tasks to be launched. |
| --ntasks-per-node=count | #SBATCH --ntasks-per-node=count | Maximum count of tasks per node. |
| -c, --cpus-per-task=count | #SBATCH --cpus-per-task=count | Number of CPUs required per (MPI-)task. |
| --mem=value_in_MB | #SBATCH --mem=value_in_MB | Memory in MegaByte per node. (You should omit the setting of this option.) |
| --mem-per-cpu=value_in_MB | #SBATCH --mem-per-cpu=value_in_MB | Minimum Memory required per allocated CPU. (You should omit the setting of this option.) |
| --mail-type=type | #SBATCH --mail-type=type | Notify user by email when certain event types occur. Valid type values are NONE, BEGIN, END, FAIL, REQUEUE, ALL. |
| --mail-user=mail-address | #SBATCH --mail-user=mail-address | The specified mail-address receives email notification of state changes as defined by --mail-type. |
| --output=name | #SBATCH --output=name | File in which job output is stored. |
| --error=name | #SBATCH --error=name | File in which job error messages are stored. |
| -J, --job-name=name | #SBATCH --job-name=name | Job name. |
| --export=[ALL,] env-variables | #SBATCH --export=[ALL,] env-variables | Identifies which environment variables from the submission environment are propagated to the launched application. Default is ALL. |
| -A, --account=group-name | #SBATCH --account=group-name | Change resources used by this job to specified group. You may need this option if your account is assigned to more than one group. By command "scontrol show job" the project group the job is accounted on can be seen behind "Account=". |
| -p, --partition=queue-name | #SBATCH --partition=queue-name | Request a specific queue for the resource allocation. |
| --reservation=reservation-name | #SBATCH --reservation=reservation-name | Use a specific reservation for the resource allocation. |
| -C, --constraint=LSDF | #SBATCH --constraint=LSDF | Job constraint LSDF filesystems. |
| -C, --constraint=BEEOND (BEEOND_4MDS, BEEOND_MAXMDS) | #SBATCH --constraint=BEEOND (BEEOND_4MDS, BEEOND_MAXMDS) | Job constraint BeeOND filesystem. |
Interactive job : salloc
If you want to run an interactive job, you can do so via the command salloc on a login node.
Considering a serial application running on a compute node that requires 5000 MByte of memory and limiting the interactive run to 2 hours the following command has to be executed:
$ salloc --partition=cpu --ntasks=1 --time=120 --mem=5000
Then you will get one core on a compute node within the partition "cpu". After execution of this command DO NOT CLOSE your current terminal session but wait until the queueing system Slurm has granted you the requested resources on the compute node. You will be logged in automatically on the granted core! To run a serial program on the granted core you only have to type the name of the executable.
$ ./<my_serial_program>
Please be aware that your serial job must run less than 2 hours in this example, else the job will be killed during runtime by the system.
You can also start now a graphical X11-terminal connecting you to the dedicated resource that is available for 2 hours. You can start it by the command:
$ xterm
Note that, once the walltime limit has been reached the resources - i.e. the compute node - will automatically be revoked.
An interactive parallel application running on one compute node or on many compute nodes (e.g. here 5 nodes) with 40 cores each requires usually an amount of memory in GByte (e.g. 50 GByte) and a maximum time (e.g. 1 hour). E.g. 5 nodes can be allocated by the following command:
$ salloc --partition=cpu --nodes=5 --ntasks-per-node=40 --time=01:00:00 --mem=50gb
Now you can run parallel jobs on 200 cores requiring 50 GByte of memory per node. Please be aware that you will be logged in on core 0 of the first node. If you want to have access to another node you have to open a new terminal, connect it also to BwUniCluster 2.0 and type the following commands to connect to the running interactive job and then to a specific node:
$ srun --jobid=XXXXXXXX --pty /bin/bash $ srun --nodelist=uc3nXXX --pty /bin/bash
With the command:
$ squeue
the jobid and the nodelist can be shown.
If you want to run MPI-programs, you can do it by simply typing mpirun <program_name>. Then your program will be run on 200 cores. A very simple example for starting a parallel job can be:
$ mpirun <my_mpi_program>
You can also start the debugger ddt by the commands:
$ module add devel/ddt $ ddt <my_mpi_program>
The above commands will execute the parallel program <my_mpi_program> on all available cores. You can also start parallel programs on a subset of cores; an example for this can be:
$ mpirun -n 50 <my_mpi_program>
If you are using Intel MPI you must start <my_mpi_program> by the command mpiexec.hydra (instead of mpirun).