slurm is is albedo's job scheduling system. It is used to submit jobs from the login nodes to the compute nodes.
...
*) For filename patterns see: https://slurm.schedmd.com/sbatch.html#SECTION_%3CB%3Efilename-pattern%3C/B%3E
Details about specific parameters
Account (-A)
Job enforcements
We implemented some enforcements to improve albedo's overall performance.
- Jobs requesting --partition=fat but only low memory are rejected.
- Jobs requesting less than 40 nodes are enforced to use only nodes connected to the very same Infiniband switch (if this is feasible within 15 Minutes).
Details about specific parameters
Account (-A)
Compute resources are attributed to (primary)Compute resources are attributed to (primary)sections and projects at AWI. Therefore it is mandatory to specify an account.
...
Partition | Nodes | Description | |||||||
---|---|---|---|---|---|---|---|---|---|
smp | prod-[001-120200] |
| |||||||
smphtmpp | prod-[121-240001-200] |
| |||||||
fat | fat-00[1-2] |
| |||||||
mpp | prod-[001-120] |
| |||||||
mppht | prod-[121-240] | like mpp but with HT | |||||||
fat | fat-00[1-2] |
| |||||||
| |||||||||
matlab | fat-00[3-4] | currently reserved for matlab users (as personal matlab licenses are node-bound). This might change later.
| |||||||
gpu | gpu-00[1-5] |
| matlab | fat-00[3-4] |
Note |
---|
To prohibit single users from allocating entire resources on these dedicated nodes, we limit the resources per user in this partition to 32 CPUs and 1TB RAM. Please get in touch with us if these limitations conflict with your use case! |
gpu
gpu-00[1-2]
like smp but...
... the two gpu nodes each contain a different number and type of GPU:
gpu-001: 2x A40
gpu-002: 4x A100
--gpus=<GpuType>
- :<GpuQuantity>
(otherwise no GPU will be allocated for you)
Quality of service (--qos)
|
Quality of service (--qos)
Slurm's QOS is a way for us to influence a job's priority (priority QOS_factor) and "cost" (UsageFactor) based on the job's size (we only take walltime into account here!). We therefore created the different QOS, which are listed below.
The default QOS is 30min; for a job with a walltime >30min you have to select and set an appropriate QOS in addition to your walltime!
To facilitate development and testing, we have reserved 20 nodes during working hours exclusively for jobs with QOS=30minA higher priority means your job is scheduled before other jobs. In addition, during working hours 10 nodes are reserved exclusively for jobs using qos=30min (to facilitate development and testing). For longer runs, another QOS (and walltime) has to be specified. Note: long running jobs (longer than 12 hours, up to 48 hours) “cost” more in terms of fairshare (meaning you priority will decrease for further jobs).
QOS | max. walltime | max. Nodes/User | UsageFactor | Priority QOS_factor | Notes | ||
---|---|---|---|---|---|---|---|
30min | 00:30 | - | 1 | 1 | default | ||
12h | 12:00 | 120 | 1 | 0 | |||
48h | 48:00 | 80 | 2 | 0 | |||
1wk | 7-00:00:00 (168h) | 1 | 10 | 0 | only available for users upon request; whenever possible try to adapt your workflow to allow for shorter walltime!
|
Job Job Scheduling
Priority
Jobs on albedo are scheduled based on a priority that is computed by Slurm depending on multiple factors (https://slurm.schedmd.com/priority_multifactor.html).
The higher the priority, the sooner your job begins. (In principle – the backfill scheduling plugin helps making best use of available resources by filling up resources that are reserved (and thus idle) for large higher priority jobs with small (lower priority) jobs.)
At AWI, only few of the possible factors are taken into account:
...
The factors (except of the nice_factor (default is zero), which can be set by the user to downgrade the jobs priority by the setting --nice=...), are numbers in the range from 0 to 1.
They are shortly explained in the following.
...
You can check the recent usage of albedo with this command:
Code Block |
---|
sreport -t Percent cluster UserUtilizationByAccount Start=$(date +%FT%T -d "1 week ago") Format=used,login,account
FairShare |
The The fairshare factor is the most important factor here, but also the most difficult factor to understand. This factor is calculated using the "classic" fairshare algorithm of Slurm (https://slurm.schedmd.com/classic_fair_share.html). It computes the fairshare for each user based on the recent usage of the system.
Note, the usage of your associated account is *not* taken into accunt here, as it was the case on ollie!
Usage is basically "CPU seconds", but weighted using the UsageFactor depending on the used QOS (see section QOS). Furthermore, the usage taken into account here decays with time (with a half life time of 7 days).
Fairshare is the calculated by
...
- sinfo shows existing queues
For example to check how many nodes are available in a given partition (mpp, fat, gpu...)Code Block language bash sinfo -p<partition_name>
- scontrol show job <JobID> shows information scontrol show job <JobID> shows information about specific job
- sstat <JobID> shows resources used by a specific job
- squeue shows information about queues and used nodes
- smap curses-graphic of queues and nodes
- sbatch <script> submits a batch job
- salloc <resources> requests access to compute nodes for interactive use
- scancel <JobID> cancels a batch job
- srun <ressources> <executable> starts a (parallel) codesshare and sprio give information on fair share value and job priority(parallel) code
- sshare and sprio give information on fair share value and job priority
- sreport -t Percent cluster UserUtilizationByAccount Start=$(date +%FT%T -d "1 month ago") Format=used,login,account | head -20 top usage users during the last month
Do's & Don'ts
- Do not use srun for simple non-parallel jobs like cp, ln, rm, cat, g[un]zip
- Do not write loops in your slurm script to start several instance of similar jobs → See Job arrays below
- Make use of parallel srun p[gu]igz instead of g[un]zip if you have allocated more than one CPU already
- Do not allocate costly resources (like fat/gpu nodes) if you not need them. Check the CPU/Memory-Efficiency of your jobs with info.sh -S
Example Scripts
Job arrays
...
Code Block | ||||
---|---|---|---|---|
| ||||
#!/bin/bash #SBATCH --account=<account> # Your account #SBATCH --time =0:10:00 #SBATCH -p mpp #SBATCH -N 2 #SBATCH --tasks-per-node =128 #SBATCH --cpus-per-task =1 #SBATCH --hint=nomultithread #SBATCH --job-name=mpi #SBATCH --output=out_%x.%j # disable hyperthreading #SBATCH --hint=nomultithread module purge module load xthi/1.0-intel-oneapi-mpi2021.6.0-oneapi2022.1.0 intel-oneapi-mpi # module load xthi/1.0-openmpi4.1.3-gcc8.5.0 openmpi/4.1.3 ## Uncomment the following line to enlarge the stacksize if needed, ## e.g., if your code crashes with a spurious segmentation fault. # ulimit -s unlimited # To be on the safe side, we emphasize that it is pure MPI, no OpenMP threads export OMP_NUM_THREADS=1 srun xthi | sort -g -k 4 |
...
Code Block | ||||
---|---|---|---|---|
| ||||
#!/bin/bash #SBATCH --account=<account> # Your account #SBATCH --time =0:10:00 #SBATCH -p mpp #SBATCH -N 2 #SBATCH --tasks-per-node =31 #SBATCH --hint=nomultithread #SBATCH --job-name=mpi_partial_node #SBATCH --output=out_%x.%j # disable hyperthreading #SBATCH --hint=nomultithread module purge module load xthi/1.0-intel-oneapi-mpi2021.6.0-oneapi2022.1.0 intel-oneapi-mpi # module load xthi/1.0-openmpi4.1.3-gcc8.5.0 openmpi/4.1.3 ## Uncomment the following line to enlarge the stacksize if needed, ## e.g., if your code crashes with a spurious segmentation fault. # ulimit -s unlimited # To be on the safe side, we emphasize that it is pure MPI, no OpenMP threads export OMP_NUM_THREADS=1 # The --cpu-bind=rank_ldom distributes the tasks via the node's cores # respecting the node's NUMA domains srun --cpu-bind=rank_ldom xthi | sort -g -k 4 |
...
Code Block | ||
---|---|---|
| ||
#!/bin/bash #SBATCH --account=<account> # Your account #SBATCH --time =0:10:00 #SBATCH -p smp #SBATCH --tasks-per-node =1 #SBATCH --cpus-per-task =64 #SBATCH --job-name=openMP #SBATCH --output=out_%x.%j # disable hyperthreading #SBATCH --hint=nomultithread module purge module load xthi/1.0-intel-oneapi-mpi2021.6.0-oneapi2022.1.0 intel-oneapi-mpi # module load xthi/1.0-openmpi4.1.3-gcc8.5.0 openmpi/4.1.3 ## Uncomment the following line to enlarge the stacksize if needed, ## e.g., if your code crashes with a spurious segmentation fault. # ulimit -s unlimited # export OMP_STACKSIZE=128M # This binds each thread to one core export OMP_PROC_BIND=TRUE # OpenMP and srun, both need to know the number of CPUs per task export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK srun xthi | sort -g -k 4 |
...
Code Block | ||
---|---|---|
| ||
#!/bin/bash #SBATCH --account=<account> # Your account #SBATCH --time =0:10:00 #SBATCH -p mpp #SBATCH -N 2 #SBATCH --tasks-per-node =8 #SBATCH --cpus-per-task =16 #SBATCH --job-name=hybrid #SBATCH --output=out_%x.%j # disable hyperthreading #SBATCH --hint=nomultithread module purge module load xthi/1.0-intel-oneapi-mpi2021.6.0-oneapi2022.1.0 intel-oneapi-mpi # module load xthi/1.0-openmpi4.1.3-gcc8.5.0 openmpi/4.1.3 ## Uncomment the following line to enlarge the stacksize if needed, ## e.g., if your code crashes with a spurious segmentation fault. # ulimit -s unlimited # export OMP_STACKSIZE=128M # This binds each thread to one core export OMP_PROC_BIND=TRUE # OpenMP and srun, both need to know the number of CPUs per task export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK export SRUN_CPUS_PER_TASK=$SLURM_CPUS_PER_TASK srun xthi | sort -g -k 4 |
...
Code Block | ||
---|---|---|
| ||
#!/bin/bash #SBATCH --account=<account> # Your account #SBATCH --time =0:10:00 #SBATCH -p gpu #SBATCH --ntasks=1 #SBATCH --gpugpus=a100:2 # allocate 2 (out of 4) A100 GPUs; to get 2 (out of 2) A40 GPUs use --gpus=a40:2 #SBATCH --hint=nomultithread #SBATCH --job-name=gpu #SBATCH --output=out_%x.%j # disable hyperthreading #SBATCH --hint=nomultithread ## Uncomment the following line to enlarge the stacksize if needed, ## e.g., if your code crashes with a spurious segmentation fault. # ulimit -s unlimited # To be on the safe side, we emphasize that it is pure MPI, no OpenMP threads export OMP_NUM_THREADS=1 srun your_code_that_runs_on_GPUs |
...