Running Jobs on Sapelo2: Difference between revisions
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If using OnDemand is not an option, and you want to run an application as an interactive job and have its graphical | If using OnDemand is not an option, and you want to run an application as an interactive job and have its graphical | ||
user interface displayed on the terminal of your local machine, you need to | user interface displayed on the terminal of your local machine, you need to | ||
enable X-forwarding when you ssh into the login node. This can be done | enable X-forwarding when you ssh into the login node. This can be done on a Linux machine | ||
by simply adding the -X option when ssh-ing into Sapelo2. For information on how | by simply adding the -X option when ssh-ing into Sapelo2. For information on how | ||
to do this on windows and mac, please see questions 5.4 and 5.5 in the [[Frequently Asked Questions]] | to do this on windows and mac, please see instructions within questions 5.4 and 5.5 in the [[Frequently Asked Questions]] | ||
page. | page. | ||
After setting up an x forwarding terminal on your local machine, start an interactive session, but add the option --x11 to the <code>interact</code> command. | |||
An interactive session on a compute node, with X forwarding enabled, can be started with the command | An interactive session on a compute node, with X forwarding enabled, can be started with the command | ||
Revision as of 16:33, 18 October 2024
Using the Queueing System
The login node for the Sapelo2 cluster should be used for text editing, and job submissions. No jobs should be run directly on the login node. Processes that use too much CPU or RAM on the login node may be terminated by GACRC staff, or automatically, in order to keep the cluster running properly. Jobs should be run using the Slurm queueing system. The queueing system should be used to run both interactive and batch jobs.
Batch partitions (queues) defined on the Sapelo2
There are different partitions defined on Sapelo2. The Slurm queueing system refers to queues as partition. Users are required to specify, in the job submission script or as job submission command line arguments, the partition and the resources needed by the job in order for it to be assigned to compute node(s) that have enough available resources (such as number of cores, amount of memory, GPU cards, etc). Please note, Slurm will not allow a job to be submitted if there are no resources matching your request. Please refer to Migrating from Torque to Slurm for more info about Slurm queueing system.
The following partitions are defined on the Sapelo2 cluster:
| Partition Name | Time limit | Max jobs | Notes |
|---|---|---|---|
| batch | 7 days | Regular nodes. | |
| batch-30d | 30 days | 2 | Regular nodes. A given user can have up to one job running at a time here, plus one pending, or two pending and none running. A user's attempt to submit a third job into this partition will be rejected. |
| highmem_p | 7 days | For high memory jobs | |
| highmem_30d_p | 30 days | 2 | For high memory jobs. A given user can have up to one job running at a time here, plus one pending, or two pending and none running. A user's attempt to submit a third job into this partition will be rejected. |
| hugemem_p | 7 days | 4 | For jobs needing up to 3TB of memory. |
| hugemem_30d_p | 30 days | 4 | For jobs needing up to 3TB of memory. |
| gpu_p | 7 days | For GPU-enabled jobs. | |
| gpu_30d_p | 30 days | 2 | For GPU-enabled jobs. A given user can have up to one job running at a time here, plus one pending, or two pending and none running. A user's attempt to submit a third job into this partition will be rejected. |
| inter_p | 2 days | Regular nodes, for interactive jobs. | |
| name_p | variable | Partitions that target different groups' buy-in nodes. The name string is specific to each group. | |
| scavenge_p | 2 hours | Partition that targets the buy-in nodes. When there are no available resources in the batch partition, short jobs submitted there might be automatically transferred into scavenge_p, to run on idle buy-in resources. Jobs cannot be submitted directly to this partition. |
For more detailed information about the partitions, please see Job Submission partitions on Sapelo2.
The table below summarizes the partitions (queues) defined and the compute nodes that they target:
| Partition Name | Node Features | Node Number | Description | Memory for jobs | Notes |
|---|---|---|---|---|---|
| batch, batch_30d | AMD, Opteron, QDR | 48-core, 128GB RAM, AMD Opteron, QDR IB interconnect | 122GB | Regular nodes. | |
| batch, batch_30d | AMD, EPYC, EDR | 64-core, 128GB RAM, AMD EPYC, IB EDR interconnect | 120GB | Regular nodes | |
| batch, batch_30d | AMD, EPYC, EDR | 32-core, 128GB RAM, AMD EPYC, IB EDR interconnect | 120GB | Regular nodes | |
| batch, batch_30d | AMD, Opteron, QDR | 48-core, 256GB RAM, AMD Opteron, QDR IB interconnect | 250GB | Regular nodes. | |
| batch, batch_30d | Intel, Skylake, EDR | 32-core, 192GB RAM, Intel Xeon Skylake, IB EDR interconnect | 180GB | Regular nodes | |
| batch, batch_30d | Intel, Broadwell, EDR | 28-core, 64GB RAM, Intel Xeon Broadwell, IB EDR interconnect | 58GB | Regular nodes | |
| highmem_p, highmem_30d_p | AMD, EPYC, EDR | 64-core, 1TB RAM, AMD EPYC, IB EDR interconnect | 950GB | For high memory jobs | |
| highmem_p, highmem_30d_p | Intel, EDR | 32-core, 1TB RAM, Intel, IB EDR interconnect | 950GB | For high memory jobs | |
| highmem_p, highmem_30d_p | AMD, Opteron, EDR | 48-core, 1TB RAM, AMD Opteron, IB EDR interconnect | 950GB | For high memory jobs | |
| highmem_p, highmem_30d_p | AMD, Opteron, QDR | 48-core, 512GB, AMD Opteron, IB QDR interconnect | 500GB | For high memory jobs | |
| highmem_p, highmem_30d_p | AMD, EPYC, EDR | 32-core, 512GB RAM, AMD EPYC, IB EDR interconnect | 490GB | For high memory jobs | |
| hugemem_p, hugemem_30d_p | AMD, EPYC, EDR | 32-core, 2TB RAM, AMD EPYC, IB EDR interconnect | 2000GB | For high memory jobs | |
| hugemem_p, hugemem_30d_p | AMD, EPYC, EDR | 48-core, 3TB RAM, AMD EPYC, IB EDR interconnect | 3000GB | For high memory jobs | |
| gpu_p, gpu_30d_p | GPU, A100, EDR | 64-core, 1000GB RAM, AMD EPYC, 4 NVIDIA A100 GPUs, EDR IB interconnect | 1000GB | For GPU-enabled jobs. | |
| gpu_p, gpu_30d_p | GPU, P100, EDR | 32-core, 192GB RAM, Intel Xeon Skylake, 1 NVIDIA P100 GPUs, EDR IB interconnect | 180GB | For GPU-enabled jobs. | |
| gpu_p, gpu_30d_p | GPU, K40, QDR | 16-core, 128GB RAM, Intel Xeon , 8 NVIDIA K40 GPUs, QDR IB interconnect | 120GB | For GPU-enabled jobs. | |
| gpu_p, gpu_30d_p | GPU, K20, QDR | 12-core, 96GB RAM, Intel Xeon , 7 NVIDIA K20Xm GPUs, QDR IB interconnect | 70GB | For GPU-enabled jobs. |
You can check all partitions (queues) defined in the cluster with the command
sinfo
Job submission Scripts
Users are required to specify the number of cores, the amount of memory, the partition (queue) name, and the maximum wallclock time needed by the job.
Header lines
Basic job submission script
At a minimum, the job submission script needs to have the following header lines:
#!/bin/bash #SBATCH --partition=batch #SBATCH --job-name=test #SBATCH --ntasks=1 #SBATCH --time=4:00:00 #SBATCH --mem=10G
Commands to run your application should be added after these header lines.
Header lines explained:
- #!/bin/bash: specify Linux default shell bash
- #SBATCH --partition=batch : specify the partition (queue) to run on, e.g. batch
- #SBATCH --job-name=test : specify the job name, e.g. test
- #SBATCH --ntasks=1 : specify the number of tasks (e.g. 1)
- #SBATCH --time=4:00:00 : specify the maximum walltime of the job in the format D-HH:MM:SS (e.g. --time=1- for one day or --time=4:00:00 for 4 hours)
- #SBATCH --mem=10G : specify the maximum memory per node required by the job (e.g. 10GB)
Below are some of the most commonly used queueing system options to configure the job.
Options to request resources for the job
- -t, --time=time
Wall clock time limit of a job running on cluster. Acceptable formats include "minutes", "minutes:seconds", "hours:minutes:seconds", "days-hours", "days-hours:minutes", and "days-hours:minutes:seconds". This is a required option.
- --mem=num
Maximum amount of memory in MegaBytes per node required by the job. Different units can be specified using the suffix [K|M|G|T].
- --mem-per-cpu=num
Minimum amount of memory in MegaBytes per allocated CPU. Different units can be specified using the suffix [K|M|G|T].
- -n, --ntasks=num
Number of tasks to run. The default is one task per node. For use with distributed parallelism. See below.
- -N, --nodes=num
Number of nodes allocated to the job. Default is one node.
- --ntasks-per-node=num
Number of tasks invoked on each node. Meant to be used with the --nodes option. For use with distributed parallelism. See below.
- -c, --cpus-per-task=ncpus
Number of CPUs allocated to each task. For use with shared memory parallelism. See below.
- -C, --constraint=<list>
List of node features required by the job. Only nodes having features matching the job constraints will be used to satisfy the request. Multiple constraints may be specified with AND, OR, matching OR, resource counts, etc.
- --gres=<list>
A comma delimited list of generic consumable resources. For example, to request one P100 GPU card: --gres=gpu:P100:1
Please try to request resources for your job as accurately as possible, because this allows your job to be dispatched to run at the earliest opportunity and it helps the system allocate resources efficiently to start as many jobs as possible, benefiting all users.
Options to manage job notification and output
- -J, --job-name jobname
Specify a name for the job. The specified name will appear along with the job id number when querying running jobs on the system. The default is the supplied executable program's name. Within the job, $SBATCH_JOB_NAME expands to the job name.
- -o, --output=path/for/stdout
Send stdout to path/for/stdout. The default filename is slurm-${SLURM_JOB_ID}.out, e.g. slurm-12345.out, in the directory from which the job was submitted.
- -e, --error=path/for/stderr
Send stderr to path/for/stderr. If --error is not specified, both stdout and stderr will directed to the file specified by --output.
- --mail-user=username@uga.edu
Send email notification to the address you specified when certain events occur.
- --mail-type=type
Notify user by email when certain event types occur. Valid type values are NONE, BEGIN, END, FAIL, REQUEUE, ALL, TIME_LIMIT, TIME_LIMIT_90 (reached 90 percent of time limit), TIME_LIMIT_80 and TIME_LIMIT_50.
By default, email notifications set for an array job will generate one email message for the array job. If you would like to receive an email message for individual array job elements (up to a certain limit), please add ARRAY_TASKS to the --mail-type option.
Options to set Array Jobs
If you wish to run an application binary or script using e.g. different input files, then you might find it convenient to use an array job. To create an array job with e.g. 10 elements, use
#SBATCH -a 0-9
or
#SBATCH --array=0-9
Each array job element runs as an independent job, so multiple elements can run concurrently if resources are available. For this reason, the job ID which is stored in SLURM_JOB_ID for each element in an array job will be different and unique. The ID of each element in an array job, i.e., array element index value, is stored in SLURM_ARRAY_TASK_ID. The ID of an array job as whole is stored in SLURM_ARRAY_JOB_ID. For this reason, it will be the same for all elements in an array job. The JodID reported by sq command is a combination of SLURM_ARRAY_JOB_ID and SLURM_ARRAY_TASK_ID connected by "_".
sbatch --array=1-3 -N1 sub.sh will generate a job array containing three jobs. If the sbatch command responds Submitted batch job 36 then the environment variables will be set as follows: SLURM_JOB_ID=36 SLURM_ARRAY_JOB_ID=36 SLURM_ARRAY_TASK_ID=1 SLURM_ARRAY_TASK_COUNT=3 SLURM_ARRAY_TASK_MAX=3 SLURM_ARRAY_TASK_MIN=1 SLURM_JOB_ID=37 SLURM_ARRAY_JOB_ID=36 SLURM_ARRAY_TASK_ID=2 SLURM_ARRAY_TASK_COUNT=3 SLURM_ARRAY_TASK_MAX=3 SLURM_ARRAY_TASK_MIN=1 SLURM_JOB_ID=38 SLURM_ARRAY_JOB_ID=36 SLURM_ARRAY_TASK_ID=3 SLURM_ARRAY_TASK_COUNT=3 SLURM_ARRAY_TASK_MAX=3 SLURM_ARRAY_TASK_MIN=1
Most Slurm commands recognize the SLURM_ARRAY_JOB_ID plus SLURM_ARRAY_TASK_ID values separated by an underscore as identifying an element of a job array, for example, 36_2 would be equivalent ways to identify the second array element of array job 36.
For more information, please see Array Jobs.
Option to set job dependency
You can set job dependency with the option -d or --dependency=dependency-list. For example, if you want to specify that one job starts to run after the job 1234 and 1235 have successfully executed (ran to completion with an exit code of zero), you can add the following header line in the job submission script of the job:
#SBATCH --dependency=afterok:1234:1235
Having this header line in the job submission script will ensure that the job is only dispatched to run after job 1234 and 1235 have completed successfully.
You can also use the following header line to specify that one job starts to run after the job 1236 and 1237 start or are cancelled:
#SBATCH --dependency=after:1236:1237
Options to requeue or not requeue a job when a node crashes
If a job is running and one or more nodes that it is using crash, the job will stop running and, by default, it will get requeued. When resources become available, the job will start running again, from the beginning, unless the program saves intermediate results and it is able to automatically pick up from where it stopped. The files with the standard error and standard output of the job will get rewritten once the job restarts. Often other output files will get rewritten as well.
If you are running a program that cannot restart, e.g. the program will fail if a certain output file or directory has already been created, or if you would like to preserve the partial results, you can use the following option to prevent the job from being requeued:
#SBATCH --no-requeue
When this option is used, the job will simply stop if a node crashes, it will not be requeued. In this case partial results and the standard error and output of the job will not get overwritten.
Although requeueing jobs is enabled by default now, you can also add the option below if you would like to ensure a job is requeued in the event of a node crash:
#SBATCH --requeue
Other content of the script
Following the header lines, users can include commands to change to the working directory, to load the modules needed to run the application, and to invoke the application. For example, to use the directory from which the job is submitted as the working directory (where to find input files or binaries), add the line
cd $SLURM_SUBMIT_DIR
(Note that Slurm jobs start from the submit directory by default, so adding the line above might not be necessary.)
You can then load the needed modules. For example, if you are running an R program, then include the line
module load R/4.3.1-foss-2022a
Then invoke your application. For example, if you are running an R program called add.R which is in your job submission directory, use
R CMD BATCH add.R
Environment Variables exported by batch jobs
When a batch job is started, a number of variables are introduced into the job's environment that can be used by the batch script in making decisions, creating output files, and so forth. Some of these variables are listed in the following table:
| Variable | Description |
|---|---|
| SLURM_ARRAY_JOB_ID | Job array's master job ID number, i.e., the first Slurm job id of a job array |
| SLURM_ARRAY_TASK_COUNT | Total number of tasks (elements) in a job array |
| SLURM_ARRAY_TASK_ID | Job array ID (index) number |
| SLURM_ARRAY_TASK_MAX | Job array's maximum ID (index) number |
| SLURM_ARRAY_TASK_MIN | Job array's minimum ID (index) number |
| SLURM_CPUS_ON_NODE | Number of CPUS on the allocated node |
| SLURM_CPUS_PER_TASK | Number of cpus requested per task. Only set if the --cpus-per-task option is specified |
| SLURM_JOB_ID | Unique Slurm job id |
| SLURM_JOB_NAME | Job name |
| SLURM_JOB_CPUS_PER_NODE | Count of processors available to the job on this node |
| SLURM_JOB_NODELIST | List of nodes allocated to the job |
| SLURM_JOB_NUM_NODES | Total number of nodes in the job's resource allocation |
| SLURM_JOB_PARTITION | Name of the partition (i.e. queue) in which the job is running |
| SLURM_MEM_PER_NODE | Same as --mem |
| SLURM_MEM_PER_CPU | Same as --mem-per-cpu |
| SLURM_NTASKS | Same as -n, --ntasks |
| SLURM_NTASKS_PER_NODE | Number of tasks requested per node. Only set if the --ntasks-per-node option is specified |
| SLURM_SUBMIT_DIR | The directory from which sbatch was invoked |
| SLURM_SUBMIT_HOST | The hostname of the computer from which sbatch was invoked |
| SLURM_TASK_PID | The process ID of the task being started |
| SLURMD_NODENAME | Name of the node running the job script |
| CUDA_VISIBLE_DEVICES | GPU devide ID that assigned to the job to use |
Sample job submission scripts
Serial (single-processor) Job
Sample job submission script (sub.sh) to run an R program called add.R using a single core:
#!/bin/bash #SBATCH --job-name=testserial # Job name #SBATCH --partition=batch # Partition (queue) name #SBATCH --ntasks=1 # Run on a single CPU #SBATCH --mem=1gb # Job memory request #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --output=testserial.%j.out # Standard output log #SBATCH --error=testserial.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR module load R/4.3.1-foss-2022a R CMD BATCH add.R
In this sample script, the standard output and error of the job will be saved into a file called testserial.o%j, where %j will be automatically replaced by the job id of the job.
Serial (single-processor) Job on an AMD EPYC Milan processor
Sample job submission script (sub.sh) to run an R program called add.R using a single core:
#!/bin/bash #SBATCH --job-name=testserial # Job name #SBATCH --partition=batch # Partition (queue) name #SBATCH --constraint=Milan # node feature #SBATCH --ntasks=1 # Run on a single CPU #SBATCH --mem=1gb # Job memory request #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --output=testserial.%j.out # Standard output log #SBATCH --error=testserial.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR module load R/4.3.1-foss-2022a R CMD BATCH add.R
In this sample script, the standard output and error of the job will be saved into a file called testserial.%j.out and testserial.%j.err, where %j will be automatically replaced by the job id of the job.
MPI Job
Sample job submission script (sub.sh) to run an OpenMPI application. In this example the job requests 16 cores and further specifies that these 16 cores need to be divided equally on 2 nodes (8 cores per node) and the binary is called mympi.exe:
#!/bin/bash #SBATCH --job-name=mpitest # Job name #SBATCH --partition=batch # Partition (queue) name #SBATCH --nodes=2 # Number of nodes #SBATCH --ntasks=16 # Number of MPI ranks #SBATCH --ntasks-per-node=8 # How many tasks on each node #SBATCH --cpus-per-task=1 # Number of cores per MPI rank #SBATCH --mem-per-cpu=600mb # Memory per processor #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --output=mpitest.%j.out # Standard output log #SBATCH --error=mpitest.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR module load OpenMPI/4.1.4-GCC-11.3.0 srun ./mympi.exe
Please note that you need to start the application with srun and not with mpirun or mpiexec.
Important note: MPI jobs need to be submitted from a Sapelo2 login node, not from an interactive session, in order to get the correct core allocation for the MPI processes.
MPI Job on nodes connected via the EDR IB fabric
Sample job submission script (sub.sh) to run an OpenMPI application. In this example the job requests 16 cores and further specifies that these 16 cores need to be divided equally on 2 nodes (8 cores per node) and the binary is called mympi.exe:
#!/bin/bash #SBATCH --job-name=mpitest # Job name #SBATCH --partition=batch # Partition (queue) name #SBATCH --constraint=EDR # node feature #SBATCH --nodes=2 # Number of nodes #SBATCH --ntasks=16 # Number of MPI ranks #SBATCH --ntasks-per-node=8 # How many tasks on each node #SBATCH --cpus-per-task=1 # Number of cores per MPI rank #SBATCH --mem-per-cpu=600mb # Memory per processor #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --output=mpitest.%j.out # Standard output log #SBATCH --error=mpitest.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR module load OpenMPI/4.1.4-GCC-11.3.0 srun ./mympi.exe
Please note that you need to start the application with srun and not with mpirun or mpiexec.
Important note: MPI jobs need to be submitted from a Sapelo2 login node, not from an interactive session, in order to get the correct core allocation for the MPI processes.
OpenMP (Multi-Thread) Job
Sample job submission script (sub.sh) to run a program that uses OpenMP with 6 threads. Please set --ntasks=1 and set --cpus-per-task to the number of threads you wish to use. The name of the binary in this example is a.out.
#!/bin/bash #SBATCH --job-name=mctest # Job name #SBATCH --partition=batch # Partition (queue) name #SBATCH --ntasks=1 # Run a single task #SBATCH --cpus-per-task=6 # Number of CPU cores per task #SBATCH --mem=4gb # Job memory request #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --output=mctest.%j.out # Standard output log #SBATCH --error=mctest.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR export OMP_NUM_THREADS=6 module load foss/2022a # load the appropriate module file, e.g. foss/2022a time ./a.out
High Memory Job
Sample job submission script (sub.sh) to run a velvet application that needs to use 200GB of memory and 4 threads:
#!/bin/bash #SBATCH --job-name=highmemtest # Job name #SBATCH --partition=highmem_p # Partition (queue) name #SBATCH --ntasks=1 # Run a single task #SBATCH --cpus-per-task=4 # Number of CPU cores per task #SBATCH --mem=200gb # Job memory request #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --output=highmemtest.%j.out # Standard output log #SBATCH --error=highmemtest.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR export OMP_NUM_THREADS=4 module load Velvet velvetg [options]
Sample job submission script (sub.sh) to run a parallel job that uses 4 MPI processes with OpenMPI and each MPI process runs with 3 threads:
#!/bin/bash #SBATCH --job-name=hybridtest #SBATCH --partition=batch # Partition (queue) name #SBATCH --nodes=2 # Number of nodes #SBATCH --ntasks=8 # Number of MPI ranks #SBATCH --ntasks-per-node=4 # Number of MPI ranks per node #SBATCH --cpus-per-task=3 # Number of OpenMP threads for each MPI process/rank #SBATCH --mem-per-cpu=2000mb # Per processor memory request #SBATCH --time=2-00:00:00 # Walltime in hh:mm:ss or d-hh:mm:ss (2 days in the example) #SBATCH --output=hybridtest.%j.out # Standard output log #SBATCH --error=hybridtest.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR module load OpenMPI/4.1.4-GCC-11.3.0 export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK srun ./myhybridprog.exe
Array job
Sample job submission script (sub.sh) to submit an array job with 10 elements. In this example, each array job element will run the a.out binary using an input file called input_0, input_1, ..., input_9.
#!/bin/bash #SBATCH --job-name=arrayjobtest # Job name #SBATCH --partition=batch # Partition (queue) name #SBATCH --ntasks=1 # Run a single task #SBATCH --mem=1gb # Job Memory #SBATCH --time=10:00:00 # Time limit hrs:min:sec #SBATCH --output=array_%A-%a.out # Standard output log #SBATCH --error=array_%A-%a.err # Standard error log #SBATCH --array=0-9 # Array range cd $SLURM_SUBMIT_DIR module load foss/2022a # load any needed module files, e.g. foss/2022a time ./a.out < input_$SLURM_ARRAY_TASK_ID
For more information, please see Array Jobs.
GPU/CUDA
Sample script to run Amber on a GPU node using one node, 2 CPU cores, and 1 GPU card:
#!/bin/bash #SBATCH --job-name=amber # Job name #SBATCH --partition=gpu_p # Partition (queue) name #SBATCH --gres=gpu:A100:1 # Requests one GPU device #SBATCH --ntasks=1 # Run a single task #SBATCH --cpus-per-task=2 # Number of CPU cores per task #SBATCH --mem=40gb # Job memory request #SBATCH --time=10:00:00 # Time limit hrs:min:sec #SBATCH --output=amber.%j.out # Standard output log #SBATCH --error=amber.%j.err # Standard error log #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR ml Amber/22.0-foss-2021b-AmberTools-22.3-CUDA-11.4.1 $AMBERHOME/bin/pmemd.cuda -O -i ./prod.in -o prod.out -p ./dimerFBP_GOL.prmtop -c ./restart.rst -r prod.rst -x prod.mdcrd
You can explicitly request a GPU device type for your job. For example:
- To request an A100 device, use
#SBATCH --gres=gpu:A100:1
- To request an H100 device, use
#SBATCH --gres=gpu:H100:1
- To request an L4 device, use
#SBATCH --gres=gpu:L4:1
- To request a P100 device, use
#SBATCH --gres=gpu:P100:1
Jobs that request a GPU, but that do not specify the device type (that is, jobs that use #SBATCH --gres=gpu:1) will get allocated any device type, some of which might not work for the application that you are running. Please check which GPU device is supported by the application or code your job is running and request the corresponding GPU device type. For more information about the GPU resources available on Sapelo2, please see https://wiki.gacrc.uga.edu/wiki/GPU and https://wiki.gacrc.uga.edu/wiki/GPU_Hardware.
Singularity job
Sample job submission script (sub.sh) to run a program (e.g. sortmerna) using a singularity container:
#!/bin/bash #SBATCH --job-name=j_sortmerna # Job name #SBATCH --partition=batch # Partition (queue) name #SBATCH --ntasks=1 # Run on a single CPU #SBATCH --mem=1gb # Job memory request #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --output=sortmerna.%j.out # Standard output log #SBATCH --error=sortmerna.%j.err # Standard error log #SBATCH --cpus-per-task=4 # Number of CPU cores per task #SBATCH --mail-type=END,FAIL # Mail events (NONE, BEGIN, END, FAIL, ALL) #SBATCH --mail-user=username@uga.edu # Where to send mail (change username@uga.edu to your email address) cd $SLURM_SUBMIT_DIR singularity exec /apps/singularity-images/sortmerna-3.0.3.simg sortmerna \ --threads 4 --ref db.fasta,db.idx --reads file.fa --aligned base_name_output
For more information about software installed as singularity containers on the cluster, please see Software_on_Sapelo2#Singularity_Containers
To run a GPU-enabled singularity container on the GPU, please submit the job to the gpu_p partition, request a GPU device and add the --nv option to the singularity command.
Sample job submission script (sub.sh) to run a program using a singularity container (e.g. gpuapp.sif) on the GPU device:
#!/bin/bash #SBATCH --job-name=myjobname # Job name #SBATCH --partition=gpu_p # Partition (queue) name #SBATCH --gres=gpu:1 # Requests one GPU device #SBATCH --ntasks=1 # Run on a single CPU #SBATCH --mem=10gb # Job memory request #SBATCH --time=02:00:00 # Time limit hrs:min:sec #SBATCH --cpus-per-task=1 # Number of CPU cores per task cd $SLURM_SUBMIT_DIR singularity exec --nv /apps/singularity-images/gpuapp.sif prog.x
For more information about software installed as singularity containers on the cluster, please see Software_on_Sapelo2#Singularity_Containers
How to submit a batch job
With the resource requirements specified in the job submission script (sub.sh), submit your job with
sbatch <scriptname>
For example
sbatch sub.sh
Once the job is submitted, the Job ID of the job (e.g. 12345) will be printed on the screen.
Discovering if a partition (queue) is busy
The nodes allocated to each partition (queue) and their state can be view with the command
sinfo
Sample output of the sinfo command:
PARTITION AVAIL TIMELIMIT NODES STATE NODELIST batch* up 7-00:00:00 1 drain* ra4-2 batch* up 7-00:00:00 3 down* d4-7,ra3-19,ra4-12 batch* up 7-00:00:00 1 mix b1-2 batch* up 7-00:00:00 1 alloc b1-3 batch* up 7-00:00:00 53 idle b1-[4-24],c1-3,c5-19,d4-[5-6,8-12],ra3-[1-18,20-24] gpu_p up 7-00:00:00 1 mix c4-23 highmem_p up 7-00:00:00 6 idle d4-[11-12],ra4-[21-24] inter_p up 2-00:00:00 2 idle ra4-[16-17]
where some common values of STATE are:
- STATE=idle indicates that those nodes are completely free.
- STATE=mix indicates that some cores on those nodes are in use (and some are free).
- STATE=alloc indicates that all cores on those nodes are in use.
- STATE=drain indicates that nodes are draining, not accepting new jobs
- STATE=down indicates that nodes are not running or accepting new jobs
This command can be used with many options. We have configured one option that shows some quantities that are commonly of interest, including node feature defined for each node. This command is
sinfo-gacrc
You can also specify the number of characters displayed in the NODELIST column (e.g. 40) and in the AVAIL_FEATURES column (e.g. 50), with
sinfo-gacrc 40 50
Sample output of the sinfo-gacrc command:
PARTITION NODELIST STATE CPUS MEMORY AVAIL_FEATURES GRES batch* ra4-2 drained* 32 126000 AMD,Opteron,QDR lscratch:230 batch* ra3-19 down* 32 126000 AMD,Opteron,QDR lscratch:230 batch* ra4-12 down* 32 126000 AMD,Opteron,QDR lscratch:230 batch* b1-3 mixed 64 126976 AMD,EPYC,Rome,EDR lscratch:890 batch* b1-2 allocated 64 126976 AMD,EPYC,Rome,EDR lscratch:890 batch* b1-[4-24] idle 64 126976 AMD,EPYC,Rome,EDR lscratch:890 batch* c1-3 idle 28 59127 Intel,Broadwell,EDR lscratch:890 batch* c5-19 idle 32 187868 Intel,Skylake,EDR lscratch:890 batch* d4-[5-6] idle 32 126976 AMD,EPYC,Naples,EDR lscratch:890 batch* d4-[8-12] idle 32 126976+ AMD,EPYC,Naples,EDR lscratch:890 batch* ra3-[1-18,20-24] idle 32 126000 AMD,Opteron,QDR lscratch:230 gpu_p c4-23 idle 32 187868 Intel,Skylake,EDR gpu:P100:1,lscratch:890 highmem_p d4-[11-12] idle 32 514048 AMD,EPYC,Naples,EDR lscratch:890 highmem_p ra4-[21-24] idle 32 126000 AMD,Opteron,QDR lscratch:230 inter_p ra4-[16-17] idle 32 126000 AMD,Opteron,QDR lscratch:230 scavenge_p rb7-18 idle 28 515780 Intel,Broadwell,QDR lscratch:180
What is the scavenge_p partition
A portion of the Sapelo2 compute nodes were purchased by UGA PIs and their group members have priority in using those resources (also referred to as buyin nodes). The GACRC purchased the rest on UGA's behalf. The agreement for the PI-owned nodes allows "other users" to also run jobs on owned nodes, as long as those jobs don't cause that lab group to wait over two hours for access to its nodes. We have implemented a partition called scavenge_p and short jobs (for example, jobs that request less than 4h) submitted to the 'batch' partition might be automatically moved into the scavenge_p partition if the 'batch' partition is busy. This is a way to reduce the wait time of the short jobs, while making use of the buyin nodes that are not in use. Jobs running on the buyin nodes (or any nodes) cannot be dynamically migrated to other nodes, so buyin-group users might have to wait up to 4h to access their nodes, if there are jobs running in the scavenge_p partition.
Users cannot submit jobs directly to the scavenge_p partition, but if you submitted short jobs to the batch partition, you might see them running on the scavenge_p partition.
How to request a specific node feature
Each compute node has a set of features, such as shown with the sinfo-gacrc command above. Common features are Intel (if the node has Intel processors), AMD (if the node has AMD processors), EPYC (if the node has AMD EPYC processors), or specific EPYC processor types, such as Rome, Milan, etc. You can request using nodes with a specific feature by adding the following header line in your job submission script:
#SBATCH --constraint=featurename
where featurename needs to be replaced by the feature you want to use. For example, to request that the job goes to a node that has a Milan processor, use
#SBATCH --constraint=Milan
How to run Intel- or AMD-specific applications
Most of the applications that GACRC installs centrally can be run on Intel and on AMD processors, but some exceptions do exist. Also, some third-party applications that you are using might have been pre-compiled for a given processor type and would fail if run on a different processor architecture If an application that you are using if only compatible with one type of processor (e.g. Intel), you can request that node feature by adding the following line in your job submission script
#SBATCH --constraint=Intel
or
#SBATCH --constraint=EPYC
or
#SBATCH --constraint=Milan
How to run a job using the local scratch /lscratch on a compute node
The IO performance of the local scratch file system /lscratch is much faster than the IO performance of the network file system /scratch. Please note that the local scratch file system can only be used for running single-node jobs, i.e., single-core jobs or multi-thread jobs. In general, MPI parallel jobs that use more than one node cannot use the local scratch file system. Detailed information and instructions about /lscratch can be found at Disk_Storage#lscratch_file_system .
To use /lscratch to run a batch job, you need a few additional steps in your job submission script to ask your job to:
- Create a job working folder in /lscratch on the compute node where your job is dispatched
- Copy any input files required to run the job from your current working space, e.g., /scratch/MyID, to the folder created in step 1
- Change directory from your current working space /scratch/MyID to the folder created in step 1 and run the software from there, i.e. from the local scratch file system /lscratch
- Copy output results from /lscratch back to your /scratch/MyID, before job finishes and exits from the node
- Clean up in /lscratch, before job finishes and exits from the node
To use /lscratch to run a batch job, you also need to:
1. Make sure that your job will use a single node by using the following line in your job submission script:
#SBATCH --nodes=1
2. Request an appropriate amount of disk storage from the local scratch file system by adding the following line in your job submission script:
#SBATCH --gres=lscratch:200
The above header requests 200GB local storage on the compute node where your job is dispatched.
Below is a sample job submission script (sub.sh) to run a batch job using /lscratch:
#!/bin/bash
#SBATCH --job-name=RM_job
#SBATCH --partition=batch
#SBATCH --nodes=1
#SBATCH --gres=lscratch:200
#SBATCH --ntasks=12
#SBATCH --mem=36G
#SBATCH --time=7-00:00:00
#SBATCH --output=log.%j.out
#SBATCH --error=log.%j.err
cd $SLURM_SUBMIT_DIR
# Step 1
mkdir -p /lscratch/${USER}/${SLURM_JOB_ID}
# Step 2
cp ./Hawaii_H3_Final.fa /lscratch/${USER}/${SLURM_JOB_ID}
# Step 3
cd /lscratch/${USER}/${SLURM_JOB_ID}
ml RepeatModeler/2.0.4-foss-2022a
BuildDatabase -name E4 -engine ncbi Hawaii_H3_Final.fa
RepeatModeler -engine ncbi -pa 3 -database E4 > E4-repeat.out
# Step 4
cp ./E4* ${SLURM_SUBMIT_DIR}
cp -r ./RM_* ${SLURM_SUBMIT_DIR}
# Step 5
rm -rf /lscratch/${USER}/${SLURM_JOB_ID}
Then submit sub.sh from your current working space /scratch/MyID with:
sbatch sub.sh
Since you submit the job from /scratch/MyID, the value stored in SLURM_SUBMIT_DIR in the above sub.sh will be /scratch/MyID.
To learn the total amount of local disk storage installed in compute nodes on Sapelo2, you can use sinfo-gacrc command. The GRES column reported is the information about the total amount of local disk storage in GB, for example, lscratch:890 means total 890GB local disk storage is installed in the compute node(s). Detailed instructions about gacrc-sinfo can be found at Running_Jobs_on_Sapelo2#Discovering_if_a_partition_.28queue.29_is_busy
How to open an interactive session
An interactive session on a compute node can be started with the command
interact
This command, invoked without any arguments, will start an interactive session with one core on one of the interactive nodes, and allocate 2GB of memory for a maximum walltime of 12h. It is equivalent to the qlogin command that we used previously, and it runs
srun --pty --cpus-per-task=1 --job-name=interact --ntasks=1 --nodes=1 --partition=inter_p --time=12:00:00 --mem=2GB /bin/bash -l
When the interact command is run, it will echo the equivalent srun command, so you can easily check the resources associated to your interactive session.
The interact command takes arguments that allow you to request cores, memory, walltime limit, specific node features, or a different partition and other resources.
The options that can be used with interact are diplayed when this command is run with the -h or --help option:
[shtsai@ss-sub2 ~]$ interact -h
Usage: interact [OPTIONS]
Description: Start an interactive job
-c, --cpus-per-task CPU cores per task (default: 1)
-J, --job-name Job name (default: interact)
-n, --ntasks Number of tasks (default: 1)
-N, --nodes Number of nodes (default: 1)
-p, --partition Partition for interactive job (default: inter_p)
-q, --qos Request a quality of service for the job.
-t, --time Maximum run time for interactive job (default: 12:00:00)
-w, --nodelist List of node name(s) on which your job should run
--constraint Job constraints
--gres Generic consumable resources
--mem Memory per node (default 2GB)
--shell Absolute path to the shell to be used in your interactive job (default: /bin/bash)
--wckey Wckey to be used with job
--x11 Start an interactive job with X Forwarding
-h, --help Display this help output
Examples:
To start an interactive session with 4 cores and 10GB of memory:
interact -c 4 --mem=10G
To start an interactive session with 1 core, 10GB of memory and a walltime limit of 18 hours:
interact --mem=10G --time=18:00:00
To start an interactive session with 1 core, 2GB of memory, on a node that has an AMD EPYC Milan processor in the batch partition:
interact --constraint=Milan -p batch
To start an interactive session with 1 core, 50GB of memory, and a A100 GPU device:
interact -p gpu_p --gres=gpu:A100:1 --mem=50G
How to run an interactive job with Graphical User Interface capabilities
A number of software installed on GACRC clusters have X Window (GUI) front ends. Examples of such applications are Matlab, Mathematica, some text editors and debuggers, etc. The best way to run such applications is using the Open OnDemand (OOD) interface to Sapelo2, either by running an interactive application in OOD or by starting an X Desktop session on the cluster and running the application therein. More information is available at OnDemand.
If using OnDemand is not an option, and you want to run an application as an interactive job and have its graphical user interface displayed on the terminal of your local machine, you need to enable X-forwarding when you ssh into the login node. This can be done on a Linux machine by simply adding the -X option when ssh-ing into Sapelo2. For information on how to do this on windows and mac, please see instructions within questions 5.4 and 5.5 in the Frequently Asked Questions page.
After setting up an x forwarding terminal on your local machine, start an interactive session, but add the option --x11 to the interact command.
An interactive session on a compute node, with X forwarding enabled, can be started with the command
interact --x11
This command will start an interactive session, with X forwarding enabled, with one core on one of the interactive nodes, and allocate 2GB of memory for a maximum walltime of 12h.
The interact --x11 command is an alias for
srun --pty --x11 --cpus-per-task=1 --job-name=interact --ntasks=1 --nodes=1 --partition=inter_p --time=12:00:00 --mem=2GB /bin/bash -l
The options available to interact, described in the previous section, can be used along with the --x11 option.
How to check on running or pending jobs
To list all running and pending jobs (by all users), use the command
squeue
or
squeue -l
This command can be used with many options. We have wrapper to this command, called sq that shows some quantities that are commonly of interest. To use the sq command to list all of your running and pending jobs, use
sq --me
For detailed information on how to monitor your jobs, please see Monitoring Jobs on Sapelo2.
How to cancel (delete) a running or pending job
To cancel one of your running or pending job, use the command
scancel <jobid>
For example, to cancel a job with Job ID 12345 use
scancel 12345
To cancel all of your jobs, use the command
scancel -u MyID
To cancel all of your pending jobs, use the command
scancel -t PENDING -u MyID
To cancel one or more jobs by job name, use the command
scancel --name <myJobName>
To cancel an element (index) of an array job
scancel <jobid>_<index>
For example, to cancel array job element 4 of an array job whose Job ID is 12345 use
scancel 12345_4
How to check resource utilization of a running or finished job
The following command can be used to show resource utilization by a running job or a job that has already completed:
sacct
This command can be used with many options. We have configured one option that shows some quantities that are commonly of interest, including the amount of memory used and the cputime used by the jobs:
sacct-gacrc
For detailed information on how to monitor your jobs, please see Monitoring Jobs on Sapelo2.