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--- public:usage:goethe-hlr [2020/05/20 09:12] – [Login] keiling
+++ public:usage:goethe-hlr [2021/03/17 16:50] (current) – created geier
@@ Line 1: / Line 1: @@
-====== Goethe-HLR Cluster Usage ======
+====== Sorry, please go to ... ======
-The [[..:service:Goethe-HLR]] is a general-purpose computer cluster based on Intel CPU architectures running Scientific Linux 7.6 and [[#running_jobs_with_slurm|SLURM]]. Please **read the following instructions and ensure that this guide is fully understood** before using the system.
+https://csc.uni-frankfurt.de/wiki/doku.php?id=public:usage:goethe
-===== Login =====
-An SSH client is required to connect to the cluster. On a Linux system the command is usually:
-<code>ssh <user_account>@goethe.hhlr-gu.de</code>
-<note important>Warnings - Security Breach - Keys etc.\\ \\
-You may receive a warning from the system that something with the security is wrong. We switched the old LOEWE Cluster IP to our new GOETHE Cluster. If you used the LOEWE Cluster in the past you receive a warning that something is wrong. This is related to the unique LOEWE key within the clientsoftware you use differs from the new unique GOETHE-HLR key. Just erase your old LOEWE key and everything is set.\\ \\
-If you may use Linux just look up ''ssh-keygen -R''.</note>
-On Windows systems please use/install a Windows SSH client (e.g. PuTTY, or the Cygwin ssh package).
-After your [[first login]] you will get the message, that your password has expired and you have to change it. Please use the password provided by CSC at the prompt, choose a new one and retype it. You will be logged out automatically. Now you can login with your new password and work on the cluster.
-<note warning>Never run heavy calculations, i.e. RAM- or CPU-time-consuming processes, on the login nodes. You can check the CPU-time limit (in seconds) by running
-''ulimit -t''
-on the command line. On a login node, any process that exceeds the CPU-time limit (e.g. a long running test program or a long running rsync) will be killed automatically.</note>
-===== Environment Modules =====
-There are several versions of software packages installed on our systems. The same name for an executable (e.g. mpirun) and/or library file may be used by more than one package. The environment module system, with its ''module'' command, helps to keep them apart and prevents name clashes. You can list the module-managed software by running ''module avail'' on the command line. Other important commands are ''module load <name>'' (loads a module) and ''module list'' (lists the already loaded modules). For instance, if you want to work with Intel MPI, run ''module load mpi/intel/2020.0''
-<note> It's important to know, which modules you really need. Loading more than one MPI module at the same time will likely lead to overlapping. </note>
-If you want to know more about module commands, the ''module help'' command will give you an overview.
-===== Compiling Software =====
-You can compile your software on the login nodes (or on any other node, inside a job allocation). On Goethe-HLR several compiler suites are available. While GCC version 4.8.5 is the built-in OS default, you can list additional compilers and libraries by running ''module avail'':
-   * GNU compilers
-   * Intel compilers
-   * MPI libraries
-For the right compilation commands please consider:
-<note important>C/C++, Fortran77, Fortran 95 \\ \\
-[[https://software.intel.com/en-us/mpi-developer-reference-linux-compilation-commands|Compilation commands for the different compilers]]
-</note>
-To compile and manage software which is not available under "''module avail''" we recommend //Spack//.  Please read this small [[public:usage:spack|introduction]] on how to use Spack on the Cluster. More information is available on the ''[[ https://spack.io/|Spack ]]'' webpage.
-===== Debugging =====
-The [[http://www.roguewave.com/products-services/totalview|TotalView]] parallel debugger is available on the Goethe-HLR cluster. Follow these steps to start a debugging session:
-   - Compile your code with your favored MPI using the debug option ''-g'', e.g.<code>
-mpicc -g -o mpi_prog mpi_prog.c</code>
-   - Load the TotalView module by running<code>
-module load debug/totalview/2019.0.4</code>
-   - Allocate the resources you need using salloc, e.g.<code>
-salloc -n 4 --partition=test --time=00:59:00</code>
-   - Start a TotalView debugging session, e.g.<code>
-totalview </code>
-   - Choose Debug a parallel session
-   - Choose your executable (mpi_prog), Parallel System (e.g. Intel MPI CSC or openmpi-m), number of tasks and load the session
-===== Storage =====
-There are various storage systems available on the cluster. In this section we describe the most relevant:
-  * your home directory ''/home/<group>/<user>'' (NFS, slow),
-  * your scratch directory ''/scratch/<group>/<user>'' (parallel file system BeeGFS, fast),
-  * the non-shared local storage (i.e. only accessible from the compute node it's connected to, max. 1.4 TB, slow) under ''/local/$SLURM_JOB_ID'' on each compute node
-  * and the two (slow) archive file systems ''/arc01'' and ''/arc02'' (explained at the end of this section).
-Please use your home directory for small permanent files, e.g. source files, libraries and executables.
-<note important>Use the scratch space for large temporary job data and delete the data as soon as you no longer need it, e.g. when it's older than 30 days.</note>
-{{ :public:loewe-storage4.png }}
-By default, the space in your home directory is limited to 10 GB and in your scratch directory to 5 TB and/or 800000 inodes (which corresponds to approximately 200000+ files). You can check your homedir and scratch usage by running the ''quota'' command on a login node.
-<note>While the data in your home directory is backed up nightly (please ask, if you want us to restore anything from there), there is no backup of your scratch directory.</note>
-If you need local storage on the compute nodes, you have to add the ''%%--%%tmp'' parameter to your job script (see SLURM section below). Set the amount of storage in megabytes, e.g. set ''%%--%%tmp=5000'' to allocate 5 GB of local disk space. The local directory (''/local/$SLURM_JOB_ID'') is deleted after the corresponding job has finished. If, for some reason, you don't want the data to be deleted (e.g. for debugging), you can use ''salloc'' instead of ''sbatch'' and work interactively (see ''man salloc''). Or, one can put an ''rsync'' at the end of the job script, in order to save the local data to ''/scratch'' just before the job exits:
-<code bash>
-...
-mkdir /scratch/<groupid>/<userid>/$SLURM_JOBID
-scontrol show hostnames $SLURM_JOB_NODELIST | xargs -i ssh {} \
-    rsync -a /local/$SLURM_JOBID/ \
-    /scratch/<groupid>/<userid>/$SLURM_JOBID/{}
-</code>
-In addition to the "volatile" ''/scratch'' and the permanent ''/home'', which come along with every user account, more permanent disk space (2 × //N//, where //N// ≤ 10 TB) can be requested by group leaders for archiving. Upon request, two file systems will be created for every group member, to be accessed through ''rsync''((For further information on how to use ''rsync'', please read its excellent man page.)), e.g. list the contents of your folder and archive a ''/scratch'' directory:
-  rsync arc01:/archive/<group>/<user>/
-  ...
-  cd /scratch/<group>/<user>/
-  rsync [--progress] -a <somefolder> arc01:/archive/<group>/<user>/
-or, for ''arc02'':
-  rsync arc02:/archive/<group>/<user>/
-  ...
-  cd /scratch/<group>/<user>/
-  rsync [--progress] -a <somefolder> arc02:/archive/<group>/<user>/
-The space is limited by //N// on each of the both systems. Limits are set for an entire group (there's no user quota). The disk usage can be checked by running
-  df -h /arc0{1,2}/archive/<group>
-on the command line. The corresponding hardware resides in separate server rooms. There is no automatic backup. However, for a user, a possible backup scenario is to backup his or her data manually to both storage systems, ''arc01'' **and** ''arc02'' (e.g. at the end of a compute job). **Note:** The archive file systems are mounted on the login nodes, but not on the compute nodes. So it's not possible to use the archive for direct job I/O. Please use ''rsync'' as described above.
-<note>All shared file systems are **shared** between users and jobs. There is no guarantee, that **you** always get the desired bandwidth and/or response time.
-Although our storage systems are protected by RAID mechanisms, we can't guarantee the safety of your data. It is within the responsibility of the user to backup important files.
-</note>
-===== Running Jobs With SLURM =====
-On our systems, compute jobs and resources are managed by SLURM (Simple Linux Utility for Resource Management). The compute nodes are organized in the partition (or queue) named ''general1''. There is also a small test partition called ''test''. Additionally we offer the use of some old compute nodes from LOEWE-CSC. Those nodes are organized in the partition ''general2''. You can see more details (the current number of nodes in each partition and their state) by running the ''sinfo'' command on a login node.
-^Partition^Node type^Implemented^
-| ''general1'' | Intel Skylake CPU|yes|
-| ''general2'' | Intel Ivy Bridge CPU \\ Intel Broadwell CPU|yes \\ yes|
-| ''gpu'' | n/a | not yet |
-| ''test'' | Intel Skylake CPU|yes|
-Nodes are used **exclusively**, i.e. only whole nodes are allocated for a job and no other job can use the same nodes concurrently.
-In this document we discuss several job types and use cases. In most cases, a compute job falls under one (or more than one) of the following categories:
-  * [[#bundling_single-threaded_tasks|embarrassingly parallel]]
-  * [[#openmp_jobs|OpenMP (multi-threaded)]]
-  * [[#mpi_jobs|MPI]]
-  * [[#hybrid_jobsmpi_openmp|hybrid MPI/OpenMP]]
-  * [[#gpu_jobs|GPU]]
-For every compute job you have to submit a job script (unless working interactively using ''salloc'' or ''srun'', see man page for more information). If ''jobscript.sh'' is such a script, then a job can be enqueued by running
-  sbatch jobscript.sh
-on a login node. A SLURM job script is a shell script which may contain SLURM directives (options), i.e. pseudo-comment lines starting with
-  #SBATCH ...
-The SLURM options define the resources to be allocated for the job (and some other properties). Otherwise the script contains the "job logic", i.e. commands to be executed.
-==== Read More ====
-The following instructions shall provide you with the basic information you need to get started with SLURM on our systems. However, the official SLURM documentation covers some more use cases (also in more detail). Please read the SLURM man pages (e.g. ''man sbatch'' or ''man salloc'') and/or visit http://www.schedmd.com/slurmdocs. It's highly recommended.
-Helpful SLURM link: [[https://slurm.schedmd.com/faq.html|SLURM FAQ]]
-==== The test Partition: Your First Job Script ====
-You can use the (very small) ''test'' partition for pre-production or tests. In ''test'' you can run jobs with a walltime of no longer than two hours. In the following example we allocate 160 CPU tasks (i.e. 160 CPU threads on two nodes = 80 tasks per node) and 512 MB per task for 5 minutes (SLURM may kill the job after that time, if it's still running):
-<code bash>
-#!/bin/bash
-#SBATCH --job-name=foo
-#SBATCH --partition=test
-#SBATCH --nodes=2
-#SBATCH --ntasks=160
-#SBATCH --cpus-per-task=1   1)
-#SBATCH --mem-per-cpu=512
-#SBATCH --time=00:05:00
-#SBATCH --no-requeue        2)
-#SBATCH --mail-type=FAIL    3)
-srun hostname
-sleep 3
-</code>
-) For SLURM, a CPU core (a CPU thread, to be more precise) is a CPU.\\
-) Prevent the job from being requeued after a failure.\\
-) Send an e-mail if sth. goes wrong.\\
-The ''srun'' command is responsible for the distribution of the program (''hostname'' in our case) across the allocated resources, so that 80 instances of ''hostname'' will run on each of the allocated nodes concurrently. Please note, that this is not the only way to run or to distribute your processes. Other cases and methods are covered later in this document. In contrast, the ''sleep'' command is executed only on the head((the first one of the two allocated nodes)) node.
-Although nodes are allocated exclusively, you should always specify a memory value that reflects the RAM requirements of your job. The scheduler treats RAM as a //consumable resource//. As a consequence, if you omit the ''%%--%%nodes'' parameter (so that only the number of CPU cores is defined) and allocate more memory per core than there actually is on a node, you'll automatically get more nodes if the job doesn't fit in otherwise. Moreover, jobs are killed through SLURM's //memory enforcement// when using more memory than requested.
-As already mentioned, after saving the above job script as e.g. ''jobscript.sh'', you can submit your job by running
-  sbatch jobscript.sh
-on the command line. The job's output streams (''stdout'' and ''stderr'') will be joined and saved to ''slurm-ID.out'', where ''ID'' is a SLURM job ID, which is assigned automatically. You can change this behavior by adding an ''%%--%%output'' and/or ''%%--%%error'' argument to the SLURM options.
-==== Job Monitoring ====
-For job monitoring (to check the current state of your jobs) you can use the ''squeue'' command. Depending on the current cluster utilization (and other factors), your job(s) may take a while to start. You can list the current queuing times by running ''sqtimes'' on the command line.
-If you need to cancel a job, you can use the ''scancel'' command (please see the manpage, ''man scancel'', for further details).
-==== Node Types And Constraints ====
-On Goethe-HLR **four different types** of compute nodes are available. There are
-^Number^Type^Vendor^CPU^Cores per CPU^Cores per Node^Hyper-Threads per Node^RAM [GB]^
-|412|dual-socket |Intel|Xeon Skylake Gold 6148   |20|40|80|192|
-|72 |dual-socket |Intel|Xeon Skylake Gold 6148   |20|40|80|772|
-|139|dual-socket |Intel|Xeon Broadwell E5-2640 v4|10|20|40|128|
-|47 |dual-socket \\ GPU|Intel \\ AMD|Xeon Ivy Bridge E5-2650 v2 \\ FirePro S10000|6|12|24|128|
-In order to separate the node types, we employ the concept of partitions. We provide three partitions  for the nodes, one for the Skylake CPU node, one for the Broadwell and one for the GPU nodes, furthermore we have a test partition. When running CPU jobs, you can select the node type you prefer by setting
-^Partition^Partition/feature^Node type^Implemented^
-|general1|''#SBATCH %%--%%partition=general1''|Intel Skylake CPU|yes|
-|general2|''#SBATCH %%--%%partition=general2'' \\ ''#SBATCH %%--%%constraint=broadwell''|Intel Broadwell CPU|yes|
-|gpu|''#SBATCH %%--%%partition=gpu''| n/a |not yet|
-|test|''#SBATCH %%--%%partition=test''|Intel Skylake CPU|yes|
-==== Per-User Resource Limits ====
-On Goethe-HLR, you have the following limits for the partitions ''general1''  and ''general2'':
-^Limit^Value^Description^
-| ''MaxTime'' | 21 days | the maximum run time for jobs |
-| ''MaxJobsPU'' |  40| max. number of jobs a user is able to run simultaneously |
-| ''MaxSubmitPU'' |  50| max. number of jobs in running or pending state	|
-| ''MaxNodesPU'' |  150|  max. number of nodes a user is able to use at the same time |
-| ''MaxArraySize'' |  1001| the maximum job array size |
-==== GPU Jobs ====
-Currently there are no GPU nodes available. In future: if you want to use GPUs in your calculations, select the ''gpu'' partition by setting
-<code>#SBATCH --partition=gpu</code>
-==== Hyper-Threading ====
-On compute nodes you can use Hyper-Threading. That means, in addition to each physical CPU core a virtual core is available. SLURM identifies all physical and virtual cores of a node, so that you have 80 logical CPU cores on an Intel Skylake node, 40 logical CPU cores on an Intel Broadwell or Ivy Bridge node, and 24 logical CPU cores on a GPU node. If you don't want to use HT, you can disable it by adding
-^Node type^sbatch command^
-|Skylake|''#SBATCH %%--%%extra-node-info=2:20:1''|
-|Broadwell / Ivy Bridge|''#SBATCH %%--%%extra-node-info=2:10:1''|
-to your job script. Then you'll get half the threads per node (which will correspond to the number of cores). This can be beneficial in some cases (some jobs may run faster and/or more stable).
-==== Bundling Single-Threaded Tasks ====
-**Note:** Please also see the Job Arrays section below. Because only full nodes are given to you, you have to ensure, that the available resources  are used efficiently. Please combine as many single-threaded jobs as possible into one. The limits for the number of combined jobs are given by the number of cores and the available memory. A simple job script to start 40 independent processes may look like this one:
-<code bash>#!/bin/bash
-#SBATCH --partition=general1
-#SBATCH --nodes=1
-#SBATCH --ntasks=40
-#SBATCH --cpus-per-task=1
-#SBATCH --mem-per-cpu=2000
-#SBATCH --time=01:00:00
-#SBATCH --mail-type=FAIL
-export OMP_NUM_THREADS=1
-#
-# Replace by a for loop.
-./program input01 >& 01.out &
-./program input02 >& 02.out &
-...
-./program input40 >& 40.out &
-# Wait for all child processes to terminate.
-wait
-</code>
-In this (SIMD) example we assume, that there is a program (called ''program'') which is run 40 times on 40 different inputs (usually input files). Both output streams (''stdout'' and ''stderr'') of each process are redirected to a file ''N.out''. A job script is always executed on the first allocated node, so we don't need to use ''srun'', since exactly one node is allocated. Further we assume that the executable is located in the same directory where the job was submitted (that is the initial working directory).
-If the running times of your processes vary a lot, consider using the //thread pool pattern//. Have a look at the ''xargs -P'' command, for instance.
-==== Job Arrays ====
-If you have a lot of single-core computations to run, job arrays are worth a look. Telling SLURM to run a job script as a job array will result in running that script multiple times (after the corresponding resources have been allocated). Each instance will have a distinct ''SLURM_ARRAY_TASK_ID'' variable defined in its environment.
-Due to our full-node policy, you still have to ensure, that your jobs don't waste any resources. Let's say, you have 320 single-core tasks. In the following example 320 tasks are run inside a job array while ensuring that only 40-core nodes are used and that each node runs exactly 40 tasks in parallel.
-<code bash>#!/bin/bash
-#SBATCH --partition=general1
-#SBATCH --nodes=1
-#SBATCH --ntasks=40
-#SBATCH --cpus-per-task=1
-#SBATCH --mem-per-cpu=2000
-#SBATCH --time=00:10:00
-#SBATCH --array=0-319:40
-#SBATCH --mail-type=FAIL
-my_task() {
-     # Print the given "global task number" with leading zeroes
-     # followed by the hostname of the executing node.
-     K=$(printf "%03d" $1)
-     echo "$K: $HOSTNAME"
-     # Do nothing, just sleep for 3 seconds.
-     sleep 3
-}
-#
-# Every 40-task block will run on a separate node.
-for I in $(seq 40); do
-     # This is the "global task number". Since we have an array of
-     # 320 tasks, J will range from 1 to 320.
-     J=$(($SLURM_ARRAY_TASK_ID+$I))
-     # Put each task into background, so that tasks are executed
-     # concurrently.
-     my_task $J &
-     # Wait a little before starting the next one.
-     sleep 1
-done
-# Wait for all child processes to terminate.
-wait
-</code>
-If the task running times vary a lot, consider using the //thread pool pattern//. Have a look at the ''xargs -P'' command, for instance.
-==== OpenMP Jobs ====
-For OpenMP jobs, set the ''%%--%%cpus-per-task'' parameter. As usual, you should also specify a ''%%--%%mem-per-cpu'' value. But in this case you have to divide the total RAM required by your program by the number of threads. E.g. if your application needs 8000 MB and you want to run 40 threads, then you have to set ''%%--%%mem-per-cpu=200'' (8000/40 = 200). Don't forget to set the ''OMP_NUM_THREADS'' environment variable. Example:
-<code bash>#!/bin/bash
-#SBATCH --partition=general1
-#SBATCH --ntasks=1
-#SBATCH --cpus-per-task=40
-#SBATCH --mem-per-cpu=200
-#SBATCH --mail-type=ALL
-#SBATCH --time=48:00:00
-export OMP_NUM_THREADS=40
-./omp_program
-</code>
-==== MPI Jobs ====
-**Remember:** Nodes are used exclusively. Each node has 40 CPU cores. If you want to run a lot of small jobs (i.e. where more than one job could be run on a single node concurrently), consider running more than one computation within a job (see next section). Otherwise it will most likely result in a waste of resources and will lead to a longer queueing time (for you and others).
-See also: http://www.schedmd.com/slurmdocs/faq.html#steps
-As an example, we want to run a program that spawns 80 Open MPI ranks and where 1200 MB of RAM are allocated for each rank.
-<code bash>#!/bin/bash
-#SBATCH --partition=general1
-#SBATCH --ntasks=80
-#SBATCH --cpus-per-task=1
-#SBATCH --mem-per-cpu=1200
-#SBATCH --mail-type=ALL
-#SBATCH --extra-node-info=2:20:1
-#SBATCH --time=48:00:00
-module load mpi/XXXX/....
-export OMP_NUM_THREADS=1
-mpirun -n 80 ./example_program
-</code>
-==== Combining Small MPI Jobs ====
-As mentioned earlier, running small jobs while full nodes are allocated leads to a waste of resources. In cases where you have, let's say, a lot of 20-rank MPI jobs (with similar runtimes and low memory consumption), you can start more than one computation within a single allocation (and on a single node). Open MPI example (running two MPI jobs concurrently on a 40-core node):
-<code bash>#!/bin/bash
-#SBATCH --partition=general1
-#SBATCH --nodes=1
-#SBATCH --ntasks=40
-#SBATCH --cpus-per-task=1
-#SBATCH --mem-per-cpu=2000
-#SBATCH --time=48:00:00
-#SBATCH --mail-type=FAIL
-export OMP_NUM_THREADS=1
-mpirun -np 20 ./program input01 >& 01.out &
-# Wait a little before starting the next one.
-sleep 3
-mpirun -np 20 ./program input02 >& 02.out &
-# Wait for all child processes to terminate.
-wait
-</code>
-You might also need to disable core binding (please see the ''mpirun'' man page, or when using MVAPICH2, set ''MV2_ENABLE_AFFINITY=0''). Otherwise the ranks of the second run will interfere with the first one.
-==== Hybrid Jobs: MPI/OpenMP ====
-MVAPICH2 example script (40 ranks, 6 threads each and 200 MB per thread, i.e. 1.2 GB per rank; so, for 40*6 threads, you'll get six 40-core nodes):
-<code bash>#!/bin/bash
-#SBATCH --partition=general1
-#SBATCH --ntasks=40
-#SBATCH --cpus-per-task=6
-#SBATCH --mem-per-cpu=200
-#SBATCH --mail-type=ALL
-#SBATCH --extra-node-info=2:20:1
-#SBATCH --time=48:00:00
-export OMP_NUM_THREADS=6
-export MV2_ENABLE_AFFINITY=0
-mpirun -np 40 ./example_program
-</code>
-Please note, that this is just an example. You may or may not run it as-it-is with your software, which is likely to have a different scalability.
-You have to disable the core affinity when running hybrid jobs with MVAPICH2. Otherwise all threads of an MPI rank will be pinned to the same core. Our example now includes the command
-<code bash>
-export MV2_ENABLE_AFFINITY=0
-</code>
-which disables this feature. The OS scheduler is now responsible for the placement of the threads during the runtime of the program. But the OS scheduler can dynamically change the thread placement during the runtime of the program. This leads to cache invalidation, which degrades performance. This can be prevented by thread pinning.
-==== Local Storage ====
-On each node there is up to 1.4 TB of local disk space (see also [[#Storage]]). If you need local storage, you have to add the ''%%--%%tmp'' parameter to your SLURM script. Set the amount of storage in megabytes, e.g. set ''%%--%%tmp=5000'' to allocate 5 GB of local disk space. The data in the local directory (''/local/$SLURM_JOB_ID'') is automatically deleted after the corresponding batch job has finished.
-==== The salloc Command ====
-For interactive workflows you can use SLURM's ''salloc'' command. With ''salloc'' almost the same options can be used as with ''sbatch'', e.g.:
-<code>[user@loginnode ~]$ salloc --nodes=4 --time=0:45:00 --mem=100g --partition=test
-salloc: Granted job allocation 197553
-salloc: Waiting for resource configuration
-salloc: Nodes node45-[002-005] are ready for job
-[user@loginnode ~]$
-</code>
-Now you can ''ssh'' into the nodes that were allocated for the job and run further commands, e.g.:
-<code>
-[user@loginnode ~]$ ssh node45-002
-[user@node45-002 ~]$ hostname
-node45-002.cm.cluster
-[user@node45-002 ~]$ logout
-Connection to node45-002 closed.
-...
-[user@loginnode ~]$ ssh node45-003
-[user@node45-003 ~]$ hostname
-node45-003.cm.cluster
-[user@node45-003 ~]$ logout
-Connection to node45-003 closed.
-...
-[user@loginnode ~]$ ssh node45-005
-[user@node45-005 ~]$ hostname
-node45-005.cm.cluster
-[user@node45-005 ~]$ logout
-Connection to node45-005 closed.
-</code>
-Or you can use ''srun'' for running a command on all allocated nodes in parallel:
-<code>[user@loginnode ~]$ srun hostname
-node45-002.cm.cluster
-node45-003.cm.cluster
-node45-005.cm.cluster
-node45-004.cm.cluster
-[user@loginnode ~]$
-</code>
-Finally you can terminate your interactive job session by running ''exit'', which will free the allocated nodes:
-<code>[user@loginnode ~]$ exit
-salloc: Relinquishing job allocation 197553
-[user@loginnode ~]$
-</code>
-==== Planning Work =====
-By using the ''%%--%%begin'' option it's possible to tell SLURM that you need the resources at some point in the future. Also, you might find it useful to use this feature for creating "user-mode reservations". E.g.
-  - Submit a sleep job (allocate twenty intel20 nodes for 3 days), you can logout after running this command (but check the output of the squeue command first, if there is no corresponding pending job, then sth. went wrong): <code>
-$ sbatch --begin=202X-07-23T08:00 --time=3-0 --nodes=20 \
-  --partition=general2 --mem=120g \
-  --wrap="sleep 3d"</code>
-  - Wait until the time has come (07/23/202X 8:00am or later, there is no guarantee, that the allocation will be made on time, but the earlier you submit the job, the more likely you'll get the resources by that time).
-  - Find out whether the sleep job is running (i.e. is in R state) and run a new job step within that allocation (see also http://slurm.schedmd.com/faq.html#multi_batch): <code>
-$ squeue
-  JOBID    PARTITION     NAME   ST      TIME  NODES
-2717365     parallel   sbatch    R   3:28:29     20
-$ srun --jobid 2717365 hostname
-...</code> **Note:** Please note, that we are using the ''srun'' command. The ''sbatch'' command is not supported in this scenario.
-  - Finally, don't forget to release the allocation, if there's time left and the sleep job is still running:<code>
-$ scancel 2717365</code>