Copying your data over from icse-data
- icse-data.cac.cornell.edu:/home/fs01 is nfs mounted on pool.cac.cornell.edu to: /mnt/migration
(Therefore you do not need to ssh to icse-data.cac to retrieve your data.)
- To copy data, one suggestion would be to use rsync:
Upon logging into pool.cac.cornell.edu, you will see you are in your /home directory (type: pwd)
- Make a directory to copy your data into & use rsync (Reminder: linux is case-sensitive):
mkdir FromIcseData rsync -av /mnt/migration/your_user_id/ FromIcseData/
(Note: the end "/" are important in the above command - it says to copy all the contents from your old home directory into the newly created directory 'FromIcseData')
- Another example would be if you do not want your data moving into a new directory:
rsync -av /mnt/migration/your_user_id/ . (use a "dot" to state copy data here in my current location)
NOTE: Once all data is copied over, we will remove the /mnt/migration mount after making an announcement.
- pool is a private cluster with restricted access to the following groups: fe13_0001, dlk15_0001, ylj2_0001
- Head node: pool.cac.cornell.edu (access via ssh)
- OpenHPC deployment running Centos 7.6
- Cluster scheduler: slurm 17.11.10
- 54 compute nodes c00[01-54]
- Current Cluster Status: Ganglia.
- data on the pool cluster is NOT backed up
- Please send any questions and report problems to: firstname.lastname@example.org
How To Login
- To get started, login to the head node pool.cac.cornell.edu via ssh.
- You will be prompted for your CAC account password
- If you are unfamiliar with Linux and ssh, we suggest reading the Linux Tutorial and looking into how to Connect to Linux before proceeding.
- There is a 1.8TB local /scratch disk on the head node only.
- Pool Hardware Details
- All nodes have a 1GB ethernet connection for eth0 on a private net served out from the pool head node.
Running Jobs / Slurm Scheduler
("Partition" is the term used by slurm for "Queues")
- hyperthreading is turned on for ALL nodes - Slurm considers each core to consist of 2 logical CPUs
- all partitions have a default time of 1 hour and are set to OverSubscribe
Current Partitions on the pool cluster:
Queue/Partition Number of nodes Node Names Limits Group Access normal (default) 27 c00[01-08], [10-28] walltime limit: 168 hours (i.e. 7 days) Domain Users plato 1 c0009 walltime limit: 168 hours (i.e. 7 days) limited access per fe13 common 18 c00[17,19,20,22,29-38,50-53] walltime limit: 168 hours (i.e. 7 days) Domain Users fe13 23 c00[01-08,18,21,23,24,40-49,54] walltime limit: 168 hours (i.e. 7 days) fe13_0001 dlk15 7 c00[10-14,25,39] walltime limit: 168 hours (i.e. 7 days) dlk15_0001 ylj2 5 c00[15-16,26-28] walltime limit: 168 hours (i.e. 7 days) ylj2_0001
As of May 15, 2019, pool partitions will be (normal goes away):
Queue/Partition Number of nodes Node Names Limits Group Access common (default) 18 c00[17,19,20,22,29-38,50-53] walltime limit: 168 hours (i.e. 7 days) Domain Users plato 1 c0009 walltime limit: 168 hours (i.e. 7 days) limited access per fe13 fe13 23 c00[01-08,18,21,23,24,40-49,54] walltime limit: 168 hours (i.e. 7 days) fe13_0001 dlk15 7 c00[10-14,25,39] walltime limit: 168 hours (i.e. 7 days) dlk15_0001 ylj2 5 c00[15-16,26-28] walltime limit: 168 hours (i.e. 7 days) ylj2_0001
Slurm Scheduler HELP
Command/Option Summary (two page PDF) - Common Slurm Commands
Slurm Workload Manager Quick Start User Guide - this page lists all of the available Slurm commands
Slurm Workload Manager Frequently Asked Questions includes FAQs for Management, Users and Administrators
Convenient SLURM Commands has examples for getting information on jobs and controlling jobs
Slurm Workload Manager - sbatch - used to submit a job script for later execution. The script will typically contain one or more srun commands to launch parallel tasks. You can also use the srun command to launch an interactive job on the compute nodes.
# A few slurm commands to initially get familiar with: scontrol show nodes scontrol show partition # To submit a batch job and an interactive job: sbatch testjob.sh srun -p normal --pty /bin/bash # Job management commands: scontrol show job <job id> scancel <job id> sinfo -l
Examples & Tips
- NOTE: All lines begining with "#SBATCH" are a directive for the scheduler to read. If you want the line ignored (i.e. a comment), you must place 2 "##" at the beginning of your line.
Example batch job to run in the partition: common
Example sbatch script to run a job with one task (default) in the 'common' partition (i.e. queue):
#!/bin/bash ## -J sets the name of job #SBATCH -J TestJob ## -p sets the partition (queue) #SBATCH -p common ## 10 min #SBATCH --time=00:10:00 ## sets the tasks per core (default=2 for hyperthreading: cores are oversubscribed) ## set to 1 if one task by itself is enough to keep a core busy #SBATCH --ntasks-per-core=1 ## request 4GB per CPU or task #SBATCH --mem-per-cpu=4GB ## define job stdout file #SBATCH -o testcommon-%j.out ## define job stderr file #SBATCH -e testcomon%j.err echo "starting at `date` on `hostname`" # Print the Slurm job ID echo "SLURM_JOB_ID=$SLURM_JOB_ID" echo "hello world `hostname`" echo "ended at `date` on `hostname`" exit 0
Submit/Run your job:
View your job:
scontrol show job <job_id>
Example MPI batch job to run in the partition: common
Example sbatch script to run a job with 60 tasks in the 'common' partition (i.e. queue):
#!/bin/bash ## -J sets the name of job #SBATCH -J TestJob ## -p sets the partition (queue) #SBATCH -p common ## 10 min #SBATCH --time=00:10:00 ## the number of slots (CPUs) to reserve #SBATCH -n 60 ## the number of nodes to use (min and max can be set separately) #SBATCH -N 3 ## typically an MPI job needs exclusive access to nodes for good load balancing #SBATCH --exclusive ## don't worry about hyperthreading, Slurm should distribute tasks evenly ##SBATCH --ntasks-per-core=1 ## define job stdout file #SBATCH -o testcommon-%j.out ## define job stderr file #SBATCH -e testcommon-%j.err echo "starting at `date` on `hostname`" # Print Slurm job properties echo "SLURM_JOB_ID = $SLURM_JOB_ID" echo "SLURM_NTASKS = $SLURM_NTASKS" echo "SLURM_JOB_NUM_NODES = $SLURM_JOB_NUM_NODES" echo "SLURM_JOB_NODELIST = $SLURM_JOB_NODELIST" echo "SLURM_JOB_CPUS_PER_NODE = $SLURM_JOB_CPUS_PER_NODE" mpiexec -n $SLURM_NTASKS ./hello_mpi echo "ended at `date` on `hostname`" exit 0
To include or exclude specific nodes in your batch script
To run on a specific node only, add the following line to your batch script:
#SBATCH -w, --nodelist=c0009
To include one or more nodes that you specifically want, add the following line to your batch script:
#SBATCH --nodelist=<node_names_you_want_to_include> ## e.g., to include c0006: #SBATCH --nodelist=c0006 ## to include c0006 and c0007 (also illustrates shorter syntax): #SBATCH -w c000[6,7]
To exclude one or more nodes, add the following line to your batch script:
#SBATCH -exclude=<node_names_you_want_to_exclude> ## e.g., to avoid c0006 through c0008, and c0013: #SBATCH -exclude=c00[06-08,13] ## to exclude c0006 (also illustrates shorter syntax): #SBATCH -x c0006
Environment variables defined for tasks that are started with srun
If you submit a batch job in which you run the following script with "srun -n $SLURM_NTASKS", you will see how the various environment variables are defined.
#!/bin/bash echo "Hello from `hostname`," \ "$SLURM_CPUS_ON_NODE CPUs are allocated here," \ "I am rank $SLURM_PROCID on node $SLURM_NODEID," \ "my task ID on this node is $SLURM_LOCALID"
These variables are not defined in the same useful way in the environments of tasks that are started with mpiexec or mpirun.
Use $HOME within your script rather than the full path to your home directory
In order to access files in your home directory, you should use $HOME rather than the full path . To test, you could add to your batch script:
echo "my home dir is $HOME"
Then view the output file you set in your batch script to get the result.
Copy your data to /tmp to avoid heavy I/O from your nfs mounted $HOME !!!
- We cannot stress enough how important this is to avoid delays on the file systems.
#!/bin/bash ## -J sets the name of job #SBATCH -J TestJob ## -p sets the partition (queue) #SBATCH -p common ## time is HH:MM:SS #SBATCH --time=00:01:30 #SBATCH --cpus-per-task=15 ## define job stdout file #SBATCH -o testcommon-%j.out ## define job stderr file #SBATCH -e testcommon-%j.err echo "starting $SLURM_JOBID at `date` on `hostname`" echo "my home dir is $HOME" ## copying my data to a local tmp space on the compute node to reduce I/O MYTMP=/tmp/$USER/$SLURM_JOB_ID srun /usr/bin/mkdir -p $MYTMP || exit $? echo "Copying my data over..." srun cp -rp $SLURM_SUBMIT_DIR/mydatadir $MYTMP || exit $? ## run your job executables here... echo "ended at `date` on `hostname`" echo "copy your data back to your $HOME" srun /usr/bin/mkdir -p $SLURM_SUBMIT_DIR/newdatadir || exit $? srun cp -rp $MYTMP $SLURM_SUBMIT_DIR/newdatadir || exit $? ## remove your data from the compute node /tmp space srun rm -rf $MYTMP exit 0
Explanation: /tmp refers to a local directory that is found on each compute node. It is faster to use /tmp because when you read and write to it, the I/O does not have to go across the network, and it does not have to compete with the other users of a shared network drive (such as the one that holds everyone's /home).
To look at files in /tmp while your job is running, you can ssh to the login node, then do a further ssh to the compute node that you were assigned. Then you can cd to /tmp on that node and inspect the files in there with
Note, if your application is producing 1000's of output files that you need to save, then it is far more efficient to put them all into a single tar or zip file before copying them into $HOME as the final step.
The 'lmod module' system is implemented for your use with listing and loading modules that will put you in the software environment needed. (For more information, type: module help)
To list the available software and the software environment you can put yourself in, type:
(to get a more complete listing, type: module spider) The software that is listed with "(L)" references what you have loaded. EXAMPLE: To be sure you are using the environment setup for gromacs, you would type:
module load gromacs/2019.1 module list (you will see gromacs is loaded (L)) * when done, either logout and log back in or type: module unload gromacs/2019.1
You can create your own modules and place them in your $HOME. Once created, type: module use $HOME/path/to/personal/modulefiles This will prepend the path to $MODULEPATH [type echo $MODULEPATH to confirm]
Reference: User Created Modules
- It is usually possible to install software in your home directory.
- List installed software via rpms: rpm -qa. Use grep to search for specific software: rpm -qa | grep sw_name [i.e. rpm -qa | grep perl ]
Build software from source into your home directory ($HOME)
* download and extract your source * cd to your extracted source directory ./configure --./configure --prefix=$HOME/appdir [You need to refer to your source documentation to get the full list of options you can provide 'configure' with.] make make install The binary would then be located in ~/appdir/bin. * Add the following to your $HOME/.bashrc: export PATH="$HOME/appdir/bin:$PATH" * Reload the .bashrc file with source ~/.bashrc. (or logout and log back in)