- ATLAS2 is a private cluster with restricted access to the bs54_0001 group.
- Head node: atlas2.cac.cornell.edu (access via ssh)
- 55 compute nodes c00[01-16, 31-48,50-70]
- Current Cluster Status: Ganglia.
- Please send any questions and report problems to: firstname.lastname@example.org
How To Login
All nodes have hyperthreading turned on and are Xeon generations that supports vector extensions: SSE4.2.
Node Names Memory per node Model name Processor count per node Core(s) per socket Sockets Thread(s) per core c00[01-12] 94GB Intel(R) Xeon(R) CPU X5690 @ 3.47GHz 24 6 2 2 c00[13-16] 47GB Intel(R) Xeon(R) CPU X5670 @ 2.93GHz 24 6 2 2 c00[31-48,50-58] 47GB Intel(R) Xeon(R) CPU X5670 @ 2.93GHz 24 6 2 2 c00[59-70] 47GB Intel(R) Xeon(R) CPU X5690 @ 3.47GHz 24 6 2 2
- All nodes have a 1GB ethernet connection for eth0 on a private net served out from the atlas2 head node.
- All nodes have an Infiniband connection:
- InfiniPath_QLE7340n (QDR speed, 8Gbits/sec)
- PLEASE NOTE: One of the 5 Infiniband switches has failed. While it is determined if it will be replaced, the following nodes do not have an "Active" state for Infiniband:
("Partition" is the term used by slurm)
- hyperthreading is turned on for ALL nodes
- all partitions have a default time of 1 hour
- ATLAS2 has 5 separate queues:
Queue/Partition Number of nodes Node Names Limits short (default) 31 c00[13-16,31-48,50-58] walltime limit: 4 hours long 22 c00[13-16,31-48] walltime limit: 504 hours inter ~Interactive 12 c00[59-70] walltime limit: 168 hours bigmem 12 servers c00[01-12] Maximum of 12 nodes, walltime limit: 168 hours normal 55 servers c00[01-16, 31-48,50-70] walltime limit: 4 hours
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.
A few slurm commands to initially get familiar with: scontrol show nodes scontrol show partition Submit a job: sbatch testjob.sh Interactive Job: srun -p short --pty /bin/bash scontrol show job [job id] scancel [job id] sinfo -l
Example in Short Partition/Queue
Example sbatch file to run a job in the short partition/queue; save as example.sh:
#!/bin/bash ## J sets the name of job #SBATCH -J TestJob ## -p sets the partition (queue) #SBATCH -p long ## 10 min #SBATCH --time=00:10:00 ## sets the tasks per core (default=2; keep default if you want to take advantage of hyperthreading) ## 2 will take whole cores, but will divide by 2 with hyperthreading #SBATCH --ntasks-per-core=1 ## request 300MB per core #SBATCH --mem-per-cpu=4GB ## define jobs stdout file #SBATCH -o testlong-%j.out ## define jobs stderr file #SBATCH -e testlong-%j.err echo "starting at `date` on `hostname`" # Print the SLURM job ID. echo "SLURM_JOBID=$SLURM_JOBID" echo "hello world `hostname`" echo "ended at `date` on `hostname`" exit 0
Submit/Run your job:
View your job:
scontrol show job 9
- To view all options of yum, type:
- To view installed repositories, type:
- To view if your requested software package is in one of the installed repositories, use:
yum search <package>
- i.e. To search whether variations of tau are available, you would type:
- To view all options of yum, type:
yum search tau
Package and Version Location module available Notes cplex studio 128 /opt/ohpc/pub/ibm/ILOG/CPLEX_Studio128/ cplex/12.8 cuda toolkit 9.0 /opt/ohpc/pub/cuda-9.0 cudnn 9.0 in targets/x86_64-linux/lib/ cuda toolkit 9.1 /opt/ohpc/pub/cuda-9.1 cudnn 9.1 in targets/x86_64-linux/lib/ cuda toolkit 9.2 /opt/ohpc/pub/cuda-9.2 cudnn 9.2 in targets/x86_64-linux/lib/ cuda toolkit 10.0 /opt/ohpc/pub/cuda-10.0 cudnn 7.4.1 for cuda10 in targets/x86_64-linux/lib/ gcc 7.2.0 /opt/ohpc/pub/compiler/gcc/7.2.0/bin/gcc gnu7/7.2.0 gcc 4.8.5 (default) /usr/bin/gcc gdal 2.2.3 /opt/ohpc/pub/gdal2.2.3 gdal/2.2.3 java openjdk 1.8.0 /usr/bin/java Python 2.7.5 (default) /usr/bin/python The system-wide installation of packages is no longer supported. See below for Anaconda/miniconda install information. R 3.5.1 /usr/bin/R The system-wide installation of packages is no longer supported. Subversion (svn) 1.7 /usr/bin/svn
- 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 ]
Since this cluster is managed with OpenHPC, the Lmod Module System is implemented. You can see detailed information and instructions at the linked page.
To be sure you are using the environment setup for
cplex, you would type:
* module avail * module load cplex - when done, either logout and log back in or type: * module unload cplex
You can also create your own modules and place them in your $HOME. For instructions, see the [Lmod Module System] page.
Once created, type
module use $HOME/path/to/personal/modulefiles. This will prepend the path to
echo $MODULEPATH to confirm.
Build software from source into your home directory ($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)
How to Install R packages in your home directory
************************************************************************************ NOTE: Steps 1) through 4) need to be done once, after your Rlibs directory has been created and your R_LIBS environment is set, you can install additional packages using step 5). ************************************************************************************ Know your R library search path: Start R and run .libPaths() Sample output is shown below: > .libPaths()  "/usr/lib64/R/library" Now we will create a local Rlibs directory and add this to the library search path. NOTE: Make sure R is NOT running before you proceed. 1) Create a directory in your home directory you would like to install the R packages, e.g. Rlibs mkdir ~/Rlibs 2) Create a .profile file in your home directory (or modify existing) using your favorite editor (emacs, vim, nano, etc) Add the following to your .profile #!/bin/sh if [ -n $R_LIBS ]; then export R_LIBS=~/Rlibs:$R_LIBS else export R_LIBS=~/Rlibs fi 3) To reset the R_LIBS path we need to run the following: "source ~/.profile" (or logout and log back in) 4) Confirm the change is in your library path: start R > .libPaths()  "$HOME/Rlibs"  "/usr/lib64/R/library" 5) Install the package in your local directory >install.packages("packagename","~/Rlibs","https://cran.r-project.org/") i.e. to install the package:snow >install.packages("snow","~/Rlibs","https://cran.r-project.org/") 6) For more help with install.packages() use >?install.packages( ) 7) To see which libraries are available in your R library path, run library() The output will show your local packages and the system wide packages >library()
How to Install Python Anaconda (miniconda) home directory
- Anaconda can be used to maintain custom environments for R (as well as other software).
- Reference to help decide if miniconda is enough: https://conda.io/docs/user-guide/install/download.html
- NOTE: Consider starting with miniconda if you do not need a multitude of packages for it will be smaller, faster to install as well as update.
- Reference for Anaconda R Essentials: https://conda.io/docs/user-guide/tasks/use-r-with-conda.html
- Reference for linux install: https://conda.io/docs/user-guide/install/linux.html
- Please take the tutorials to assist you with your management of conda packages: