home directory quota
There is a 10GB quota limit enforced on $HOME directory (/global/home/users/username) usage. Please keep your usage below this limit. There will NETAPP snapshots in place in this file system so we suggest you store only your source code and scripts in this area and store all your data under /clusterfs/cortex (see below).
In order to see your current quota and usage, use the following command:
For large amounts of data, please create a directory
and store the data inside that directory.
get a password
- press the PASS WORD button on your crypto card
- enter passoword
- press enter
- the 7 digit password is given (without the dash)
ssh to the gateway computer (hadley)
note: please don't use the gateway for computations (e.g. matlab)!
ssh -Y neuro-calhpc.berkeley.edu (or hadley.berkeley.edu)
and use your crypto password
- put all your customizations into your .bashrc
- for login shells, .bash_profile is used, which in turn loads .bashrc
Start interactive session on compute node
- start interactive session:
qsub -X -I
- start interactive session on particular node (nodes n0000.cortex and n0001.cortex have GPUs):
qsub -X -I -l nodes=n0001.cortex
The perceus manual is here
- listing available cluster nodes:
- list cluster usage
- to restrict the scope of these commands to cortex cluster, add the following line to your .bashrc
- module list
- module avail
- module help
- help pages are here
Resource Manager PBS
- Job Scheduler MOAB
- List running jobs:
- List jobs of a given node:
qstat -n 98
- sample script
#!/bin/bash #PBS -q cortex #PBS -l nodes=1:ppn=2:cortex #PBS -l walltime=01:00:00 #PBS -o path-to-output #PBS -e path-to-error cd /global/home/users/kilian/sample_executables cat $PBS_NODEFILE mpirun -np 8 /bin/hostname sleep 60
- submit script
- interactive session
qsub -I -q cortex -l nodes=1:ppn=2:cortex -l walltime=00:15:00
- flush STDOUT and STDERR to files in your home directory so you can tail the output of the job while it's running
qsub -k oe scriptname
- remove a queued/running job (you can get the job_id from qstat)
- list nodes that your job is running on
- run the program on several cores
mpirun -np 4 -mca btl ^openib sample_executables/mpi_hello
Finding out the list of occupants on each cluster node
- One can find out the list of users using a particular node by ssh into the node, e.g.
- After logging into the node, type
- This is useful if you believe someone is abusing the machine and would like to send him/her a friendly reminder.
note: remember to start an interactive session before starting matlab!
In order to use matlab, you have to load the matlab environment:
module load matlab/R2010a -or- module load matlab/R2007a
Once the matlab environment is loaded, you can start a matlab session by running
matlab -nojvm -nodesktop
An example PBS script for running matlab code is
#!/bin/bash #PBS -q cortex # request 1 nodes with 2 CPUs #PBS -l nodes=1:ppn=2 # reserve time on the selected cores #PBS -l walltime=01:00:00 module load matlab matlab -nodisplay -nojvm << EOF test # here you should have whatever you would normally type in the Matlab prompt exit EOF
If you would like to see who is using matlab licenses, enter
We have several Python Distributions installed: The Enthought Python Distribution (EPD), the Source Python Distribution (SPD) and Sage. The easiest way to get started is probably to use EPD (see below).
Enthought Python Distribution (EPD)
We have the Enthought Python Distribution 6.3.1 installed [EPD]. In order to use it, you have to follow the following steps:
- login to the gateway server using "ssh -Y" (see above)
- start an interactive session using "qsub -I -X" (see above)
- load the python environment module:
module load python/epd
- start ipython:
- run the following commands inside ipython to test the setup:
from enthought.mayavi import mlab mlab.test_contour3d()
CUDA is a library to use the graphics processing units (GPU) on the graphics card for general-purpose computing. We have a separate wiki page to collect information on how to do general-purpose computing on the GPU: GPGPU. We have installed the CUDA 3.0 driver and toolkit.
In order to use CUDA, you have to load the CUDA environment:
module load cuda
CUDA SDK (Outdated since version change to 3.0)
You can install the CUDA SDK by running
You can compile all the code examples by running
module load X11 module load Mesa/7.4.4 cd ~/NVIDIA_GPU_Computing_SDK/C make
The compiled examples can be found in the directory
note: The examples using graphics with OpenGL don't seem to run on a remote X server. In order to make them work, we probably need to install something like virtualgl.
Here are some tips on how to effectively use the cluster.
Mounting Cluster File System
Mounting the cluster file system remotely allows you to easily access files on the cluster, and allows you to use local programs to edit code or examine simulation outputs locally (very useful). I often edit the remote code using a text editor running on my local machine. This allows you to take advantage of the niceties of a native editor without having to copy code back and forth before you run a simulation on the cluster.
On linux distributions you can mount your cluster home directory locally using sshfs 
sshfs hadley.berkeley.edu: <mount-dir>
On Mac and Windows machines the program ExpanDrive works well (uses Fuse under the hood): 
- If you have a problem that is not covered on this page, you can send an email to our user list:
- If you need additional help from the LBL group, send an email to their email list. Please always cc our email list as well.
- In urgent cases, you can also email Krishna Muriki (LBL User Services) directly.