Latest revision as of 00:37, 11 January 2017

General Information

The Redwood computing cluster consists of about a dozen somewhat heterogeneous machines, some with graphics cards (GPUs), and one very clever wombat who can optimize your neural network for you if you ask nicely. The typical use cases for the cluster are that you have jobs that run in parallel which are independent, so having several machines will complete the task faster, even though any one machine might not be faster than your own laptop. Or you have a long running job which may take a day, and you don't want to worry about having to leave your laptop on at all times and not be able to use it. Another reason is that your code leverages a communication scheme (such as MPI) to have multiple machines cooperatively work on a problem. Lastly, if you want to do long GPU computations.

In order for the cluster to be useful and well-utilized, it works best for everyone to submit jobs TODO (see SLURM further down on this page for the details) to the queue. A job may not start right away, but will get run once its turn comes. Please do not run extended interactive sessions or ssh directly to worker nodes for performing computation.

Cluster Administration

ClusterAdmin has information about cluster administration.

Hardware Overview

The current hardware and node configuration is listed here.

In addition to the compute nodes we ave a 17TB file server at

 /clusterfs/cortex/users

which is mounted as scratch space.

In brief, we have 14 nodes with over 60 cores and 4 GPUs.

Getting an account and one-time password service

In order to get an account on the cluster, please send an email to Bruno (baolshausen AT berk...edu) with the following information:

   Full Name <emailaddress> desiredusername

Please also include a note about which PI you are working with. Note: the desireusername must be 3-8 characters long, so it would have been truncated to desireus in this case.

OTP (One Time Password) Service

Once you have a username, you will need to follow the instructions found [1] to set up the Google Authenticator application, which gives you a one-time password for logging into the cluster.

Directory setup

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 be 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: TODO

 quota -s

Data

For large amounts of data, please create a directory

 /clusterfs/cortex/users/username

and store the data inside that directory. Note that unlike the home directory, scratch space is not backed up and permanence of your data is not guaranteed. There is a total limit of 17 TB for this drive that is shared by everyone at the Redwood Center.

Connect

ssh to a login node

 ssh -Y username@hpc.brc.berkeley.edu

and use your one-time password.

If you intend on working with a remote GUI session you can add a -C flag to the command above to enable compression data to be sent through the ssh tunnel.

note: please don't use the login nodes for computations (e.g. matlab, python)!

Google Authenticator App (get a password)

Open the google Authenticator App
Enter your personal pin
Enter the one-time pin

Setup environment

put all your customizations into your .bashrc
for login shells, .bash_profile is used, which in turn loads .bashrc

Using a Windows machine

Windows is not a Unix-based operating system and as a result does not natively interface with a Unix environment. Download the 2 following pieces of software to create a workaround:

Install a Unix environment emulator to interface directly with the cluster. Cygwin [2] seems to work well. During installation make sure to install Net -> "openssh". Editors -> "vim" is also recommended. Then you can use the instructions detailed in ssh to gateway above
Install an SFTP/SCP/FTP client to allow for file sharing between the cluster and your local machine. WinSCP [3] is recommended. ExpanDrive can also be used to create a cluster-based network drive on your local machine.

Useful commands

See https://sites.google.com/a/lbl.gov/high-performance-computing-services-group/scheduler/ucb-supercluster-slurm-migration for a detailed FAQ on the SLURM job manager.

Full description of our system by the LBL folks is at http://go.lbl.gov/hpcs-user-svcs/ucb-supercluster/cortex

SLURM

SLURM is our scheduler. It is very important you understand SLURM well to have a good time doing research on the cluster. SLURM is our administrator on the cluster, it helps you find resources for your job. It also helps others do the same, so we are not stepping on each others' toes. There are some do's and don'ts with using SLURM.

Logging in -- when you login to the cluster, you end up landing on the login node. We do not own the login node and share this with other members of the Berkeley Research Consortium. So, it is important not to run anything here *at all*

Information on Submitting, Monitoring, Reviewing Jobs can be found here. You can do many simple BASH tricks to submit a large number of embarrassingly parallel jobs on the cluster. This is great for parameter sweeps.

Storage -- every user gets a 10 GB quota gratis from the BRC. This is your home folder or where you land when you login. In addition to this there's a 20TB scratch space (/clusterfs/cortex/scratch) shared by all members of the Redwood Center. We have a log of how much space is being used by each member who writes into the scratch folder at (TODO)

We have 4 GPU nodes and information on requesting and using them can be found here. When you request a GPU as a resource, you get the whole node along with it.

We have a debug queue that can be requested for research here

Submitting a Job

From the login node, you can submit jobs to the compute nodes using the syntax

 sbatch myscript.sh

where the myscript.sh is an shell script containing the call to the executable to be submitted to the cluster. Typically, for a matlab job, it would look like

 #!/bin/bash -l
 #SBATCH -p cortex
 #SBATCH --time=03:30:00
 #SBATCH --mem-per-cpu=2G
 cd /clusterfs/cortex/scratch/working/dir/for/your/code
 module load matlab/R2013a
 matlab -nodisplay -nojvm -r "mymatlabfunction( parameters); exit"
 exit

the --time defines the walltime of the job, which is an upper bound on the estimated runtime. The job will be killed after this time is elapsed. --mem specifies how much memory the job requires, the default is 1GB per job.

Monitoring Jobs

Additional options can be passed to sbatch to monitor outputs from the running jobs

   sbatch -o outputfile.txt -e errofile.txt -J jobdescriptor myscript.sh

the output of the job will be piped to outputfile.txt and any errors if the job crashes to errofile.txt

Cluster usage

Use

 squeue

to get a list of pending and running jobs on the cluster. It will show user names jobdescriptor passed to sbatch, runtime and nodes.

To start an interactive session on the cluster (requires specifying the cluster and walltime as is shown here):

 srun -u -p cortex -t 2:0:0 --pty bash -i

Perceus commands

The perceus manual is here

listing available cluster nodes:

 wwstats
 wwnodes

list cluster usage

 wwtop

to restrict the scope of these commands to cortex cluster, add the following line to your .bashrc

 export NODES='*cortex'

module list
module avail
module help

help pages are here

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.

 ssh n0000.cortex

After logging into the node, type

top

This is useful if you believe someone is abusing the machine and would like to send him/her a friendly reminder.

Job Management

In order to coordinate our cluster usage patterns fairly, our cluster uses a job manager known as SLURM. If your are planning to run jobs on the cluster you should be using SLURM! Learn how here.

Software

Information on what software is installed on the cluster and how to access it is here.

Matlab

Matlab instructions are here.

Python

Python instructions are here.

Usage Tips TODO

Here are some tips on how to effectively use the cluster.

Embarrassingly Parallel Submissions

Here is an alternate script to do embarrassingly parallel submissions on the cluster.

iterate.sh

 #!/bin/sh
 #Leap Size
 param1=11
 param2=1.2
 param3=.75
 #LeapSize
 for i in 14 15 16
 do
 #Epsilon
  for j in $(seq .8 .1 $param2);
      do
      #Beta
      for k in $(seq .65 .01 $param3);
            do
                echo $i,$j,$k
                qsub param_test.sh  -v "LeapSize=$i,Epsilon=$j,Beta=$k"
            done
      done
  done

param_test.sh

 #!/bin/bash
 #PBS -q cortex
 #PBS -l nodes=1:ppn=2:gpu
 #PBS -l walltime=10:35:00
 #PBS -o /global/home/users/mayur/Logs
 #PBS -e /global/home/users/mayur/Errors
 cd /global/home/users/mayur/HMC_reducedflip/
 module load matlab
 echo "Epsilon = ",$Epsilon
 echo "Leap Size = ",$LeapSize
 echo "Beta = ",$Beta
 matlab -nodisplay -nojvm -r "make_figures_fneval_cluster $LeapSize $Epsilon $Beta"

  Now run ./iterate.sh

Support Requests

If you have a problem that is not covered on this page, you can send an email to our user list:

 redwood_cluster@lists.berkeley.edu

If you need additional help from the LBL group, send an email to their email list. Please always cc our email list as well. Or visit their website[4].

 hpcshelp@lbl.gov

In urgent cases, you can also email Krishna Muriki (LBL User Services) directly.

Cluster: Difference between revisions