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(General Information)
(General Information)
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The Redwood computing cluster consists of about a dozen somewhat heterogeneous machines, some with graphics cards (GPUs).  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.  
 
The Redwood computing cluster consists of about a dozen somewhat heterogeneous machines, some with graphics cards (GPUs).  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 '''sbatch''' 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.
+
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.
  
 
[[ClusterAdmin]] has information about cluster administration.
 
[[ClusterAdmin]] has information about cluster administration.

Revision as of 23:16, 11 September 2015

General Information

The Redwood computing cluster consists of about a dozen somewhat heterogeneous machines, some with graphics cards (GPUs). 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.

ClusterAdmin has information about cluster administration.

Hardware Overview

The current hardware and node configuration is listed here.

In addition to the compute nodes we own a file server TODO

 NetOp 4TB

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 here to set up the Pledge application, which gives you a one-time password for logging into the cluster (see Installing and Configuring the OTP Token).

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/scratch/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 4 TB for this drive that is shared by everyone at the Redwood center.

Connect

Pledge App (get a password)

  • Run the pledge app and click "Generate one-time password"
  • Enter your PIN and press "Enter"
  • The application will present your 7 digit one time password

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)!

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 [1] 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 [2] 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 usage

  • 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.

Software

For information on what software is installed on the cluster and how to access it, head 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

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 [3]

 sshfs hadley.berkeley.edu: <mount-dir>

On Mac and Windows machines the program ExpanDrive works well (uses Fuse under the hood): [4]

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[5].
 hpcshelp@lbl.gov
  • In urgent cases, you can also email Krishna Muriki (LBL User Services) directly.
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