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= General Information =
= General Information =


== Directory setup ==
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.


=== home directory quota ===
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.


There is a 10GB quota limit enforced on $HOME directory
== Cluster Administration ==
(/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:
[[ClusterAdmin]] has information about cluster administration.


  quota -s
== Hardware Overview ==


=== data ===
The current hardware and node configuration is listed [https://sites.google.com/a/lbl.gov/high-performance-computing-services-group/ucb-non-supercluster/cortex here].


For large amounts of data, please create a directory
In addition to the compute nodes we ave a 17TB file server at
  /clusterfs/cortex/users
which is mounted as scratch space.


  /clusterfs/cortex/scratch/username
In brief, we have 14 nodes with over 60 cores and 4 GPUs.


and store the data inside that directory.
== 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:


== Connect ==
    Full Name <emailaddress> desiredusername


=== get a password ===
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.


* press the PASS WORD button on your crypto card
'''OTP (One Time Password) Service'''
* enter passoword
* press enter
* the 7 digit password is given (without the dash)


=== ssh to the gateway computer (hadley) ===
Once you have a username, you will need to follow the instructions found [https://sites.google.com/a/lbl.gov/high-performance-computing-services-group/authentication/linotp-usage] to set up the Google Authenticator application, which gives you a one-time password for logging into the cluster.


''' note: please don't use the gateway for computations (e.g. matlab)! '''
== Directory setup ==


  ssh -Y neuro-calhpc.berkeley.edu (or hadley.berkeley.edu)
=== Home Directory Quota ===


and use your crypto password
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).


=== Setup environment ===
In order to see your current quota and usage, use the following command: TODO


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


== Useful commands ==
=== Data ===


=== Start interactive session on compute node ===
For large amounts of data, please create a directory


* start interactive session:
  /clusterfs/cortex/users/username


  qsub -X -I
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.


* start interactive session on particular node (nodes n0000.cortex and n0001.cortex have GPUs):
== Connect ==


  qsub -X -I -l nodes=n0001.cortex
=== ssh to a login node ===


=== Perceus commands ===
  ssh -Y username@hpc.brc.berkeley.edu


The perceus manual is [http://www.warewulf-cluster.org/portal/book/export/html/7 here]
and use your one-time password.


* listing available cluster nodes:
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.


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


* list cluster usage
==== Google Authenticator App (get a password) ====


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


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


  export NODES='*cortex'
* put all your customizations into your .bashrc
* for login shells, .bash_profile is used, which in turn loads .bashrc


* module list
=== Using a Windows machine ===
* module avail
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:
* module help
* Install a Unix environment emulator to interface directly with the cluster. Cygwin [http://www.cygwin.com] 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 [http://www.winscp.net] is recommended. ExpanDrive can also be used to create a cluster-based network drive on your local machine.


* help pages are [http://lrc.lbl.gov/html/guide.html here]
== 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.


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


* Job Scheduler MOAB
=== SLURM  ===
* List running jobs:


  qstat -a
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.


* List jobs of a given node:
* 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*


  qstat -n 98
* 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.


* sample script
* 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)


  #!/bin/bash
* 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.
 
  #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
* We have a debug queue that can be requested for research here


  qsub scriptname


* interactive session
* Submitting a Job


  qsub -I -q cortex -l nodes=1:ppn=2:cortex -l walltime=00:15:00
From the login node, you can submit jobs to the compute nodes using the syntax


* flush STDOUT and STDERR to files in your home directory so you can tail the output of the job while it's running
  sbatch myscript.sh


  qsub -k oe scriptname
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


* remove a queued/running job (you can get the job_id from qstat)
  #!/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


  qdel job_id
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.


* list nodes that your job is running on
* Monitoring Jobs


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


* run the program on several cores
    sbatch -o outputfile.txt -e errofile.txt -J jobdescriptor myscript.sh


  mpirun -np 4 -mca btl ^openib sample_executables/mpi_hello
the output of the job will be piped to outputfile.txt and any errors if the job crashes to errofile.txt


= Software =
* Cluster usage


== Matlab ==
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.


'''note: remember to start an interactive session before starting matlab!'''


In order to use matlab, you have to load the matlab environment:
To start an interactive session on the cluster (requires specifying the cluster and walltime as is shown here):


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


Once the matlab environment is loaded, you can start a matlab session by running
=== Perceus commands ===


  matlab -nojvm -nodesktop
The perceus manual is [http://www.warewulf-cluster.org/portal/book/export/html/7 here]


== Python ==
* listing available cluster nodes:


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).
  wwstats
  wwnodes


=== Enthought Python Distribution (EPD) ===
* list cluster usage


We have the Enthought Python Distribution 5.0.0 installed [[http://www.enthought.com/products/epd.php EPD]]. In order to use it, you have to follow the following steps:
  wwtop


* login to the gateway server using "ssh -Y" (see above)
* to restrict the scope of these commands to cortex cluster, add the following line to your .bashrc
* start an interactive session using "qsub -I -X" (see above)
* load the python environment module:


   module load python/epd
   export NODES='*cortex'


* start ipython:
* module list
* module avail
* module help


  ipython -pylab
* help pages are [http://lrc.lbl.gov/html/guide.html here]


* run the following commands inside ipython to test the setup:
=== Finding out the list of occupants on each cluster node ===


  from enthought.mayavi import mlab
* One can find out the list of users using a particular node by ssh into the node, e.g.
  mlab.test_contour3d()


=== Source Python Distribution (SPD) ===
  ssh n0000.cortex


We have the Source Python Distribution installed [[http://code.google.com/p/spdproject/ SPD]]. In order to use it, you have to first load the python environment module:
* After logging into the node, type


   module load python/spd
   top


Afterwards, you can run ipython
* This is useful if you believe someone is abusing the machine and would like to send him/her a friendly reminder.


  % ipython -pylab
= Job Management =


At the moment, we have numpy, scipy, and matplotlib installed. If you would like to have additional modules installed, let me know [[mailto:kilian@berkeley.edu kilian]]
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 [http://redwood.berkeley.edu/wiki/Cluster_Job_Management here].


=== Sage ===
= Software =
Information on what software is installed on the cluster and how to access it is [http://redwood.berkeley.edu/wiki/Cluster-Software here].


Sage is [http://sagemath.org http://sagemath.org]. In order to use sage, you have to first load the sage environment module
== Matlab ==
Matlab instructions are [http://redwood.berkeley.edu/wiki/Cluster-Software#Matlab here].


  module load python/sage
== Python ==
Python instructions are [http://redwood.berkeley.edu/wiki/Cluster-Software#Python here].


After loading the sage module, if you want to have a scipy environment (run ipython, etc) in your interactive session, first do:
= Usage Tips TODO =
Here are some tips on how to effectively use the cluster.


  % sage -sh
== Embarrassingly Parallel Submissions ==


then you can run:
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


  % ipython
param_test.sh
 
   #!/bin/bash
or you can just do:
   #PBS -q cortex
 
   #PBS -l nodes=1:ppn=2:gpu
  % sage -ipython
   #PBS -l walltime=10:35:00
 
   #PBS -o /global/home/users/mayur/Logs
This is a temporary solution for people wanting use scipy with mpi on the cluster. It was built against the default openmpi (1.2.8) (icc) and mpi4py 1.1.0. For those using hdf5, I also built hdf5 1.8.3 (gcc)  and h5py 1.2.
  #PBS -e /global/home/users/mayur/Errors
 
   cd /global/home/users/mayur/HMC_reducedflip/
Sample pbs and mpi script is here:
  module load matlab
 
  echo "Epsilon = ",$Epsilon
   ~amirk/test
  echo "Leap Size = ",$LeapSize
 
  echo "Beta = ",$Beta
You can run it as:
  matlab -nodisplay -nojvm -r "make_figures_fneval_cluster $LeapSize $Epsilon $Beta"
 
   % mkdir -p ~/jobs
   % cd ~amirk/test
  % qsub pbs
 
--Amir
 
== CUDA ==
 
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 2.3 driver and toolkit.
 
In order to use CUDA, you have to load the CUDA environment:
 
   module load cuda
 
=== CUDA SDK ===
 
You can install the CUDA SDK by running
 
   bash /clusterfs/cortex/software/cuda-2.3/src/cudasdk_2.3_linux.run
 
You can compile all the code examples by running
 
  module load X11
   cd ~/NVIDIA_GPU_Computing_SDK/C
  make
 
The compiled examples can be found in the directory
 
  ~/NVIDIA_GPU_Computing_SDK/C/bin/linux/release
 
=== PyCuda ===


PyCuda 0.93 is installed as part of the Source Python Distribution (SPD). This is how you run all unit tests:
  Now run ./iterate.sh
 
  module load python/spd
  cd /clusterfs/cortex/software/src/pycuda-0.93/test/
  nosetests


= Support Requests =
= Support Requests =
Line 250: Line 238:
   [mailto:redwood_cluster@lists.berkeley.edu redwood_cluster@lists.berkeley.edu]
   [mailto:redwood_cluster@lists.berkeley.edu 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.
* 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[https://sites.google.com/a/lbl.gov/high-performance-computing-services-group/].


   [mailto:scs@lbl.gov scs@lbl.gov]
   [mailto:hpcshelp@lbl.gov hpcshelp@lbl.gov]


* In urgent cases, you can also email [mailto:kmuriki@lbl.gov Krishna Muriki] (LBL User Services) directly.
* In urgent cases, you can also email [mailto:kmuriki@lbl.gov Krishna Muriki] (LBL User Services) directly.
== OpenCL ==
'''note: OpenCL doesn't work yet'''
The OpenCL applications in the NVIDIA GPU Computing SDK require a GPU with CUDA Compute Architecture to run properly. In order to use OpenCL, you have to load the CUDA environment:
  module load cuda
=== GPU Computing SDK ===
Install version 2.3 of the NVIDIA GPU Computing SDK by executing the followin command
  bash /clusterfs/cortex/software/opencl-2.3/src/gpucomputingsdk_2.3a_linux.run
You can compile all the code examples by running
  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/NVIDIA_GPU_Computing_SDK/OpenCL/common/lib/Linux64
  module load X11
  cd ~/NVIDIA_GPU_Computing_SDK/OpenCL
  make
The compiled examples can be found in the directory
  ~/NVIDIA_GPU_Computing_SDK/OpenCL/bin/linux/release
=== PyOpenCL ===

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

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.