User Guide

User Guide

System Access

Using Secure Shell (ssh) commands:

ssh -X


ssh -Y -l userid


To avoid having to authenticate (entering the verification code followed by your password) every time you open an ssh connection or use scp, you can sign in once and use the same connection for all your needs.

On your local Unix-based computer, enter the following text into the file (create it if necessary) ~/.ssh/config

ControlMaster auto
ControlPath ~/.ssh/control:%h:%p:%r

Then, start the master connection in the background (adding -X if you use X11 forwarding):

ssh -fNM -l userid

Now, every time you ssh or scp into it should use the authenticated connection and not ask for any password. The only downside is that simultaneously transferring large files and trying to work on a terminal through the same connection may cause lag in your terminal. To open a new connection without going through the master, use the option -o ControlMaster=no. To close this background connection:

ssh -O stop

but note that this will close all open connections. You can alternatively choose to run the master connection interactively by omitting the -fNM argument, in which case closing the master connection in the normal way will close all open connections.

To set up a multiplexed connection on Windows, use Plink with PuTTY.

Rockfish will be accessible only to those users and research groups that have been given time and resources on the system. Hopkins PIs may request projects and allocations of resources at the Rockfish portal. After the allocation is approved and created users can request an account at the Rockfish portal and PIs or proxy should add user accounts to their allocations. This VIDEO shows the process to follow.

Once an account has been created users need to follow directions to configure their Two factor Authentication codes (using Google authenticator) [to be implemented in February 2021]

ssh {-XY] -l userid

The Rockfish portal should be used by PIs and users to manage accounts and allocations. PIs can create projects, request allocations (which must be approved by the advanced computing committee), add or remove users to their allocations, upload publications, grants and other items for ROI.


Each compute node has a local 1 TB NVMe hard drive mounted as “/tmp”. Users who read/write small files may want to use this space instead of the scratch file sets. It will most likely provide better performance. make sure you write files back to “scratch” or “data” before the job ends. Likewise, make sure you delete files and directories at the end of jobs.

Rockfish has two file sets optimized for small files (4MB block size) and large files (16MB blocksize). Users are expected to use one or the other according to their workflows. If you need assistance in exploring which scratch filesystem is best for your workflow, reach out to us at

Users can create aliases to easily access “scratch” filesets from any directory. For example, add to your .bashrc file:

alias cd16=”cd /scratch16/$(id -gn)”  This alias will change directory to /scratch16/PI-userid

This fileset has lower performance but larger capacity. Users are expected to store files for a longer time but also avoid batch computing to this file set.

Rockfish has two data Transfer Nodes (DTNs) to be used by Globus to transfer data at high speeds. One set of DTNs is in the Science DMZ network and can connect Rockfish with any site that uses Globus. The name of the end point is “Rockfish Data” DTNs should be used to move files to Rockfish using applications like Filezilla or plan secure copy. For example: scp file-name

Follow the instructions here: site

SLURM & Running Jobs

Rockfish uses the Simple Linux Utility for Resource Management (SLURM) batch environment. When you run in batch mode, you submit jobs to be run on the compute nodes using the “sbatch” command as described below. Remember that production or debugging jobs should be run only on the compute nodes and not the login nodes.

Rockfish places limits on the number of jobs queued and running on a per group (allocation) and partition basis. Please note that submitting a large number of jobs (especially very short ones) can impact the overall  scheduler response for all users. If you are anticipating submitting a lot of jobs, please contact before submission. We can work to check if there are bundling options (job arrays or gnu parallel) that make your workflow more efficient and reduce the impact on the scheduler.
Access to the compute node where your job is running.  Use the “srun” command to gain access to the compute node (driver node) where your job is running:  “srun –jobid=JOB12345 -w Node-name –pty /bin/bash”

Rockfish allocations are made in core-hours. The recommended method for estimating your resource needs for an allocation request is to perform benchmark runs. The core-hours used for a job are calculated by multiplying the number of processor cores used by the wall-clock duration in hours. Rockfish core-hour calculations should assume that all jobs will run in the regular queue.

For example: if you request one core on one node for an hour your allocation will be charged one core-hour. If you request 24 cores on one node, and the job runs for one hour, your account will be charged 24 core-hours. For parallel jobs, compute nodes are dedicated to the job. If you request 2 compute nodes and the job runs for one hour, your allocation will be charged 96 core-hours.

Job accounting is independent of the number of processes you run on compute nodes. You can request 2 cores for your job for one hour. If you run only one process, your allocation will be charged for 2 core-hours.

Charge = Number of cores x wall-time.

“defq queue”: This partition is designed to run a mix of jobs, from sequential jobs needing just one core to small parallel jobs (l.e. 48 cores), or large parallel job that span multiple nodes. Nodes on this queue may share resources with many other jobs. The memory per core is set by default to 1 GB. If the jobs needs more than 1GB memory users can set the flag: “ –mem-per-cpu=4GB “, for example to request 4GB per core.

“a100 queue”: This partition/queue is designed to run jobs that require the use of GPU nodes. This allocation is defined by the account “PI-userid_gpu”. This account should be included in the SLURM script as

“#SBATCH -A PI-userid_gpu”

“bigmem queue”: This queue contains nodes with 1500GB of memory and it is designed to run jobs that require more than 192GB of memory. Use of this queue requires a separate allocation of the type “PI-userid_bigmem”. This account should be included in the SLURM script as

“#SBATCH -A PI-userid_bigmem”

Submitting jobs using “sbatch”

Ideally users write a script that contains SLURM flags to set the environment, load modules and run commands to execute codes.

For example:

#!/bin/bash -l Recommended Linux shell

#SBATCH –job-name=”MyJob”

name of the Job

#SBATCH –output=”myLog-file”

IOf you want to explicitly direct the output to a file. The default is slurm-jobid.out

#SBATCH –partition=defq

Name of the queue

#SBATCH —t 00-01:30:15

Time limit (0 days, 1 hour, 30 minutes and 15 seconds)

#SBATCH –nodes=2 (or -N 2)

Number of nodes requested

#SBATCH –ntasks-per-node=48

Number of cores per node. 48 in this case as the parallel queue is exclusive

#SBATCH –mem-per-cpu=4GB

Define memory per core. The default is set to 1 GB. This should NOT be set for the parallel queue

#SBATCH –account=XYAWA

Make sure the propoer accouint is used

#SBATCH –export=ALL

#This script will run a job with 2 nodes, 48 cores per node in the parallel queue

#load modules

ml purge

ml intel intel-mpi

mpirun -np 96 ./my-mpi-code.x < my-input-file

Basic openMP Job Script:
#!/bin/bash -l
#SBATCH –job-name=”OpenMP-Job”
#SBATCH –output=”myLog-file”
#SBATCH –partition=defq
#SBATCH —t 00-01:30:15
#SBATCH –nodes=1
#SBATCH –ntasks-per-node=8
#SBATCH –mem-per-cpu=4GB
#SBATCH –account=XYAWA
#SBATCH –export=ALL 
ml purge
ml intel intel-mpi intel-mkl
time ./a.out > MyOutput.log
Hybrid MPI-OpenMP Job Script:
#!/bin/bash -l
#SBATCH --job-name=”MyLMJob”
#SBATCH --output=”myLog-file”
#SBATCH --partition=defq
#SBATCH ---t 02-01:30:15 
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=8
#SBATCH --account=XYAWA
#SBATCH --cpus-per-task=6
#SBATCH --export=ALL
ml gcc openmpi
module load hwloc
module load boost
module load gromacs/2016-mpi-plumed
module load plumed/2.3b

mpirun -np 8 bin/gmx_mpi mdrun -deffnm options
Big Mem Job Script:
#!/bin/bash -l
#SBATCH --job-name=”MyLMJob”
#SBATCH --output=”myLog-file”
#SBATCH --partition=bigmem 
#SBATCH -A PI-userid_bigmem
#SBATCH ---t 02-01:30:15 
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=8
#SBATCH --export=ALL
ml purge
ml intel
a.out > MyLog.out
GPU Job Script: This script is an example to run a NAMD job that uses 44 cores and 4 GPUS.
#!/bin/bash -l
#SBATCH –job-name=namd4gpu
#SBATCH –time=48:0:0
#SBATCH –partition=a100
#SBATCH –nodes=1
#SBATCH –ntasks-per-node=48
#SBATCH –mem-per-cpu=4G
#SBATCH –gres=gpu:4
#SBATCH -A “Slurm-account”_gpu ###  Slurm-account is usually the PI’s userid
#load modules and check
ml purge
module load namd/2.14-cuda-smp
export CONV_RSH=ssh
JOBNODES=$(scontrol show hostname $SLURM_JOB_NODELIST)
echo group main > $NODELIST
for nodes in $JOBNODES ; do
echo host $nodes >> $NODELIST
COMMAND=”`which namd2` +ignoresharing +idlepoll +p 44 +setcpuaffinity +pemap 1-11,13-23,25-35,37-47 +commap 0,12,24,36 +devices 0,1,2,3″
$COMMAND step1_NPT.conf > npt/npt.log
$COMMAND step2_NVT.conf > nvt/nvt.log
$COMMAND step3_Production.conf > production/production.log
Job Arrays: This script is an example to run a set of 5,000 jobs. Only 480 jobs will run at a time. The input files are located in a directory ($workdir). A temporary directory ($tmpdir) will be created in “scratch” where all the jobs will be run. At the end of each run the temporary directory is deleted.
#!/bin/bash -l
#SBATCH –job-name=small-array
#SBATCH –time=48:0:0
#SBATCH –partition=defq
#SBATCH –nodes=1
#SBATCH –ntasks-per-node=1
#SBATCH –mem-per-cpu=4G
#SBATCH –array=1-1000%480
#load modules and check
ml purge
module load intel
# set variable “file” to read all the files in $wordir (zmatabcde where “abcde” goes from 00001 to 05000) and assign them to the job array
file=$(ls zmat*  | sed -n ${SLURM_ARRAY_TASK_ID}p)
echo $file
Get the number for each file (abcde)
export basisdir=/scratch16/jcombar1/LC-tests
export workdir=/scratch16/jcombar1/LC-tests
export tmpdir=/scratch16/jcombar1/TMP/$SLURM_JOBID
export PATH=/scratch16/jcombar1/LC/bin:$PATH
mkdir -p $tmpdir
cd $tmpdir
# run your job
cp $workdir/$file     ZMAT
cp $basisdir/GENBAS GENBAS
./a.out > $workdir/out.$newstring
cd ..
\rm -rf $tmpdir
Jupyter Notebook Job Script:
#SBATCH –ntasks-per-node 1
#SBATCH –mem-per-cpu 4G
#SBATCH –time 1:00:00
#SBATCH –job-name jupyter-notebook
#SBATCH –output jupyter-notebook-%J.log
ml anaconda
## or use your own python/conda enviromnent
port=$(shuf -i8000-9999 -n1)
echo $port
node=$(hostname -s)
jupyter-notebook –no-browser –port=${port} –ip=${node}
NOTE: look at the file “jupyter-notebook-JOBID.log” for information on # new ssh command from a different windows in your local machine (ssh -N -L ${port}:${node}:${port} ${user} # the line to copy and paste in your browser (http://127.0.0.:PORT-number/?token ………..)
The open-source RStudio server provides a fully-featured IDE for R users. The ARCH users can access the RStudio Server on Rockfish using the command. It will create a Slurm script to run on the system. Usage examples to start the RStudio service:
1 2 3 4 $ -h $ -n 1 -c 2 -m 8G -t 1-02:0 -p defq (default) $ -c 2 -t 4:0:0 -p defq -e $ -c 24 -g 2 -p a100 -a _gpu Executing the
1 $
Copying R-Studio-Server singularity image: rstudio_1.4.1106.sif
 R-Studio-server singularity image is stored in:
Sign in with your Rockfish Login credentials:
Enter the $USER password:
Attempt 1 of 3
How to login to RStudio Server see details in:
Nodes:           1
Cores/task:   1
Total cores:   1
Walltime:       00-02:00
Queue:           defq
 The R-Studio-Server.rockfish.slurm is running…
—————————–   How to login   —————————–
1 $ cat rstudio-server.job.3248065.out 1. SSH tunnel from your workstation using the following command: ssh -N -L 44539:c124:44539 $ 2. log in to RStudio Server in your web browser using the Rockfish cluster credentials (username and password) at: http://localhost:44539 user: $USER password: < ARCH password > 3. When done using RStudio Server, terminate the job by: a. Exit the RStudio Session (“power” button in the top right corner of the RStudio window) b. Issue the following command on the login node: scancel -f 3248065 ———————————————— The command reserves 1 processor for 2 hours (default) on the defq partition. You will see an output file written by SLURM called e.g. rstudio-server.job.3248065.out, after the job started in your current directory, the number corresponds to your SLURM log file for this job. The file contains instructions how to login to RStudio Server. You will also see a folder called $USER/singularity/r-studio/ which contains the R-Studio-server singularity image Run this command (How to login 1) in a dedicated terminal (In your local machine). The specific compute node and port numbers will be unique to your session (How to login 2) in your web browser, it will ask to you the the Rockfish cluster credentials. After you run it, you can check the status of the resulting SLURM job by running the sqme command. When you are finished it you SHOULD cancel the job by using the scancel -f 3248065 command (How to login 3), where the number corresponds to your SLURM job ID. Note: If you do not cancel it, the job will be finished only when the time wall is reached.

The JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning.

The users can access the JupyterLab on Rockfish using the command. It will create a SLURM script.

Usage examples to start the JupyterLab service:

$ -h
$ -n 1 -c 2 -m 4 -t 0-02:0:0 -p defq (default)
$ -c 2 -t 4:0:0 -p defq -e

—————————–    Example     —————————– 


The script will create a slurm script for multiple environments with jupyterlab and #SBATCH with default parameters.

Use –help for more details.

1) Slurm script to run jupyterlab (jupyter_lab.slurm.script)
2) File with login information (Jupyter_lab.job..login)
3) File related to slurm INPUT ENVIRONMENT VARIABLES and HTTPS server information (
4) Notebook server file (.jupyter/
5) The jupyter-lab, ipykernal, pip will be installed/updated in: /home/$USER/jp_lab

<Ctrl+C> to cancel

Sign in with your Rockfish Login credentials:

Enter the $USER password:

Creating slurm script: /home/$USER/jupyter_lab.slurm.script

SLURM job script for run Jupyter Lab

The Jupyter Lab is ready to run.

  1. Usage:
    • $ sbatch jupyter_lab.slurm.script
  2. How to login see login file (after step 1)
    • $ cat Jupyter_lab.job..login
  3. Further information
    • $ cat

Instructions for adding multiple envs:

First, change to the proper version of Python or Conda

  • For Python Virtual environment
    • $ module load python; source /bin/activate
  • For Conda environment
    • $ module load conda; conda activate


  • (myenv)$ pip install ipykernel
  • # Install Jupyter kernel
  • (myenv)$ ipython kernel install –user –name= –display-name “Python (myenv)”
  • # List kernels
  • (myenv)$ jupyter kernelspec list
Users can request interactive sessions to compute nodes via the “interact” command (based on srun).

interact -n 1 -c 1 -p queue-name -t “time in minutes”


The Rockfish cluster uses Modules (lua modules version 8.3, developed at TACC) to dynamically manage users’ shell environments. “module” commands will set, modify, or delete environment variables in support of a scientific application. Modules allow users to select a particular version of an application or a combination of packages.

For example, if the Intel and intel-mpi modules are loaded and the user compiles with “mpif90”, the executable generated is compiled with the Intel compilers and links intel-mpi libraries.

Several modules are loaded by default at login time. These include Slurm, gcc/9.3 and openmpi/3.1 We strongly recommend that users utilize these combination of modules whenever possible to get the best performance.




module list


List scientific packages that are currently loaded

module avail

ml av

List all scientific packages that are available

module show

ml show

Show the environment variables and in some cases compilation options

module load


Load the named

module unload

ml –

Unload the named

module spider

ml spider

Show if the is available and different versions. It will show if there are any requirements

module save

ml save (module-env>

Save current modules into a session (default) or named session.

module swap


It automatically swaps versions or modules

To avoid conflicts users may need to remove some modules before they can load other modules. At Rockfish and with Lua modules, this is done automatically for most cases. For example, if you load intel and intel-mpi modules and later your load gcc, users will get a message: “Lmod is automatically replacing intel/202.1 with gcc/9.3.0”. In addition, users will get a message (screen) “Inactive Modules: intel-mpi”. If users need to load a set of modules on a regular basis, this process can be automated in two ways:
  1. Add the module load into the .bashrc file
  2. Load all necessary modules and save them. For example, “ml matlab intel intel-mpi” will load these three modules. Then type: “module save” to save them into the default module session. These modules will be loaded each time you connect. Alternatively, users can save the loaded modules and give the environment a new name. It can be “restored” later. “module save My-env”;
    and load then as “module restore My_env”


The Rockfish cluster will have three different compilers for compute nodes, GNU, Intel and PGI. There are also MPI libraries (openmpi, Intelmpi and Mvapich2). The majority of applications have been built using GNU compilers version 9.3.0. Users should evaluate which compiler gives the best performance for the applications.

The intel compilers and intel-mpi libraries can be loaded by executing the following command:

ml intel intel-mpi intel-mkl

a standard command to compile a Fortran or C-code will be:

ifort (icc) -O3 -xHOST -o code.x code.f90 (or code.c)
[add as many flags as needed]

g++ -O3 -march=native -mtune=native  -march=cascadelake-avx2


Singularity (version 3.7.4-1) is available on compute nodes. No need to use a module. To request an interactive session on compute nodes use:  interact -p defq -n 1 -c 1 -t 120