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Shark CentOS Slurm - User guide

Shark CentOS Slurm - User guide

Contact

For accounts, storage, requests, anything related to the cluster, use the Topdesk Self-Service Portal:

Topdesk Self-Service Portal [Use Self-Service Portal]

HPC-Linux team

General email: ITenDI_Infra-Linux@lumc.nl

Naam	Locatie	Email
John Berbee	D-01-133	J.A.M.Berbee@lumc.nl
Tom Brusche	D-01-128	T.A.W.Brusche@lumc.nl
Michel Villerius	D-01-133	M.P.Villerius@lumc.nl
Pieter van Vliet	D-01-133	P.Y.B.van_Vliet@lumc.nl

Cluster overview:

OS (Linux): CentOS 8
Workload manager: Slurm version 20.11

Cluster hardware - Network overview

Hardware overview

Hostname	IP address	CPU	Cores	Memory	GPUs	Purpose	Type machine
res-hpc-lo01	145.88.76.243	Intel E5-2660	32	128Gb	0	Login node	Dell PowerEdge M620
res-hpc-lo02	145.88.76.217	Intel Xe 6248	80	128Gb	1	Login node + Rem Vis*	Dell PowerEdge R740
res-hpc-exe007	145.88.76.220	Intel E5-2697	24	384Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe008	145.88.76.224	Intel E5-2697	24	384Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe009	145.88.76.222	Intel E5-2697	24	384Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe010	145.88.76.221	Intel E5-2690	24	384Gb	0	Execution node	Dell PowerEdge M630
res-hpc-exe011	145.88.76.223	Intel E5-2697	24	384Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe012	145.88.76.233	Intel E5-2690	24	384Gb	0	Execution node	Dell PowerEdge M630
res-hpc-exe013	145.88.76.247	Intel E5-2670	16	128Gb	0	Executio---	n node
res-hpc-exe014	145.88.76.242	Intel E5-2697	24	384Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe015	145.88.76.235	Intel E5-2660	16	96Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe016	145.88.76.236	Intel E5-2660	16	128Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe019	145.88.76.239	Intel E5-2660	16	128Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe020	145.88.76.229	Intel E5-2697	24	192Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe021	145.88.76.228	Intel E5-2660	16	96Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe022	145.88.76.227	Intel E5-2697	24	192Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe023	145.88.76.225	Intel E5-2697	24	192Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe024	145.88.76.232	Intel E5-2690	24	128Gb	0	Execution node	Dell PowerEdge M630
res-hpc-exe025	145.88.76.213	Intel E5-2670	16	96Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe027	145.88.76.209	Intel E5-2690	28	128Gb	0	Execution node	Dell PowerEdge M630
res-hpc-exe028	145.88.76.212	Intel E5-2670	16	96Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe029	145.88.76.210	Intel E5-2690	28	384Gb	0	Execution node	Dell PowerEdge M630
res-hpc-exe030	145.88.76.215	Intel E5-2697	24	384Gb	0	Execution node	Dell PowerEdge M620
res-hpc-exe031	145.88.76.214	Intel E5-2697	24	512Gb	0	Execution node	Dell PowerEdge M620
res-hpc-gpu01	145.88.76.237	Intel 8160	48	512Gb	3	GPU node	Dell PowerEdge R740
res-hpc-gpu02	145.88.76.234	Intel 8160	48	512Gb	3	GPU node	Dell PowerEdge R740
res-hpc-lkeb03	145.88.76.226	Intel E5-2698	40	256Gb	4	GPU node	NVIDIA DGX Station
res-hpc-lkeb04	145.88.76.248	Intel E5-1650	12	256Gb	4	GPU node	Asus X99-E-10G WS
res-hpc-lkeb05	145.88.76.244	Intel Xe 6134	16	256Gb	3	GPU node	Dell Precision 7920
res-hpc-lkeb06	145.88.76.238	Intel Xe 6234	16	256Gb	3	GPU node	Dell Precision 7920
res-hpc-lkeb07	145.88.76.211	Intel Xe 6234	16	256Gb	3	GPU node	Dell Precision 7920
res-hpc-mem01	145.88.76.230	Intel E7-4890	60	3Tb	0	High mem	Dell PowerEdge R920
res-hpc-mem02	145.88.76.218	Intel E5-4657L	48	1Tb	0	High mem	Dell PowerEdge M820
res-hpc-mem03	145.88.76.216	Intel E5-4657L	48	1Tb	0	High mem	Dell PowerEdge M820

res-hpc-ma01	145.88.76.246	Intel E5-2697	2	4Gb	0	Controller node 1	VM: VMware
res-hpc-ma02	145.88.76.249	Intel E5-4650	1	4Gb	0	Controller node 2	VM: VMware
res-hpc-db01	145.88.76.245	Intel E5-2697	1	4Gb	0	Slurm DB node	VM: VMware
res-hpc-ood01	145.88.76.231	Intel E5-4650	2	4Gb	0	OpenOnDemand portal	VM: VMware

Rem Vis = Remote Visualization

GPU overview

Hostname	Access	GPU 0 (cores/mem)	GPU 1 (cores/mem)	GPU 2 (cores/mem)	GPU 3 (cores/mem)
res-hpc-lo02	Everyone	Tesla T4 (2560/16Gb)
res-hpc-gpu01	Everyone	TITAN Xp (3840/12Gb)	TITAN Xp (3840/12Gb)	TITAN Xp (3840/12Gb)
res-hpc-gpu02	Everyone	TITAN Xp (3840/12Gb)	TITAN Xp (3840/12Gb)	TITAN Xp (3840/12Gb)
res-hpc-lkeb03	LKEB	Tesla V100-DGXS (5120/16Gb)	Tesla V100-DGXS (5120/16Gb)	Tesla V100-DGXS (5120/16Gb)	Tesla V100-DGXS (5120/16Gb)
res-hpc-lkeb04	LKEB	GeForce GTX 1080 Ti (3584/12Gb)	GeForce GTX 1080 Ti (3584/12Gb)	GeForce GTX 1080 Ti (3584/12Gb)	GeForce GTX 1080 Ti (3584/12Gb)
res-hpc-lkeb05	LKEB	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)
res-hpc-lkeb06	LKEB	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)
res-hpc-lkeb07	LKEB	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)
res-hpc-path01	Pathologie	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)	Quadro RTX 6000 (4608/24Gb)
res-ai-app92	LKEB	VGPU GRID V100S-32C

Rules

Always login/connect to the login node res-hpc-lo01.researchlumc.nl or res-hpc-lo02.researchlumc.nl
Always use the workload manager (Slurm) to run/submit jobs or use it interactive
Never run a job outside the workload manager (Slurm), not on the login node nor on the execution nodes
Never run (heavy) calculations on the login node, but do this on a compute node

How to get a Shark cluster account

To get a Shark account you first need to have some basic Linux knowledge.
A basic Linux introduction course will be given once a month in the LUMC practical Linux course
Without basic Linux knowledge you cannot work on the Shark cluster.
Create a self service desk call and ask for a Shark account.
A default cluster account will be created, you will receive an email with your RESEARCHLUMC/username and password.

How to get access to the HPC and LTS storage.

Here you can read how to get access to a HPC or LTS share on Shark

Shark cluster introduction course

A Shark introduction course can be followed where you will receive basic information on how to start using a HPC cluster.

Schedule 2020

Date	Time	Room	Room size	Seats left
2020	Canceled until further notice due to coronavirus (SARS-CoV-2)

Presentations

Shark SLURM introduction

How to connect to the login node / hpc cluster

From a Linux workstation

You are free to choose your Linux distribution, but we recommend the following distributions:

Other distributions:

CentOS 8
Debian 10.7.0
Red Hat 8 Commercial license required
Arch Linux Rolling distribution

From the command line (ssh)

If your login user name from your workstation is the same as the username on the HPC cluster, you can use:

ssh res-hpc-lo01.researchlumc.nl

ssh res-hpc-lo02.researchlumc.nl

Otherwise:

ssh username@res-hpc-lo01.researchlumc.nl

ssh username@res-hpc-lo02.researchlumc.nl

You can make your life easier by editing the file:

vi ~/.ssh/config
Host res-hpc-lo01
	Hostname 145.88.76.243
	User user-name
        ServerAliveInterval 60
Host res-hpc-lo02
	Hostname 145.88.76.217
	User user-name
        ServerAliveInterval 60

Where you adapt the user-name.

X11 forwarding

You can show graphical output when you enable X11 forwarding

ssh -X res-hpc-lo01.researchlumc.nl

ssh -X res-hpc-lo02.researchlumc.nl

ssh -Y res-hpc-lo01.researchlumc.nl

ssh -Y res-hpc-lo02.researchlumc.nl

Once you are logged in, you should be able to run a graphical program, for example:

xterm
xclock
xeyes

A remote desktop

Install the X2Go client:

CentOS/Fedora/Red Hat: yum install x2goclient
Ubuntu/Debian: apt-get install x2goclient
Arch Linux: pacman -S x2goclient

X2Go enables to access a graphical desktop of a computer over the network. The protocol is tunneled through the Secure Shell protocol, so it is encrypted.

Start the x2goclient:

Go to Session and create a new session:

For the Host:

res-hpc-lo01.researchlumc.nl

res-hpc-lo02.researchlumc.nl

For the Session type:

XFCE
ICEWM
MATE [only for res-hpc-lo02.researchlumc.nl]

After you have created a new session

Start the new session:

You can ignore this error:

The XFCE desktop:

How to logout:

The ICEWM desktop:

How to logout:

The MATE desktop:

How to logout:

When using the Linux client of X2Go then you should disable the screensaver which can be done in the power manager tool. Otherwise your session will crash when the screensaver activates.

Working from home with the SSH proxy server

If you are working from home or from outside the LUMC (network), you can use the SSH proxy server to connect to the cluster.

IP address: 145.88.35.10
Hostname: res-ssh-alg01.researchlumc.nl

With the X2Go client, you have to enable the following options:

Use Proxy server for SSH connection
Type: SSH
Same login as on X2Go Server
Same password as on X2Go Server
Host: 145.88.35.10
Port: 22

From a Windows workstation

From the command line (ssh)

A simple terminal ssh shell is putty. Download the client from the putty homepage

Direct client download putty

Windows 64-bit MSI installer

Once you have started the putty program, you will see:

Fill in at the Host Name (or IP address): res-hpc-lo01.researchlumc.nl or res-hpc-lo02.researchlumc.nl

At the connection setting, fill in 60 at the Seconds between keepalives. If needed, enable the Enable TCP keepalives

At the X11 setting, you can enable the Enable X11 forwarding. You want this if you need graphical output, but for this to get this working, you need to install separately a X11 server for Windows. It is better and easier to install mobaXterm, which will be explained below.

Now press Open to connect to the login node.

If you connect for the first time to the login node, you will see the warning. Press Yes to continue.

Now you are logged in and you can start using the cluster.

If you press on the putty symbol at the left corner of the terminal window, you have multiple options, like:

New Session
Duplicate Session
Change Settings
Copy All to Clipboard
Clear Scrollback
Reset Terminal

You can give your session a name so that you can save it and reuse it later.

Give you session a name, for example "res-hpc-lo01". Save

Later you can load your saved session. Select your saved sessions and press Load

MobaXterm

With mobaXterm, you have a buildin SSH shell/terminal and an embedded X11 server. With this, you don't have to worry about setting up a special X11 server for showing graphical output on your Windows workstation.

Go to the MobaXterm website: MobaXterm

Here is the direct download link: Download

Choose the MobaXterm Home Editon v20.6 and install it on your Windows workstation. Once you have installed it, you can start it and you have to create a SSH session:

Choose for the session type: SSH and press OK

Fill in the Remote host and Specify username

All the default settings should be ok, but here you can check your Advanced SSH settings

The Terminal settings

Network settings including the SSH Gateway (proxy server/jump host)

Also here you can set the settings for the ssh jump host (Gateway host), if needed (if you are working from home)

Pres OK to start the connection

On the left screen you can have multiple session saved

You can also add a, for example, a "SFTP" session, so that you can easily transfer files from and to your workstation.

Right clicking on one of your session, you can, for example, edit you session settings

A remote desktop

For the windows version, we can use the same version as we used with Linux:

x2go x2go

Install the X2Go client

The setup is already described with the Linux client.

Module environment

When you will be working on the cluster, you probably need to load the correct module, which will set the correct environment for your library, compiler or program.

Here below are some useful commands and examples:

List all available modules

module av

----------------------------------------------------------------------------------------------- /share/modulefiles ------------------------------------------------------------------------------------------------
   bioimage/QuPath/0.2.3                                       library/cuda/11.0/gcc.8.3.1                                 neuroImaging/SimpleElastix/0.10.0/python3.6.8
   bioinformatics/tools/SNP/flashpca/2.1/gcc-8.3.1             library/cuda/11.1/gcc.8.3.1                                 neuroImaging/freesurfer/stable-pub-v6.0.0.patched
   bioinformatics/tools/assembly-stats/1.0.1/gcc-8.3.1         library/cuda/11.2/gcc.8.3.1                                 neuroImaging/freesurfer/7.1.0                     (D)
   bioinformatics/tools/ncbi/sra/2.10.8                        library/cuda/7.5/gcc.8.3.1                                  neuroImaging/fsl/5.0.10
   container/singularity/3.5.3/gcc.8.3.1                       library/cuda/8.0/gcc.8.3.1                                  neuroImaging/fsl/5.0.11
   container/singularity/3.6.1/gcc.8.3.1                       library/cuda/9.0/gcc.8.3.1                                  neuroImaging/fsl/5.0.9
   container/singularity/3.6.4/gcc.8.3.1                       library/cuda/9.1/gcc.8.3.1                                  neuroImaging/fsl/6.0.0
   container/singularity/3.7.0/gcc.8.3.1                       library/cuda/9.2/gcc.8.3.1                                  neuroImaging/fsl/6.0.1
   cryogenicEM/chimera/1.14/gcc-8.3.1                          library/cudnn/10.0/cudnn                                    neuroImaging/fsl/6.0.2
   cryogenicEM/ctffind/4.1.13/gcc-8.3.1                        library/cudnn/10.1/cudnn                                    neuroImaging/fsl/6.0.3
   cryogenicEM/eman2/2.31                                      library/cudnn/10.2/cudnn                                    neuroImaging/fsl/6.0.4
   cryogenicEM/gctf/1.06                                       library/cudnn/9.0/cudnn                                     neuroImaging/fsl/fix/1.06.12
   cryogenicEM/imod/4.9.12                                     library/cudnn/9.2/cudnn                                     neuroImaging/mrtrix/3.0.0/gcc-8.3.1
   cryogenicEM/motioncor2/1.31                                 library/fltk/1.3.5/gcc-8.3.1                                pharmaceutical/PsN/4.9.0
   cryogenicEM/relion/3.0.8/gcc-8.3.1                          library/ftgl/2.1.3/gcc-8.3.1                                pharmaceutical/PsN/5.0.0                          (D)
   cryogenicEM/relion/3.1-beta/gcc-8.3.1                       library/gdal/2.4.4/gcc-8.3.1                                pharmaceutical/nonmem/7.4.4/gcc-8.3.1
   cryogenicEM/resmap/1.1.4                                    library/gdal/3.0.4/gcc-8.3.1                                pharmaceutical/pirana/2.9.7
   genomics/hmmer/openmpi-3.1.5/3.3/gcc-8.3.1                  library/hdf5/1.10.5/gcc-8.3.1                               pharmaceutical/pirana/2.9.8                       (D)
   genomics/ngs/aligners/bowtie2/2.4.2/gcc-8.3.1               library/htslib/1.10.2/gcc-8.3.1                             statistical/MATLAB/R2016b
   genomics/ngs/aligners/bwa/0.7.17/gcc-8.3.1                  library/htslib/1.11/gcc-8.3.1                               statistical/MATLAB/R2018b
   genomics/ngs/aligners/minimap2/2.17/gcc-8.3.1               library/java/OpenJDK-11.0.2                                 statistical/MATLAB/R2019b
   genomics/ngs/assembler/canu/2.2/gcc-8.3.1                   library/java/OpenJDK-12.0.2                                 statistical/MATLAB/R2020b
   genomics/ngs/assembler/flye/2.8.1/gcc-8.3.1                 library/java/OpenJDK-13.0.2                                 statistical/MATLAB/v93/MCR2017b
   genomics/ngs/assembler/miniasm/0.3/gcc-8.3.1                library/java/OpenJDK-14.0.1                                 statistical/MATLAB/v97/MCR2019b
   genomics/ngs/assembler/trinityrnaseq/2.8.4/gcc-8.3.1        library/java/OracleJDK-1.8.0_251                     (D)    statistical/R/3.4.4/gcc.8.3.1
   genomics/ngs/bcftools/1.10.2/gcc-8.3.1                      library/lapack/3.9.0/gcc-8.3.1                              statistical/R/3.5.3/gcc.8.3.1
   genomics/ngs/bcftools/1.11/gcc-8.3.1                        library/mpi/mpich/3.3.2/gcc-8.3.1                           statistical/R/3.6.0/gcc.8.3.1
   genomics/ngs/bcl2fastq/2.20.0                               library/mpi/openmpi/3.1.5/gcc-8.3.1                         statistical/R/3.6.2/gcc.8.3.1
   genomics/ngs/bedtools2/2.29.1/gcc-8.3.1                     library/mpi/openmpi/4.0.2/gcc-8.3.1                         statistical/R/4.0.2/gcc.8.3.1
   genomics/ngs/samtools/1.10/gcc-8.3.1                        library/mpi/openmpi/4.0.3/gcc-8.3.1                         statistical/RStudio/1.2.5033/gcc-8.3.1
   genomics/ngs/samtools/1.11/gcc-8.3.1                        library/mpi/openmpi/4.0.4/gcc-8.3.1                         statistical/RStudio/1.3.959/gcc-8.3.1
   genomics/ngs/shapeit4/4.1.3/gcc-8.3.1                       library/mpi/openmpi/4.0.5/gcc-8.3.1                         statistical/RStudio/1.3.959/server/gcc-8.3.1
   genomics/ngs/tools/HISAT2/2.1.0/gcc-8.3.1                   library/nlopt/2.6.2/gcc-8.3.1                               system/go/1.13.7
   genomics/ngs/tools/LTR_Finder/1.0.7/gcc-8.3.1               library/openslide/3.4.1/gcc-8.3.1                           system/hwloc/1.11.13/gcc-8.3.1
   genomics/ngs/tools/infernal/1.1.3/gcc-8.3.1                 library/pmi/openpmix/2.2.3/gcc-8.3.1                        system/hwloc/2.1.0/gcc-8.3.1
   genomics/ngs/tools/liftoff/1.5.1/python-3.8                 library/pmi/openpmix/3.1.4/gcc-8.3.1                        system/knem/1.1.3/gcc-8.3.1
   genomics/ngs/tools/pilon/1.23                               library/sparsehash/2.0.3/gcc-8.3.1                          system/knem/1.1.4/gcc-8.3.1
   genomics/ngs/tools/stringtie/2.1.1/gcc-8.3.1                library/spectra/0.9.0                                       system/python/2.7.17
   genomics/ngs/tools/transdecoder/5.0.2/gcc-8.3.1             library/wxwidgets/3.1.3/gcc-8.3.1                           system/python/3.7.6
   genomics/ngs/vcftools/0.1.16/gcc-8.3.1                      mathematical/octave/5.2.0/gcc-8.3.1                         system/python/3.8.1                               (D)
   graphics/gnuplot/5.2.8/gcc-8.3.1                            mathematical/octave/libs/SuiteSparse/5.7.2/gcc-8.3.1        system/qt/5.14.2/gcc-8.3.1
   graphics/graphicsmagick/1.3.35/gcc-8.3.1                    mathematical/octave/libs/arpack/3.7.0/gcc-8.3.1             system/swi-prolog/8.2.0
   gwas/depict/1.rel194                                        mathematical/octave/libs/gl2ps/1.4.2/gcc-8.3.1              tools/biomake/0.1.5
   gwas/plink/1.07                                             mathematical/octave/libs/glpk/4.65/gcc-8.3.1                tools/cmake/3.11.4
   gwas/plink/1.90b6.17                                        mathematical/octave/libs/qhull/8.0.0/gcc-8.3.1              tools/gitlab-runner/12.8.0
   gwas/plink/1.90p                                            mathematical/octave/libs/qrupdate/1.1.2/gcc-8.3.1           tools/hashdeep/4.3/gcc-8.3.1
   gwas/plink/2.00a3LM                                  (D)    mathematical/octave/libs/sundials/5.3.0/gcc-8.3.1           tools/jupyterlab/4.3.1
   library/blas/0.3.10/gcc-8.3.1                               medicalImaging/minc-stuffs/0.1.25/gcc-8.3.1                 tools/luarocks/3.3.1/gcc-8.3.1
   library/boost/1.69.0/gcc-8.3.1                              medicalImaging/minc-toolkit-v2/1.9.18                       tools/miniconda/python2.7/4.7.12
   library/boost/1.72.0/gcc-8.3.1                              medicalImaging/minc2-simple/2.2.30/gcc-8.3.1                tools/miniconda/python3.8/4.8.5
   library/cuda/10.0/gcc.8.3.1                                 medicalImaging/pydpiper/2.0.14                              tools/miniconda/python3.8/4.9.2                   (D)
   library/cuda/10.1/gcc.8.3.1                                 neuroImaging/Elastix/5.0.0/gcc-7.4.0                        tools/virtualgl/2.6.4/gcc-8.3.1
   library/cuda/10.2/gcc.8.3.1                                 neuroImaging/FSLeyes/0.32.3                                 tools/websockify/0.9.0

-------------------------------------------------------------------------------------- /usr/share/lmod/lmod/modulefiles/Core --------------------------------------------------------------------------------------
   lmod    settarg

  Where:
   D:  Default Module

Use "module spider" to find all possible modules and extensions.
Use "module keyword key1 key2 ..." to search for all possible modules matching any of the "keys".

load a module

module load library/mpi/openmpi/4.0.2/gcc-8.3.1

show loaded modules

module li

Currently Loaded Modules:
  1) library/mpi/openmpi/4.0.2/gcc-8.3.1

delete one module

module del library/mpi/openmpi/4.0.2/gcc-8.3.1

purge all modules

module purge

Compiling programs

We are going to compile a very simple MPI (message passing interface) program, which is quite common on a cluster.

vi hello.c

#include <stdio.h>
#include <mpi.h>

int main (int argc, char *argv[])
{
  int id, np, i;
  char processor_name[MPI_MAX_PROCESSOR_NAME];
  int processor_name_len;

  MPI_Init (&argc, &argv);

  MPI_Comm_size (MPI_COMM_WORLD, &np);
  MPI_Comm_rank (MPI_COMM_WORLD, &id);
  MPI_Get_processor_name (processor_name, &processor_name_len);

  for (i=1; i<2; i++)
    printf ("Hello world from process %03d out of %03d, processor name %s \n", id, np, processor_name);

  MPI_Finalize ();
  return 0;
}

If you are going to compile this program without the correct loaded module(s), you would see something like this:

$ module li
No modules loaded
$ gcc hello.c -o hello
hello.c:2:10: fatal error: mpi.h: No such file or directory
 #include <mpi.h>
          ^~~~~~~
compilation terminated.

So we need to load the correct module and use the correct compiler

$ module add library/mpi/openmpi/4.0.2/gcc-8.3.1

mpicc hello.c -o hello

Handy reference:

Language	C	C++	Fortran77	Fortran90	Fortran95
Command	mpicc	mpiCC	mpif77	mpif90	mpif95

./hello

Hello world from process 000 out of 001, processor name res-hpc-lo01.researchlumc.nl

Here you can see that we ran the program only on 1 core of the cpu. (which is the same as running: mpirun -np 1 ./hello) (np = number of processes to launch)

To make use of the MPI capabilities of the program, we have to run the program with the "mpirun" which comes with the loaded module library/mpi/openmpi/4.0.2/gcc-8.3.1

mpirun ./hello

Hello world from process 003 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 006 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 013 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 015 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 000 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 005 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 010 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 011 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 012 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 002 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 004 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 007 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 001 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 008 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 009 out of 016, processor name res-hpc-lo01.researchlumc.nl 
Hello world from process 014 out of 016, processor name res-hpc-lo01.researchlumc.nl

Now the program is using all the cores of the local machine. (which is the same as: mpirun -np 16 ./hello)

Workload manager: Slurm

The workload manager Slurm is installed on the cluster. This is a resource manager and a scheduler all in one.

resource manager: are there resources free (memory, cpu, gpus, etc) on the nodes for the job?
scheduler: when to run the job?

Slurm commands

User commands

Command	Info
salloc	Obtain a Slurm job allocation (a set of nodes), execute a command, and then release the allocation when the command is finished
sbatch	Submit a batch script to Slurm
scancel	Used to signal jobs or job steps that are under the control of Slurm
scontrol	Used view and modify Slurm configuration and state
sinfo	View information about Slurm nodes and partitions
squeue	View information about jobs located in the Slurm scheduling queue
srun	Run parallel jobs

Accounting info

Command	Info
sacct	Displays accounting data for all jobs and job steps in the Slurm job accounting log or Slurm database
sstat	Display various status information of a running job/step
sacctmgr	Used to view and modify Slurm account information
sreport	Generate reports from the slurm accounting data

Scheduling info

Command	Info
sprio	View the factors that comprise a job's scheduling priority
sshare	Tool for listing the shares of associations to a cluster

Partitions (info) (in SGE called queues)

Available partitions

Partition name	Nodes	Default	MemPerCpu	DefaultTime	Remark
all	res-hpc-exe[007-031]	Yes	2048	1:00:00	Default partition
gpu	res-hpc-gpu[01-02]	No	2048	1:00:00	Only for GPU/CUDA calculations
highmem	res-hpc-mem[01-03]	No	2048	1:00:00	For memory intensive applications
LKEBgpu	res-hpc-lkeb[03-07]	No	2048	-	Only for GPU/CUDA calculations
short	res-hpc-gpu[01-02]	No	2048	-	max 60 cores, 1 hour walltime, for non GPU calculations

A job must always be submitted to a partition (queue). By default, the jobs will be submitted to the "all" partition unless you specify a different queue.

The following commands are useful:

sinfo
sinfo -a
sinfo -l
sinfo -N -l

[user@res-hpc-lo01 ~]$ sinfo 
PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST 
all*         up   infinite      2    mix res-hpc-exe[014,019] 
all*         up   infinite      1  alloc res-hpc-exe018 
all*         up   infinite      4   idle res-hpc-exe[013,015-017] 
gpu          up   infinite      2    mix res-hpc-gpu[01-02] 
highmem      up   infinite      1    mix res-hpc-mem01 

[user@res-hpc-lo01 ~]$ sinfo -a
PARTITION AVAIL  TIMELIMIT  NODES  STATE NODELIST 
all*         up   infinite      2    mix res-hpc-exe[014,019] 
all*         up   infinite      1  alloc res-hpc-exe018 
all*         up   infinite      4   idle res-hpc-exe[013,015-017] 
gpu          up   infinite      2    mix res-hpc-gpu[01-02] 
highmem      up   infinite      1    mix res-hpc-mem01 
LKEBgpu      up   infinite      1  comp* res-hpc-lkeb02 
LKEBgpu      up   infinite      1  down* res-hpc-lkeb03 
LKEBgpu      up   infinite      2    mix res-hpc-lkeb[04-05] 
LKEBgpu      up   infinite      1   idle res-hpc-lkeb01 

[user@res-hpc-lo01 ~]$ sinfo -l
Mon Mar 23 09:21:27 2020
PARTITION AVAIL  TIMELIMIT   JOB_SIZE ROOT OVERSUBS     GROUPS  NODES       STATE NODELIST 
all*         up   infinite 1-infinite   no       NO        all      2       mixed res-hpc-exe[014,019] 
all*         up   infinite 1-infinite   no       NO        all      1   allocated res-hpc-exe018 
all*         up   infinite 1-infinite   no       NO        all      4        idle res-hpc-exe[013,015-017] 
gpu          up   infinite 1-infinite   no       NO        all      2       mixed res-hpc-gpu[01-02] 
highmem      up   infinite 1-infinite   no       NO        all      1       mixed res-hpc-mem01

[user@res-hpc-lo01 ~]$ sinfo -l -N -a
Mon Mar 23 09:34:14 2020
NODELIST        NODES PARTITION       STATE CPUS    S:C:T MEMORY TMP_DISK WEIGHT AVAIL_FE REASON               
res-hpc-exe013      1      all*       mixed 16      2:8:1 128800        0      1   (null) none                 
res-hpc-exe014      1      all*       mixed 24     2:12:1 386800        0      1   (null) none                 
res-hpc-exe015      1      all*        idle 16      2:8:1  96000        0      1   (null) none                 
res-hpc-exe016      1      all*        idle 16      2:8:1 128800        0      1   (null) none                 
res-hpc-exe017      1      all*        idle 16      2:8:1  64000        0      1   (null) none                 
res-hpc-exe018      1      all*   allocated 16      2:8:1  64000        0      1   (null) none                 
res-hpc-exe019      1      all*       mixed 16      2:8:1 128800        0      1   (null) none                 
res-hpc-gpu01       1       gpu       mixed 48     2:24:1 515000        0      1   (null) none                 
res-hpc-gpu02       1       gpu       mixed 48     2:24:1 515000        0      1   (null) none                 
res-hpc-lkeb01      1   LKEBgpu        idle 8       1:4:2  63000        0      1   (null) none                 
res-hpc-lkeb02      1   LKEBgpu completing* 12      1:6:2  15000        0      1   (null) none                 
res-hpc-lkeb03      1   LKEBgpu       down* 40     1:20:2 250000        0      1   (null) Not responding       
res-hpc-lkeb04      1   LKEBgpu       mixed 12      1:6:2 257000        0      1   (null) none                 
res-hpc-lkeb05      1   LKEBgpu       mixed 16      2:8:1 256000        0      1   (null) none                 
res-hpc-mem01       1   highmem       mixed 60     4:15:1 300000        0      1   (null) none

idle: this node has no jobs running on it
alloc(ated): the whole node is allocated by 1 or more jobs
mix(ed): there is 1 or more jobs running on the node, but there are still cores free on this node

Jobs info

With the following command, you can get information about your running jobs and jobs from other users:

squeue
squeue -a
squeue -l

[user@res-hpc-lo01 mpi-benchmarks]$ squeue 
             JOBID  PARTITION      USER  ST        TIME   NODES NODELIST(REASON) 
               258        all      user   R        0:03       2 res-hpc-exe[013-014] 

[user@res-hpc-lo01 mpi-benchmarks]$ squeue -a
             JOBID  PARTITION      USER  ST        TIME   NODES NODELIST(REASON) 
               258        all      user   R        0:06       2 res-hpc-exe[013-014] 

[user@res-hpc-lo01 mpi-benchmarks]$ squeue -l
Thu Jan 23 09:14:22 2020
             JOBID  PARTITION      USER     STATE        TIME TIME_LIMIT   NODES NODELIST(REASON) 
               258        all      user   RUNNING        0:12      30:00       2 res-hpc-exe[013-014]

Jobs typically pass through several states in the course of their execution.
The typical states are PENDING, RUNNING, SUSPENDED, COMPLETING, and COMPLETED. An explanation of some state follows:

State	State (full)	Explanation
CA	CANCELLED	Job was explicitly cancelled by the user or system administrator. The job may or may not have been initiated.
CD	COMPLETED	Job has terminated all processes on all nodes with an exit code of zero.
CG	COMPLETING	Job is in the process of completing. Some processes on some nodes may still be active.
F	FAILED	Job terminated with non-zero exit code or other failure condition.
PD	PENDING	Job is awaiting resource allocation.
R	RUNNING	Job currently has an allocation.
S	SUSPENDED	Job has an allocation, but execution has been suspended and CPUs have been released for other jobs.

scontrol

With the slurm command scontrol you can get a more detailed overview of your running job, node hardware and partitions:

[user@res-hpc-lo01 ~]$ scontrol show job 260
JobId=260 JobName=IMB
   UserId=user(225812) GroupId=Domain Users(513) MCS_label=N/A
   Priority=35603 Nice=0 Account=dnst-ict QOS=normal
   JobState=RUNNING Reason=None Dependency=(null)
   Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
   RunTime=00:00:13 TimeLimit=00:30:00 TimeMin=N/A
   SubmitTime=2020-01-23T10:27:45 EligibleTime=2020-01-23T10:27:45
   AccrueTime=2020-01-23T10:27:45
   StartTime=2020-01-23T10:27:45 EndTime=2020-01-23T10:57:45 Deadline=N/A
   SuspendTime=None SecsPreSuspend=0 LastSchedEval=2020-01-23T10:27:45
   Partition=all AllocNode:Sid=res-hpc-ma01:46428
   ReqNodeList=(null) ExcNodeList=(null)
   NodeList=res-hpc-exe[013-014]
   BatchHost=res-hpc-exe013
   NumNodes=2 NumCPUs=32 NumTasks=32 CPUs/Task=1 ReqB:S:C:T=0:0:*:*
   TRES=cpu=32,mem=64G,node=2,billing=32
   Socks/Node=* NtasksPerN:B:S:C=16:0:*:* CoreSpec=*
   MinCPUsNode=16 MinMemoryCPU=2G MinTmpDiskNode=0
   Features=(null) DelayBoot=00:00:00
   OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
   Command=/home/user/Software/imb/mpi-benchmarks/imb.slurm
   WorkDir=/home/user/Software/imb/mpi-benchmarks
   StdErr=/home/user/Software/imb/mpi-benchmarks/job.%J.err
   StdIn=/dev/null
   StdOut=/home/user/Software/imb/mpi-benchmarks/job.%J.out
   Power=
   MailUser=user@gmail.com MailType=BEGIN,END,FAIL

[user@res-hpc-lo01 ~]$ scontrol show node res-hpc-exe014
NodeName=res-hpc-exe014 Arch=x86_64 CoresPerSocket=12 
   CPUAlloc=16 CPUTot=24 CPULoad=0.00
   AvailableFeatures=(null)
   ActiveFeatures=(null)
   Gres=(null)
   NodeAddr=res-hpc-exe014 NodeHostName=res-hpc-exe014 Version=20.02.0-0pre1
   OS=Linux 4.18.0-80.11.2.el8_0.x86_64 #1 SMP Tue Sep 24 11:32:19 UTC 2019 
   RealMemory=386800 AllocMem=32768 FreeMem=380208 Sockets=2 Boards=1
   State=MIXED ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
   Partitions=all 
   BootTime=2019-12-11T11:51:40 SlurmdStartTime=2020-01-14T15:36:20
   CfgTRES=cpu=24,mem=386800M,billing=24
   AllocTRES=cpu=16,mem=32G
   CapWatts=n/a
   CurrentWatts=0 AveWatts=0
   ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s

[user@res-hpc-lo01 ~]$ scontrol show partition all
PartitionName=all
   AllowGroups=ALL AllowAccounts=ALL AllowQos=ALL
   AllocNodes=ALL Default=YES QoS=N/A
   DefaultTime=01:00:00 DisableRootJobs=NO ExclusiveUser=NO GraceTime=0 Hidden=NO
   MaxNodes=UNLIMITED MaxTime=UNLIMITED MinNodes=0 LLN=NO MaxCPUsPerNode=UNLIMITED
   Nodes=res-hpc-exe[013-014]
   PriorityJobFactor=1 PriorityTier=1 RootOnly=NO ReqResv=NO OverSubscribe=NO
   OverTimeLimit=NONE PreemptMode=OFF
   State=UP TotalCPUs=40 TotalNodes=2 SelectTypeParameters=NONE
   JobDefaults=(null)
   DefMemPerCPU=2048 MaxMemPerNode=UNLIMITED

Interactive jobs

salloc

You can open an interactive session with the salloc command:

[user@res-hpc-lo01 ~]$ salloc -N1
salloc: Granted job allocation 267
salloc: Waiting for resource configuration
salloc: Nodes res-hpc-exe013 are ready for job

[user@res-hpc-exe013 ~]$ squeue 
             JOBID  PARTITION      USER  ST        TIME   NODES NODELIST(REASON) 
               267        all      user   R        0:04       1 res-hpc-exe013 
[user@res-hpc-exe013 ~]$ exit
exit
salloc: Relinquishing job allocation 267

[user@res-hpc-lo01 ~]$

In the example above, we won't run a command so we ended up in the bash environment. With exit we leave the environment and we release the node.

[user@res-hpc-lo01 ~]$ salloc -N1 mpirun ./hello1
salloc: Granted job allocation 268
salloc: Waiting for resource configuration
salloc: Nodes res-hpc-exe013 are ready for job
Hello world from process 000 out of 001, processor name res-hpc-exe013.researchlumc.nl 
salloc: Relinquishing job allocation 268
salloc: Job allocation 268 has been revoked.

Here we allocated 1 node with one core and ran the openmpi compiled "hello1" program.

Now the same with 2 nodes, 16 cores on each machine:

[user@res-hpc-lo01 ~]$ salloc -N2 --ntasks-per-node=16 mpirun ./hello1
salloc: Granted job allocation 270
salloc: Waiting for resource configuration
salloc: Nodes res-hpc-exe[013-014] are ready for job
Hello world from process 003 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 021 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 004 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 005 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 027 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 000 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 029 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 006 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 031 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 007 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 016 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 010 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 019 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 011 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 030 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 012 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 017 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 013 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 018 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 014 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 020 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 015 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 022 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 001 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 023 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 024 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 002 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 025 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 008 out of 032, processor name res-hpc-exe013.researchlumc.nl 
Hello world from process 026 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 028 out of 032, processor name res-hpc-exe014.researchlumc.nl 
Hello world from process 009 out of 032, processor name res-hpc-exe013.researchlumc.nl 
salloc: Relinquishing job allocation 270

srun

With the srun command you can also open an interactive session or you can run a program through the scheduler.

Interactive:

[user@res-hpc-lo01 ~]$ srun --pty bash
[user@res-hpc-exe013 ~]$ exit
exit

Running a program:

[user@res-hpc-lo01 ~]$ cat hello.sh
#!/bin/bash
#

echo "Hello from $(hostname)"
echo "It is currently $(date)"
echo ""
echo "SLURM_JOB_NAME: $SLURM_JOB_NAME"
echo "SLURM_JOBID: " $SLURM_JOBID

[user@res-hpc-lo01 ~]$ chmod +x hello.sh 

[user@res-hpc-lo01 ~]$ srun -N1 hello.sh
Hello from res-hpc-exe013.researchlumc.nl
It is currently Thu Jan 23 12:35:18 CET 2020

SLURM_JOB_NAME: hello.sh
SLURM_JOBID:  282

sbatch

The normal and correct way to submit a job is with a slurm batch file. This is a normal bash script with special directives for slurm.

In SBATCH lines, “#SBATCH” is used to submit options. The various meanings of lines starting with “#” are:

Line starts with	Treated as
#	Comment in shell and Slurm
#SBATCH	Comment in shell, option in Slurm
# SBATCH	Comment in shell and Slurm

Options, sometimes called “directives”, can be set in the job script file using this line format for each option:

#SBATCH {option} {parameter}

Directive Description	Specified As #SBATCH
Name the job < jobname >	-J < jobname >
Request at least < minnodes > nodes	-N < minnodes >
Request < minnodes > to < maxnodes > nodes	-N < minnodes >-< maxnodes >
Request at least < MB > amount of temporary disk space	--tmp < MB >
Run the job for a time of < walltime >	-t < walltime >
Run the job at < time >	--begin < time >
Set the working directory to < directorypath >	-D < directorypath >
Set error log name to < jobname.err >	-e < jobname.err >
Set output log name to < jobname.log >	-o < jobname.log >
Mail < user@address >	--mail-user < user@address >
Mail on any event	--mail-type=ALL
Mail on job end	--mail-type=END
Run job in partition	-p < destination >
Run job using GPU with ID < number >	--gres=gpu:< number >
Specify the real memory required per node. Suffix [K\|M\|G\|T]. Better use "--mem-per-cpu"	--mem=<size[units]>
Minimum memory required per allocated CPU. Suffix [K\|M\|G\|T]	--mem-per-cpu=<size[units]>

Node-Core reservation:

Short option	Long option	Description
-N	--nodes=	Request this many nodes on the cluster. Use 1 core on each node by default
-n	--ntasks=	Request this many tasks on the cluster. Defaults to 1 task per node
(none)	--ntasks-per-node=	Request this number of tasks per node

For example:

Options	Description
-N2	use 2 nodes, 1 core on each node, so in total 2 cores
-N2 --ntasks-per-node=16	use 2 nodes, 16 cores on each node, so in total 32 cores
-n32	use 32 cores in total, let Slurm decide where to run (one or multiple nodes)

Submitting jobs

module purge
module add library/mpi/openmpi/4.0.2/gcc-8.3.1

git clone https://github.com/intel/mpi-benchmarks
cd mpi-benchmarks
make clean
cd src_c
make clean
make -f Makefile TARGET=MPI1

Please notice in the Makefile: CC=mpicc

ldd ./IMB-MPI1 
	linux-vdso.so.1 (0x00007fff6e9f6000)
	libmpi.so.40 => /share/software/library/mpi/openmpi/4.0.2/gcc-8.3.1/lib/libmpi.so.40 (0x00007f7c6acb7000)
	libpthread.so.0 => /lib64/libpthread.so.0 (0x00007f7c6aa97000)
	libc.so.6 => /lib64/libc.so.6 (0x00007f7c6a6d4000)
	libopen-rte.so.40 => /share/software/library/mpi/openmpi/4.0.2/gcc-8.3.1/lib/libopen-rte.so.40 (0x00007f7c6a41e000)
	libopen-pal.so.40 => /share/software/library/mpi/openmpi/4.0.2/gcc-8.3.1/lib/libopen-pal.so.40 (0x00007f7c6a12e000)
	libdl.so.2 => /lib64/libdl.so.2 (0x00007f7c69f2a000)
	libudev.so.1 => /lib64/libudev.so.1 (0x00007f7c69d03000)
	libpciaccess.so.0 => /lib64/libpciaccess.so.0 (0x00007f7c69af9000)
	librt.so.1 => /lib64/librt.so.1 (0x00007f7c698f0000)
	libm.so.6 => /lib64/libm.so.6 (0x00007f7c6956e000)
	libutil.so.1 => /lib64/libutil.so.1 (0x00007f7c6936a000)
	libz.so.1 => /lib64/libz.so.1 (0x00007f7c69153000)
	libevent-2.1.so.6 => /lib64/libevent-2.1.so.6 (0x00007f7c68efa000)
	libevent_pthreads-2.1.so.6 => /lib64/libevent_pthreads-2.1.so.6 (0x00007f7c68cf7000)
	/lib64/ld-linux-x86-64.so.2 (0x00007f7c6afda000)
	libmount.so.1 => /lib64/libmount.so.1 (0x00007f7c68a9d000)
	libgcc_s.so.1 => /lib64/libgcc_s.so.1 (0x00007f7c68885000)
	libcrypto.so.1.1 => /lib64/libcrypto.so.1.1 (0x00007f7c683a7000)
	libblkid.so.1 => /lib64/libblkid.so.1 (0x00007f7c68155000)
	libuuid.so.1 => /lib64/libuuid.so.1 (0x00007f7c67f4d000)
	libselinux.so.1 => /lib64/libselinux.so.1 (0x00007f7c67d22000)
	libpcre2-8.so.0 => /lib64/libpcre2-8.so.0 (0x00007f7c67a9e000)

#!/bin/bash
#SBATCH -J IMB
#SBATCH -N 2
# SBATCH --ntasks-per-node=16
# SBATCH --ntasks-per-node=6
# SBATCH -n 32
# SBATCH --exclusive
#SBATCH --time=00:30:00 
#SBATCH --error=job.%J.err
#SBATCH --output=job.%J.out
#SBATCH --mail-type=BEGIN,END,FAIL
#SBATCH --mail-user=user@lumc.nl

# Clear the environment from any previously loaded modules
module purge > /dev/null 2>&1

# Load the module environment suitable for the job
# module load library/mpi/openmpi/3.1.5/gcc-8.3.1
module load library/mpi/openmpi/4.0.2/gcc-8.3.1

echo "Starting at `date`"

echo "Running on hosts: $SLURM_JOB_NODELIST"
echo "Running on $SLURM_JOB_NUM_NODES nodes."
echo "Running $SLURM_NTASKS tasks."
echo "Account: $SLURM_JOB_ACCOUNT"
echo "Job ID: $SLURM_JOB_ID"
echo "Job name: $SLURM_JOB_NAME"
echo "Node running script: $SLURMD_NODENAME"
echo "Submit host: $SLURM_SUBMIT_HOST"

echo "Current working directory is `pwd`"

mpirun ./IMB-MPI1 
echo "Program finished with exit code $? at: `date`"

scancel

With the scancel command you can cancel your running job or your scheduled job:

scancel jobid

where the jobid is your job identifier.

scontrol update

While the scontrol show a powerful command is to show info about your job, with the scontrol update command, you can change certain settings as long as your job is on hold or pending. First put your job on hold, update the settings and then release your job:

scontrol hold jobid
scontrol update job jobid NumNodes=2-2 NumTasks=2 Features=intel16
scontrol release jobid

See the man page for the scontrol command.

X11 forwarding

You can enable X11 forwarding with the "--x11" parameter, for example:

srun -n1 --pty --x11 xclock

Using GPU's

The SLURM scheduler will chose a free GPU for you, the Linux cgroups will make sure you will see only the GPU(s) that are chosen for you. This means the the variable CUDA_VISIBLE_DEVICES is not the right variable that denotes your GPU index.
The SLURM variable that will return the right GPU index number is SLURM_STEP_GPUS.
You can use a GPU with the --gres parameter, for example:

--partition=gpu
--gres=gpu:1
--ntasks=1
--cpus-per-task=1

Syntax:

--gres=gpu:[type gpu]:[number of gpus]

Normally you don't have to specify the type of GPU. But if there are different kind of GPUs in a single machine or you want to run on a certain type of GPU you have to specify on which GPU you want to run, for example:

--partition=LKEBgpu
--gres=gpu:1080Ti:1

Hostname	Partition	Type GPU and number
res-hpc-lkeb03	LKEBgpu	Gres=gpu:V100:4
res-hpc-lkeb04	LKEBgpu	Gres=gpu:1080Ti:4
res-hpc-lkeb05	LKEBgpu	Gres=gpu:RTX6000:3
res-hpc-lkeb06	LKEBgpu	Gres=gpu:RTX6000:3
res-hpc-lkeb07	LKEBgpu	Gres=gpu:RTX6000:3
res-hpc-gpu[01-02]	gpu	Gres=gpu:TitanXp:3

Simple GPU example

cat test-gpu.slurm

#!/bin/bash
#
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --time=3:00

module purge
module add library/cuda/11.2/gcc.8.3.1

hostname
echo "Cuda devices: $CUDA_VISIBLE_DEVICES"
nvidia-smi
sleep 10

Output:

cat slurm-206044.out

res-hpc-gpu01.researchlumc.nl
Cuda devices: 0
Mon Dec 21 11:38:54 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.27.04    Driver Version: 460.27.04    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  TITAN Xp            On   | 00000000:AF:00.0 Off |                  N/A |
| 23%   23C    P8    10W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

You can see from the output that we have 1 GPU: Cuda devices: 0

The same, but now we make a reservation for 3 GPU's:

#!/bin/bash
#
#SBATCH --partition=gpu
#SBATCH --gres=gpu:3
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --time=3:00

module purge
module add library/cuda/10.1/gcc.8.3.1

hostname
echo "Cuda devices: $CUDA_VISIBLE_DEVICES"
nvidia-smi
sleep 10

cat slurm-206045.out

res-hpc-gpu02.researchlumc.nl
Cuda devices: 0,1,2
Mon Dec 21 11:41:37 2020       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.27.04    Driver Version: 460.27.04    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  TITAN Xp            On   | 00000000:3B:00.0 Off |                  N/A |
| 23%   25C    P8     8W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  TITAN Xp            On   | 00000000:AF:00.0 Off |                  N/A |
| 23%   24C    P8     9W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   2  TITAN Xp            On   | 00000000:D8:00.0 Off |                  N/A |
| 23%   25C    P8     9W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

You can see from the output that we have 3 GPU's: Cuda devices: 0,1,2

Compiling and running GPU programs

First download and compile the samples from NVidia: Cuda samples

module purge
module add library/cuda/11.2/gcc.8.3.1

cd
git clone https://github.com/NVIDIA/cuda-samples.git
cd cuda-samples/Samples/UnifiedMemoryPerf/
make

Create a slurm batch script:

cat gpu-test.slurm
#!/bin/bash
#
#SBATCH --partition=gpu
#SBATCH --gres=gpu:1
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=1
#SBATCH --time=3:00

module purge
module add library/cuda/11.2/gcc.8.3.1

hostname
echo "Cuda devices: $CUDA_VISIBLE_DEVICES"
$HOME/cuda-samples/Samples/UnifiedMemoryPerf/UnifiedMemoryPerf

sbatch gpu-test.slurm

While running, ssh to the node (in this case res-hpc-gpu01) and run the command nvidia-smi. This will show that the "UnifiedMemoryPerf" program is running on a GPU.

[user@res-hpc-gpu01 GPU]$ nvidia-smi
Mon Dec 21 11:29:55 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.27.04    Driver Version: 460.27.04    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  TITAN Xp            Off  | 00000000:3B:00.0 Off |                  N/A |
| 17%   31C    P0    61W / 250W |      0MiB / 12196MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  TITAN Xp            Off  | 00000000:AF:00.0 Off |                  N/A |
| 23%   34C    P2    69W / 250W |    259MiB / 12196MiB |      2%      Default |
+-------------------------------+----------------------+----------------------+
|   2  TITAN Xp            Off  | 00000000:D8:00.0 Off |                  N/A |
| 18%   31C    P0    61W / 250W |      0MiB / 12196MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     28550      C   ...oryPerf/UnifiedMemoryPerf      145MiB |
+-----------------------------------------------------------------------------+

Output:

cat slurm-206625.out 
res-hpc-gpu01.researchlumc.nl
Cuda devices: 0
GPU Device 0: "Pascal" with compute capability 6.1

Running ........................................................

Overall Time For matrixMultiplyPerf 

Printing Average of 100 measurements in (ms)
Size_KB	 UMhint	UMhntAs	 UMeasy	  0Copy	MemCopy	CpAsync	CpHpglk	CpPglAs
4	 10.879	 23.178	  0.222	  0.014	  0.031	  0.026	  0.035	  0.026
16	 10.657	 25.849	  0.580	  0.030	  0.051	  0.046	  0.052	  0.039
64	 21.117	 37.351	  0.852	  0.103	  0.124	  0.116	  0.095	  0.081
256	 21.184	 38.074	  1.387	  0.587	  0.450	  0.415	  0.313	  0.302
1024	 24.174	 33.124	  3.032	  3.650	  1.741	  1.649	  1.211	  1.199
4096	 21.668	 35.167	 11.067	 25.803	  7.119	  7.104	  5.329	  5.333
16384	 51.674	 62.263	 49.300	191.051	 34.179	 34.632	 28.582	 28.054

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

Slurm Environment Variables

Available environment variables include:

Variable	Meaning
SLURM_CPUS_ON_NODE	processors available to the job on this node
SLURM_JOB_ID	job ID of executing job
SLURM_LAUNCH_NODE_IPADDR	IP address of node where job launched
SLURM_NNODES	total number of nodes
SLURM_NODEID	relative node ID of current node
SLURM_NODELIST	list of nodes allocated to job
SLURM_NTASKS	total number of processes in current job
SLURM_PROCID	MPI rank (or relative process ID) of the current process
SLURM_SUBMIT_DIR	directory from with job was launched
SLURM_TASK_PID	process ID of task started
SLURM_TASKS_PER_NODE	number of task to be run on each node
CUDA_VISIBLE_DEVICES	which GPUs are available for use

Job arrays

For job arrays see the slurm web page

Batch option:

--array=0-31

You can cancel your job array with the command:

scancel jobid_[0-31]

Environment variables

Environment variable	Comment
SLURM_ARRAY_JOB_ID	set to the first job ID of the array
SLURM_ARRAY_TASK_ID	set to the job array index value
SLURM_ARRAY_TASK_COUNT	set to the number of tasks in the job array
SLURM_ARRAY_TASK_MAX	set to the highest job array index value
SLURM_ARRAY_TASK_MIN	set to the lowest job array index value

Limitations - Restrictions

For now we have set the maximum amount of job arrays you can summit at once to 121

scontrol show config | grep MaxArraySize
MaxArraySize            = 121

If we don't set the limitation, some users are submitting large amounts of job arrays which will occupy the cluster so that other users can't run.

Limit the amount of simultaneously running jobs

You can limit the amount of simultaneously running jobs with the following command (for example):

"--array=0-100%4" will limit the number of simultaneously running tasks from this job array to 25

We recommend to use this option.

Remote GPU-accelerated visualization on res-hpc-lo02

If you want to run a graphical program that will show 3D animation, movies or any other kind of simulation/visualization, we have the second login node res-hpc-lo02.researchlumc.nl for this. This server has a powerful Tesla T4 GPU card (16Gb memory).

Steps for setting up a remote GPU-accelerated visualization:

connect your remote desktop (X2Go) to the second login node res-hpc-lo02.researchlumc.nl
start your visualization program (with "vglrun" in front of it if needed)

Once you are in your remote desktop, open a terminal.

For the GPU acceleration, you have to run the VirtualGL command: vglrun in front of the real program you want to run.

Examples:

/opt/VirtualGL/bin/vglrun /opt/VirtualGL/bin/glxinfo
/opt/VirtualGL/bin/vglrun /opt/VirtualGL/bin/glxspheres64
/opt/VirtualGL/bin/vglrun glxgears

With the "glxinfo" program, you should check for the strings:

direct rendering: Yes
OpenGL renderer string: Tesla T4/PCIe/SSE2

vglrun glxinfo | egrep "rendering|OpenGL"
direct rendering: Yes
OpenGL vendor string: NVIDIA Corporation
OpenGL renderer string: Tesla T4/PCIe/SSE2

If you see llvmpipe then you are using software rendering in stead of hardware accelerating.

glxinfo | egrep "rendering|OpenGL"
direct rendering: Yes
OpenGL vendor string: VMware, Inc.
OpenGL renderer string: llvmpipe (LLVM 9.0.0, 256 bits)

Programs that can run with "vglrun"

fsleyes

How to run:

module add neuroImaging/FSLeyes/0.32.3
/opt/VirtualGL/bin/vglrun fsleyes

Check for: OpenGL renderer: Tesla T4/PCIe/SSE2

If you see llvmpipe then you are using software rendering:

With the nvidia-smi command, you can also check if your program is running on the GPU. Below you can see 2 programs running on the GPU: the Xorg server and the fsleyes program:

nvidia-smi 
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     25565      G   /usr/libexec/Xorg                  25MiB |
|    0   N/A  N/A     28059      G   ...ng/fsl/FSLeyes/bin/python        2MiB |
+-----------------------------------------------------------------------------+

Create a vncserver setup and start a vncserver session

If you are setting up a vcnserver session for the first time, it will ask you a few questions and after that you have to adapt the configuration and startup file:

vncserver

You will require a password to access your desktops.

Password: 
Verify:   
Would you like to enter a view-only password (y/n)? n

Desktop 'TurboVNC: res-hpc-lo02.researchlumc.nl:1 (username)' started on display res-hpc-lo02.researchlumc.nl:1

Creating default startup script /home/username/.vnc/xstartup.turbovnc
Starting applications specified in /home/username/.vnc/xstartup.turbovnc
Log file is /home/username/.vnc/res-hpc-lo02.researchlumc.nl:1.log

You should choose a strong password, the password should not be the same as the user login password.

Kill the vncserver connection:

vncserver -kill :1

Now adapt the xstartup.turbovnc file:

vi $HOME/.vnc/xstartup.turbovnc

#!/bin/sh

unset SESSION_MANAGER
unset DBUS_SESSION_BUS_ADDRESS
XDG_SESSION_TYPE=x11;  export XDG_SESSION_TYPE

exec icewm-session

Adapt/create a turbovncserver.conf file for the vncserver with some useful settings:

vi $HOME/.vnc/turbovncserver.conf

$geometry="1280x1024"
$depth=24

Now start the vncserver:

vncserver 

Desktop 'TurboVNC: res-hpc-lo02.researchlumc.nl:1 (username)' started on display res-hpc-lo02.researchlumc.nl:1

Starting applications specified in /home/username/.vnc/xstartup.turbovnc
Log file is /home/username/.vnc/res-hpc-lo02.researchlumc.nl:1.log

You can list you vncserver session with the following command:

vncserver -list

vncserver -list
TurboVNC sessions:

X DISPLAY #     PROCESS ID
:1              33915

You can/should kill your vncserver session when you are done with running your own application:

vncserver -kill :1

vncserver -kill :1
Killing Xvnc process ID 47947

vncserver and port numbers

Everytime someone start a vcnsession and there is already a vncsession running, the port number will increase. The first connection will be on :1 which is port 5900 + 1, where the port 5900 is the standard port number for VNC. For example:

Desktop 'TurboVNC: res-hpc-lo02.researchlumc.nl:3 (username)' started on display res-hpc-lo02.researchlumc.nl:3

In this case, the port to connect to is number 3. So you connect to "res-hpc-lo02.researchlumc.nl:3".

You can always list your own open vnc session with the vncserver -list command.

Remember to kill you vnc session when you are done running your own application.

Remote visualization with a "reverse SSH tunnel" and a vncserver/client

With a reverse SSH tunnel you can make a quick connection to a remote desktop. We assume that you already have setup correctly your vncserver.

We are using the SSH proxy server for this:

IP address: 145.88.35.10
Hostname: res-ssh-alg01.researchlumc.nl

Setting up the reverse SSH tunnel

You have to follow the following steps:

[on res-hpc-lo02:] vncserver

Desktop 'TurboVNC: res-hpc-lo02.researchlumc.nl:1 (username)' started on display res-hpc-lo02.researchlumc.nl:1

Starting applications specified in /home/username/.vnc/xstartup.turbovnc
Log file is /home/username/.vnc/res-hpc-lo02.researchlumc.nl:1.log

[on res-hpc-lo02:] ssh -R 8899:localhost:5901 -l username 145.88.35.10

You are now logged in on "username@res-ssh-alg01" [keep this window open]

At home (terminal 1):

ssh -L 5901:localhost:8899 -l username 145.88.35.10 [keep this window open]

At home (terminal 2):

first install a vnc client for your OS, for example:
- TurboVNC
  - https://sourceforge.net/projects/turbovnc/files/
- TigerVNC
  - https://bintray.com/tigervnc/stable/tigervnc
vncviewer localhost:1

This will open a desktop (icewm)

From here:

terminal
module add neuroImaging/FSLeyes/0.32.3
/opt/VirtualGL/bin/vglrun fsleyes

Remember: the first vnc connection will start at port 5901 (:1), the second connection at port 5902 (:2) and so on. The same for the port 8899. Only 1 user can connect to this port. If this port is occupied, you can't get a connection, so you have to try another port, for example: 8900 or 8898, etc.

Closing the reverse SSH tunnel

vncserver -kill :1
close both connection/terminals where you are logged in [to the ssh proxy server] with "exit"

More Slurm info

Sview

sview is a graphical frontend for slurm which can be handy sometimes. It only gives you some minimal functionality.

Don't forget to enable X11 forwarding.

Comparison between SGE and Slurm

User commands

User command	SGE	Slurm
Interactive login	qlogin	srun --pty bash or srun (-p "partition name"--time=4:0:0 --pty bash)
Job submission	qsub [script_file]	sbatch [script_file]
Job deletion	qdel [job_id]	scancel [job_id]
Job status by job	qstat -u “*” [-j job_id]	squeue [job_id]
Job status by user	qstat [-u user_name]	squeue -u [user_name]
Job hold	qhold [job_id]	scontrol hold [job_id]
Job release	qrls [job_id]	scontrol release [job_id]
Queue list	qconf -sql	squeue
List nodes	qhost	sinfo -N or scontrol show nodes
Cluster status	qhost -q	sinfo
GUI	qmon	sview

Environmental

Environmental	SGE	SLURM
Job ID	$JOB_ID	$SLURM_JOB_ID
Submit directory	$SGE_O_WORKDIR	$SLURM_SUBMIT_DIR
Submit host	$SGE_O_HOST	$SLURM_SUBMIT_HOST
Node list	$PE_HOSTFILE	$SLURM_NODELIST
Job Array Index	$SGE_TASK_ID	$SLURM_ARRAY_TASK_ID
Number of CPUs	$NSLOTS	$SLURM_NPROCS

Slurm	Comment
$SLURM_CPUS_ON_NODE	processors available to the job on this node
$SLURM_LAUNCH_NODE_IPADDR	IP address of node where job launched
$SLURM_NNODES	total number of nodes
$SLURM_NODEID	relative node ID of current node
$SLURM_NTASKS	total number of processes in current job
$SLURM_PROCID	MPI rank (or relative process ID) of the current process
$SLURM_TASK_PID	process ID of task started
$SLURM_TASKS_PER_NODE	number of task to be run on each node.
$CUDA_VISIBLE_DEVICES	which GPUs are available for use

Job Specification

Job Specification	SGE	SLURM
Script directive	#$	#SBATCH
queue	-q [queue]	-p [partition]
count of nodes	N/A	-N [min[-max]]
CPU count	-pe [PE] [count]	-n [count]
Wall clock limit	-l h_rt=[seconds]	-t [min] OR -t [days-hh:mm:ss]
Standard out file	-o [file_name]	-o [file_name]
Standard error file	-e [file_name]	-e [file_name]
Combine STDOUT & STDERR files	-j yes	(use -o without -e)
Copy environment	-V	--export=[ALL \| NONE \| variables]
Event notification	-m abe	--mail-type=[events] --mail-type=ALL (any event) --mail-type=END (job end)
Send notification email	-M [address]	--mail-user=[address]
Job name	-N [name]	--job-name=[name] [-J]
Restart job	-r [yes\|no]	--requeue OR --no-requeue NOTE: configurable default)
Set working directory	-wd [directory]	--workdir=[dir_name] [-D]
Resource sharing	-l exclusive	--exclusive OR --shared
Memory size	-l mem_free=[memory][K\|M\|G]	--mem=[mem][M\|G\|T] OR --mem-per-cpu=[mem][M\|G\|T]
Charge to an account	-A [account]	--account=[account]
Tasks per node	(Fixed allocation_rule in PE)	--tasks-per-node=[count] --cpus-per-task=[count]
Job dependency	-hold_jid [job_id \| job_name]	--depend=[state:job_id]
Job project	-P [name]	--wckey=[name]
Job host preference	-q [queue]@[node] OR -q [queue]@@[hostgroup]	--nodelist=[nodes] AND/OR --exclude=[nodes]
Quality of service		--qos=[name]
Job arrays	-t [array_spec]	--array=[array_spec]
Generic Resources	-l [resource]=[value]	--gres=[resource_spec]
Licenses	-l [license]=[count]	--licenses=[license_spec]
Begin Time	-a [YYMMDDhhmm]	--begin=YYYY-MM-DD[HH:MM[:SS]]

Working with sensitive data on the Shark cluster

Data should be acted on in concordance with the General Data Protection Regulation (EU) 2016/697 (GDPR)
Data analyzed on the Shark cluster should at least be pseudomised.

Data type definitions

Personal data
Personal data, also known as personal information or personally identifiable information is any information relating to an identifiable person.
Pseudonymisation
Is a required process for stored data that transforms personal data in such a way that the resulting data cannot be attributed to a specific data subject without the use of additional information
anonymization
Data anonymization has been defined as a "process by which personal data is irreversibly altered in such a way that a data subject can no longer be identified directly or indirectly".

Data information links (access only from within LUMC)

Data storage / access

This shark cluster has multiple data storage types.
HPC and BeegFS storage do not have backups!!

Storage solutions

HPC Isilon storage.
This is fast storage for direct acces to your data on the cluster, which can be purchased from the IT&DI department through Topdesk. Once purchased this storage will be NFS v4 mounted on all the nodes on the cluster. The default mountpoint will be /exports/.
Access to this mountpoint is handeld by an Active Directory group. The default mount access rights are set by an Ansible playbook. To grant users access to this share you need to have them added to the Active Directory group attached to the share. To find out which group is attached to your data storage use the following command ls -aldh /exports/<storage-share-name> | awk '{print $4}'
No backups or snapshots are available for the HPC isilon storage.
research LTS Isilon storage
This is slow storage for archiving data which can be purchased from the IT&DI department through Topdesk. Once purchased this storage will be NFS v4 mounted on all the execution/gpu/mem nodes on the cluster with read only access, on the login nodes you wil have read and write access. The deafult mountpoint will be /exports/archive/. Access to this mountpoint is handeld by an Active Directory group. The default mount access rights are set by an Ansible playbook. To grant users access to this share you need to have them added to the Active Directory group attached to the share. To find out which group is attached to your data storage use the following command ls -aldh /exports/archive/<storage-share-name> | awk '{print $4}'
BeeGFS
BeeGFS is a parallel file system, created within the LUMC with 4 Virtual Macines (8 vcpus and 32G memory), each have a 5TB unity storage attached. Each Virtual Machine is connected with each a 10GbE network to the Shark slurm cluster. The 4 VMs with the 5TB storage creates a total of 20TB BeeGFS storage mounted on /scratch/shared/. This BeeGFS is faster compared to the HPC Isilon if you have a lot of small files and do a lot of read write operations.
This storage is a so called scratch space, everyone can use this BeeGFS scratch space for staging your data. Please do not use this scratch space for storage.
The HPC Isilon and the LTS Isilon are for storage, the BeeGFS is only for staging.
Staging your data is a normal procedure on large clusters. No backups or snapshots are available for the BeeGFS storage.

Howto Stage your data:
From within your submit script
Create a directory on the /scratch/shared mkdir -p /scratch/shared/$USER/$SLURM_JOB_ID
Change permissions so only the owner can view the files chmod 700 /scratch/shared/$USER
Copy your data cp <path>/to/Data /scratch/shared/$USER/$SLURM_JOB_ID/
Execute your binary and save output to /scratch/shared/$USER/$SLURM_JOB_ID/
At the end of your sbatch script move/copy your output to your HPC/LTS storage.
Now remove the complete directory on the BeeGFS `rm -Rf /scratch/shared/$USER/$SLURM_JOB_ID/'

Importand information for BeeGFS /scratch/shared

Data is scratch and will be treated as such
If needed data can be removed by the admins without notice !!
There is no backup for /scratch/shared
Data and empty folders will be removed automatically after 31 days
There is no quota do not abuse this FileSystem, data can and will be removed if necessary
Set the security to read,write,execute for the owner only chmod 700

Special directories on the cluster

/bam-export/ This directory is created for sharing your Binary Alignment/Map files. This is a temporarely share, first in first out principle. Data here should not contain any patient related data and can be deleted at any time (make sure you have a copy somwhere else). This directory can be used in the UCSC Genome Browser to view your data tracks. The files here are accessable through a webbrowser with the following URL https://barmsijs.lumc.nl/. The files can only be accessed if you know the exact file name and the files are on the /bam-export/ directory word readable.
/home The /home directory is an Isilon HPC export mounted on the /home directory for all the nodes in the cluster. This export is limited to 10 Gb per person. Your home directory is automatically created the first time you log into the cluster and is the same as your username. By default your /home/ directory is world readable and world executable. This directory should not be used for data storage.

mount point	Storage	Size	Usage	owner	group	security rights	mount option login nodes	mount option compute nodes
/home	HPC Isilon	10GB	For small personel storage	AD username	AD Group	rwxr-xr-x	read/write	read/write
/exports	HPC Isilon	-	Mount point for depertment/project storage	AD username	AD Group	rwxrws---	read/write	read/write
/exports/archive	LTS Isilon	-	Mount point for department/project Long Term Storage	AD username	AD Group	rwxrws---	read/write	read/only
/bam-export	HPC Isilon	2TB	For displaying BAM files on https://barmsijs.lumc.nl	AD username	AD Group	rwxrwxrwx	read/write	read/write

How to get access to a HPC or LTS share.

The shares are mounted on a folder on Shark and an Active Directory group grants access to the folder where the share is mounted.
To get access to the storage share only your Department ACPr (Automation Contact Person) can grant access to the share through a self service desk form Aanvraag: Mutaties netwerkrechten of folders (add user to a security group)

Applications

R

R is a programming language for statistical computing and graphics.

You can load R with one of the modules:

statistical/R/3.4.4/gcc.8.3.1
statistical/R/3.5.3/gcc.8.3.1
statistical/R/3.6.0/gcc.8.3.1
statistical/R/3.6.2/gcc.8.3.1
statistical/R/4.0.2/gcc.8.3.1

R environment

You can set your R environment in the file:

$HOME/.Renviron

In here, you can set, for example, the location of your R packages:

R_LIBS_USER=/some/path

R package management

Local R packages are installed in:

$HOME/R/x86_64-pc-linux-gnu-library/version

Global R packages are installed in:

/share/software/statistical/R/R-version/lib64/R/library/

Getting a list of installed R packages and information:

installed.packages()
packageDescription("package")
help(package = "package")
packageVersion("package")

Example:

packageDescription("stats")
help(package = "stats")
packageVersion("stats")

Installing R packages

You can find a list of R packages here:

Get a list of mirrors:

getCRANmirrors()

Choose a mirror:

chooseCRANmirror()

For example, choose: 53: Netherlands [https]

Install a R package:

install.packages("package")

Install a R package with all the dependencies:

install.packages("dplyr", dependencies = TRUE)

Install a R package with a mirror url link:

install.packages("vioplot", repo = "https://lib.ugent.be/CRAN/")

Install 2 or more R packages: install.packages(c("vioplot", "MASS"))

Removing R packages

Remove R packages with:

remove.packages("package")
remove.packages("vioplot")

You can only remove R packages which are installed in your own home folder. You can not remove globally installed R packages.

Updating R packages

You can check what packages need an update with a call to the function:

old.packages()

Updating packages (local):

update.packages()

Or:

update.packages(ask = FALSE)

for without asking

If a global R package needs to be updated, contact the Linux team.

Running R interactively

You can start to run R interactively, just as an exercise and test. The recommended way is to run R in batch mode.

[username@res-hpc-lo01 ~]$ salloc -N1 -n1
salloc: Pending job allocation 386499
salloc: job 386499 queued and waiting for resources
salloc: job 386499 has been allocated resources
salloc: Granted job allocation 386499
salloc: Waiting for resource configuration
salloc: Nodes res-hpc-exe017 are ready for job

[username@res-hpc-exe017 ~]$ module add statistical/R/4.0.0/gcc.8.3.1
[username@res-hpc-exe017 ~]$ R

R version 4.0.0 (2020-04-24) -- "Arbor Day"
...
Type 'q()' to quit R.

> q()
Save workspace image? [y/n/c]: n

[username@res-hpc-exe017 ~]$ exit
exit
salloc: Relinquishing job allocation 386499
salloc: Job allocation 386499 has been revoked.

Running a R script in batch mode

First example

HelloWorld.R

print ("Hello world!")

myscript.sh

#!/bin/bash

#SBATCH --job-name=HelloWord            # Job name
#SBATCH --output=slurm.out              # Output file name
#SBATCH --error=slurm.err               # Error file name
#SBATCH --partition=short               # Partition
#SBATCH --time=00:05:00                 # Time limit 
#SBATCH --nodes=1                       # Number of nodes
#SBATCH --ntasks-per-node=1             # MPI processes per node

module purge
module add statistical/R/4.0.0/gcc.8.3.1

Rscript --vanilla HelloWorld.R

sbatch myscript.sh

Submitted batch job 386860

[username@res-hpc-lo01 R]$ cat slurm.out

[1] "Hello world!"

Second example

driver.R

x <- rnorm(50)
cat("My sample from N(0,1) is:\n")
print(x)

run.slurm

#!/bin/bash

#SBATCH --job-name=serialR              # Job name
#SBATCH --output=slurm.out              # Output file name
#SBATCH --error=slurm.err               # Error file name
#SBATCH --partition=short               # Partition
#SBATCH --time=00:05:00                 # Time limit 
#SBATCH --nodes=1                       # Number of nodes
#SBATCH --ntasks-per-node=1             # MPI processes per node

module purge
module add statistical/R/4.0.0/gcc.8.3.1

Rscript driver.R

[username@res-hpc-lo01 R]$ sbatch run.slurm 
Submitted batch job 386568

[username@res-hpc-lo01 R]$ ls -l
total 78
-rw-r--r-- 1 username Domain Users  59 Jun  5 11:42 driver.R
-rw-r--r-- 1 username Domain Users 483 Jun  5 11:42 run.slurm
-rw-r--r-- 1 username Domain Users   0 Jun  5 11:43 slurm.err
-rw-r--r-- 1 username Domain Users 671 Jun  5 11:43 slurm.out

[username@res-hpc-lo01 R]$ cat slurm.out 
My sample from N(0,1) is:
 [1]  0.32241013 -0.78250675 -0.28872991  0.12559634 -0.29176358  0.57962942
 [7] -0.38277807 -0.21266343  0.86537064  1.06636737  0.96487417  0.31699518
[13]  0.38003556  0.78275327 -0.85745177 -1.47682958 -0.16192662  0.09207091
[19] -0.64508782  1.01504976 -0.07736039 -1.08819811  1.17762738 -0.22819258
[25]  0.79564029  1.36863520 -0.63137494 -0.58452239 -0.96832479 -1.56506037
[31]  1.68344229  1.03967058 -0.20854621  1.39479829 -0.95509839  0.80826154
[37] -0.89781029  0.99954821 -1.25047597 -1.11034908 -1.10759254  1.32150663
[43] -0.04589279 -0.62886137  0.63947415  0.18295622  0.63929410  0.16774740
[49]  0.92311091 -0.13370228

[username@res-hpc-lo01 R]$ scontrol show job 386568
JobId=386568 JobName=serialR
   UserId=username(225812) GroupId=Domain Users(513) MCS_label=N/A
   Priority=449759 Nice=0 Account=dnst-ict QOS=normal
   JobState=COMPLETED Reason=None Dependency=(null)
   Requeue=1 Restarts=0 BatchFlag=1 Reboot=0 ExitCode=0:0
   RunTime=00:00:02 TimeLimit=00:05:00 TimeMin=N/A
   SubmitTime=2020-06-05T11:43:02 EligibleTime=2020-06-05T11:43:02
   AccrueTime=2020-06-05T11:43:02
   StartTime=2020-06-05T11:43:02 EndTime=2020-06-05T11:43:04 Deadline=N/A
   SuspendTime=None SecsPreSuspend=0 LastSchedEval=2020-06-05T11:43:02
   Partition=short AllocNode:Sid=res-hpc-ma01:27472
   ReqNodeList=(null) ExcNodeList=(null)
   NodeList=res-hpc-gpu01
   BatchHost=res-hpc-gpu01
   NumNodes=1 NumCPUs=1 NumTasks=1 CPUs/Task=1 ReqB:S:C:T=0:0:*:*
   TRES=cpu=1,mem=2G,node=1,billing=1
   Socks/Node=* NtasksPerN:B:S:C=1:0:*:* CoreSpec=*
   MinCPUsNode=1 MinMemoryCPU=2G MinTmpDiskNode=0
   Features=(null) DelayBoot=00:00:00
   OverSubscribe=OK Contiguous=0 Licenses=(null) Network=(null)
   Command=/home/username/R/run.slurm
   WorkDir=/home/username/R
   StdErr=/home/username/R/slurm.err
   StdIn=/dev/null
   StdOut=/home/username/R/slurm.out
   Power=
   MailUser=(null) MailType=NONE

Some useful links:

SLURM Job Submission with R, Python, Bash

R (Programming Language)

How to run R programs on taki

How to useRon the Bioinformatics cluster

Running R parallel

Unfornately, "R" is not very efficient when running on a HPC cluster. Basically every R instance is running on only 1 core. To make your R program to run parallel and more efficient, we have installed for now the following libraries:

Rmpi
snow
snowfall
parallel

Loading one of these libraires does not make your program to run parallel. For that, you have to adapt your R program.

Example

hello.R

library(Rmpi)

id <- mpi.comm.rank(comm = 0)
np <- mpi.comm.size(comm = 0)
hostname <- mpi.get.processor.name()

msg <- sprintf("Hello world from process %03d of %03d, on host %s\n", id, np, hostname)
cat(msg)

mpi.barrier(comm = 0)
mpi.finalize()

run-rpmi.slurm

#!/bin/bash

#SBATCH --job-name=hello_parallel       # Job name
#SBATCH --output=slurm-rmpi.out         # Output file name
#SBATCH --error=slurm-rmpi.err          # Error file name
#SBATCH --partition=short               # Partition
#SBATCH --time=00:05:00                 # Time limit
#SBATCH --nodes=2                       # Number of nodes
#SBATCH --ntasks-per-node=4             # MPI processes per node

module purge
module add statistical/R/4.0.0/gcc.8.3.1
module add library/mpi/openmpi/4.0.3/gcc-8.3.1

mpirun Rscript hello.R

[username@res-hpc-lo01 R]$ cat slurm-rmpi.out 
Hello world from process 000 of 008, on host res-hpc-gpu01
Hello world from process 001 of 008, on host res-hpc-gpu01
Hello world from process 002 of 008, on host res-hpc-gpu01
Hello world from process 003 of 008, on host res-hpc-gpu01
Hello world from process 004 of 008, on host res-hpc-gpu02
Hello world from process 005 of 008, on host res-hpc-gpu02
Hello world from process 006 of 008, on host res-hpc-gpu02
Hello world from process 007 of 008, on host res-hpc-gpu02

We recommend to have a look at the following web pages:

High-Performance and Parallel Computing with R

Quick Intro to Parallel Computing in R

Parallel Processing in R

How-to go parallel in R - basics + tips

Rmpi

Parallel Computing: Introduction to MPI

MPI Tutorial for R (Rmpi)

Rmpi Tutorial 2: Sending Data

RStudio

RStudio is an integrated development environment for R.

You can run RStudio on the login node if you want (X11 forwarding enabled or connected with X2Go or MobaXterm):

module purge
module add statistical/RStudio/1.3.959/gcc-8.3.1
rstudio

RStudio on a compute node

You can also start RStudio on a compute node:

[username@res-hpc-lo01 ~]$ srun --x11 --pty bash
[username@res-hpc-exe014 ~]$ module purge
[username@res-hpc-exe014 ~]$ module add statistical/RStudio/1.3.959/gcc-8.3.1
[username@res-hpc-exe014 ~]$ rstudio

RStudio on the OOD (OpenOnDemand portal)

You can also start a RStudio server on the OOD portal:

OOD portal

FSLeyes

See: Programs that can run with "vglrun"

Python

As a researcher, student, scientist or health care worker, there is a big change you have to work with the programming language Python. Basically it is almost de facto programming language in the research world.

By default, Python version 3.6.8 is installed as part of the opererating system (CentOS 8.X).

python --version
Python 3.6.8

If you need another version of Python (older or newer), you can load them with the module command (module add).

Python versions

We have the following extra Python versions installed on the cluster:

2.7.17
3.7.6
3.8.1

You can load one of these with:

module add system/python/2.7.17
module add system/python/3.7.6
module add system/python/3.8.1

Installing Python packages

Some Python packages are already installed on the cluster and you can load/use them with the "module load" command. If you need an extra Python package you can use the pip3 command to install the Python package.

pip

For Python version 3 you should use the pip3 command. For Python version 2 you should use the pip2 command.

Edit first your pip config file:

$HOME/.config/pip/pip.conf
[list]
format=columns

Useful commmands:

pip install packageName
pip uninstall packageName
pip search packageName
pip help
pip install --help

As a normal user you should always install a Python packages (when you are not running in a virtual Python environment) with the command:

pip install --user

So do not use pip install --user some_pkg inside a virtual environment, otherwise, virtual environment's pip will be confused.

Example

pip3 install nibabel --user
Collecting nibabel
  Downloading https://files.pythonhosted.org/packages/8b/8c/cf676b9b3cf69164ba0703a9dcb86ed895ab172e09bece4480db4f03fcce/nibabel-3.1.1-py3-none-any.whl (3.3MB)
    100% |████████████████████████████████| 3.3MB 200kB/s 
Collecting packaging>=14.3 (from nibabel)
  Downloading https://files.pythonhosted.org/packages/46/19/c5ab91b1b05cfe63cccd5cfc971db9214c6dd6ced54e33c30d5af1d2bc43/packaging-20.4-py2.py3-none-any.whl
Requirement already satisfied: numpy>=1.13 in /usr/local/lib64/python3.6/site-packages (from nibabel)
Requirement already satisfied: six in /usr/lib/python3.6/site-packages (from packaging>=14.3->nibabel)
Requirement already satisfied: pyparsing>=2.0.2 in /usr/lib/python3.6/site-packages (from packaging>=14.3->nibabel)
Installing collected packages: packaging, nibabel
Successfully installed nibabel-3.1.1 packaging-20.4


pip3 show nibabel 
Name: nibabel
Version: 3.1.1
Summary: Access a multitude of neuroimaging data formats
Home-page: https://nipy.org/nibabel
Author: nibabel developers
Author-email: neuroimaging@python.org
License: MIT License
Location: /home/username/.local/lib/python3.6/site-packages
Requires: numpy, packaging


pip3 list
Package                  Version     
------------------------ ------------
...
nibabel                  3.1.1       
...

As you can see from the example above, the Python package(s) will be installed in your local user environment:

Location: /home/username/.local/lib/python3.6/site-packages

Python virtual environments

If you are working on different projects and you need different Python packages for each project, it is better to work in a special virtual environment.

When you activate this virtual environment, it will create a special virtual Python environment for you. In this virtual environment you can use the pip command (without the --user option) and other commands.

You create a new virtual environment with one of the following commands:

$ virtualenv python3 -m venv /path/to/new/virtual/environmentname
$ python3 -m venv /path/to/new/virtual/environmentname

You activate a new virtual environment with the command:

$ source /path/to/new/virtual/environmentname/bin/activate

You deactivate a virtual environment with the command (it will not be destroyed):

(envname) $ deactivate

Example:

virtualenv /exports/example/projects/Project-A
Using base prefix '/usr'
New python executable in /exports/example/projects/Project-A/bin/python3.6
Also creating executable in /exports/example/projects/Project-A/bin/python
Installing setuptools, pip, wheel...done.


[username@res-hpc-lo01 ~]$ source /exports/example/projects/Project-A/bin/activate
(Project-A) [username@res-hpc-lo01 ~]$ 


(Project-A) [username@res-hpc-lo01 python3.6]$ pip3 list
Package    Version
---------- -------
pip        20.1.1
setuptools 49.1.0
wheel      0.34.2


(Project-A) [username@res-hpc-lo01 ~]$ deactivate 
[username@res-hpc-lo01 ~]$

To remove your Python virtual environment delete the virtual environment directory.

$ rm -Rf /path/to/virtual/environmentname

Conda, Anaconda, Miniconda and Bioconda

If you have to install, setup and work with a complex program/project you should make use of the conda tool. Conda it self is a package management system, while anaconda, miniconda and bioconda provides you with a virtual Python environment and a lot of optimized Python packages, especially for researchers and scientists. These packages you can easily install within this environment.

Anaconda - collection with the most packages (> 7,500 data science and machine learning packages)
Miniconda - light-weighted Anaconda version (you should start with this version)
Bioconda - specializing in bio-informatics software

Conda

Conda is an open source package management system and environment management system that runs on Windows, macOS and Linux. Conda quickly installs, runs and updates packages and their dependencies. Conda easily creates, saves, loads and switches between environments on your local computer. It was created for Python programs, but it can package and distribute software for any language.

Conda website

Conda documentation

Anaconda

Anaconda is a package manager, an environment manager, a Python/R data science distribution, and a collection of over 7,500+ open-source packages.

Anaconda website

Anaconda product

Miniconda

Miniconda is a free minimal installer for conda. It is a small, bootstrap version of Anaconda that includes only conda, Python, the packages they depend on, and a small number of other useful packages, including pip, zlib and a few others. Use the conda install command to install 720+ additional conda packages from the Anaconda repository.

Miniconda website

Bioconda

Bioconda is a channel for the conda package manager specializing in bioinformatics software.

Bioconda website

Bioconda documentation

Overview useful commands

Desciption	command
Verify Conda is installed, check version number	conda info
Create a new environment named ENVNAME	conda create --name ENVNAME
Activate a named Conda environment	conda activate ENVNAME
Deactivate current environment	conda deactivate
List all packages and versions in the active environment	conda list
Delete an entire environment	conda remove --name ENVNAME --all
Search for a package in currently configured channels	conda search PKGNAME
Install a package	conda install PKGNAME
Detailed information about package versions	conda search PKGNAME --info
Remove a package from an environment	conda uninstall PKGNAME --name ENVNAME
Add a channel to your Conda configuration	conda config --add channels CHANNELNAME

Example

module purge
module add tools/miniconda/python3.7/4.7.12

conda info

     active environment : None
            shell level : 0
       user config file : /home/username/.condarc
 populated config files : /home/username/.condarc
          conda version : 4.7.12
    conda-build version : not installed
         python version : 3.7.4.final.0
       virtual packages : 
       base environment : /share/software/tools/miniconda/3.7/4.7.12  (read only)
           channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
                          https://repo.anaconda.com/pkgs/main/noarch
                          https://repo.anaconda.com/pkgs/r/linux-64
                          https://repo.anaconda.com/pkgs/r/noarch
          package cache : /share/software/tools/miniconda/3.7/4.7.12/pkgs
                          /home/username/.conda/pkgs
       envs directories : /home/username/.conda/envs
                          /share/software/tools/miniconda/3.7/4.7.12/envs
               platform : linux-64
             user-agent : conda/4.7.12 requests/2.22.0 CPython/3.7.4 Linux/4.18.0-147.8.1.el8_1.x86_64 centos/8.1.1911 glibc/2.28
                UID:GID : 225812:513
             netrc file : None
           offline mode : False

conda create --name Project-B
Collecting package metadata (current_repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /home/username/.conda/envs/Project-B

Proceed ([y]/n)? y

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
#     $ conda activate Project-B
#
# To deactivate an active environment, use
#
#     $ conda deactivate

conda init bash
no change     /share/software/tools/miniconda/3.7/4.7.12/condabin/conda
no change     /share/software/tools/miniconda/3.7/4.7.12/bin/conda
no change     /share/software/tools/miniconda/3.7/4.7.12/bin/conda-env
no change     /share/software/tools/miniconda/3.7/4.7.12/bin/activate
no change     /share/software/tools/miniconda/3.7/4.7.12/bin/deactivate
no change     /share/software/tools/miniconda/3.7/4.7.12/etc/profile.d/conda.sh
no change     /share/software/tools/miniconda/3.7/4.7.12/etc/fish/conf.d/conda.fish
no change     /share/software/tools/miniconda/3.7/4.7.12/shell/condabin/Conda.psm1
no change     /share/software/tools/miniconda/3.7/4.7.12/shell/condabin/conda-hook.ps1
no change     /share/software/tools/miniconda/3.7/4.7.12/lib/python3.7/site-packages/xontrib/conda.xsh
no change     /share/software/tools/miniconda/3.7/4.7.12/etc/profile.d/conda.csh
modified      /home/username/.bashrc

==> For changes to take effect, close and re-open your current shell. <==

[username@res-hpc-lo01 ~]$ conda activate Project-B
(Project-B) [username@res-hpc-lo01 ~]$

conda search beautifulsoup4
Loading channels: done
# Name                       Version           Build  Channel             
beautifulsoup4                 4.6.0          py27_1  pkgs/main           
...
beautifulsoup4                 4.9.1          py38_0  pkgs/main

conda install beautifulsoup4
Collecting package metadata (current_repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /home/username/.conda/envs/Project-B

  added / updated specs:
    - beautifulsoup4


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    beautifulsoup4-4.9.1       |           py38_0         171 KB
    ca-certificates-2020.6.24  |                0         125 KB
    certifi-2020.6.20          |           py38_0         156 KB
    libedit-3.1.20191231       |       h7b6447c_0         167 KB
    libffi-3.3                 |       he6710b0_2          50 KB
    ncurses-6.2                |       he6710b0_1         817 KB
    openssl-1.1.1g             |       h7b6447c_0         2.5 MB
    pip-20.1.1                 |           py38_1         1.7 MB
    python-3.8.3               |       hcff3b4d_2        49.1 MB
    readline-8.0               |       h7b6447c_0         356 KB
    setuptools-47.3.1          |           py38_0         515 KB
    soupsieve-2.0.1            |             py_0          33 KB
    sqlite-3.32.3              |       h62c20be_0         1.1 MB
    tk-8.6.10                  |       hbc83047_0         3.0 MB
    xz-5.2.5                   |       h7b6447c_0         341 KB
    ------------------------------------------------------------
                                           Total:        60.1 MB

The following NEW packages will be INSTALLED:

  _libgcc_mutex      pkgs/main/linux-64::_libgcc_mutex-0.1-main
  beautifulsoup4     pkgs/main/linux-64::beautifulsoup4-4.9.1-py38_0
  ca-certificates    pkgs/main/linux-64::ca-certificates-2020.6.24-0
  certifi            pkgs/main/linux-64::certifi-2020.6.20-py38_0
  ld_impl_linux-64   pkgs/main/linux-64::ld_impl_linux-64-2.33.1-h53a641e_7
  libedit            pkgs/main/linux-64::libedit-3.1.20191231-h7b6447c_0
  libffi             pkgs/main/linux-64::libffi-3.3-he6710b0_2
  libgcc-ng          pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0
  libstdcxx-ng       pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0
  ncurses            pkgs/main/linux-64::ncurses-6.2-he6710b0_1
  openssl            pkgs/main/linux-64::openssl-1.1.1g-h7b6447c_0
  pip                pkgs/main/linux-64::pip-20.1.1-py38_1
  python             pkgs/main/linux-64::python-3.8.3-hcff3b4d_2
  readline           pkgs/main/linux-64::readline-8.0-h7b6447c_0
  setuptools         pkgs/main/linux-64::setuptools-47.3.1-py38_0
  soupsieve          pkgs/main/noarch::soupsieve-2.0.1-py_0
  sqlite             pkgs/main/linux-64::sqlite-3.32.3-h62c20be_0
  tk                 pkgs/main/linux-64::tk-8.6.10-hbc83047_0
  wheel              pkgs/main/linux-64::wheel-0.34.2-py38_0
  xz                 pkgs/main/linux-64::xz-5.2.5-h7b6447c_0
  zlib               pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3


Proceed ([y]/n)? y


Downloading and Extracting Packages
libedit-3.1.20191231 | 167 KB    | #################################### | 100% 
sqlite-3.32.3        | 1.1 MB    | #################################### | 100% 
readline-8.0         | 356 KB    | #################################### | 100% 
pip-20.1.1           | 1.7 MB    | #################################### | 100% 
python-3.8.3         | 49.1 MB   | #################################### | 100% 
certifi-2020.6.20    | 156 KB    | #################################### | 100% 
ncurses-6.2          | 817 KB    | #################################### | 100% 
ca-certificates-2020 | 125 KB    | #################################### | 100% 
setuptools-47.3.1    | 515 KB    | #################################### | 100% 
xz-5.2.5             | 341 KB    | #################################### | 100% 
openssl-1.1.1g       | 2.5 MB    | #################################### | 100% 
libffi-3.3           | 50 KB     | #################################### | 100% 
soupsieve-2.0.1      | 33 KB     | #################################### | 100% 
beautifulsoup4-4.9.1 | 171 KB    | #################################### | 100% 
tk-8.6.10            | 3.0 MB    | #################################### | 100% 
Preparing transaction: done
Verifying transaction: done
Executing transaction: done

conda list
# packages in environment at /home/username/.conda/envs/Project-B:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                        main  
beautifulsoup4            4.9.1                    py38_0  
ca-certificates           2020.6.24                     0  
certifi                   2020.6.20                py38_0  
ld_impl_linux-64          2.33.1               h53a641e_7  
libedit                   3.1.20191231         h7b6447c_0  
libffi                    3.3                  he6710b0_2  
libgcc-ng                 9.1.0                hdf63c60_0  
libstdcxx-ng              9.1.0                hdf63c60_0  
ncurses                   6.2                  he6710b0_1  
openssl                   1.1.1g               h7b6447c_0  
pip                       20.1.1                   py38_1  
python                    3.8.3                hcff3b4d_2  
readline                  8.0                  h7b6447c_0  
setuptools                47.3.1                   py38_0  
soupsieve                 2.0.1                      py_0  
sqlite                    3.32.3               h62c20be_0  
tk                        8.6.10               hbc83047_0  
wheel                     0.34.2                   py38_0  
xz                        5.2.5                h7b6447c_0  
zlib                      1.2.11               h7b6447c_3

conda uninstall -y beautifulsoup4
Collecting package metadata (repodata.json): done
Solving environment: done

## Package Plan ##

  environment location: /home/username/.conda/envs/Project-B

  removed specs:
    - beautifulsoup4


The following packages will be REMOVED:

  beautifulsoup4-4.9.1-py38_0
  soupsieve-2.0.1-py_0


Preparing transaction: done
Verifying transaction: done
Executing transaction: done

(Project-B) [username@res-hpc-lo01 ~]$ conda deactivate
[username@res-hpc-lo01 ~]$

conda remove --name Project-B --all -y

Remove all packages in environment /home/username/.conda/envs/Project-B:


## Package Plan ##

  environment location: /home/username/.conda/envs/Project-B


The following packages will be REMOVED:

  _libgcc_mutex-0.1-main
  ca-certificates-2020.6.24-0
  certifi-2020.6.20-py38_0
  ld_impl_linux-64-2.33.1-h53a641e_7
  libedit-3.1.20191231-h7b6447c_0
  libffi-3.3-he6710b0_2
  libgcc-ng-9.1.0-hdf63c60_0
  libstdcxx-ng-9.1.0-hdf63c60_0
  ncurses-6.2-he6710b0_1
  openssl-1.1.1g-h7b6447c_0
  pip-20.1.1-py38_1
  python-3.8.3-hcff3b4d_2
  readline-8.0-h7b6447c_0
  setuptools-47.3.1-py38_0
  sqlite-3.32.3-h62c20be_0
  tk-8.6.10-hbc83047_0
  wheel-0.34.2-py38_0
  xz-5.2.5-h7b6447c_0
  zlib-1.2.11-h7b6447c_3


Preparing transaction: done
Verifying transaction: done
Executing transaction: done

Open OnDemand [OOD]

Open OnDemand [OOD] provides an integrated, single access point for all of your HPC resources.

OOD

This will give you a web interface with the following options:

Files
- Home directory/File Explorer
Jobs
- Active/Completed Jobs
- Job Composer
Clusters
- Cluster Shell Access
Interactive Apps
- Shark cluster Desktop
- RStudio Server
- Jupyter Notebook (with GPU support)