Available hardware¶

While CÉCI clusters are mostly homogeneous, Manneback is made of several different generations of hardware.

Use the sinfo command to learn about the available hardware on Manneback

"Partitions":
Def* (5days)        keira (5days)   pelican (5days) cp3 (5days)     cp3-gpu (5days) gpu (5days)
"Nodes":
#Nodes  Partition  Features                                       Cores/Slots  Memory  GPUs
16      cp3        CascadeLake,Xeon,4214,CentOS7                  48           187G
1       cp3-gpu    SandyBridge,Xeon,E5-2640,CentOS7               20           63G     TeslaK80:2
13      cp3        IvyBridge,Xeon,E5-2695v2,CentOS7               48           126G
2       cp3        K10,Opteron,6134,CentOS7                       16           31G
1       cp3        Milan,EPYC,7452,CentOS7                        128          504G
8       cp3        Rome,EPYC,7452,CentOS7                         128          504G
2       cp3        Rome,EPYC,7452,Rocky8                          128          503G
18      cp3        SkyLake,Xeon,4116,CentOS7                      48           187G
24      Def*       Haswell,Xeon,E5-2630v3,CentOS7                 16           63G
8       Def*       IvyBridge,Xeon,E5-2650v2,CentOS7               16           63G
13      Def*       SandyBridge,Xeon,E5-2650,CentOS7               16           63G
1       Def*       SandyBridge,Xeon,E5-4620,CentOS7               32           126G
1       Def*       SandyBridge,Xeon,E5-4640,CentOS7               32           252G
6       Def*       SkyLake,Xeon,5118,CentOS7                      24           94G
1       Def*       Zen,EPYC,7551,CentOS7                          128          504G
1       gpu        CascadeLake,Xeon,5217,Tesla,TeslaV100,CentOS7  16           377G    TeslaV100:2
1       gpu        CascadeLake,Xeon,5217,Tesla,TeslaV100,CentOS7  32           377G    TeslaV100:2
1       gpu        CascadeLake,Xeon,6244,GeForce,GeForceRTX2080T  32           376G    GeForceRTX2080Ti:6
1       gpu        IceLake,Xeon,6346,Tesla,TeslaA10,Rocky8        64           251G    TeslaA10:4
1       gpu        Milan,EPYC,7313,TeslaMIG,TeslaA100_80,CentOS7  64           252G    TeslaA100_80:2
1       gpu        Milan,EPYC,7313,Tesla,TeslaA100_80,CentOS7     64           252G    TeslaA100_80:2
1       gpu        Milan,EPYC,7313,Tesla,TeslaA100,TeslaMIG,Rock  64           251G    TeslaA100:2
2       gpu        Rome,EPYC,7302,Tesla,TeslaA100,CentOS7         64           504G    TeslaA100:2
1       gpu        Rome,EPYC,7352,GeForce,GeForceRTX3090,CentOS7  96           504G    GeForceRTX3090:4
2       keira      Rome,EPYC,7742,CentOS7                         256          252G
4       keira      Rome,EPYC,7742,CentOS7                         256          504G
3       pelican    IceLake,Xeon,6326,Rocky8                       64           251G
"Filesystems":
Filesystem      quota
$CECIHOME       100.0GiB
$CECITRSF       1.0TiB
$HOME           50G
$GLOBALSCRATCH  unlimited

Multiple CPU vendors and generations are represented. The Features column in the above list show a triplet with the CPU code name, the CPU family (Xeon is Intel’s server CPU brand, EPYC is AMD’s) and the CPU reference.

The code name is representative of the generation of the CPU (from older to more recent):

• Intel: Nehalem > Westmere > SandyBridge > IvyBridge > Haswell > Broadwell > SkyLake > CascadeLake > IceLake
• AMD: K10 > Zen > Rome > Milan

In your submission script, you can select one or more specific features with the --constraint= option, like --constraint="Nehalem|Westmere" for instance to choose a compute node with an older CPU. The --constraint option accepts quit complex expressions, you can find the documentation here.

Another distinctive feature of Manneback is that some nodes are equiped with GPUs. They are listed in the last column as a GPU name followed by the number of GPUs in each node.

As for CPUs multiple generations are available, in chronological order:

• nVidia: TeslaM10 > TeslaV100 (Volta) > GeForceRTX2080Ti (Turing) > TeslaA100, GeForceRTX3090, TeslaA10 (Ampere)

The GPU’s are considered a “generic resource” in Slurm, meaning that you can reserve the GPU for your job with a command like --gres="gpu:TeslaV100:2" in your submission script. This would request two V100 GPUs. If you do not need to choose a particular type of GPU, you can also simply request --gres=gpu:1 for instance for just one GPU. The --gres (documentation) option will allow you to request a number of GPUs per node. You can also use --gpus (documentation) option to request a total number of GPUs per job and not per node.

The --gres and --gpus options do not allow as much flexibility as the --constraint. Therefore, we also define features related to GPUs, such as Testla, Tesla100, Geforce, etc. You can thus specify

--gpus=1
--constraint=Tesla

to request one Tesla GPU if you do not care about the difference between a TeslaV100 and a TeslaA100.

As GPU resources are much scarcer that CPU resources, GPUs will impact the fairshare much more than CPUs, depending on the performance of the GPU and the ratio of CPUs to GPU on the compute nodes.

Partitions¶

The nodes are organised into partitions, which are logical sets of nodes with either similar hardware or similar policies/configurations. Three CPU partitions are available on Manneback: The default one (Def) which is opened to everyone, cp3, which is open to everyone but CP3 users have a higher priority there, keira which is open to everyone but NAPS users have a higher priority there, and GPU partitions. The GPU nodes are grouped into two GPU partitions: gpu and cp3-gpu. Note that you can specify #SBATCH -–partition=Def,cp3 to submit a job that will run on the earliest available partition.

Quality of Service¶

A Quality Of Service (QOS) is a parameter that a job can request to bring specific privileges for the job, for instance a higher priority, or a longer run time. A QOS is typically granted to department/groups who participate in the funding of the Manneback hardware.

Currently, the following QOS’es are defined and available on the respective partitions for the listed groups:

A QOS is chosen in Slurm with `--qos=.... For instance, to use the interactive QOS, add #SBATCH --qos=interactive in your submission script.

The cp3 and keira QOS’es bring a higher priority for jobs on the partitions that their corresponding groups funded.

The configuration of the GPU partition is specific and was designed in order to make interactive jobs and production jobs run as smoothly as possible together on that partition.

By default, a job submitted to the GPU partition will be subjected to the following restrictions: maximum two jobs running at all times, for a maximum duration of 2 days. To run more jobs and/or longer jobs, users must use the preemptiable QOS, that will allow up to 30 5-days-long jobs. The drawback is that those jobs will be preemptible, meaning that they can potentially be stopped by Slurm to let a higher-priority job run immediatley. As a consequence,

The interactive QOS will allow 1 9-hour-long job with a very high priority per user, intended for interactive jobs running during the day.

Disk space¶

Every user has access to a home directory with a 100GB quota.

A global scratch /globalscratch is available, offering a 90TB space NFS mounted on all the compute nodes. There is no quota enforced on that filesystem ; it is the responsibility of the users to remove the files that are not needed anymore by any job. This space is cleaned periodically.

Each node furthermore has a local /scratch space. Local scratch is a smaller file system directly attached to the worker node. It is created at the beginning of a job and deleted automatically at the end. Again here no quota is enforced, but the available space is limited by the hardware.

Jupyter notebooks¶

For heavy computations, you can run a Jupyterhub service in a Slurm job allocation on any cluster and then use the nice little tool named Sshuttle tool to access it.

Sshuttle is a program that, according to its documentation, acts as a “Transparent proxy server that works as a poor man’s VPN. Forwards over ssh. Doesn’t require admin. Works with Linux and MacOS.” Installation on a Linux or MacOS laptop can be done with apt, pacman, dnf depending on your Linux distribution, or with brew on MacOS, but you can also install it with pip or git clone from GitHub directly. See the GitHub page for details.

After you have installed Sshuttle, make sure you can access the cluster using an SSH gateway or VPN if necessary. In the following, we will assume the connection to Manneback through gwceci is properly configured in .ssh/config as manneback%gwceci.

Next, find out in which module Jupyterhub is installed on the cluster or install it by yourself with pip. In the example, it installed in the user’s directory for the default Python module. Also find out which private network range is used on the cluster for the compute nodes. Often, it will be 10.0.0.0/8, but if you are unsure, ask the cluster admin.

Those operations will only be needed once.

Then, create a tunnel with Sshuttle. Open a terminal window that will be dedicated to that and run the following in it.

$ sshuttle -r manneback%gwceci 10.0.0.0/8
[local sudo] Password:
Warning: No xauth data; using fake authentication data for X11 forwarding.
client: Connected.

It will ask for your password in order to elevate privileges (sudo). That is needed because it will modify temporarily low-level network routing. As long as it runs, the private network of the cluster will be accessible from your laptop, wherever you are connected. Leave that terminal session open for as long as you need access to the Jupyterhub service in your job. About the job, connect to the frontend (in another terminal) and submit it with

[dfr@manneback ~]$ srun --pty bash -c 'ml Python ; jupyter notebook --ip $(hostname -i)'

Adapt the srun options to your liking. Once the job start, you should see something like this:

[I 15:59:54.435 NotebookApp] Serving notebooks from local directory: /auto/home/users/d/f/dfr
[I 15:59:54.435 NotebookApp] The Jupyter Notebook is running at:
[I 15:59:54.435 NotebookApp] http://10.3.220.101:8888/?token=4bd6038c1f0df6e51d901891b95b98ebf8e899646cc68223
[I 15:59:54.435 NotebookApp]  or http://127.0.0.1:8888/?token=4bd6038c1f0df6e51d901891b95b98ebf8e899646cc68223
[I 15:59:54.435 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 15:59:54.477 NotebookApp] No web browser found: could not locate runnable browser.
[C 15:59:54.478 NotebookApp]

    To access the notebook, open this file in a browser:
        file:///auto/home/users/d/f/dfr/.local/share/jupyter/runtime/nbserver-14230-open.html
    Or copy and paste one of these URLs:
        http://10.3.220.101:8888/?token=4bd6038c1f0df6e51d901891b95b98ebf8e899646cc68223
     or http://127.0.0.1:8888/?token=4bd6038c1f0df6e51d901891b95b98ebf8e899646cc68223

If your terminal supports it, you can then click on the URL that starts with http://10.. Otherwise simply copy that URL in your browser and you should see the Jupyterhub interface.

Once you are finished hit CTRL-C in both terminals to stop everything.

^C[I 16:02:40.877 NotebookApp] interrupted
Serving notebooks from local directory: /auto/home/users/d/f/dfr
0 active kernels
The Jupyter Notebook is running at:
http://10.3.220.101:8888/?token=4bd6038c1f0df6e51d901891b95b98ebf8e899646cc68223
 or http://127.0.0.1:8888/?token=4bd6038c1f0df6e51d901891b95b98ebf8e899646cc68223
Shutdown this notebook server (y/[n])? y
[C 16:02:43.185 NotebookApp] Shutdown confirmed
[I 16:02:43.186 NotebookApp] Shutting down 0 kernels
[dfr@manneback ~]$

^Cclient:
client: Keyboard interrupt: exiting.

See it in action below:

First connection warning¶

When you connect for the first time, the SSH client will ask you to confirm the identity of the remote host, you can do so by verifying that the fingerprint shown to you is among the ones listed below:

Frontend 1:

• SHA256:Q3IYMwb5QElBkqmVbJyi8UgFoyKZMZQsWRRU3CEvV8s
• SHA256:iR1HQsjGvKxo4uwswD/xLepW6DA3e45jUbNEZTntWRc
• SHA256:i2Hb6HDaeMz6h99/qHu3lIqGUX6Zrx8Yuz0ELTQzsjc

Frontend 2

• SHA256:Q3IYMwb5QElBkqmVbJyi8UgFoyKZMZQsWRRU3CEvV8s
• SHA256:iR1HQsjGvKxo4uwswD/xLepW6DA3e45jUbNEZTntWRc
• SHA256:i2Hb6HDaeMz6h99/qHu3lIqGUX6Zrx8Yuz0ELTQzsjc

User self-assessment test¶

Users must pass a simple test to use Manneback. The test is very short (5 questions) and easy; 5 minutes should be enough. It exists only to make sure every one uses the cluster in the intended way and does not harm other users’ experience on the cluster. New users have a few weeks to pass the test voluntarily before the test is enforced upon login. This is recalled upon every login in the message of the day.

To start the test, you simply run selftest.py. As soon as you respond correctly to the questions, the message will disappear. As a hint, each question contains a link to the corresponding section in the documentation.