A High Performance Computing (HPC) is a specially designed network of computers capable of running applications that can exchange data efficiently.

VU MIF HPC consists of a supercomputer from the clusters (the first number is the actual and available amount):

Total 40/41 nodes, 1912 CPU cores with 17TB RAM, 32 GPU with 1TB RAM.

The processor below = CPU = core - a single core of the processor (with all hyperthreads if they are turned on).

In main and gpu partitions there are installed Qlustar 11 OS. It is based on Ubuntu 18.04 LTS. In power partition there is installed Ubuntu 18.04 LTS.

You can check the list of OS package with the command dpkg -l (in login node hpc or in power nodes).

With the command singularity it is possible to make use of ready-made copies of container files in directories /apps/local/hpc, /apps/local/nvidia, /apps/local/intel, /apps/local/lang or to download from singularity and docker online repositories. You can also create your own singularity containers using the MIF cloud service.

You can prepare your container with singularity, for example:

$ singularity build --sandbox /tmp/python docker://python:3.8
$ singularity exec -w /tmp/python pip install package
$ singularity build python.sif /tmp/python
$ rm -rf /tmp/python

Similarly, you can use R, Julia or other containers that do not require root privileges to install packages.

If you want to add OS packages to the singularity container, you need root/superuser privileges. With fakeroot, we simulate them, and copy the required library libfakeroot-sysv.so into the container, for example:

$ singularity build --sandbox /tmp/python docker://ubuntu:18.04
$ cp /libfakeroot-sysv.so /tmp/python/
$ fakeroot -l /libfakeroot-sysv.so singularity exec -w /tmp/python apt-get update
$ fakeroot -l /libfakeroot-sysv.so singularity exec -w /tmp/python apt-get install python3.8 ...
$ fakeroot -l /libfakeroot-sysv.so singularity exec -w /tmp/python apt-get clean
$ rm -rf /tmp/python/libfakeroot-sysv.so /tmp/python/var/lib/apt/lists (you can clean up more of what you don't need)
$ singularity build python.sif /tmp/python
$ rm -rf /tmp/python

There are ready-made scripts to run your hadoop tasks using the Magpie set in the directory /apps/local/bigdata.

With JupyterHub you can run calculations with the python command line in a web browser and use the JupyterLab environment. If you install your own JupyterLab environment in your home directory, you need to install the additional batchspawner package - this will start your environment, for example:

$ python3.7 -m pip install --upgrade pip setuptools wheel
$ python3.7 -m pip install --ignore-installed batchspawner jupyterlab

Alternatively, you can use a container that you made via JupyterHub. In that container, you need to install the batchswapner and jupyterlab packages, and to create a script ~/.local/bin/batchspawner-singleuser with execution permissions (chmod +x ~/.local/bin/batchspawner-singleuser).

#!/bin/sh
exec singularity exec --nv myjupyterlab.sif batchspawner-singleuser "$@"

Fill in the registration form to order IT Research center services. Once the request has been approved, a VU MIF computer network user ID will be assigned. If you are a VU employee or student and you have provided your VU email address during the registration, then you must enter your original password using VU EID (available via the forgotten password change procedure). Otherwise, you have to come to the VU MIF at 47 Didlaukio Street, Room 302/304 during office hours (for the exact time you can call 8 5219 5005 or 8 5219 5006).

With the given (selected) username and the password entered by the user, the user has the right to connect to the server uosis.mif.vu.lt, to the VU MIF classrooms and to some of the VU MIF workstations computers.

Adresu Waldur yra savitarnos portalas, kur su savo universiteto (per eduGAIN arba LITNET) prisijungimu galima pačiam susikurti HPC prisijungimą. Daugiau info apie tai čia.