# HPC Resources

HPC resources in ZIH systems comprise the *High Performance Computing and Storage Complex* and its
extension *High Performance Computing – Data Analytics*. In total it offers scientists
about 100,000 CPU cores and a peak performance of more than 1.5 quadrillion floating point
operations per second. The architecture specifically tailored to data-intensive computing, Big Data
analytics, and artificial intelligence methods with extensive capabilities for energy measurement
and performance monitoring provides ideal conditions to achieve the ambitious research goals of the
users and the ZIH.

## Architectural Design

Over the last decade we have been running our HPC system of high heterogeneity with a single
Slurm batch system. This made things very complicated, especially to inexperienced users. With
the replacement of the Taurus system by the cluster [Barnard](#barnard) in 2023 we have a new
archtictural design comprising **six homogeneous clusters with their own Slurm instances and with
cluster specific login nodes** running on the same CPU. Job submission is possible only from
within the corresponding cluster (compute or login node).

All clusters are integrated to the new InfiniBand fabric and have the same access to
the shared filesystems. You find a comprehensive documentation on the available working and
permanent filesystems on the page [Filesystems](../data_lifecycle/file_systems.md).

![Architecture overview 2023](../jobs_and_resources/misc/architecture_2024.png)
{: align=center}

HPC resources at ZIH comprise a total of the **six systems**:

| Name                                | Description           | Year of Installation | DNS |
| ----------------------------------- | ----------------------| -------------------- | --- |
| [`Capella`](capella.md)               | GPU cluster           | 2024                 | `c[1-144].capella.hpc.tu-dresden.de` |
| [`Barnard`](barnard.md)               | CPU cluster           | 2023                 | `n[1001-1630].barnard.hpc.tu-dresden.de` |
| [`Alpha Centauri`](alpha-centauri.md) | GPU cluster           | 2021                 | `i[8001-8037].alpha.hpc.tu-dresden.de` |
| [`Julia`](julia.md)                   | Single SMP system     | 2021                 | `julia.hpc.tu-dresden.de` |
| [`Romeo`](romeo.md)                   | CPU cluster           | 2020                 | `i[8001-8190].romeo.hpc.tu-dresden.de` |
| [`Power9`](power9.md)                 | IBM Power/GPU cluster | 2018                 | `ml[1-29].power9.hpc.tu-dresden.de` |

All clusters will run with their own [Slurm batch system](slurm.md) and job submission is possible
only from their respective login nodes.

## Login and Dataport Nodes

- Login-Nodes
    - Individual for each cluster. See the specifics in each cluster chapter
- 2 Data-Transfer-Nodes
    - 2 servers without interactive login, only available via file transfer protocols
      (`rsync`, `ftp`)
    - `dataport[3-4].hpc.tu-dresden.de`
    - IPs: 141.30.73.\[4,5\]
    - Further information on the usage is documented on the site
      [dataport Nodes](../data_transfer/dataport_nodes.md)

## Barnard

The cluster `Barnard` is a general purpose cluster by Bull. It is based on Intel Sapphire Rapids CPUs.
Further details in [`Barnard` Chapter](barnard.md).

## Alpha Centauri

The cluster `Alpha Centauri` (short: `Alpha`) by NEC provides AMD Rome CPUs and NVIDIA A100 GPUs
and is designed for AI and ML tasks.
Further details in [`Alpha Centauri` Chapter](alpha_centauri.md).

## Capella

The cluster `Capella` by MEGWARE provides AMD Genoa CPUs and NVIDIA H100 GPUs
and is designed for AI and ML tasks.
Further details in [`Capella` Chapter](capella.md).

## Romeo

The cluster `Romeo` is a general purpose cluster by NEC based on AMD Rome CPUs.
Further details in [`Romeo` Chapter](romeo.md).

## Julia

The cluster `Julia` is a large SMP (shared memory parallel) system by HPE based on Superdome Flex
architecture.
Further details in [`Julia` Chapter](julia.md).

## Power9

The cluster `Power9` by IBM is based on Power9 CPUs and provides NVIDIA V100 GPUs.
`Power9` is specifically designed for machine learning (ML) tasks.
Further details in [`Power9` Chapter](power9.md).