neue files für 2023

doc.zih.tu-dresden.de/docs/jobs_and_resources/hardware_overview_2023.md

+# HPC Resources
+
+The architecture specifically tailored to data-intensive computing, Big Data
+analytics, and artificial intelligence methods with extensive capabilities
+for performance monitoring provides ideal conditions to achieve the ambitious
+research goals of the users and the ZIH.
+
+## Overview
+
+From the users' pespective, there are seperate clusters, all of them with their subdomains:
+
+| Name | Description | Year| DNS |
+| --- | --- | --- | --- |
+| **Barnard** | CPU cluster |2023| n[1001-1630].barnard.hpc.tu-dresden.de |
+| **Romeo** | CPU cluster |2020|i[8001-8190].romeo.hpc.tu-dresden.de |
+| **Alpha Centauri** | GPU cluster |2021|i[8001-8037].alpha.hpc.tu-dresden.de |
+| **Julia** | single SMP system |2021|smp8.julia.hpc.tu-dresden.de |
+| **Power** | IBM Power/GPU system |2018|ml[1-29].power9.hpc.tu-dresden.de |
+
+They run with their own Slurm batch system. Job submission is possible only from
+their respective login nodes.
+
+All clusters have access to these shared parallel file systems:
+
+| File system | Usable directory | Type | Capacity | Purpose |
+| --- | --- | --- | --- | --- |
+| Home | `/home` | Lustre | quota per user: 20 GB | permanant user data |
+| Project | `/projects` | Lustre | quota per project | permanant project data |
+| Scratch for large data / streaming | `/data/horse` | Lustre | 20 PB | h
+| Scratch for random access | `/data/rabbit` | Lustre | 2 PB |
+
+These mount points are planned (September 2023):
+
+| Scratch for random access | `/data/weasel` | WEKA | 232 TB |
+| Scratch for random access | `/data/squirrel` | BeeGFS | xxx TB |
+
+## Barnard - Intel Sapphire Rapids CPUs
+
+- 630 diskless nodes, each with
+    - 2 x Intel(R) Xeon(R) CPU E5-2680 v3 (52 cores) @ 2.50 GHz, Multithreading enabled
+    - 512 GB RAM
+- Hostnames: `n1[001-630].barnard.hpc.tu-dresden.de`
+- Login nodes: `login[1-4].barnard.hpc.tu-dresden.de`
+
+## AMD Rome CPUs + NVIDIA A100
+
+- 34 nodes, each with
+    - 8 x NVIDIA A100-SXM4 Tensor Core-GPUs
+    - 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz, Multithreading available
+    - 1 TB RAM
+    - 3.5 TB local memory on NVMe device at `/tmp`
+- Hostnames: `taurusi[8001-8034]`  -> `i[8001-8037].alpha.hpc.tu-dresden.de`
+- Login nodes: `login[1-2].alpha.hpc.tu-dresden.de`
+- Further information on the usage is documented on the site [Alpha Centauri Nodes](alpha_centauri.md)
+
+## Island 7 - AMD Rome CPUs
+
+- 192 nodes, each with
+    - 2 x AMD EPYC CPU 7702 (64 cores) @ 2.0 GHz, Multithreading available
+    - 512 GB RAM
+    - 200 GB local memory on SSD at `/tmp`
+- Hostnames: `taurusi[7001-7192]` -> `i[7001-7190].romeo.hpc.tu-dresden.de`
+- Login nodes: `login[1-2].romeo.hpc.tu-dresden.de`
+- Further information on the usage is documented on the site [AMD Rome Nodes](rome_nodes.md)
+
+## Large SMP System HPE Superdome Flex
+
+- 1 node, with
+    - 32 x Intel(R) Xeon(R) Platinum 8276M CPU @ 2.20 GHz (28 cores)
+    - 47 TB RAM
+- Configured as one single node
+- 48 TB RAM (usable: 47 TB - one TB is used for cache coherence protocols)
+- 370 TB of fast NVME storage available at `/nvme/<projectname>`
+- Hostname: `taurussmp8` -> `smp8.julia.hpc.tu-dresden.de`
+- Further information on the usage is documented on the site [HPE Superdome Flex](sd_flex.md)
+
+## IBM Power9 Nodes for Machine Learning
+
+For machine learning, we have IBM AC922 nodes installed with this configuration:
+
+- 32 nodes, each with
+    - 2 x IBM Power9 CPU (2.80 GHz, 3.10 GHz boost, 22 cores)
+    - 256 GB RAM DDR4 2666 MHz
+    - 6 x NVIDIA VOLTA V100 with 32 GB HBM2
+    - NVLINK bandwidth 150 GB/s between GPUs and host
+- Hostnames: `taurusml[1-32]` -> `ml[1-29].power9.hpc.tu-dresden.de`
+- Login nodes: `login[1-2].power9.hpc.tu-dresden.de``

doc.zih.tu-dresden.de/mkdocs.yml

@@ -100,6 +100,7 @@ nav:
     - Overview: jobs_and_resources/overview.md
     - HPC Resources:
       - Overview: jobs_and_resources/hardware_overview.md
+      - Overview 2023: jobs_and_resources/hardware_overview_2023.md
       - Architecture 2023: jobs_and_resources/architecture_2023.md
       - AMD Rome Nodes: jobs_and_resources/rome_nodes.md
       - NVMe Storage: jobs_and_resources/nvme_storage.md