diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md
index 561d0ed622ae6b514866404a38ee5bc7d2f0c4ba..5a0e15613665ececfaa2d0915fd7022c742a9288 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/alpha_centauri.md
@@ -1,22 +1,17 @@
# Alpha Centauri
-The multi-GPU sub-cluster "Alpha Centauri" had been installed for AI-related computations (ScaDS.AI).
-It has 34 nodes, each with:
+The multi-GPU sub-cluster "Alpha Centauri" has been installed for AI-related computations (ScaDS.AI).
-* 8 x NVIDIA A100-SXM4 (40 GB RAM)
-* 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz with multi-threading enabled
-* 1 TB RAM
-* 3.5 TB `/tmp` local NVMe device
-* Hostnames: `taurusi[8001-8034]`
-* Slurm partition `alpha` for batch jobs and `alpha-interactive` for interactive jobs
+The hardware specification is documented on the page
+[HPC Resources](hardware_overview.md#amd-rome-cpus-nvidia-a100).
+
+## Usage
!!! note
The NVIDIA A100 GPUs may only be used with **CUDA 11** or later. Earlier versions do not
recognize the new hardware properly. Make sure the software you are using is built with CUDA11.
-## Usage
-
### Modules
The easiest way is using the [module system](../software/modules.md).
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/hardware_overview.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/hardware_overview.md
index 7405eec766fa216e5eccdab2b4a1856ca5f98b2b..1d06a620e89ae43b796286e6add849644beae530 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/hardware_overview.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/hardware_overview.md
@@ -1,6 +1,6 @@
# HPC Resources
-HPC resources in ZIH systems comprises the *High Performance Computing and Storage Complex* and its
+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 60,000 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
@@ -8,80 +8,76 @@ analytics, and artificial intelligence methods with extensive capabilities for e
and performance monitoring provides ideal conditions to achieve the ambitious research goals of the
users and the ZIH.
-## Login Nodes
+## Login and Export Nodes
-- Login-Nodes (`tauruslogin[3-6].hrsk.tu-dresden.de`)
- - each with 2x Intel(R) Xeon(R) CPU E5-2680 v3 each with 12 cores
- 2.50GHz, Multithreading Disabled, 64 GB RAM, 128 GB SSD local disk
+- 4 Login-Nodes `tauruslogin[3-6].hrsk.tu-dresden.de`
+ - Each login node is equipped with 2x Intel(R) Xeon(R) CPU E5-2680 v3 with 24 cores in total @
+ 2.50 GHz, Multithreading disabled, 64 GB RAM, 128 GB SSD local disk
- IPs: 141.30.73.\[102-105\]
-- Transfer-Nodes (`taurusexport[3-4].hrsk.tu-dresden.de`, DNS Alias
- `taurusexport.hrsk.tu-dresden.de`)
- - 2 Servers without interactive login, only available via file transfer protocols (`rsync`, `ftp`)
- - IPs: 141.30.73.82/83
-- Direct access to these nodes is granted via IP whitelisting (contact
- hpcsupport@zih.tu-dresden.de) - otherwise use TU Dresden VPN.
-
-!!! warning "Run time limit"
-
- Any process on login nodes is stopped after 5 minutes.
+- 2 Data-Transfer-Nodes `taurusexport[3-4].hrsk.tu-dresden.de`
+ - DNS Alias `taurusexport.hrsk.tu-dresden.de`
+ - 2 Servers without interactive login, only available via file transfer protocols
+ (`rsync`, `ftp`)
+ - IPs: 141.30.73.\[82,83\]
+ - Further information on the usage is documented on the site
+ [Export Nodes](../data_transfer/export_nodes.md)
## AMD Rome CPUs + NVIDIA A100
- 34 nodes, each with
- - 8 x NVIDIA A100-SXM4
+ - 8 x NVIDIA A100-SXM4 Tensor Core-GPUs
- 2 x AMD EPYC CPU 7352 (24 cores) @ 2.3 GHz, Multithreading disabled
- 1 TB RAM
- - 3.5 TB local memory at NVMe device at `/tmp`
+ - 3.5 TB local memory on NVMe device at `/tmp`
- Hostnames: `taurusi[8001-8034]`
-- Slurm partition `alpha`
-- Dedicated mostly for ScaDS-AI
+- Slurm partition: `alpha`
+- Further information on the usage is documented on the site [AMD Rome Nodes](rome_nodes.md)
## Island 7 - AMD Rome CPUs
- 192 nodes, each with
- - 2x AMD EPYC CPU 7702 (64 cores) @ 2.0GHz, Multithreading
- enabled,
+ - 2 x AMD EPYC CPU 7702 (64 cores) @ 2.0 GHz, Multithreading enabled,
- 512 GB RAM
- - 200 GB /tmp on local SSD local disk
+ - 200 GB local memory on SSD at `/tmp`
- Hostnames: `taurusi[7001-7192]`
-- Slurm partition `romeo`
-- More information under [Rome Nodes](rome_nodes.md)
+- Slurm partition: `romeo`
+- 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 @ 2.20GHz (28 cores)
+ - 32 x Intel(R) Xeon(R) Platinum 8276M CPU @ 2.20 GHz (28 cores)
- 47 TB RAM
-- Currently configured as one single node
+- 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`
-- Slurm partition `julia`
-- More information under [HPE SD Flex](sd_flex.md)
+- Slurm partition: `julia`
+- 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 32 IBM AC922 nodes installed with this configuration:
+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 2666MHz
- - 6x NVIDIA VOLTA V100 with 32GB HBM2
+ - 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]`
-- Slurm partition `ml`
+- Slurm partition: `ml`
## Island 6 - Intel Haswell CPUs
- 612 nodes, each with
- - 2x Intel(R) Xeon(R) CPU E5-2680 v3 (12 cores)
- @ 2.50GHz, Multithreading disabled, 128 GB SSD local disk
-- Varying amounts of main memory (selected automatically by the batch
- system for you according to your job requirements)
- - 594 nodes with 2.67 GB RAM per core (64 GB total):
- `taurusi[6001-6540,6559-6612]`
- - 18 nodes with 10.67 GB RAM per core (256 GB total):
- `taurusi[6541-6558]`
+ - 2 x Intel(R) Xeon(R) CPU E5-2680 v3 (12 cores) @ 2.50 GHz, Multithreading disabled
+ - 128 GB local memory on SSD
+- Varying amounts of main memory (selected automatically by the batch system for you according to
+ your job requirements)
+ * 594 nodes with 2.67 GB RAM per core (64 GB in total): `taurusi[6001-6540,6559-6612]`
+ - 18 nodes with 10.67 GB RAM per core (256 GB in total): `taurusi[6541-6558]`
- Hostnames: `taurusi[6001-6612]`
-- Slurm Partition `haswell`
+- Slurm Partition: `haswell`
??? hint "Node topology"
@@ -91,23 +87,21 @@ For machine learning, we have 32 IBM AC922 nodes installed with this configurati
## Island 2 Phase 2 - Intel Haswell CPUs + NVIDIA K80 GPUs
- 64 nodes, each with
- - 2x Intel(R) Xeon(R) CPU E5-E5-2680 v3 (12 cores)
- @ 2.50GHz, Multithreading Disabled
+ - 2 x Intel(R) Xeon(R) CPU E5-E5-2680 v3 (12 cores) @ 2.50 GHz, Multithreading disabled
- 64 GB RAM (2.67 GB per core)
- - 128 GB SSD local disk
- - 4x NVIDIA Tesla K80 (12 GB GDDR RAM) GPUs
+ - 128 GB local memory on SSD
+ - 4 x NVIDIA Tesla K80 (12 GB GDDR RAM) GPUs
- Hostnames: `taurusi[2045-2108]`
-- Slurm Partition `gpu2`
+- Slurm Partition: `gpu2`
- Node topology, same as [island 4 - 6](#island-6-intel-haswell-cpus)
## SMP Nodes - up to 2 TB RAM
- 5 Nodes, each with
- - 4x Intel(R) Xeon(R) CPU E7-4850 v3 (14 cores) @
- 2.20GHz, Multithreading Disabled
+ - 4 x Intel(R) Xeon(R) CPU E7-4850 v3 (14 cores) @ 2.20 GHz, Multithreading disabled
- 2 TB RAM
- Hostnames: `taurussmp[3-7]`
-- Slurm partition `smp2`
+- Slurm partition: `smp2`
??? hint "Node topology"
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/rome_nodes.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/rome_nodes.md
index 530e2714668c0695e5fd57df8052c609bdc9a28f..905110c775721ded6ce280ef069b0b05e7ce146f 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/rome_nodes.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/rome_nodes.md
@@ -1,13 +1,7 @@
# Island 7 - AMD Rome Nodes
-## Hardware
-
-- Slurm partition: `romeo`
-- Module architecture: `rome`
-- 192 nodes `taurusi[7001-7192]`, each:
- - 2x AMD EPYC CPU 7702 (64 cores) @ 2.0GHz, Simultaneous Multithreading (SMT)
- - 512 GB RAM
- - 200 GB SSD disk mounted on `/tmp`
+The hardware specification is documented on the page
+[HPC Resources](hardware_overview.md#island-7-amd-rome-cpus).
## Usage
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/sd_flex.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/sd_flex.md
index 34505f93de1673aea883574459157b41c9f56357..27a39b06a6444ebd13a5a4e86c74cf1b17317e8d 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/sd_flex.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/sd_flex.md
@@ -4,16 +4,10 @@ The HPE Superdome Flex is a large shared memory node. It is especially well suit
intensive application scenarios, for example to process extremely large data sets completely in main
memory or in very fast NVMe memory.
-## Configuration Details
+The hardware specification is documented on the page
+[HPC Resources](hardware_overview.md#large-smp-system-hpe-superdome-flex).
-- Hostname: `taurussmp8`
-- Access to all shared filesystems
-- Slurm partition `julia`
-- 32 x Intel(R) Xeon(R) Platinum 8276M CPU @ 2.20GHz (28 cores)
-- 48 TB RAM (usable: 47 TB - one TB is used for cache coherence protocols)
-- 370 TB of fast NVME storage available at `/nvme/<projectname>`
-
-## Local Temporary NVMe Storage
+## Local Temporary on NVMe Storage
There are 370 TB of NVMe devices installed. For immediate access for all projects, a volume of 87 TB
of fast NVMe storage is available at `/nvme/1/<projectname>`. A quota of
@@ -28,7 +22,13 @@ project's quota can be increased or dedicated volumes of up to the full capacity
- Granularity should be a socket (28 cores)
- Can be used for OpenMP applications with large memory demands
- To use OpenMPI it is necessary to export the following environment
- variables, so that OpenMPI uses shared memory instead of Infiniband
- for message transport. `export OMPI_MCA_pml=ob1;   export OMPI_MCA_mtl=^mxm`
+ variables, so that OpenMPI uses shared-memory instead of Infiniband
+ for message transport:
+
+ ```
+ export OMPI_MCA_pml=ob1
+ export OMPI_MCA_mtl=^mxm
+ ```
+
- Use `I_MPI_FABRICS=shm` so that Intel MPI doesn't even consider
using Infiniband devices itself, but only shared-memory instead