diff --git a/doc.zih.tu-dresden.de/docs/archive/beegfs_on_demand.md b/doc.zih.tu-dresden.de/docs/archive/beegfs_on_demand.md
index 29274c54a77ce954a478325f7d43110557a325e5..610670c6961b0d73a68e92b6487ad47a6804776f 100644
--- a/doc.zih.tu-dresden.de/docs/archive/beegfs_on_demand.md
+++ b/doc.zih.tu-dresden.de/docs/archive/beegfs_on_demand.md
@@ -61,8 +61,8 @@ Check the status of the job with `squeue -u \<username>`.
## Mount BeeGFS Filesystem
-You can mount BeeGFS filesystem on the ML partition (PowerPC architecture) or on the Haswell
-[partition](../jobs_and_resources/partitions_and_limits.md) (x86_64 architecture)
+You can mount BeeGFS filesystem on the partition ml (PowerPC architecture) or on the
+partition haswell (x86_64 architecture), more information [here](../jobs_and_resources/partitions_and_limits.md).
### Mount BeeGFS Filesystem on the Partition `ml`
diff --git a/doc.zih.tu-dresden.de/docs/archive/install_jupyter.md b/doc.zih.tu-dresden.de/docs/archive/install_jupyter.md
index c6924fc4f716a0f7bdab1ab5b66bdcfe71019151..0d50ecc6c8ec26c30fccaf7882abee6f2070d55b 100644
--- a/doc.zih.tu-dresden.de/docs/archive/install_jupyter.md
+++ b/doc.zih.tu-dresden.de/docs/archive/install_jupyter.md
@@ -131,7 +131,7 @@ c.NotebookApp.allow_remote_access = True
```console
#!/bin/bash -l
#SBATCH --gres=gpu:1 # request GPU
-#SBATCH --partition=gpu2 # use GPU partition
+#SBATCH --partition=gpu2 # use partition GPU 2
#SBATCH --output=notebook_output.txt
#SBATCH --nodes=1
#SBATCH --ntasks=1
diff --git a/doc.zih.tu-dresden.de/docs/archive/unicore_rest_api.md b/doc.zih.tu-dresden.de/docs/archive/unicore_rest_api.md
index 721aca76357bc1b004178d06c30ae0e8f0362b64..839028f327e069e912f59ffb688ccd1f54b58a40 100644
--- a/doc.zih.tu-dresden.de/docs/archive/unicore_rest_api.md
+++ b/doc.zih.tu-dresden.de/docs/archive/unicore_rest_api.md
@@ -11,5 +11,5 @@ Most of the UNICORE features are also available using its REST API.
* [https://sourceforge.net/p/unicore/wiki/REST_API/](https://sourceforge.net/p/unicore/wiki/REST_API/)
* Some useful examples of job submission via REST are available at:
* [https://sourceforge.net/p/unicore/wiki/REST_API_Examples/](https://sourceforge.net/p/unicore/wiki/REST_API_Examples/)
-* The base address for the Taurus system at the ZIH is:
- * *unicore.zih.tu-dresden.de:8080/TAURUS/rest/core*
+* The base address for the system at the ZIH is:
+ * `unicore.zih.tu-dresden.de:8080/TAURUS/rest/core`
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 67c3168d23b7edb4a0128ead8ac3fcf0bf96520a..ca813dbe4b627f2ac74b33163f285c6caa93348b 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
@@ -19,7 +19,7 @@ It has 34 nodes, each with:
### Modules
The easiest way is using the [module system](../software/modules.md).
-The software for the `alpha` partition is available in `modenv/hiera` module environment.
+The software for the partition alpha is available in `modenv/hiera` module environment.
To check the available modules for `modenv/hiera`, use the command
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/checkpoint_restart.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/checkpoint_restart.md
index 619e9bfa84413b6f37e99ae8838abd354f059ba5..38d6686d7a655c1c5d7161d6607be9d6f55d8b5c 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/checkpoint_restart.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/checkpoint_restart.md
@@ -56,8 +56,8 @@ checkpoint/restart bits transparently to your batch script. You just have to spe
total runtime of your calculation and the interval in which you wish to do checkpoints. The latter
(plus the time it takes to write the checkpoint) will then be the runtime of the individual jobs.
This should be targeted at below 24 hours in order to be able to run on all
-[haswell64 partitions](../jobs_and_resources/partitions_and_limits.md#runtime-limits). For increased
-fault-tolerance, it can be chosen even shorter.
+[partitions haswell64](../jobs_and_resources/partitions_and_limits.md#runtime-limits). For
+increased fault-tolerance, it can be chosen even shorter.
To use it, first add a `dmtcp_launch` before your application call in your batch script. In the case
of MPI applications, you have to add the parameters `--ib --rm` and put it between `srun` and your
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 8a6e3f5904582a8316fdcde363144a174b59e4d3..b65ba24eb3f78d51d4cde97dc6e6214b5ffec2c6 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
@@ -1,11 +1,11 @@
# ZIH Systems
-ZIH systems comprises the *High Performance Computing and Storage Complex* (HRSK-II) and its
-extension *High Performance Computing – Data Analytics* (HPC-DA). In totoal 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 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
+ZIH systems comprises 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
+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.
## Login Nodes
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/overview.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/overview.md
index caa501d3034e868a43743d1a102e3f478638af81..edb48c108690f54884d8c7837d1b4de2e56a62c8 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/overview.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/overview.md
@@ -62,7 +62,7 @@ Normal compute nodes are perfect for this task.
**OpenMP jobs:** SMP-parallel applications can only run **within a node**, so it is necessary to
include the [batch system](slurm.md) options `-N 1` and `-n 1`. Using `--cpus-per-task N` Slurm will
start one task and you will have `N` CPUs. The maximum number of processors for an SMP-parallel
-program is 896 on [partition `julia`](partitions_and_limits.md).
+program is 896 on partition `julia`, see [here](partitions_and_limits.md).
**GPUs** partitions are best suited for **repetitive** and **highly-parallel** computing tasks. If
you have a task with potential [data parallelism](../software/gpu_programming.md) most likely that
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 6816f97581303b41bd9247969f4e4c398932bfb1..c09260cf8d814a6a6835f981a25d1e8700c71df2 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
@@ -1,7 +1,7 @@
# Large Shared-Memory Node - HPE Superdome Flex
- Hostname: `taurussmp8`
-- Access to all shared file systems
+- 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)
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_examples.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_examples.md
index 5f4c9b50c6b7d64474609d3f1753571bb9ec0999..396657db06766eaab6f8694ca4bed4f8014cf7f4 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_examples.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_examples.md
@@ -238,13 +238,13 @@ resources.
Setting `--exclusive` **only** makes sure that there will be **no other jobs running on your nodes**.
It does not, however, mean that you automatically get access to all the resources which the node
might provide without explicitly requesting them, e.g. you still have to request a GPU via the
-generic resources parameter (`gres`) to run on the GPU partitions, or you still have to request all
-cores of a node if you need them. CPU cores can either to be used for a task (`--ntasks`) or for
-multi-threading within the same task (`--cpus-per-task`). Since those two options are semantically
-different (e.g., the former will influence how many MPI processes will be spawned by `srun` whereas
-the latter does not), Slurm cannot determine automatically which of the two you might want to use.
-Since we use cgroups for separation of jobs, your job is not allowed to use more resources than
-requested.*
+generic resources parameter (`gres`) to run on the partitions with GPU, or you still have to
+request all cores of a node if you need them. CPU cores can either to be used for a task
+(`--ntasks`) or for multi-threading within the same task (`--cpus-per-task`). Since those two
+options are semantically different (e.g., the former will influence how many MPI processes will be
+spawned by `srun` whereas the latter does not), Slurm cannot determine automatically which of the
+two you might want to use. Since we use cgroups for separation of jobs, your job is not allowed to
+use more resources than requested.*
If you just want to use all available cores in a node, you have to specify how Slurm should organize
them, like with `-p haswell -c 24` or `-p haswell --ntasks-per-node=24`.
diff --git a/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_profiling.md b/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_profiling.md
index 0c3dc773898143ca25086a97370b079a6c74cebe..273a87710602b62feb97c342335b4c44f30ad09e 100644
--- a/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_profiling.md
+++ b/doc.zih.tu-dresden.de/docs/jobs_and_resources/slurm_profiling.md
@@ -1,7 +1,7 @@
# Job Profiling
Slurm offers the option to gather profiling data from every task/node of the job. Analyzing this
-data allows for a better understanding of your jobs in terms of elapsed time, runtime and IO
+data allows for a better understanding of your jobs in terms of elapsed time, runtime and I/O
behavior, and many more.
The following data can be gathered:
diff --git a/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md b/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md
index 7eb432bd9f963ed2ecc0133db2ad5f04c9b67b8c..2cb43c19cb9ad53ed20884451bfbd2a2370e1fe1 100644
--- a/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md
+++ b/doc.zih.tu-dresden.de/docs/software/big_data_frameworks_spark.md
@@ -7,7 +7,7 @@
[Apache Spark](https://spark.apache.org/), [Apache Flink](https://flink.apache.org/)
and [Apache Hadoop](https://hadoop.apache.org/) are frameworks for processing and integrating
Big Data. These frameworks are also offered as software [modules](modules.md) on both `ml` and
-`scs5` partition. You can check module versions and availability with the command
+`scs5` software environments. You can check module versions and availability with the command
```console
marie@login$ module avail Spark
@@ -46,20 +46,20 @@ as via [Jupyter notebook](#jupyter-notebook). All three ways are outlined in the
### Default Configuration
-The Spark module is available for both `scs5` and `ml` partitions.
+The Spark module is available in both `scs5` and `ml` environments.
Thus, Spark can be executed using different CPU architectures, e.g., Haswell and Power9.
Let us assume that two nodes should be used for the computation. Use a
`srun` command similar to the following to start an interactive session
-using the Haswell partition. The following code snippet shows a job submission
-to Haswell nodes with an allocation of two nodes with 60 GB main memory
+using the partition haswell. The following code snippet shows a job submission
+to haswell nodes with an allocation of two nodes with 60 GB main memory
exclusively for one hour:
```console
marie@login$ srun --partition=haswell -N 2 --mem=60g --exclusive --time=01:00:00 --pty bash -l
```
-The command for different resource allocation on the `ml` partition is
+The command for different resource allocation on the partition `ml` is
similar, e. g. for a job submission to `ml` nodes with an allocation of one
node, one task per node, two CPUs per task, one GPU per node, with 10000 MB for one hour:
diff --git a/doc.zih.tu-dresden.de/docs/software/distributed_training.md b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
index 1548afa1aef1dd3377490b4f6b757194f320bdea..1c84d44b08f6311719802b9e22ce3c6c3af6cfe9 100644
--- a/doc.zih.tu-dresden.de/docs/software/distributed_training.md
+++ b/doc.zih.tu-dresden.de/docs/software/distributed_training.md
@@ -65,9 +65,9 @@ parameters: `--ntasks-per-node` -parameter to the number of GPUs you use
per node. Also, it could be useful to increase `memomy/cpu` parameters
if you run larger models. Memory can be set up to:
-`--mem=250000` and `--cpus-per-task=7` for the `ml` partition.
+`--mem=250000` and `--cpus-per-task=7` for the partition `ml`.
-`--mem=60000` and `--cpus-per-task=6` for the `gpu2` partition.
+`--mem=60000` and `--cpus-per-task=6` for the partition `gpu2`.
Keep in mind that only one memory parameter (`--mem-per-cpu` = <MB> or `--mem`=<MB>) can be
specified
diff --git a/doc.zih.tu-dresden.de/docs/software/machine_learning.md b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
index ecbb9e146276aff67d6079579f2163fa6d7dbf74..7245ac128d81624b6dd9be39de8098cc6145010a 100644
--- a/doc.zih.tu-dresden.de/docs/software/machine_learning.md
+++ b/doc.zih.tu-dresden.de/docs/software/machine_learning.md
@@ -1,10 +1,9 @@
# Machine Learning
This is an introduction of how to run machine learning applications on ZIH systems.
-For machine learning purposes, we recommend to use the partitions [Alpha](#alpha-partition) and/or
-[ML](#ml-partition).
+For machine learning purposes, we recommend to use the partitions `alpha` and/or `ml`.
-## ML Partition
+## Partition `ml`
The compute nodes of the partition ML are built on the base of
[Power9 architecture](https://www.ibm.com/it-infrastructure/power/power9) from IBM. The system was created
@@ -36,7 +35,7 @@ The following have been reloaded with a version change: 1) modenv/scs5 => moden
There are tools provided by IBM, that work on partition ML and are related to AI tasks.
For more information see our [Power AI documentation](power_ai.md).
-## Alpha Partition
+## Partition `alpha`
Another partition for machine learning tasks is Alpha. It is mainly dedicated to
[ScaDS.AI](https://scads.ai/) topics. Each node on Alpha has 2x AMD EPYC CPUs, 8x NVIDIA A100-SXM4
@@ -45,7 +44,7 @@ partition in our [Alpha Centauri](../jobs_and_resources/alpha_centauri.md) docum
### Modules
-On the partition **Alpha** load the module environment:
+On the partition alpha load the module environment:
```console
marie@alpha$ module load modenv/hiera
diff --git a/doc.zih.tu-dresden.de/docs/software/power_ai.md b/doc.zih.tu-dresden.de/docs/software/power_ai.md
index 37de0d0a05ecf8113b86ca9a550285184cf202a7..b4beda5cec2b8b2e1ede4729df7434b6e8c8e7d5 100644
--- a/doc.zih.tu-dresden.de/docs/software/power_ai.md
+++ b/doc.zih.tu-dresden.de/docs/software/power_ai.md
@@ -5,7 +5,7 @@ the PowerAI Framework for Machine Learning. In the following the links
are valid for PowerAI version 1.5.4.
!!! warning
- The information provided here is available from IBM and can be used on `ml` partition only!
+ The information provided here is available from IBM and can be used on partition ml only!
## General Overview
@@ -47,7 +47,7 @@ are valid for PowerAI version 1.5.4.
(Open Neural Network Exchange) provides support for moving models
between those frameworks.
- [Distributed Deep Learning](https://www.ibm.com/support/knowledgecenter/SS5SF7_1.5.4/navigation/pai_getstarted_ddl.html?view=kc)
- Distributed Deep Learning (DDL). Works on up to 4 nodes on `ml` partition.
+ Distributed Deep Learning (DDL). Works on up to 4 nodes on partition `ml`.
## PowerAI Container
diff --git a/doc.zih.tu-dresden.de/docs/software/pytorch.md b/doc.zih.tu-dresden.de/docs/software/pytorch.md
index 63d3eb91e516d24559a85d80c70a30b73e9af73c..3c2e88a6c9fc209c246ede0e50410771be541c3f 100644
--- a/doc.zih.tu-dresden.de/docs/software/pytorch.md
+++ b/doc.zih.tu-dresden.de/docs/software/pytorch.md
@@ -15,14 +15,14 @@ marie@login$ module spider pytorch
to find out, which PyTorch modules are available on your partition.
-We recommend using **Alpha** and/or **ML** partitions when working with machine learning workflows
+We recommend using partitions alpha and/or ml when working with machine learning workflows
and the PyTorch library.
You can find detailed hardware specification in our
[hardware documentation](../jobs_and_resources/hardware_overview.md).
## PyTorch Console
-On the **Alpha** partition, load the module environment:
+On the partition `alpha`, load the module environment:
```console
marie@login$ srun -p alpha --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=800 bash #Job submission on alpha nodes with 1 gpu on 1 node with 800 Mb per CPU
@@ -33,8 +33,8 @@ Die folgenden Module wurden in einer anderen Version erneut geladen:
Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies loaded.
```
-??? hint "Torchvision on alpha partition"
- On the **Alpha** partition, the module torchvision is not yet available within the module
+??? hint "Torchvision on partition `alpha`"
+ On the partition `alpha`, the module torchvision is not yet available within the module
system. (19.08.2021)
Torchvision can be made available by using a virtual environment:
@@ -47,7 +47,7 @@ Module GCC/10.2.0, CUDA/11.1.1, OpenMPI/4.0.5, PyTorch/1.9.0 and 54 dependencies
Using the **--no-deps** option for "pip install" is necessary here as otherwise the PyTorch
version might be replaced and you will run into trouble with the cuda drivers.
-On the **ML** partition:
+On the partition `ml`:
```console
marie@login$ srun -p ml --gres=gpu:1 -n 1 -c 7 --pty --mem-per-cpu=800 bash #Job submission in ml nodes with 1 gpu on 1 node with 800 Mb per CPU
diff --git a/doc.zih.tu-dresden.de/docs/software/virtual_machines.md b/doc.zih.tu-dresden.de/docs/software/virtual_machines.md
index 9fd64d01dddbfde3119b74fa0e8f9decfe5b49f0..c6c660d3c5ac052f3362ad950f6ad395e4420bdf 100644
--- a/doc.zih.tu-dresden.de/docs/software/virtual_machines.md
+++ b/doc.zih.tu-dresden.de/docs/software/virtual_machines.md
@@ -45,10 +45,10 @@ times till it succeeds.
bash-4.2$ cat /tmp/marie_2759627/activate
#!/bin/bash
-if ! grep -q -- "Key for the VM on the ml partition" "/home/rotscher/.ssh/authorized_keys" >& /dev/null; then
+if ! grep -q -- "Key for the VM on the partition ml" "/home/rotscher/.ssh/authorized_keys" >& /dev/null; then
cat "/tmp/marie_2759627/kvm.pub" >> "/home/marie/.ssh/authorized_keys"
else
- sed -i "s|.*Key for the VM on the ml partition.*|ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC3siZfQ6vQ6PtXPG0RPZwtJXYYFY73TwGYgM6mhKoWHvg+ZzclbBWVU0OoU42B3Ddofld7TFE8sqkHM6M+9jh8u+pYH4rPZte0irw5/27yM73M93q1FyQLQ8Rbi2hurYl5gihCEqomda7NQVQUjdUNVc6fDAvF72giaoOxNYfvqAkw8lFyStpqTHSpcOIL7pm6f76Jx+DJg98sXAXkuf9QK8MurezYVj1qFMho570tY+83ukA04qQSMEY5QeZ+MJDhF0gh8NXjX/6+YQrdh8TklPgOCmcIOI8lwnPTUUieK109ndLsUFB5H0vKL27dA2LZ3ZK+XRCENdUbpdoG2Czz Key for the VM on the ml partition|" "/home/marie/.ssh/authorized_keys"
+ sed -i "s|.*Key for the VM on the partition ml.*|ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQC3siZfQ6vQ6PtXPG0RPZwtJXYYFY73TwGYgM6mhKoWHvg+ZzclbBWVU0OoU42B3Ddofld7TFE8sqkHM6M+9jh8u+pYH4rPZte0irw5/27yM73M93q1FyQLQ8Rbi2hurYl5gihCEqomda7NQVQUjdUNVc6fDAvF72giaoOxNYfvqAkw8lFyStpqTHSpcOIL7pm6f76Jx+DJg98sXAXkuf9QK8MurezYVj1qFMho570tY+83ukA04qQSMEY5QeZ+MJDhF0gh8NXjX/6+YQrdh8TklPgOCmcIOI8lwnPTUUieK109ndLsUFB5H0vKL27dA2LZ3ZK+XRCENdUbpdoG2Czz Key for the VM on the partition ml|" "/home/marie/.ssh/authorized_keys"
fi
ssh -i /tmp/marie_2759627/kvm root@192.168.0.6
diff --git a/doc.zih.tu-dresden.de/util/grep-forbidden-words.sh b/doc.zih.tu-dresden.de/util/grep-forbidden-words.sh
index aed418887e876384de76080ed2100477037609ba..456eb55e192634bf4e159ce0096c83076989f2fc 100755
--- a/doc.zih.tu-dresden.de/util/grep-forbidden-words.sh
+++ b/doc.zih.tu-dresden.de/util/grep-forbidden-words.sh
@@ -15,7 +15,7 @@ basedir=`dirname "$basedir"`
ruleset="i \<io\> \.io
s \<SLURM\>
i file \+system HDFS
-i \<taurus\> taurus\.hrsk /taurus
+i \<taurus\> taurus\.hrsk /taurus /TAURUS
i \<hrskii\>
i hpc[ -]\+da\>
i \(alpha\|ml\|haswell\|romeo\|gpu\|smp\|julia\|hpdlf\|scs5\)-\?\(interactive\)\?[^a-z]*partition