Removed some whitespace at end of lines. Corrected forbidden words where

necessary.

doc.zih.tu-dresden.de/docs/software/cfd.md

@@ -42,7 +42,7 @@ marie@login$ # source $FOAM_CSH
     module load OpenFOAM
     source $FOAM_BASH
     cd /scratch/ws/1/marie-example-workspace  # work directory using workspace
-    srun pimpleFoam -parallel > "$OUTFILE" 
+    srun pimpleFoam -parallel > "$OUTFILE"
     ```
 
 ## Ansys CFX
@@ -62,7 +62,7 @@ geometry and mesh generator cfx5pre, and the post-processor cfx5post.
 
     module load ANSYS
     cd /scratch/ws/1/marie-example-workspace                   # work directory using workspace
-    cfx-parallel.sh -double -def StaticMixer.def 
+    cfx-parallel.sh -double -def StaticMixer.def
     ```
 
 ## Ansys Fluent

doc.zih.tu-dresden.de/docs/software/data_analytics_with_python.md

@@ -212,11 +212,11 @@ for the partition `alpha` (queue at the dask terms) on the ZIH system:
 ```python
 from dask_jobqueue import SLURMCluster
 
-cluster = SLURMCluster(queue='alpha', 
+cluster = SLURMCluster(queue='alpha',
   cores=8,
-  processes=2, 
-  project='p_marie', 
-  memory="8GB", 
+  processes=2,
+  project='p_marie',
+  memory="8GB",
   walltime="00:30:00")
 
 ```
@@ -235,15 +235,15 @@ from distributed import Client
 from dask_jobqueue import SLURMCluster
 from dask import delayed
 
-cluster = SLURMCluster(queue='alpha', 
+cluster = SLURMCluster(queue='alpha',
   cores=8,
-  processes=2, 
-  project='p_marie', 
-  memory="80GB", 
+  processes=2,
+  project='p_marie',
+  memory="80GB",
   walltime="00:30:00",
   extra=['--resources gpu=1'])
 
-cluster.scale(2)             #scale it to 2 workers! 
+cluster.scale(2)             #scale it to 2 workers!
 client = Client(cluster)     #command will show you number of workers (python objects corresponds to jobs)
 ```
 
@@ -288,7 +288,7 @@ for the Monte-Carlo estimation of Pi.
     uid = int( sp.check_output('id -u', shell=True).decode('utf-8').replace('\n','') )
     portdash = 10001 + uid
 
-    #create a Slurm cluster, please specify your project 
+    #create a Slurm cluster, please specify your project
 
     cluster = SLURMCluster(queue='alpha', cores=2, project='p_marie', memory="8GB", walltime="00:30:00", extra=['--resources gpu=1'], scheduler_options={"dashboard_address": f":{portdash}"})
 
@@ -309,12 +309,12 @@ for the Monte-Carlo estimation of Pi.
 
     def calc_pi_mc(size_in_bytes, chunksize_in_bytes=200e6):
       """Calculate PI using a Monte Carlo estimate."""
-    
+
       size = int(size_in_bytes / 8)
       chunksize = int(chunksize_in_bytes / 8)
-    
+
       xy = da.random.uniform(0, 1, size=(size / 2, 2), chunks=(chunksize / 2, 2))
-    
+
       in_circle = ((xy ** 2).sum(axis=-1) < 1)
       pi = 4 * in_circle.mean()
 
@@ -327,11 +327,11 @@ for the Monte-Carlo estimation of Pi.
             f"\tErr: {abs(pi - np.pi) : 10.3e}\n"
             f"\tWorkers: {num_workers}"
             f"\t\tTime: {time_delta : 7.3f}s")
-          
+
     #let's loop over different volumes of double-precision random numbers and estimate it
 
     for size in (1e9 * n for n in (1, 10, 100)):
-    
+
       start = time()
       pi = calc_pi_mc(size).compute()
       elaps = time() - start
@@ -339,7 +339,7 @@ for the Monte-Carlo estimation of Pi.
       print_pi_stats(size, pi, time_delta=elaps, num_workers=len(cluster.scheduler.workers))
 
     #Scaling the Cluster to twice its size and re-run the experiments
-                   
+
     new_num_workers = 2 * len(cluster.scheduler.workers)
 
     print(f"Scaling from {len(cluster.scheduler.workers)} to {new_num_workers} workers.")
@@ -349,11 +349,11 @@ for the Monte-Carlo estimation of Pi.
     sleep(120)
 
     client
-                   
+
     #Re-run same experiments with doubled cluster
 
-    for size in (1e9 * n for n in (1, 10, 100)):    
-        
+    for size in (1e9 * n for n in (1, 10, 100)):
+
       start = time()
       pi = calc_pi_mc(size).compute()
       elaps = time() - start

doc.zih.tu-dresden.de/docs/software/distributed_training.md

@@ -183,7 +183,7 @@ DDP uses collective communications in the
 [torch.distributed](https://pytorch.org/tutorials/intermediate/dist_tuto.html) package to
 synchronize gradients and buffers.
 
-The tutorial can be found [here](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
+Please also look at the [official tutorial](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html).
 
 To use distributed data parallelism on ZIH systems, please make sure the `--ntasks-per-node`
 parameter is equal to the number of GPUs you use per node.
@@ -234,7 +234,7 @@ marie@compute$ module spider Horovod           # Check available modules
         Horovod/0.19.5-fosscuda-2019b-TensorFlow-2.2.0-Python-3.7.4
         Horovod/0.21.1-TensorFlow-2.4.1
 [...]
-marie@compute$ module load Horovod/0.19.5-fosscuda-2019b-TensorFlow-2.2.0-Python-3.7.4  
+marie@compute$ module load Horovod/0.19.5-fosscuda-2019b-TensorFlow-2.2.0-Python-3.7.4
 ```
 
 Or if you want to use Horovod on the partition `alpha`, you can load it with the dependencies:

doc.zih.tu-dresden.de/docs/software/ngc_containers.md

@@ -53,7 +53,7 @@ Create a container from the image from the NGC catalog.
 (For this example, the alpha is used):
 
 ```console
-marie@login$ srun --partition=alpha --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 bash 
+marie@login$ srun --partition=alpha --nodes=1 --ntasks-per-node=1 --ntasks=1 --gres=gpu:1 --time=08:00:00 --pty --mem=50000 bash
 
 marie@compute$ cd /scratch/ws/<name_of_your_workspace>/containers   #please create a Workspace
 

doc.zih.tu-dresden.de/docs/software/perf_tools.md

 # Introduction
 
 `perf` consists of two parts: the kernel space implementation and the userland tools. This wiki
-entry focusses on the latter. These tools are installed on taurus, and others and provides support
-for sampling applications and reading performance counters.
+entry focusses on the latter. These tools are installed on ZIH systems, and others and provides
+support for sampling applications and reading performance counters.
 
 ## Configuration
 
@@ -34,18 +34,18 @@ Run `perf stat <Your application>`. This will provide you with a general
 overview on some counters.
 
 ```Bash
-Performance counter stats for 'ls':= 
-          2,524235 task-clock                #    0,352 CPUs utilized           
-                15 context-switches          #    0,006 M/sec                   
-                 0 CPU-migrations            #    0,000 M/sec                   
-               292 page-faults               #    0,116 M/sec                   
-         6.431.241 cycles                    #    2,548 GHz                     
-         3.537.620 stalled-cycles-frontend   #   55,01% frontend cycles idle    
-         2.634.293 stalled-cycles-backend    #   40,96% backend  cycles idle    
-         6.157.440 instructions              #    0,96  insns per cycle         
-                                             #    0,57  stalled cycles per insn 
-         1.248.527 branches                  #  494,616 M/sec                   
-            34.044 branch-misses             #    2,73% of all branches         
+Performance counter stats for 'ls':=
+          2,524235 task-clock                #    0,352 CPUs utilized
+                15 context-switches          #    0,006 M/sec
+                 0 CPU-migrations            #    0,000 M/sec
+               292 page-faults               #    0,116 M/sec
+         6.431.241 cycles                    #    2,548 GHz
+         3.537.620 stalled-cycles-frontend   #   55,01% frontend cycles idle
+         2.634.293 stalled-cycles-backend    #   40,96% backend  cycles idle
+         6.157.440 instructions              #    0,96  insns per cycle
+                                             #    0,57  stalled cycles per insn
+         1.248.527 branches                  #  494,616 M/sec
+            34.044 branch-misses             #    2,73% of all branches
        0,007167707 seconds time elapsed
 ```
 
@@ -142,10 +142,10 @@ If you added a callchain, it also gives you a callchain profile.\<br /> \*Discla
 not an appropriate way to gain exact numbers. So this is merely a rough overview and not guaranteed
 to be absolutely correct.\*\<span style="font-size: 1em;"> \</span>
 
-### On Taurus
+### On ZIH systems
 
-On Taurus, users are not allowed to see the kernel functions. If you have multiple events defined,
-then the first thing you select in `perf report` is the type of event. Press right
+On ZIH systems, users are not allowed to see the kernel functions. If you have multiple events
+defined, then the first thing you select in `perf report` is the type of event. Press right
 
 ```Bash
 Available samples
@@ -165,7 +165,7 @@ If you'd select cycles, you would get such a screen:
 ```Bash
 Events: 96  cycles
 +  49,13%  test_gcc_perf  test_gcc_perf      [.] main.omp_fn.0
-+  34,48%  test_gcc_perf  test_gcc_perf      [.] 
++  34,48%  test_gcc_perf  test_gcc_perf      [.]
 +   6,92%  test_gcc_perf  test_gcc_perf      [.] omp_get_thread_num@plt
 +   5,20%  test_gcc_perf  libgomp.so.1.0.0   [.] omp_get_thread_num
 +   2,25%  test_gcc_perf  test_gcc_perf      [.] main.omp_fn.1