diff --git a/doc.zih.tu-dresden.de/docs/DataAnalyticsWithR.md b/doc.zih.tu-dresden.de/docs/DataAnalyticsWithR.md
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+# R for data analytics
+
+
+
+[ **R** ](https://www.r-project.org/about.html)\<span style="font-size:
+1em;"> is a programming language and environment for statistical
+computing and graphics. R provides a wide variety of statistical (linear
+and nonlinear modelling, classical statistical tests, time-series
+analysis, classification, etc) and graphical techniques. R is an
+integrated suite of software facilities for data manipulation,
+calculation and graphing.\</span>
+
+R possesses an extensive catalogue of statistical and graphical methods.
+It includes machine learning algorithms, linear regression, time series,
+statistical inference.
+
+**Aim** of this page is to introduce users on how to start working with
+the R language on Taurus in general as well as on the HPC-DA system.\<br
+/>**Prerequisites:** To work with the R on Taurus you obviously need
+access for the Taurus system and basic knowledge about programming and
+[SLURM](Slurm) system.
+
+For general information on using the HPC-DA system, see the [Get started
+with HPC-DA system](GetStartedWithHPCDA) page.
+
+You can also find the information you need on the HPC-Introduction and
+HPC-DA-Introduction presentation slides.
+
+\<br />\<span style="font-size: 1em;">We recommend using
+\</span>**Haswell**\<span style="font-size: 1em;">and/or\</span> **
+[Romeo](RomeNodes)**\<span style="font-size: 1em;">partitions to work
+with R. Please use the ml partition only if you need GPUs! \</span>
+
+## R console
+
+This is a quickstart example. The `srun` command is used to submit a
+real-time execution job designed for interactive use with output
+monitoring. Please check [the page](Slurm) for details. The R language
+available for both types of Taurus nodes/architectures x86 (scs5
+software environment) and Power9 (ml software environment).
+
+Haswell partition:
+
+ srun --partition=haswell --ntasks=1 --nodes=1 --cpus-per-task=4 --mem-per-cpu=2583 --time=01:00:00 --pty bash #job submission in haswell nodes with allocating: 1 task per node, 1 node, 4 CPUs per task with 2583 mb per CPU(core) on 1 hour
+
+ module load modenv/scs5 #Ensure that you are using the scs5 partition. Example output: The following have been reloaded with a version change: 1) modenv/ml => modenv/scs5
+ module available R/3.6 #Check all availble modules with R version 3.6. You could use also "ml av R" but it gives huge output.
+ module load R #Load default R module Example output: Module R/3.6.0-foss 2019a and 56 dependencies loaded.
+ which R #Checking of current version of R
+ R #Start R console
+
+Here are the parameters of the job with all the details to show you the
+correct and optimal way to do it. Please allocate the job with respect
+to [hardware specification](HardwareTaurus)! Besides, it should be noted
+that the value of the \<span>--mem-per-cpu\</span> parameter is
+different for the different partitions. it is important to respect \<a
+href="SystemTaurus#Memory_Limits" target="\_blank">memory limits\</a>.
+Please note that the default limit is 300 MB per cpu.
+
+However, using srun directly on the shell will lead to blocking and
+launch an interactive job. Apart from short test runs, it is
+**recommended to launch your jobs into the background by using batch
+jobs**. For that, you can conveniently place the parameters directly
+into the job file which can be submitted using
+`sbatch [options] <job file><span>. </span>`\<span style="font-size:
+1em;">The examples could be found [here](GetStartedWithHPCDA) or
+[here](Slurm). Furthermore, you could work with simple examples in your
+home directory but according to \<a href="HPCStorageConcept2019"
+target="\_blank">storage concept\</a>** please use \<a href="WorkSpaces"
+target="\_blank">workspaces\</a> for your study and work
+projects!**\</span>
+
+It is also possible to run Rscript directly (after loading the module):
+
+ Rscript /path/to/script/your_script.R param1 param2 #run Rscript directly. For instance: Rscript /scratch/ws/mastermann-study_project/da_script.r
+
+## R with Jupyter notebook
+
+In addition to using interactive srun jobs and batch jobs, there is
+another way to work with the **R** on Taurus. JipyterHub is a quick and
+easy way to work with jupyter notebooks on Taurus. See\<span
+style="font-size: 1em;"> the \<a href="JupyterHub"
+target="\_blank">JupyterHub page\</a> for detailed instructions.\</span>
+
+The [production environment](JupyterHub#Standard_environments) of
+JupyterHub contains R as a module for all partitions. R could be run in
+the Notebook or Console for [JupyterLab](JupyterHub#JupyterLab).
+
+## RStudio
+
+\<a href="<https://rstudio.com/>" target="\_blank">RStudio\</a> is an
+integrated development environment (IDE) for R. It includes a console,
+syntax-highlighting editor that supports direct code execution, as well
+as tools for plotting, history, debugging and workspace management.
+RStudio is also available for both Taurus x86 (scs5) and Power9 (ml)
+nodes/architectures.
+
+The best option to run RStudio is to use JupyterHub. RStudio will work
+in a browser. It is currently available in the **test** environment on
+both x86 (**scs5**) and Power9 (**ml**) architectures/partitions. It can
+be started similarly as a new kernel from \<a
+href="JupyterHub#JupyterLab" target="\_blank">JupyterLab\</a> launcher.
+See the picture below.
+
+\<img alt="environments.png" height="70"
+src="%ATTACHURL%/environments.png" title="environments.png" width="300"
+/>
+
+\<img alt="Launcher.png" height="205" src="%ATTACHURL%/Launcher.png"
+title="Launcher.png" width="195" />
+
+Please keep in mind that it is not currently recommended to use the
+interactive x11 job with the desktop version of Rstudio, as described,
+for example, [here](Slurm#Interactive_X11_47GUI_Jobs) or in introduction
+HPC-DA slides. This method is unstable.
+
+## Install packages in R
+
+By default, user-installed packages are stored in the
+\<span>/$HOME/R\</span>/ folder inside a subfolder depending on the
+architecture (on Taurus: x86 or PowerPC). Install packages using the
+shell:
+
+ srun -p haswell -N 1 -n 1 -c 4 --mem-per-cpu=2583 --time=01:00:00 --pty bash #job submission to the haswell nodes with allocating: 1 task per node, 1 node, 4 CPUs per task with 2583 mb per CPU(core) in 1 hour
+ module purge
+ module load modenv/scs5 #Changing the environment. Example output: The following have been reloaded with a version change: 1) modenv/ml => modenv/scs5
+
+ module load R #Load R module Example output: Module R/3.6.0-foss-2019a and 56 dependencies loaded.
+ which R #Checking of current version of R
+ R #Start of R console
+ install.packages("package_name") #For instance: install.packages("ggplot2")
+
+Note that to allocate the job the slurm parameters are used with
+different (short) notations, but with the same values as in the previous
+example.
+
+## Deep Learning with R
+
+This chapter will briefly describe working with **ml partition** (Power9
+architecture). This means that it will focus on the work with the GPUs,
+and the main scenarios will be explained.
+
+\*Important: Please use the ml partition if you need GPUs\* \<span
+style="font-size: 1em;"> Otherwise using the x86 partitions (e.g
+Haswell) would most likely be more beneficial. \</span>
+
+### R Interface to Tensorflow
+
+The ["Tensorflow" R package](https://tensorflow.rstudio.com/) provides R
+users access to the Tensorflow toolset.
+[TensorFlow](https://www.tensorflow.org/) is an open-source software
+library for numerical computation using data flow graphs.
+
+ srun -p ml -N 1 -n 1 -c 7 --mem-per-cpu=5772 --gres=gpu:1 --time=04:00:00 --pty bash
+
+ module purge #clear modules
+ ml modenv/ml #load ml environment
+ ml TensorFlow
+ ml R
+
+ which python
+ mkdir python-virtual-environments #Create folder. Please use Workspaces!
+ cd python-virtual-environments #Go to folder
+ python3 -m venv --system-site-packages R-TensorFlow #create python virtual environment
+ source R-TensorFlow/bin/activate #activate environment
+ module list
+ which R
+
+Please allocate the job with respect to [hardware
+specification](HardwareTaurus)! Note that the ML nodes have 4way-SMT, so
+for every physical core allocated, you will always get 4\*1443mb
+=5772mb.
+
+To configure "reticulate" R library to point to the Python executable in
+your virtual environment, create a file \<span style="font-size:
+1em;">nam\</span>\<span style="font-size: 1em;">ed .Rprofile in your
+project directory (e.g. R-TensorFlow) with the following
+contents:\</span>
+
+ Sys.setenv(RETICULATE_PYTHON = "/sw/installed/Anaconda3/2019.03/bin/python") #assign the output of the 'which python' to the RETICULATE_PYTHON
+
+Let's start R, install some libraries and evaluate the result
+
+ R
+ install.packages("reticulate")
+ library(reticulate)
+ reticulate::py_config()
+ install.packages("tensorflow")
+ library(tensorflow)
+ tf$constant("Hellow Tensorflow") #In the output 'Tesla V100-SXM2-32GB' should be mentioned
+
+Please find the example of the code in the
+[attachment](%ATTACHURL%/TensorflowMNIST.R?t=1597837603). The example
+shows the use of the Tensorflow package with the R for the
+classification problem related to the MNIST dataset.\<br />\<span
+style="font-size: 1em;">As an alternative to the TensorFlow rTorch could
+be used. \</span>\<a
+href="<https://cran.r-project.org/web/packages/rTorch/index.html>"
+target="\_blank">rTorch\</a>\<span style="font-size: 1em;"> is an 'R'
+implementation and interface for the \<a href="<https://pytorch.org/>"
+target="\_blank">PyTorch\</a> Machine Learning framework\</span>\<span
+style="font-size: 1em;">.\</span>
+
+## Parallel computing with R
+
+Generally, the R code is serial. However, many computations in R can be
+made faster by the use of parallel computations. Taurus allows a vast
+number of options for parallel computations. Large amounts of data
+and/or use of complex models are indications of the use of parallelism.
+
+### General information about the R parallelism
+
+There are various techniques and packages in R that allow
+parallelization. This chapter concentrates on most general methods and
+examples. The Information here is Taurus-specific. The \<a
+href="<https://www.rdocumentation.org/packages/parallel/versions/3.6.2>"
+target="\_blank">parallel package\</a> will be used for the purpose of
+the chapter.
+
+%RED%Note:<span class="twiki-macro ENDCOLOR"></span> Please do not
+install or update R packages related to parallelism it could lead to
+conflict with other pre-installed packages.
+
+### \<span style="font-size: 1em;">Basic lapply-based parallelism \</span>
+
+**`lapply()`** function is a part of base R. lapply is useful for
+performing operations on list-objects. Roughly speaking, lapply is a
+vectorisation of the source code but it could be used for
+parallelization. To use more than one core with lapply-style
+parallelism, you have to use some type of networking so that each node
+can communicate with each other and shuffle the relevant data around.
+The simple example of using the "pure" lapply parallelism could be found
+as the [attachment](%ATTACHURL%/lapply.R).
+
+### Shared-memory parallelism
+
+The `parallel` library includes the `mclapply()` function which is a
+shared memory version of lapply. The "mc" stands for "multicore". This
+function distributes the `lapply` tasks across multiple CPU cores to be
+executed in parallel.
+
+This is a simple option for parallelisation. It doesn't require much
+effort to rewrite the serial code to use mclapply function. Check out an
+[example](%ATTACHURL%/multicore.R). The cons of using shared-memory
+parallelism approach that it is limited by the number of cores(cpus) on
+a single node.
+
+<span class="twiki-macro RED"></span> **Important:** <span
+class="twiki-macro ENDCOLOR"></span> Please allocate the job with
+respect to [hardware specification](HardwareTaurus). The current maximum
+number of processors (read as cores) for an SMP-parallel program on
+Taurus is 56 (smp2 partition), for the Haswell partition, it is a 24.
+The large SMP system (Julia) is coming soon with a total number of 896
+nodes.
+
+Submitting a multicore R job to SLURM is very similar to [Submitting an
+OpenMP Job](Slurm#Binding_and_Distribution_of_Tasks) since both are
+running multicore jobs on a **single** node. Below is an example:
+
+ #!/bin/bash
+ #SBATCH --nodes=1
+ #SBATCH --tasks-per-node=1
+ #SBATCH --cpus-per-task=16
+ #SBATCH --time=00:10:00
+ #SBATCH -o test_Rmpi.out
+ #SBATCH -e test_Rmpi.err
+
+ module purge
+ module load modenv/scs5
+ module load R
+
+ R CMD BATCH Rcode.R
+
+Examples of R scripts with the shared-memory parallelism can be found as
+an [attachment](%ATTACHURL%/multicore.R) on the bottom of the page.
+
+### Distributed memory parallelism
+
+To use this option we need to start by setting up a cluster, a
+collection of workers that will do the job in parallel. There are three
+main options for it: MPI cluster, PSOCK cluster and FORK cluster. We use
+\<span>makeCluster {parallel}\</span> function to create a set of copies
+of **R** running in parallel and communicating over sockets, the type of
+the cluster could be specified by the \<span>TYPE \</span>variable.
+
+#### MPI cluster
+
+This way of the R parallelism uses the
+[Rmpi](http://cran.r-project.org/web/packages/Rmpi/index.html) package
+and the [MPI](https://en.wikipedia.org/wiki/Message_Passing_Interface)
+(Message Passing Interface) as a "backend" for its parallel operations.
+Parallel R codes submitting a multinode MPI R job to SLURM is very
+similar to \<a href="Slurm#Binding_and_Distribution_of_Tasks"
+target="\_blank">submitting an MPI Job\</a> since both are running
+multicore jobs on multiple nodes. Below is an example of running R
+script with the Rmpi on Taurus:
+
+ #!/bin/bash
+ #SBATCH --partition=haswell #specify the partition
+ #SBATCH --ntasks=16 #This parameter determines how many processes will be spawned. Please use >= 8.
+ #SBATCH --cpus-per-task=1
+ #SBATCH --time=00:10:00
+ #SBATCH -o test_Rmpi.out
+ #SBATCH -e test_Rmpi.err
+
+ module purge
+ module load modenv/scs5
+ module load R
+
+ mpirun -n 1 R CMD BATCH Rmpi.R #specify the absolute path to the R script, like: /scratch/ws/max1234-Work/R/Rmpi.R
+
+ # when finished writing, submit with sbatch <script_name>
+
+\<span class="WYSIWYG_TT"> **-ntasks** \</span> SLURM option is the best
+and simplest way to run your application with MPI. The number of nodes
+required to complete this number of tasks will then be selected. Each
+MPI rank is assigned 1 core(CPU).
+
+However, in some specific cases, you can specify the number of nodes and
+the number of necessary tasks per node:
+
+ #!/bin/bash
+ #SBATCH --nodes=2
+ #SBATCH --tasks-per-node=16
+ #SBATCH --cpus-per-task=1
+ module purge
+ module load modenv/scs5
+ module load R
+
+ time mpirun -quiet -np 1 R CMD BATCH --no-save --no-restore Rmpi_c.R #this command will calculate the time of completion for your script
+
+The illustration above shows the binding of an MPI-job. Use an
+[example](%ATTACHURL%/Rmpi_c.R) from the attachment. In which 32 global
+ranks are distributed over 2 nodes with 16 cores(CPUs) each. Each MPI
+rank has 1 core assigned to it.
+
+To use Rmpi and MPI please use one of these partitions: **Haswell**,
+**Broadwell** or **Rome**.\<br />**%RED%Important:<span
+class="twiki-macro ENDCOLOR"></span>**\<span
+style`"font-size: 1em;"> Please allocate the required number of nodes and cores according to the hardware specification: 1 Haswell's node: 2 x [Intel Xeon (12 cores)]; 1 Broadwell's Node: 2 x [Intel Xeon (14 cores)]; 1 Rome's node: 2 x [AMD EPYC (64 cores)]. Please also check the </span><a href="HardwareTaurus" target="_blank">hardware specification</a><span style="font-size: 1em;"> (number of nodes etc). The =sinfo`
+command gives you a quick overview of the status of partitions.\</span>
+
+\<span style="font-size: 1em;">Please use \</span>\<span>mpirun\</span>
+command \<span style="font-size: 1em;">to run the Rmpi script. It is a
+wrapper that enables the communication between processes running on
+different machines. \</span>\<span style="font-size: 1em;">We recommend
+always use \</span>\<span style="font-size: 1em;">"\</span>\<span>-np
+1\</span>\<span style="font-size: 1em;">"\</span>\<span
+style="font-size: 1em;"> (the number of MPI processes to launch)\</span>
+\<span style="font-size: 1em;">because otherwise, it spawns additional
+processes dynamically.\</span>
+
+Examples of R scripts with the Rmpi can be found as attachments at the
+bottom of the page.
+
+#### PSOCK cluster
+
+The `type="PSOCK"` uses TCP sockets to transfer data between nodes.
+PSOCK is the default on *all* systems. However, if your parallel code
+will be executed on Windows as well you should use the PSOCK method. The
+advantage of this method is that It does not require external libraries
+such as Rmpi. On the other hand, TCP sockets are relatively
+[slow](http://glennklockwood.blogspot.com/2013/06/whats-killing-cloud-interconnect.html).
+Creating a PSOCK cluster is similar to launching an MPI cluster, but
+instead of simply saying how many parallel workers you want, you have to
+manually specify the number of nodes according to the hardware
+specification and parameters of your job. The example of the code could
+be found as an [attachment](%ATTACHURL%/RPSOCK.R?t=1597043002).
+
+#### FORK cluster
+
+The `type="FORK"` behaves exactly like the `mclapply` function discussed
+in the previous section. Like `mclapply`, it can only use the cores
+available on a single node, but this does not require clustered data
+export since all cores use the same memory. You may find it more
+convenient to use a FORK cluster with `parLapply` than `mclapply` if you
+anticipate using the same code across multicore *and* multinode systems.
+
+### Other parallel options
+
+There are numerous different parallel options for R. However for general
+users, we would recommend using the options listed above. However, the
+alternatives should be mentioned:
+
+- \<span>
+ [foreach](https://cran.r-project.org/web/packages/foreach/index.html)
+ \</span>package. It is functionally equivalent to the [lapply-based
+ parallelism](https://www.glennklockwood.com/data-intensive/r/lapply-parallelism.html)
+ discussed before but based on the for-loop;
+- [future](https://cran.r-project.org/web/packages/future/index.html)
+ package. The purpose of this package is to provide a lightweight and
+ unified Future API for sequential and parallel processing of R
+ expression via futures;
+- [Poor-man's
+ parallelism](https://www.glennklockwood.com/data-intensive/r/alternative-parallelism.html#6-1-poor-man-s-parallelism)
+ (simple data parallelism). It is the simplest, but not an elegant
+ way to parallelize R code. It runs several copies of the same R
+ script where's each read different sectors of the input data;
+- \<a
+ href="<https://www.glennklockwood.com/data-intensive/r/alternative-parallelism.html#6-2-hands-off-parallelism>"
+ target="\_blank">Hands-off (OpenMP) method\</a>. R has
+ [OpenMP](https://www.openmp.org/resources/) support. Thus using
+ OpenMP is a simple method where you don't need to know a much about
+ the parallelism options in your code. Please be careful and don't
+ mix this technique with other methods!
+
+-- Main.AndreiPolitov - 2020-05-18
+
+- [TensorflowMNIST.R](%ATTACHURL%/TensorflowMNIST.R?t=1597837603)\<span
+ style="font-size: 13px;">: TensorflowMNIST.R\</span>
+- [lapply.R](%ATTACHURL%/lapply.R)\<span style="font-size: 13px;">:
+ lapply.R\</span>
+- [multicore.R](%ATTACHURL%/multicore.R)\<span style="font-size:
+ 13px;">: multicore.R\</span>
+- [Rmpi.R](%ATTACHURL%/Rmpi.R)\<span style="font-size: 13px;">:
+ Rmpi.R\</span>
+- [Rmpi_c.R](%ATTACHURL%/Rmpi_c.R)\<span style="font-size: 13px;">:
+ Rmpi_c.R\</span>
+- [RPSOCK.R](%ATTACHURL%/RPSOCK.R)\<span style="font-size: 13px;">:
+ RPSOCK.R\</span>
+
+\<div id="gtx-trans" style="position: absolute; left: 35px; top:
+5011.8px;"> \</div>
diff --git a/doc.zih.tu-dresden.de/mkdocs.yml b/doc.zih.tu-dresden.de/mkdocs.yml
index 1a1ff9012a96e1a3d79acee857a4378f04ccdf00..95a3dce56adde700de14d88d7984c76ad0dfe3e0 100644
--- a/doc.zih.tu-dresden.de/mkdocs.yml
+++ b/doc.zih.tu-dresden.de/mkdocs.yml
@@ -17,6 +17,7 @@ nav:
- Support: support.md
- Running Jobs:
- Overview: jobs/index.md
+ - HPDCA: DataAnalyticsWithR.md
# - Queue Policy: jobs/policy.md
# - Examples: jobs/examples/index.md
# - Affinity: jobs/affinity/index.md