diff --git a/content/FAQ/_index.md b/content/FAQ/_index.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..c51a547e16877530d974885b005a35eca8fcc221 100644
--- a/content/FAQ/_index.md
+++ b/content/FAQ/_index.md
@@ -0,0 +1,177 @@
++++
+title = "FAQ"
+description = "HCC Frequently Asked Questions"
+weight = "10"
++++
+
+- [I have an account, now what?](#i-have-an-account-now-what)
+- [How do I change my password?](#how-do-i-change-my-password)
+- [I forgot my password, how can I retrieve it?](#i-forgot-my-password-how-can-i-retrieve-it)
+- [I just deleted some files and didn't mean to! Can I get them back?](#i-just-deleted-some-files-and-didn-t-mean-to-can-i-get-them-back)
+- [How do I (re)activate Duo?](#how-do-i-re-activate-duo)
+- [How many nodes/memory/time should I request?](#how-many-nodes-memory-time-should-i-request)
+- [I am trying to run a job but nothing happens?](#i-am-trying-to-run-a-job-but-nothing-happens)
+- [I keep getting the error "slurmstepd: error: Exceeded step memory limit at some point." What does this mean and how do I fix it?](#i-keep-getting-the-error-slurmstepd-error-exceeded-step-memory-limit-at-some-point-what-does-this-mean-and-how-do-i-fix-it)
+- [I want to talk to a human about my problem. Can I do that?](#i-want-to-talk-to-a-human-about-my-problem-can-i-do-that)
+
+---
+
+#### I have an account, now what?
+
+Congrats on getting an HCC account! Now you need to connect to a Holland
+cluster. To do this, we use an SSH connection. SSH stands for Secure
+Shell, and it allows you to securely connect to a remote computer and
+operate it just like you would a personal machine.
+
+Depending on your operating system, you may need to install software to
+make this connection. Check out on Quick Start Guides for information on
+how to install the necessary software for your operating system
+
+- [For Mac/Linux Users]({{< relref "for_maclinux_users" >}})
+- [For Windows Users]({{< relref "for_windows_users" >}})
+
+#### How do I change my password?
+
+#### I forgot my password, how can I retrieve it?
+
+Information on how to change or retrieve your password can be found on
+the documentation page: [How to change your
+password]({{< relref "/accounts/how_to_change_your_password" >}})
+
+
+All passwords must be at least 8 characters in length and must contain
+at least one capital letter and one numeric digit. Passwords also cannot
+contain any dictionary words. If you need help picking a good password,
+consider using a (secure!) password generator such as
+[this one provided by Random.org](https://www.random.org/passwords)
+
+To preserve the security of your account, we recommend changing the
+default password you were given as soon as possible.
+
+#### I just deleted some files and didn't mean to! Can I get them back?
+
+That depends. Where were the files you deleted?
+
+**If the files were in your $HOME directory (/home/group/user/):** It's
+possible.
+
+$HOME directories are backed up daily and we can restore your files as
+they were at the time of our last backup. Please note that any changes
+made to the files between when the backup was made and when you deleted
+them will not be preserved. To have these files restored, please contact
+HCC Support at
+{{< icon name="envelope" >}}[hcc-support@unl.edu] (mailto:hcc-support@unl.edu)
+as soon as possible.
+
+**If the files were in your $WORK directory (/work/group/user/):** No.
+
+Unfortunately, the $WORK directories are created as a short term place
+to hold job files. This storage was designed to be quickly and easily
+accessed by our worker nodes and as such is not conducive to backups.
+Any irreplaceable files should be backed up in a secondary location,
+such as Attic, the cloud, or on your personal machine. For more
+information on how to prevent file loss, check out [Preventing File
+Loss]({{< relref "preventing_file_loss" >}}).
+
+#### How do I (re)activate Duo?
+
+**If you have not activated Duo before:**
+
+Please stop by
+[our offices](http://hcc.unl.edu/location)
+along with a photo ID and we will be happy to activate it for you. If
+you are not local to Omaha or Lincoln, contact us at
+{{< icon name="envelope" >}}[hcc-support@unl.edu] (mailto:hcc-support@unl.edu)
+and we will help you activate Duo remotely.
+
+**If you have activated Duo previously but now have a different phone
+number:**
+
+Stop by our offices along with a photo ID and we can help you reactivate
+Duo and update your account with your new phone number.
+
+**If you have activated Duo previously and have the same phone number:**
+
+Email us at
+{{< icon name="envelope" >}}[hcc-support@unl.edu] (mailto:hcc-support@unl.edu)
+from the email address your account is registered under and we will send
+you a new link that you can use to activate Duo.
+
+#### How many nodes/memory/time should I request?
+
+**Short answer:** We don’t know.
+
+**Long answer:** The amount of resources required is highly dependent on
+the application you are using, the input file sizes and the parameters
+you select. Sometimes it can help to speak with someone else who has
+used the software before to see if they can give you an idea of what has
+worked for them.
+
+But ultimately, it comes down to trial and error; try different
+combinations and see what works and what doesn’t. Good practice is to
+check the output and utilization of each job you run. This will help you
+determine what parameters you will need in the future.
+
+For more information on how to determine how many resources a completed
+job used, check out the documentation on [Monitoring Jobs]({{< relref "monitoring_jobs" >}}).
+
+#### I am trying to run a job but nothing happens?
+
+Where are you trying to run the job from? You can check this by typing
+the command \`pwd\` into the terminal.
+
+**If you are running from inside your $HOME directory
+(/home/group/user/)**:
+
+Move your files to your $WORK directory (/work/group/user) and resubmit
+your job.
+
+The worker nodes on our clusters have read-only access to the files in
+$HOME directories. This means that when a job is submitted from $HOME,
+the scheduler cannot write the output and error files in the directory
+and the job is killed. It appears the job does nothing because no output
+is produced.
+
+**If you are running from inside your $WORK directory:**
+
+Contact us at
+{{< icon name="envelope" >}}[hcc-support@unl.edu] (mailto:hcc-support@unl.edu)
+with your login, the name of the cluster you are running on, and the
+full path to your submit script and we will be happy to help solve the
+issue.
+
+##### I keep getting the error "slurmstepd: error: Exceeded step memory limit at some point." What does this mean and how do I fix it?
+
+This error occurs when the job you are running uses more memory than was
+requested in your submit script.
+
+If you specified `--mem` or `--mem-per-cpu` in your submit script, try
+increasing this value and resubmitting your job.
+
+If you did not specify `--mem` or `--mem-per-cpu` in your submit script,
+chances are the default amount allotted is not sufficient. Add the line
+
+{{< highlight batch >}}
+#SBATCH --mem=<memory_amount>
+{{< /highlight >}}
+
+to your script with a reasonable amount of memory and try running it again. If you keep
+getting this error, continue to increase the requested memory amount and
+resubmit the job until it finishes successfully.
+
+For additional details on how to monitor usage on jobs, check out the
+documentation on [Monitoring Jobs]({{< relref "monitoring_jobs" >}}).
+
+If you continue to run into issues, please contact us at
+{{< icon name="envelope" >}}[hcc-support@unl.edu] (mailto:hcc-support@unl.edu)
+for additional assistance.
+
+#### I want to talk to a human about my problem. Can I do that?
+
+Of course! We have an open door policy and invite you to stop by
+[either of our offices](http://hcc.unl.edu/location)
+anytime Monday through Friday between 9 am and 5 pm. One of the HCC
+staff would be happy to help you with whatever problem or question you
+have. Alternatively, you can drop one of us a line and we'll arrange a
+time to meet: [Contact Us](https://hcc.unl.edu/contact-us).
+
diff --git a/content/applications/app_specific/Jupyter.md b/content/applications/app_specific/Jupyter.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..3a7e3f9f9fd06ec168a6ec0229bcaa7a1a70b22d 100644
--- a/content/applications/app_specific/Jupyter.md
+++ b/content/applications/app_specific/Jupyter.md
@@ -0,0 +1,58 @@
++++
+title = "Jupyter Notebooks on Crane"
+description = "How to access and use a Jupyter Notebook"
+weight = 20
++++
+
+- [Connecting to Crane] (#connecting-to-crane)
+- [Running Code] (#running-code)
+- [Opening a Terminal] (#opening-a-terminal)
+- [Using Custom Packages] (#using-custom-packages)
+
+## Connecting to Crane
+-----------------------
+ Jupyter defines it's notebooks ("Jupyter Notebooks") as
+ an open-source web application that allows you to create and share documents that contain live code,
+ equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation,
+ statistical modeling, data visualization, machine learning, and much more.
+
+1. To open a Jupyter notebook, [Sign in](https://crane.unl.edu) to crane.unl.edu using your hcc credentials (NOT your
+ campus credentials).
+{{< figure src="/images/jupyterLogin.png" >}}
+
+2. Select your preferred authentication method.
+
+ {{< figure src="/images/jupyterPush.png" >}}
+
+3. Choose a job profile. Select "Noteboook via SLURM Job | Small (1 core, 4GB RAM, 8 hours)" for light tasks such as debugging or small-scale testing.
+Select the other options based on your computing needs. Note that a SLURM Job will save to your "work" directory.
+
+{{< figure src="/images/jupyterjob.png" >}}
+
+## Running Code
+
+1. Select the "New" dropdown menu and select the file type you want to create.
+
+{{< figure src="/images/jupyterNew.png" >}}
+2. A new tab will open, where you can enter your code. Run your code by selecting the "play" icon.
+
+{{< figure src="/images/jupyterCode.png">}}
+
+## Opening a Terminal
+
+1. From your user home page, select "terminal" from the "New" drop-down menu.
+{{< figure src="/images/jupyterTerminal.png">}}
+2. A terminal opens in a new tab. You can enter [Linux commands] ({{< relref "basic_linux_commands" >}})
+ at the prompt.
+{{< figure src="/images/jupyterTerminal2.png">}}
+
+## Using Custom Packages
+
+Many popular `python` and `R` packages are already installed and available within Jupyter Notebooks.
+However, it is possible to install custom packages to be used in notebooks by creating a custom Anaconda
+Environment. Detailed information on how to create such an environment can be found at
+ [Using an Anaconda Environment in a Jupyter Notebook on Crane]({{< relref "/applications/user_software/using_anaconda_package_manager#using-an-anaconda-environment-in-a-jupyter-notebook-on-crane" >}}).
+
+---
+
+
diff --git a/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/blast_with_allinea_performance_reports.md b/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/blast_with_allinea_performance_reports.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..ec4291ed4d4fb318fac1d71327279306107d2bb2 100644
--- a/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/blast_with_allinea_performance_reports.md
+++ b/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/blast_with_allinea_performance_reports.md
@@ -0,0 +1,65 @@
++++
+title = "BLAST with Allinea Performance Reports"
+description = "Example of how to profile BLAST using Allinea Performance Reports."
++++
+
+Simple example of using
+[BLAST]({{< relref "/applications/app_specific/bioinformatics_tools/alignment_tools/blast/running_blast_alignment" >}})
+with Allinea Performance Reports (`perf-report`) on Crane is shown below:
+
+{{% panel theme="info" header="blastn_perf_report.submit" %}}
+{{< highlight batch >}}
+#!/bin/sh
+#SBATCH --job-name=BlastN
+#SBATCH --nodes=1
+#SBATCH --ntasks=16
+#SBATCH --time=20:00:00
+#SBATCH --mem=50gb
+#SBATCH --output=BlastN.info
+#SBATCH --error=BlastN.error
+
+module load allinea
+module load blast/2.2.29
+
+cd $WORK/<project_folder>
+cp -r /work/HCC/DATA/blastdb/nt/ /tmp/
+cp input_reads.fasta /tmp/
+
+perf-report --openmp-threads=$SLURM_NTASKS_PER_NODE --nompi `which blastn` \
+-query /tmp/input_reads.fasta -db /tmp/nt/nt -out \
+blastn_output.alignments -num_threads $SLURM_NTASKS_PER_NODE
+
+cp blastn\_output.alignments .
+{{< /highlight >}}
+{{% /panel %}}
+
+BLAST uses OpenMP and therefore the Allinea Performance Reports options
+`--openmp-threads` and `--nompi` are used. The perf-report
+part, `perf-report --openmp-threads=$SLURM_NTASKS_PER_NODE --nompi`,
+is placed in front of the actual `blastn` command we want
+to analyze.
+
+{{% notice info %}}
+If you see the error "**Allinea Performance Reports - target file
+'application' does not exist on this machine... exiting**", this means
+that instead of just using the executable '*application*', the full path
+to that application is required. This is the reason why in the script
+above, instead of using "*blastn*", we use *\`which blastn\`* which
+gives the full path of the *blastn* executable.
+{{% /notice %}}
+
+When the application finishes, the performance report is generated in
+the working directory.
+For the executed application, this is how the report looks like:
+
+{{< figure src="/images/11635296.png" width="850" >}}
+
+From the report, we can see that **blastn** is Compute-Bound
+application. The difference between mean (11.1 GB) and peak (26.3 GB)
+memory is significant, and this may be sign of workload imbalance or a
+memory leak. Moreover, 89.6% of the time is spent in synchronizing
+threads in parallel regions which can lead to workload imbalance.
+
+Running Allinea Performance Reports and identifying application
+bottlenecks is really useful for improving the application and better
+utilization of the available resources.
diff --git a/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/ray_with_allinea_performance_reports.md b/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/ray_with_allinea_performance_reports.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..ba18fd08924a2d40eb7fbd5bbce34ce16145dfe2 100644
--- a/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/ray_with_allinea_performance_reports.md
+++ b/content/applications/app_specific/allinea_profiling_and_debugging/allinea_performance_reports/ray_with_allinea_performance_reports.md
@@ -0,0 +1,44 @@
++++
+title = "Ray with Allinea Performance Reports"
+description = "Example of how to profile Ray using Allinea Performance Reports"
++++
+
+Simple example of using [Ray]({{< relref "/applications/app_specific/bioinformatics_tools/de_novo_assembly_tools/ray" >}})
+with Allinea PerformanceReports (`perf-report`) on Tusker is shown below:
+
+{{% panel theme="info" header="ray_perf_report.submit" %}}
+{{< highlight batch >}}
+#!/bin/sh
+#SBATCH --job-name=Ray
+#SBATCH --ntasks-per-node=16
+#SBATCH --time=10:00:00
+#SBATCH --mem=70gb
+#SBATCH --output=Ray.info
+#SBATCH --error=Ray.error
+
+module load allinea
+module load compiler/gcc/4.7 openmpi/1.6 ray/2.3
+
+perf-report mpiexec -n 16 Ray -k 31 -p -p input_reads_pair_1.fasta input_reads\_pair_2.fasta -o output_directory
+{{< /highlight >}}
+{{% /panel %}}
+
+Ray is MPI and therefore additional Allinea Performance Reports options
+are not required. The `perf-report` command is placed in front of the
+actual `Ray` command we want to analyze.
+
+When the application finishes, the performance report is generated in
+the working directory.
+For the executed application, this is how the report looks like:
+
+{{< figure src="/images/11635303.png" width="850" >}}
+
+From the report, we can see that **Ray **is Compute-Bound application.
+Most of the running time is spent in point-to-point calls with a low
+transfer rate which may be caused by inefficient message sizes.
+Therefore, running this application with fewer MPI processes and more
+data on each process may be more efficient.
+
+Running Allinea Performance Reports and identifying application
+bottlenecks is really useful for improving the application and better
+utilization of the available resources.
diff --git a/content/applications/app_specific/bioinformatics_tools/alignment_tools/blast/running_blast_alignment.md b/content/applications/app_specific/bioinformatics_tools/alignment_tools/blast/running_blast_alignment.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..b93fb6d8811a6e1ce83d17d6fbb4a4d7e645d02e 100644
--- a/content/applications/app_specific/bioinformatics_tools/alignment_tools/blast/running_blast_alignment.md
+++ b/content/applications/app_specific/bioinformatics_tools/alignment_tools/blast/running_blast_alignment.md
@@ -0,0 +1,124 @@
++++
+title = " Running BLAST Alignment"
+description = "How to run BLAST alignment on HCC resources"
+weight = "10"
++++
+
+
+Basic BLAST has the following commands:
+
+- **blastn**: search nucleotide database using a nucleotide query
+- **blastp**: search protein database using a protein query
+- **blastx**: search protein database using a translated nucleotide query
+- **tblastn**: search translated nucleotide database using a protein query
+- **tblastx**: search translated nucleotide database using a translated nucleotide query
+
+
+The basic usage of **blastn** is:
+{{< highlight bash >}}
+$ blastn -query input_reads.fasta -db input_reads_db -out blastn_output.alignments [options]
+{{< /highlight >}}
+where **input_reads.fasta** is an input file of sequence data in fasta format, **input_reads_db** is the generated BLAST database, and **blastn_output.alignments** is the output file where the alignments are stored.
+
+Additional parameters can be found in the [BLAST manual] (https://www.ncbi.nlm.nih.gov/books/NBK279690/), or by typing:
+{{< highlight bash >}}
+$ blastn -help
+{{< /highlight >}}
+
+These BLAST alignment commands are multi-threaded, and therefore using the BLAST option **-num_threads <number_of_CPUs>** is recommended.
+
+
+HCC hosts multiple BLAST databases and indices on Crane. In order to use these resources, the ["biodata" module] ({{<relref "/applications/app_specific/bioinformatics_tools/biodata_module">}}) needs to be loaded first. The **$BLAST** variable contains the following currently available databases:
+
+- **16SMicrobial**
+- **env_nt**
+- **est**
+- **est_human**
+- **est_mouse**
+- **est_others**
+- **gss**
+- **human_genomic**
+- **human_genomic_transcript**
+- **mouse_genomic_transcript**
+- **nr**
+- **nt**
+- **other_genomic**
+- **refseq_genomic**
+- **refseq_rna**
+- **sts**
+- **swissprot**
+- **tsa_nr**
+- **tsa_nt**
+
+If you want to create and use a BLAST database that is not mentioned above, check [Create Local BLAST Database]({{<relref "create_local_blast_database" >}}).
+
+
+Basic SLURM example of nucleotide BLAST run against the non-redundant **nt** BLAST database with `8 CPUs` is provided below. When running BLAST alignment, it is recommended to first copy the query and database files to the **/scratch/** directory of the worker node. Moreover, the BLAST output is also saved in this directory (**/scratch/blastn_output.alignments**). After BLAST finishes, the output file is copied from the worker node to your current work directory.
+{{% notice info %}}
+**Please note that the worker nodes can not write to the */home/* directories and therefore you need to run your job from your */work/* directory.**
+**This example will first copy your database to faster local storage called “scratch”. This can greatly improve performance!**
+{{% /notice %}}
+
+{{% panel header="`blastn_alignment.submit`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --job-name=BlastN
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --time=168:00:00
+#SBATCH --mem=20gb
+#SBATCH --output=BlastN.%J.out
+#SBATCH --error=BlastN.%J.err
+
+module load blast/2.7
+module load biodata/1.0
+
+cd $WORK/<project_folder>
+cp $BLAST/nt.* /scratch/
+cp input_reads.fasta /scratch/
+
+blastn -query /scratch/input_reads.fasta -db /scratch/nt -out /scratch/blastn_output.alignments -num_threads $SLURM_NTASKS_PER_NODE
+
+cp /scratch/blastn_output.alignments $WORK/<project_folder>
+{{< /highlight >}}
+{{% /panel %}}
+
+
+One important BLAST parameter is the **e-value threshold** that changes the number of hits returned by showing only those with value lower than the given. To show the hits with **e-value** lower than 1e-10, modify the given script as follows:
+{{< highlight bash >}}
+$ blastn -query input_reads.fasta -db input_reads_db -out blastn_output.alignments -num_threads $SLURM_NTASKS_PER_NODE -evalue 1e-10
+{{< /highlight >}}
+
+
+The default BLAST output is in pairwise format. However, BLAST’s parameter **-outfmt** supports output in [different formats] (https://www.ncbi.nlm.nih.gov/books/NBK279684/) that are easier for parsing.
+
+
+Basic SLURM example of protein BLAST run against the non-redundant **nr **BLAST database with tabular output format and `8 CPUs` is shown below. Similarly as before, the query and database files are copied to the **/scratch/** directory. The BLAST output is also saved in this directory (**/scratch/blastx_output.alignments**). After BLAST finishes, the output file is copied from the worker node to your current work directory.
+{{% notice info %}}
+**Please note that the worker nodes can not write to the */home/* directories and therefore you need to run your job from your */work/* directory.**
+**This example will first copy your database to faster local storage called “scratch”. This can greatly improve performance!**
+{{% /notice %}}
+
+{{% panel header="`blastx_alignment.submit`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --job-name=BlastX
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --time=168:00:00
+#SBATCH --mem=20gb
+#SBATCH --output=BlastX.%J.out
+#SBATCH --error=BlastX.%J.err
+
+module load blast/2.7
+module load biodata/1.0
+
+cd $WORK/<project_folder>
+cp $BLAST/nr.* /scratch/
+cp input_reads.fasta /scratch/
+
+blastx -query /scratch/input_reads.fasta -db /scratch/nr -outfmt 6 -out /scratch/blastx_output.alignments -num_threads $SLURM_NTASKS_PER_NODE
+
+cp /scratch/blastx_output.alignments $WORK/<project_folder>
+{{< /highlight >}}
+{{% /panel %}}
diff --git a/content/applications/app_specific/bioinformatics_tools/biodata_module.md b/content/applications/app_specific/bioinformatics_tools/biodata_module.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0257bcf5f52bc1ede31253e14e143cf558449d4f 100644
--- a/content/applications/app_specific/bioinformatics_tools/biodata_module.md
+++ b/content/applications/app_specific/bioinformatics_tools/biodata_module.md
@@ -0,0 +1,88 @@
++++
+title = "Biodata Module"
+description = "How to use Biodata Module on HCC machines"
+scripts = ["https://cdnjs.cloudflare.com/ajax/libs/jquery.tablesorter/2.31.1/js/jquery.tablesorter.min.js", "https://cdnjs.cloudflare.com/ajax/libs/jquery.tablesorter/2.31.1/js/widgets/widget-pager.min.js","https://cdnjs.cloudflare.com/ajax/libs/jquery.tablesorter/2.31.1/js/widgets/widget-filter.min.js","/js/sort-table.js"]
+css = ["http://mottie.github.io/tablesorter/css/theme.default.css","https://mottie.github.io/tablesorter/css/theme.dropbox.css", "https://cdnjs.cloudflare.com/ajax/libs/jquery.tablesorter/2.31.1/css/jquery.tablesorter.pager.min.css","https://cdnjs.cloudflare.com/ajax/libs/jquery.tablesorter/2.31.1/css/filter.formatter.min.css"]
+weight = "52"
++++
+
+
+HCC hosts multiple databases (BLAST, KEGG, PANTHER, InterProScan), genome files, short read aligned indices etc. on Crane.
+In order to use these resources, the "**biodata**" module needs to be loaded first.
+For how to load module, please check [Module Commands]({{< relref "/applications/modules/_index.md" >}}).
+
+Loading the "**biodata**" module will pre-set many environment variables, but most likely you will only need a subset of them. Environment variables can be used in your command or script by prefixing `$` to the name.
+
+The major environment variables are:
+**$DATA** - main directory
+**$BLAST** - Directory containing all available BLAST (nucleotide and protein) databases
+**$KEGG** - KEGG database main entry point (requires license)
+**$PANTHER** - PANTHER database main entry point (latest)
+**$IPR** - InterProScan database main entry point (latest)
+**$GENOMES** - Directory containing all available genomes (multiple sources, builds possible
+**$INDICES** - Directory containing indices for bowtie, bowtie2, bwa for all available genomes
+**$UNIPROT** - Directory containing latest release of full UniProt database
+
+
+In order to check what genomes are available, you can type:
+{{< highlight bash >}}
+$ ls $GENOMES
+{{< /highlight >}}
+
+
+In order to check what BLAST databases are available, you can just type:
+{{< highlight bash >}}
+$ ls $BLAST
+{{< /highlight >}}
+
+
+An example of how to run Bowtie2 local alignment on Crane utilizing the default Horse, *Equus caballus* index (*BOWTIE2\_HORSE*) with paired-end fasta files and 8 CPUs is shown below:
+{{% panel header="`bowtie2_alignment.submit`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --job-name=Bowtie2
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --time=168:00:00
+#SBATCH --mem=10gb
+#SBATCH --output=Bowtie2.%J.out
+#SBATCH --error=Bowtie2.%J.err
+
+module load bowtie/2.2
+module load biodata
+
+bowtie2 -x $BOWTIE2_HORSE -f -1 input_reads_pair_1.fasta -2 input_reads_pair_2.fasta -S bowtie2_alignments.sam --local -p $SLURM_NTASKS_PER_NODE
+
+{{< /highlight >}}
+{{% /panel %}}
+
+
+An example of BLAST run against the non-redundant nucleotide database available on Crane is provided below:
+{{% panel header="`blastn_alignment.submit`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --job-name=BlastN
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --time=168:00:00
+#SBATCH --mem=10gb
+#SBATCH --output=BlastN.%J.out
+#SBATCH --error=BlastN.%J.err
+
+module load blast/2.7
+module load biodata
+cp $BLAST/nt.* /scratch
+cp input_reads.fasta /scratch
+
+blastn -db /scratch/nt -query /scratch/input_reads.fasta -out /scratch/blast_nucleotide.results
+cp /scratch/blast_nucleotide.results .
+
+{{< /highlight >}}
+{{% /panel %}}
+
+
+### Available Organisms
+
+The organisms and their appropriate environmental variables for all genomes and chromosome files, as well as indices are shown in the table below.
+
+{{< table url="http://rhino-head.unl.edu:8192/bio/data/json" >}}
diff --git a/content/applications/app_specific/dmtcp_checkpointing.md b/content/applications/app_specific/dmtcp_checkpointing.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..9de1c055fe6790b4d29caea3f04d16b112047bb2 100644
--- a/content/applications/app_specific/dmtcp_checkpointing.md
+++ b/content/applications/app_specific/dmtcp_checkpointing.md
@@ -0,0 +1,138 @@
++++
+title = "DMTCP Checkpointing"
+description = "How to use the DMTCP utility to checkpoint your application."
++++
+
+[DMTCP](http://dmtcp.sourceforge.net)
+(Distributed MultiThreaded Checkpointing) is a checkpointing package for
+applications. Using checkpointing allows resuming of a failing
+simulation due to failing resources (e.g. hardware, software, exceeded
+time and memory resources).
+
+DMTCP supports both sequential and multi-threaded applications. Some
+examples of binary programs on Linux distributions that can be used with
+DMTCP are OpenMP, MATLAB, Python, Perl, MySQL, bash, gdb, X-Windows etc.
+
+DMTCP provides support for several resource managers, including SLURM,
+the resource manager used in HCC. The DMTCP module is available both on
+Crane, and is enabled by typing:
+
+{{< highlight bash >}}
+module load dmtcp
+{{< /highlight >}}
+
+After the module is loaded, the first step is to run the command:
+
+{{< highlight bash >}}
+[<username>@login.crane ~]$ dmtcp_launch --new-coordinator --rm --interval <interval_time_seconds> <your_command>
+{{< /highlight >}}
+
+where `--rm` option enables SLURM support,
+**\<interval_time_seconds\>** is the time in seconds between
+automatic checkpoints, and **\<your_command\>** is the actual
+command you want to run and checkpoint.
+
+Beside the general options shown above, more `dmtcp_launch` options
+can be seen by using:
+
+{{< highlight bash >}}
+[<username>@login.crane ~]$ dmtcp_launch --help
+{{< /highlight >}}
+
+`dmtcp_launch` creates few files that are used to resume the
+cancelled job, such as *ckpt\_\*.dmtcp* and
+*dmtcp\_restart\_script\*.sh*. Unless otherwise stated
+(using `--ckptdir` option), these files are stored in the current
+working directory.
+
+
+The second step of DMTCP is to restart the cancelled job, and there are
+two ways of doing that:
+
+- `dmtcp_restart ckpt_*.dmtcp` *\<options\>* (before running
+ this command delete any old *ckp\_\*.dmtcp* files in your current
+ directory)
+
+- `./dmtcp_restart_script.sh` *\<options\>*
+
+If there are no options defined in the *<options>* field, DMTCP
+will keep running with the options defined in the initial
+**dmtcp\_launch** call (such as interval time, output directory etc).
+
+
+Simple example of using DMTCP with
+[BLAST]({{< relref "/applications/app_specific/bioinformatics_tools/alignment_tools/blast/running_blast_alignment" >}})
+on crane is shown below:
+
+{{% panel theme="info" header="dmtcp_blastx.submit" %}}
+{{< highlight batch >}}
+#!/bin/sh
+#SBATCH --job-name=BlastX
+#SBATCH --nodes=1
+#SBATCH --ntasks=8
+#SBATCH --time=50:00:00
+#SBATCH --mem=20gb
+#SBATCH --output=BlastX_info_1.txt
+#SBATCH --error=BlastX_error_1.txt
+
+module load dmtcp
+module load blast/2.4
+
+cd $WORK/<project_folder>
+cp -r /work/HCC/DATA/blastdb/nr/ /tmp/
+cp input_reads.fasta /tmp/
+
+dmtcp_launch --new-coordinator --rm --interval 3600 blastx -query \
+/tmp/input_reads.fasta -db /tmp/nr/nr -out blastx_output.alignments \
+-num_threads $SLURM_NTASKS_PER_NODE
+{{< /highlight >}}
+{{% /panel %}}
+
+In this example, DMTCP takes checkpoints every hour (`--interval 3600`),
+and the actual command we want to checkpoint is `blastx` with
+some general BLAST options defined with `-query`, `-db`, `-out`,
+`-num_threads`.
+
+If this job is killed for various reasons, it can be restarted using the
+following submit file:
+
+{{% panel theme="info" header="dmtcp_restart_blastx.submit" %}}
+{{< highlight batch >}}
+#!/bin/sh
+#SBATCH --job-name=BlastX
+#SBATCH --nodes=1
+#SBATCH --ntasks=8
+#SBATCH --time=50:00:00
+#SBATCH --mem=20gb
+#SBATCH --output=BlastX_info_2.txt
+#SBATCH --error=BlastX_error_2.txt
+
+module load dmtcp
+module load blast/2.4
+
+cd $WORK/<project_folder>
+cp -r /work/HCC/DATA/blastdb/nr/ /tmp/
+cp input_reads.fasta /tmp/
+
+# Start DMTCP
+dmtcp_coordinator --daemon --port 0 --port-file /tmp/port
+export DMTCP_COORD_HOST=`hostname`
+export DMTCP_COORD_PORT=$(</tmp/port)
+
+# Restart job
+./dmtcp_restart_script.sh
+{{< /highlight >}}
+{{% /panel %}}
+
+{{% notice info %}}
+`dmtcp_restart` generates new
+`ckpt_*.dmtcp` and `dmtcp_restart_script*.sh` files. Therefore, if
+the restarted job is also killed due to unavailable/exceeded resources,
+you can resubmit the same job again without any changes in the submit
+file shown above (just don't forget to delete the old `ckpt_*.dmtcp`
+files if you are using these files instead of `dmtcp_restart_script.sh`)
+{{% /notice %}}
+
+Even though DMTCP tries to support most mainstream and commonly used
+applications, there is no guarantee that every application can be
+checkpointed and restarted.
diff --git a/content/applications/app_specific/fortran_c_on_hcc.md b/content/applications/app_specific/fortran_c_on_hcc.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..beec01f8505f52c39bb67e9af09aa8fc338a7ba2 100644
--- a/content/applications/app_specific/fortran_c_on_hcc.md
+++ b/content/applications/app_specific/fortran_c_on_hcc.md
@@ -0,0 +1,219 @@
++++
+title = "Fortran/C on HCC"
+description = "How to compile and run Fortran/C program on HCC machines"
+weight = "50"
++++
+
+This quick start demonstrates how to implement a Fortran/C program on
+HCC supercomputers. The sample codes and submit scripts can be
+downloaded from [serial_dir.zip](/attachments/serial_dir.zip).
+
+#### Login to a HCC Cluster
+
+Log in to a HCC cluster through PuTTY ([For Windows Users]({{< relref "/connecting/for_windows_users">}})) or Terminal ([For Mac/Linux
+Users]({{< relref "/connecting/for_maclinux_users">}})) and make a subdirectory called `serial_dir` under the `$WORK` directory.
+
+{{< highlight bash >}}
+$ cd $WORK
+$ mkdir serial_dir
+{{< /highlight >}}
+
+In the subdirectory `serial_dir`, save all the relevant Fortran/C codes. Here we include two demo
+programs, `demo_f_serial.f90` and `demo_c_serial.c`, that compute the sum from 1 to 20.
+
+{{%expand "demo_f_serial.f90" %}}
+{{< highlight bash >}}
+Program demo_f_serial
+ implicit none
+ integer, parameter :: N = 20
+ real*8 w
+ integer i
+ common/sol/ x
+ real*8 x
+ real*8, dimension(N) :: y
+ do i = 1,N
+ w = i*1d0
+ call proc(w)
+ y(i) = x
+ write(6,*) 'i,x = ', i, y(i)
+ enddo
+ write(6,*) 'sum(y) =',sum(y)
+Stop
+End Program
+Subroutine proc(w)
+ real*8, intent(in) :: w
+ common/sol/ x
+ real*8 x
+ x = w
+Return
+End Subroutine
+{{< /highlight >}}
+{{% /expand %}}
+
+
+{{%expand "demo_c_serial.c" %}}
+{{< highlight c >}}
+//demo_c_serial
+#include <stdio.h>
+
+double proc(double w){
+ double x;
+ x = w;
+ return x;
+}
+
+int main(int argc, char* argv[]){
+ int N=20;
+ double w;
+ int i;
+ double x;
+ double y[N];
+ double sum;
+ for (i = 1; i <= N; i++){
+ w = i*1e0;
+ x = proc(w);
+ y[i-1] = x;
+ printf("i,x= %d %lf\n", i, y[i-1]) ;
+ }
+
+ sum = 0e0;
+ for (i = 1; i<= N; i++){
+ sum = sum + y[i-1];
+ }
+
+ printf("sum(y)= %lf\n", sum);
+
+return 0;
+}
+{{< /highlight >}}
+{{% /expand %}}
+
+---
+
+#### Compiling the Code
+
+The compiling of a Fortran/C++ code to executable is usually done behind
+the scene in a Graphical User Interface (GUI) environment, such as
+Microsoft Visual Studio. In a HCC cluster, the compiling is done
+explicitly by first loading a choice compiler and then executing the
+corresponding compiling command. Here we will use the GNU Complier
+Collection, `gcc`, for demonstration. Other available compilers such as
+`intel` or `pgi` can be looked up using the command
+line `module avail`. Before compiling the code, make sure there is no
+dependency on any numerical library in the code. If invoking a numerical
+library is necessary, contact a HCC specialist
+({{< icon name="envelope" >}}[hcc-support@unl.edu] (mailto:hcc-support@unl.edu)) to
+discuss implementation options.
+
+{{< highlight bash >}}
+$ module load compiler/gcc/8.2
+$ gfortran demo_f_serial.f90 -o demo_f_serial.x
+$ gcc demo_c_serial.c -o demo_c_serial.x
+{{< /highlight >}}
+
+The above commends load the `gcc` complier and use the compiling
+commands `gfortran` or `gcc` to compile the codes to`.x` files
+(executables).
+
+#### Creating a Submit Script
+
+Create a submit script to request one core (default) and 1-min run time
+on the supercomputer. The name of the main program enters at the last
+line.
+
+{{% panel header="`submit_f.serial`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --mem-per-cpu=1024
+#SBATCH --time=00:01:00
+#SBATCH --job-name=Fortran
+#SBATCH --error=Fortran.%J.err
+#SBATCH --output=Fortran.%J.out
+
+module load compiler/gcc/4.9
+./demo_f_serial.x
+{{< /highlight >}}
+{{% /panel %}}
+
+{{% panel header="`submit_c.serial`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --mem-per-cpu=1024
+#SBATCH --time=00:01:00
+#SBATCH --job-name=C
+#SBATCH --error=C.%J.err
+#SBATCH --output=C.%J.out
+
+module load compiler/gcc/4.9
+./demo_c_serial.x
+{{< /highlight >}}
+{{% /panel %}}
+
+#### Submit the Job
+
+The job can be submitted through the command `sbatch`. The job status
+can be monitored by entering `squeue` with the `-u` option.
+
+{{< highlight bash >}}
+$ sbatch submit_f.serial
+$ sbatch submit_c.serial
+$ squeue -u <username>
+{{< /highlight >}}
+
+Replace `<username>` with your HCC username.
+
+#### Sample Output
+
+The sum from 1 to 20 is computed and printed to the `.out` file (see
+below).
+{{%expand "Fortran.out" %}}
+{{< highlight batchfile>}}
+ i,x = 1 1.0000000000000000
+ i,x = 2 2.0000000000000000
+ i,x = 3 3.0000000000000000
+ i,x = 4 4.0000000000000000
+ i,x = 5 5.0000000000000000
+ i,x = 6 6.0000000000000000
+ i,x = 7 7.0000000000000000
+ i,x = 8 8.0000000000000000
+ i,x = 9 9.0000000000000000
+ i,x = 10 10.000000000000000
+ i,x = 11 11.000000000000000
+ i,x = 12 12.000000000000000
+ i,x = 13 13.000000000000000
+ i,x = 14 14.000000000000000
+ i,x = 15 15.000000000000000
+ i,x = 16 16.000000000000000
+ i,x = 17 17.000000000000000
+ i,x = 18 18.000000000000000
+ i,x = 19 19.000000000000000
+ i,x = 20 20.000000000000000
+ sum(y) = 210.00000000000000
+{{< /highlight >}}
+{{% /expand %}}
+
+{{%expand "C.out" %}}
+{{< highlight batchfile>}}
+i,x= 1 1.000000
+i,x= 2 2.000000
+i,x= 3 3.000000
+i,x= 4 4.000000
+i,x= 5 5.000000
+i,x= 6 6.000000
+i,x= 7 7.000000
+i,x= 8 8.000000
+i,x= 9 9.000000
+i,x= 10 10.000000
+i,x= 11 11.000000
+i,x= 12 12.000000
+i,x= 13 13.000000
+i,x= 14 14.000000
+i,x= 15 15.000000
+i,x= 16 16.000000
+i,x= 17 17.000000
+i,x= 18 18.000000
+i,x= 19 19.000000
+i,x= 20 20.000000
+sum(y)= 210.000000
+{{< /highlight >}}
+{{% /expand %}}
diff --git a/content/applications/app_specific/mpi_jobs_on_hcc.md b/content/applications/app_specific/mpi_jobs_on_hcc.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..bfb9abd7f86588f33dd98b4ea2ef7e10c6d147c4 100644
--- a/content/applications/app_specific/mpi_jobs_on_hcc.md
+++ b/content/applications/app_specific/mpi_jobs_on_hcc.md
@@ -0,0 +1,322 @@
++++
+title = "MPI Jobs on HCC"
+description = "How to compile and run MPI programs on HCC machines"
+weight = "52"
++++
+
+This quick start demonstrates how to implement a parallel (MPI)
+Fortran/C program on HCC supercomputers. The sample codes and submit
+scripts can be downloaded from [mpi_dir.zip](/attachments/mpi_dir.zip).
+
+#### Login to a HCC Cluster
+
+Log in to a HCC cluster through PuTTY ([For Windows Users]({{< relref "/connecting/for_windows_users">}})) or Terminal ([For Mac/Linux
+Users]({{< relref "/connecting/for_maclinux_users">}})) and make a subdirectory called `mpi_dir` under the `$WORK` directory.
+
+{{< highlight bash >}}
+$ cd $WORK
+$ mkdir mpi_dir
+{{< /highlight >}}
+
+In the subdirectory `mpi_dir`, save all the relevant codes. Here we
+include two demo programs, `demo_f_mpi.f90` and `demo_c_mpi.c`, that
+compute the sum from 1 to 20 through parallel processes. A
+straightforward parallelization scheme is used for demonstration
+purpose. First, the master core (i.e. `myid=0`) distributes equal
+computation workload to a certain number of cores (as specified by
+`--ntasks `in the submit script). Then, each worker core computes a
+partial summation as output. Finally, the master core collects the
+outputs from all worker cores and perform an overall summation. For easy
+comparison with the serial code ([Fortran/C on HCC]({{< relref "fortran_c_on_hcc">}})), the
+added lines in the parallel code (MPI) are marked with "!=" or "//=".
+
+{{%expand "demo_f_mpi.f90" %}}
+{{< highlight fortran >}}
+Program demo_f_mpi
+!====== MPI =====
+ use mpi
+!================
+ implicit none
+ integer, parameter :: N = 20
+ real*8 w
+ integer i
+ common/sol/ x
+ real*8 x
+ real*8, dimension(N) :: y
+!============================== MPI =================================
+ integer ind
+ real*8, dimension(:), allocatable :: y_local
+ integer numnodes,myid,rc,ierr,start_local,end_local,N_local
+ real*8 allsum
+!====================================================================
+
+!============================== MPI =================================
+ call mpi_init( ierr )
+ call mpi_comm_rank ( mpi_comm_world, myid, ierr )
+ call mpi_comm_size ( mpi_comm_world, numnodes, ierr )
+ !
+ N_local = N/numnodes
+ allocate ( y_local(N_local) )
+ start_local = N_local*myid + 1
+ end_local = N_local*myid + N_local
+!====================================================================
+ do i = start_local, end_local
+ w = i*1d0
+ call proc(w)
+ ind = i - N_local*myid
+ y_local(ind) = x
+! y(i) = x
+! write(6,*) 'i, y(i)', i, y(i)
+ enddo
+! write(6,*) 'sum(y) =',sum(y)
+!============================================== MPI =====================================================
+ call mpi_reduce( sum(y_local), allsum, 1, mpi_real8, mpi_sum, 0, mpi_comm_world, ierr )
+ call mpi_gather ( y_local, N_local, mpi_real8, y, N_local, mpi_real8, 0, mpi_comm_world, ierr )
+
+ if (myid == 0) then
+ write(6,*) '-----------------------------------------'
+ write(6,*) '*Final output from... myid=', myid
+ write(6,*) 'numnodes =', numnodes
+ write(6,*) 'mpi_sum =', allsum
+ write(6,*) 'y=...'
+ do i = 1, N
+ write(6,*) y(i)
+ enddo
+ write(6,*) 'sum(y)=', sum(y)
+ endif
+
+ deallocate( y_local )
+ call mpi_finalize(rc)
+!========================================================================================================
+
+Stop
+End Program
+Subroutine proc(w)
+ real*8, intent(in) :: w
+ common/sol/ x
+ real*8 x
+
+ x = w
+
+Return
+End Subroutine
+{{< /highlight >}}
+{{% /expand %}}
+
+{{%expand "demo_c_mpi.c" %}}
+{{< highlight c >}}
+//demo_c_mpi
+#include <stdio.h>
+//======= MPI ========
+#include "mpi.h"
+#include <stdlib.h>
+//====================
+
+double proc(double w){
+ double x;
+ x = w;
+ return x;
+}
+
+int main(int argc, char* argv[]){
+ int N=20;
+ double w;
+ int i;
+ double x;
+ double y[N];
+ double sum;
+//=============================== MPI ============================
+ int ind;
+ double *y_local;
+ int numnodes,myid,rc,ierr,start_local,end_local,N_local;
+ double allsum;
+//================================================================
+//=============================== MPI ============================
+ MPI_Init(&argc, &argv);
+ MPI_Comm_rank( MPI_COMM_WORLD, &myid );
+ MPI_Comm_size ( MPI_COMM_WORLD, &numnodes );
+ N_local = N/numnodes;
+ y_local=(double *) malloc(N_local*sizeof(double));
+ start_local = N_local*myid + 1;
+ end_local = N_local*myid + N_local;
+//================================================================
+
+ for (i = start_local; i <= end_local; i++){
+ w = i*1e0;
+ x = proc(w);
+ ind = i - N_local*myid;
+ y_local[ind-1] = x;
+// y[i-1] = x;
+// printf("i,x= %d %lf\n", i, y[i-1]) ;
+ }
+ sum = 0e0;
+ for (i = 1; i<= N_local; i++){
+ sum = sum + y_local[i-1];
+ }
+// printf("sum(y)= %lf\n", sum);
+//====================================== MPI ===========================================
+ MPI_Reduce( &sum, &allsum, 1, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD );
+ MPI_Gather( &y_local[0], N_local, MPI_DOUBLE, &y[0], N_local, MPI_DOUBLE, 0, MPI_COMM_WORLD );
+
+ if (myid == 0){
+ printf("-----------------------------------\n");
+ printf("*Final output from... myid= %d\n", myid);
+ printf("numnodes = %d\n", numnodes);
+ printf("mpi_sum = %lf\n", allsum);
+ printf("y=...\n");
+ for (i = 1; i <= N; i++){
+ printf("%lf\n", y[i-1]);
+ }
+ sum = 0e0;
+ for (i = 1; i<= N; i++){
+ sum = sum + y[i-1];
+ }
+
+ printf("sum(y) = %lf\n", sum);
+
+ }
+
+ free( y_local );
+ MPI_Finalize ();
+//======================================================================================
+
+return 0;
+}
+{{< /highlight >}}
+{{% /expand %}}
+
+---
+
+#### Compiling the Code
+
+The compiling of a MPI code requires first loading a compiler "engine"
+such as `gcc`, `intel`, or `pgi` and then loading a MPI wrapper
+`openmpi`. Here we will use the GNU Complier Collection, `gcc`, for
+demonstration.
+
+{{< highlight bash >}}
+$ module load compiler/gcc/6.1 openmpi/2.1
+$ mpif90 demo_f_mpi.f90 -o demo_f_mpi.x
+$ mpicc demo_c_mpi.c -o demo_c_mpi.x
+{{< /highlight >}}
+
+The above commends load the `gcc` complier with the `openmpi` wrapper.
+The compiling commands `mpif90` or `mpicc` are used to compile the codes
+to`.x` files (executables).
+
+### Creating a Submit Script
+
+Create a submit script to request 5 cores (with `--ntasks`). A parallel
+execution command `mpirun ./` needs to enter to last line before the
+main program name.
+
+{{% panel header="`submit_f.mpi`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --ntasks=5
+#SBATCH --mem-per-cpu=1024
+#SBATCH --time=00:01:00
+#SBATCH --job-name=Fortran
+#SBATCH --error=Fortran.%J.err
+#SBATCH --output=Fortran.%J.out
+
+mpirun ./demo_f_mpi.x
+{{< /highlight >}}
+{{% /panel %}}
+
+{{% panel header="`submit_c.mpi`"%}}
+{{< highlight bash >}}
+#!/bin/sh
+#SBATCH --ntasks=5
+#SBATCH --mem-per-cpu=1024
+#SBATCH --time=00:01:00
+#SBATCH --job-name=C
+#SBATCH --error=C.%J.err
+#SBATCH --output=C.%J.out
+
+mpirun ./demo_c_mpi.x
+{{< /highlight >}}
+{{% /panel %}}
+
+#### Submit the Job
+
+The job can be submitted through the command `sbatch`. The job status
+can be monitored by entering `squeue` with the `-u` option.
+
+{{< highlight bash >}}
+$ sbatch submit_f.mpi
+$ sbatch submit_c.mpi
+$ squeue -u <username>
+{{< /highlight >}}
+
+Replace `<username>` with your HCC username.
+
+Sample Output
+-------------
+
+The sum from 1 to 20 is computed and printed to the `.out` file (see
+below). The outputs from the 5 cores are collected and processed by the
+master core (i.e. `myid=0`).
+
+{{%expand "Fortran.out" %}}
+{{< highlight batchfile>}}
+ -----------------------------------------
+ *Final output from... myid= 0
+ numnodes = 5
+ mpi_sum = 210.00000000000000
+ y=...
+ 1.0000000000000000
+ 2.0000000000000000
+ 3.0000000000000000
+ 4.0000000000000000
+ 5.0000000000000000
+ 6.0000000000000000
+ 7.0000000000000000
+ 8.0000000000000000
+ 9.0000000000000000
+ 10.000000000000000
+ 11.000000000000000
+ 12.000000000000000
+ 13.000000000000000
+ 14.000000000000000
+ 15.000000000000000
+ 16.000000000000000
+ 17.000000000000000
+ 18.000000000000000
+ 19.000000000000000
+ 20.000000000000000
+ sum(y)= 210.00000000000000
+{{< /highlight >}}
+{{% /expand %}}
+
+{{%expand "C.out" %}}
+{{< highlight batchfile>}}
+-----------------------------------
+*Final output from... myid= 0
+numnodes = 5
+mpi_sum = 210.000000
+y=...
+1.000000
+2.000000
+3.000000
+4.000000
+5.000000
+6.000000
+7.000000
+8.000000
+9.000000
+10.000000
+11.000000
+12.000000
+13.000000
+14.000000
+15.000000
+16.000000
+17.000000
+18.000000
+19.000000
+20.000000
+sum(y) = 210.000000
+{{< /highlight >}}
+{{% /expand %}}
+
diff --git a/content/handling_data/data_storage/linux_file_permissions.md b/content/handling_data/data_storage/linux_file_permissions.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..9a3d709945b45a42efe74850471671a34e7252e8 100644
--- a/content/handling_data/data_storage/linux_file_permissions.md
+++ b/content/handling_data/data_storage/linux_file_permissions.md
@@ -0,0 +1,48 @@
++++
+title = "Linux File Permissions"
+description = "How to view and change file permissions with Linux commands"
+weight = 20
++++
+
+- [Opening a Terminal Window] (#opening-a-terminal-window)
+- [Listing File Permissions] (#listing-file-permissions)
+- [Changing File Permissions] (#changing-file-permissions)
+
+## Opening a Terminal Window
+-----------------------
+
+Use your local terminal to connect to a cluster, or open a new terminal window on [Crane](https://crane.unl.edu).
+
+Click [here](https://hcc.unl.edu/docs/Quickstarts/connecting/) if you need help connecting to a cluster
+with a local terminal.
+
+Click [here](https://hcc.unl.edu/docs/guides/running_applications/jupyter/) if you need
+help opening a new terminal window within JupyterHub.
+
+## Listing File Permissions
+
+Type the command `ls -l` to list the files and directories with file permissions for your current location.
+
+{{< figure src="/images/LinuxList.png" >}}
+
+The first character denotes whether an item is a file or a directory. If 'd' is shown, it's a directory, and if '-' is shown, it's a file.
+ Following the first character you will see some
+combination of r,w,x, and -. The first rwx is the ‘read’ ‘write’ ‘execute’ file permissions for the creator
+ of that file or directory. A ‘-‘ instead means a particular permission has not been granted. For example “rw-“ means the
+ ‘execute’ permission has not been granted. The next three entries are the permissions for ‘group’ and the last three are the
+ permissions for everyone else.
+
+ Following the file permissions are the name of the creator, the name of the group, the size of the file, the date it was created, and finally
+the name of the file.
+
+
+## Changing File Permissions
+
+To change file permissions, use the command "chmod [permissions] [filename]" where permissions are indicated by a three-digit code.
+Each digit in the code correspondes to the three digits mentioned above in the permissions printout: One for the creater permissions,
+one for the group permissions, and one for everyone else. The command is interpreted as follows: 4=read 2=write 1=execute and any combination of these is given by summing their codes.
+Each chmod command will include 3 codes.
+For example, to give the creator of mars.txt rights to read, write and execute, the group rights to read and execute, and everone else only the right to read,
+we would use the command `chmod 754 mars.txt`
+
+{{< figure src="/images/LinuxChange.png" >}}
diff --git a/content/handling_data/data_storage/preventing_file_loss.md b/content/handling_data/data_storage/preventing_file_loss.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..1ce628aad84316e96cce4cfe7818e91e050902d7 100644
--- a/content/handling_data/data_storage/preventing_file_loss.md
+++ b/content/handling_data/data_storage/preventing_file_loss.md
@@ -0,0 +1,169 @@
++++
+title = "Preventing File Loss"
+description = "How to prevent file loss on HCC clusters"
+weight = 40
++++
+
+Each research group is allocated 50TB of storage in `/work` on HCC
+clusters. With over 400 active groups, HCC does not have the resources
+to provide regular backups of `/work` without sacrificing the
+performance of the existing filesystem. No matter how careful a user
+might be, there is always the risk of file loss due to user error,
+natural disasters, or equipment failure.
+
+However, there are a number of solutions available for backing up your
+data. By carefully considering the benefits and limitations of each,
+users can select the backup methods that work best for their particular
+needs. For truly robust file backups, we recommend combining multiple
+methods. For example, use Git regularly along with manual backups to an
+external hard-drive at regular intervals such as monthly or biannually.
+
+---
+### 1. Use your local machine:
+
+If you have sufficient hard drive space, regularly backup your `/work`
+directories to your personal computer. To avoid filling up your personal
+hard-drives, consider using an external drive that can easily be placed
+in a fireproof safe or at an off-site location for an extra level of
+protection. To do this, you can either use [Globus
+Connect]({{< relref "/handling_data/data_transfer/globus_connect/_index.md" >}}) or an
+SCP client, such
+as <a href="https://cyberduck.io/" class="external-link">Cyberduck</a> or <a href="https://winscp.net/eng/index.php" class="external-link">WinSCP</a>.
+For help setting up an SCP client, check out our [Connecting Guides]({{< relref "/connecting" >}}).
+
+For those worried about personal hard drive crashes, UNL
+offers <a href="http://nsave.unl.edu/" class="external-link">the backup service NSave</a>.
+For a small monthly fee, users can install software that will
+automatically backup selected files from their personal machine.
+
+Benefits:
+
+- Gives you full control over what is backed up and when.
+- Doesn't require the use of third party servers (when using SCP
+ clients).
+- Take advantage of our high speed data transfers (10 Gb/s) when using
+ Globus Connect or [setup your SCP client to use our dedicated high
+ speed transfer
+ servers]({{< relref "/handling_data/data_transfer/high_speed_data_transfers.md" >}})
+
+Limitations:
+
+- The amount you can backup is limited by available hard-drive space.
+- Manual backups of many files can be time consuming.
+
+---
+### 2. Use Git to preserve files and revision history:
+
+Git is a revision control service which can be run locally or can be
+paired with a repository hosting service, such
+as <a href="http://www.github.com/" class="external-link">GitHub</a>, to
+provide a remote backup of your files. Git works best with smaller files
+such as source code and manuscripts. Anyone with an InCommon login can
+utilize <a href="http://git.unl.edu/" class="external-link">UNL's GitLab Instance</a>,
+for free.
+
+Benefits:
+
+- Git is naturally collaboration-friendly, allowing multiple people to
+ easily work on the same project and provides great built-in tools to
+ control contributions and managing conflicting changes.
+- Create individual repositories for each project, allowing you to
+ compartmentalize your work.
+- Using UNL's GitLab instance allows you to create private or internal
+ (accessible by anyone within your organization) repositories.
+
+Limitations:
+
+- Git is not designed to handle large files. GitHub does not allow
+ files larger than 100MB unless using
+ their <a href="https://help.github.com/articles/about-git-large-file-storage/" class="external-link">Git Large File Storage</a> and
+ tracking files over 1GB in size can be time consuming and lead to
+ errors when using other repository hosts.
+
+---
+### 3. Use Attic:
+
+HCC offers
+long-term, <a href="https://en.wikipedia.org/wiki/Nearline_storage" class="external-link">near-line</a> data
+storage
+through [Attic]({{< relref "using_attic" >}}).
+HCC users with an existing account
+can <a href="http://hcc.unl.edu/attic" class="external-link">apply for an Attic account</a> for
+a <a href="http://hcc.unl.edu/priority-access-pricing" class="external-link">small annual fee</a> that
+is substantially less than other cloud services.
+
+Benefits:
+
+- Attic files are backed up regularly at both HCC locations in Omaha
+ and Lincoln to help provide disaster tolerance and a second security
+ layer against file loss.
+- No limits on individual or total file sizes.
+- High speed data transfers between Attic and the clusters when using
+ [Globus Connect]({{< relref "/handling_data/data_transfer/globus_connect/_index.md" >}}) and [HCC's high-speed data
+ servers]({{< relref "/handling_data/data_transfer/high_speed_data_transfers.md" >}}).
+
+Limitations:
+
+- Backups must be done manually which can be time consuming. Setting
+ up automated scripts can help speed up this process.
+
+---
+### 4. Use a cloud-based service, such as Box:
+
+Many of us are familiar with services such as Google Drive, Dropbox, Box
+and OneDrive. These cloud-based services provide a convenient portal for
+accessing your files from any computer. NU offers OneDrive and Box
+services to all students, staff and faculty. But did you know that you
+can link your Box account to HCC’s clusters to provide quick and easy
+access to files stored there? [Follow a few set-up
+steps]({{< relref "integrating_box_with_hcc" >}}) and
+you can add files to and access files stored in your Box account
+directly from HCC clusters. Setup your submit scripts to automatically
+upload results as they are generated or use it interactively to store
+important workflow scripts and maintain a backup of your analysis
+results.
+
+Benefits:
+
+- <a href="http://box.unl.edu/" class="external-link">Box@UNL</a> offers
+ unlimited file storage while you are associated with UNL.
+- Integrating with HCC clusters provides a quick and easy way to
+ automate backups of analysis results and workflow scripts.
+
+Limitations:
+
+- Box has individual file size limitations, larger files will need to
+ be backed up using an alternate method.
+
+---
+### 5. Copy important files to `/home`:
+
+While `/work` files and directories are not backed up, files and
+directories in `/home` are backed up on a daily basis. Due to the
+limitations of the `/home` filesystem, we strongly recommend that only
+source code and compiled programs are backed up to `/home`. If you do
+use `/home` to backup datasets, please keep a working copy in your
+`/work` directories to prevent negatively impacting the functionality of
+the cluster.
+
+Benefits:
+
+- No need to make manual backups. `\home` files are automatically backed
+ up daily.
+- Files in `/home` are not subject to the 6 month purge policy that
+ exists on `/work`.
+- Doesn't require the use of third-party software or tools.
+
+Limitations:
+
+- Home storage is limited to 20GB per user. Larger files sets will
+ need to be backed up using an alternate method.
+- Home is read-only on the cluster worker nodes so results cannot be
+ directly written or altered from within a submitted job.
+
+
+If you would like more information or assistance in setting up any of
+these methods, contact us
+at <a href="mailto:hcc-support@unl.edu" class="external-link">hcc-support@unl.edu</a>.
+
+
diff --git a/content/handling_data/data_storage/using_attic.md b/content/handling_data/data_storage/using_attic.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..cdc6c1f4dad5dfb6b6af575a9fa1a2c809bc627f 100644
--- a/content/handling_data/data_storage/using_attic.md
+++ b/content/handling_data/data_storage/using_attic.md
@@ -0,0 +1,105 @@
++++
+title = "Using Attic"
+description = "How to store data on Attic"
+weight = 20
++++
+
+For users who need long-term storage for large amount of data, HCC
+provides an economical solution called Attic. Attic is a reliable
+<a href="https://en.wikipedia.org/wiki/Nearline_storage" class="external-link">near-line data archive</a> storage
+system. The files in Attic can be accessed and shared from anywhere
+using [Globus
+Connect]({{< relref "/handling_data/data_transfer/globus_connect" >}}),
+with a fast 10Gb/s link. Also, the data in Attic is backed up between
+our Lincoln and Omaha facilities to ensure high availability and
+disaster tolerance. The data and user activities on Attic are subject to
+our
+<a href="http://hcc.unl.edu/hcc-policies" class="external-link">HCC Policies</a>.
+
+---
+### Accounts and Cost
+
+To use Attic you will first need an
+<a href="https://hcc.unl.edu/new-user-request" class="external-link">HCC account</a>, and
+then you may request an
+<a href="http://hcc.unl.edu/attic" class="external-link">Attic allocation</a>.
+We charge a small fee per TB per year, but it is cheaper than most
+commercial cloud storage solutions. For the user application form and
+cost, please see the
+<a href="http://hcc.unl.edu/attic" class="external-link">HCC Attic page</a>.
+
+---
+### Transfer Files Using Globus Connect
+
+The easiest and fastest way to access Attic is via Globus. You can
+transfer files between your computer, our clusters ($HOME, $WORK, and $COMMON on
+Crane or Rhino), and Attic. Here is a detailed tutorial on
+how to set up and use [Globus Connect]({{< relref "/handling_data/data_transfer/globus_connect" >}}). For
+Attic, use the Globus Endpoint **hcc\#attic**. Your Attic files are
+located at `~, `which is a shortcut
+for `/attic/<groupname>/<username>`.
+**Note:** *If you are accessing Attic files from your supplementary
+group, you should explicitly set the path to
+/attic/<supplementary\_groupname>/. If you don't do that, by
+default the endpoint will try to place you in your primary group's Attic
+path, to which access will be denied if the primary group doesn't have an Attic allocation.*
+
+---
+### Transfer Files Using SCP/SFTP/RSYNC
+
+The transfer server for Attic storage is `attic.unl.edu` (or `attic-xfer.unl.edu`).
+
+{{% panel theme="info" header="SCP Example" %}}
+{{< highlight bash >}}
+scp /source/file <username>@attic.unl.edu:~/destination/file
+{{< /highlight >}}
+{{% /panel %}}
+
+{{% panel theme="info" header="SFTP Example" %}}
+{{< highlight bash >}}
+sftp <username>@attic.unl.edu
+Password:
+Duo two-factor login for <username>
+Connected to attic.unl.edu.
+sftp> pwd
+Remote working directory: /attic/<groupname>/<username>
+sftp> put source/file destination/file
+sftp> exit
+{{< /highlight >}}
+{{% /panel %}}
+
+{{% panel theme="info" header="RSYNC Example" %}}
+{{< highlight bash >}}
+# local to remote rsync command
+rsync -avz /local/source/path <username>@attic.unl.edu:remote/destination/path
+
+# remote to local rsync command
+rsync -avz <username>@attic.unl.edu:remote/source/path /local/destination/path
+{{< /highlight >}}
+{{% /panel %}}
+
+You can also access your data on Attic using our [high-speed
+transfer servers]({{< relref "/handling_data/data_transfer/high_speed_data_transfers" >}}) if you prefer.
+Simply use scp or sftp to connect to one of the transfer servers, and
+your directory is mounted at `/attic/<groupname>/<username>`.
+
+---
+### Check Attic Usage
+
+The usage and quota information for your group and the users in the
+group are stored in a file named "disk\_usage.txt" in your group's
+directory (`/attic/<groupname>`). You can use either [Globus Connect]({{< relref "/handling_data/data_transfer/globus_connect" >}}) or
+scp to download it. Your usage and expiration is also shown in the web
+interface (see below).
+
+---
+### Use the web interface
+
+For convenience, a web interface is also provided. Simply go to
+<a href="https://attic.unl.edu" class="external-link">https://attic.unl.edu</a>
+and login with your HCC credentials. Using this interface, you can see
+your quota usage and expiration, manage files, etc. **Please note we do
+not recommend uploading/downloading large files this way**. Use one of
+the other transfer methods above for large datasets.
+
+
diff --git a/content/handling_data/data_transfer/globus_connect/activating_hcc_cluster_endpoints.md b/content/handling_data/data_transfer/globus_connect/activating_hcc_cluster_endpoints.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..a4b1a339824ae92bc6410c506c86ed60fe8b4968 100644
--- a/content/handling_data/data_transfer/globus_connect/activating_hcc_cluster_endpoints.md
+++ b/content/handling_data/data_transfer/globus_connect/activating_hcc_cluster_endpoints.md
@@ -0,0 +1,39 @@
++++
+title = "Activating HCC Cluster Endpoints"
+description = "How to activate HCC endpoints on Globus"
+weight = 20
++++
+
+You will not be able to transfer files to or from an HCC endpoint using Globus Connect without first activating the endpoint. Endpoints are available for Crane (`hcc#crane`), Rhino, (`hcc#rhino`), and Attic (`hcc#attic`). Follow the instructions below to activate any of these endpoints and begin making transfers.
+
+1. [Sign in](https://www.globus.org/SignIn) to your Globus account using your campus credentials or your Globus ID (if you have one). Then click on 'Endpoints' in the left sidebar.
+{{< figure src="/images/Glogin.png" >}}
+{{< figure src="/images/endpoints.png" >}}
+
+2. Find the endpoint you want by entering '`hcc#crane`', '`hcc#rhino`', or '`hcc#attic`' in the search box and hit 'enter'. Once you have found and selected the endpoint, click the green 'activate' icon. On the following page, click 'continue'.
+{{< figure src="/images/activateEndpoint.png" >}}
+{{< figure src="/images/EndpointContinue.png" >}}
+
+3. You will be redirected to the HCC Globus Endpoint Activation page. Enter your *HCC* username and password (the password you usually use to log into the HCC clusters).
+{{< figure src="/images/hccEndpoint.png" >}}
+
+4. Next you will be prompted to
+ provide your *Duo* credentials. If you use the Duo Mobile app on
+ your smartphone or tablet, select 'Duo Push'. Once you approve the notification that is sent to your phone,
+ the activation will be complete. If you use a Yubikey for
+ authentication, select the 'Passcode' option and then press your
+ Yubikey to complete the activation. Upon successful activation, you
+ will be redirected to your Globus *Manage Endpoints* page.
+{{< figure src="/images/EndpointPush.png" >}}
+{{< figure src="/images/endpointComplete.png" >}}
+
+The endpoint should now be ready
+and will not have to be activated again for the next 7 days.
+To transfer files between any two HCC clusters, you will need to
+activate both endpoints individually.
+
+Next, learn how to [make file transfers between HCC endpoints]({{< relref "/handling_data/data_transfer/globus_connect/file_transfers_between_endpoints" >}}) or how to [transfer between HCC endpoints and a personal computer]({{< relref "/handling_data/data_transfer/globus_connect/file_transfers_to_and_from_personal_workstations" >}}).
+
+---
+
+
diff --git a/content/handling_data/data_transfer/globus_connect/file_transfers_between_endpoints.md b/content/handling_data/data_transfer/globus_connect/file_transfers_between_endpoints.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..4379b43de6922a206b9b0f9c0c9522729e5dc7ba 100644
--- a/content/handling_data/data_transfer/globus_connect/file_transfers_between_endpoints.md
+++ b/content/handling_data/data_transfer/globus_connect/file_transfers_between_endpoints.md
@@ -0,0 +1,50 @@
++++
+title = "File Transfers Between Endpoints"
+description = "How to transfer files between HCC clusters using Globus"
+weight = 30
++++
+
+To transfer files between HCC clusters, you will first need to
+[activate]({{< relref "/handling_data/data_transfer/globus_connect/activating_hcc_cluster_endpoints" >}}) the
+two endpoints you would like to use (the available endpoints
+are: `hcc#crane` `hcc#rhino`, and `hcc#attic`). Once
+that has been completed, follow the steps below to begin transferring
+files. (Note: You can also transfer files between an HCC endpoint and
+any other Globus endpoint for which you have authorized access. That
+may include a [personal
+endpoint]({{< relref "/handling_data/data_transfer/globus_connect/file_transfers_to_and_from_personal_workstations" >}}),
+a [shared
+endpoint]({{< relref "/handling_data/data_transfer/globus_connect/file_sharing" >}}),
+or an endpoint on another computing resource or cluster. Once the
+endpoints have been activated, the file transfer process is generally
+the same regardless of the type of endpoints you use. For demonstration
+purposes we use two HCC endpoints.)
+
+1. Once both endpoints for the desired file transfer have been
+ activated, [sign in](https://www.globus.org/SignIn) to
+ your Globus account (if you are not already) and select
+ "Transfer or Sync to.." from the right sidebar. If you have
+ a small screen, you may have to click the menu icon
+ first.
+{{< figure src="/images/Transfer.png">}}
+
+2. Enter the names of the two endpoints you would like to use, or
+ select from the drop-down menus (for
+ example, `hcc#attic` and `hcc#crane`). Enter the
+ directory paths for both the source and destination (the 'from' and
+ 'to' paths on the respective endpoints). Press 'Enter' to view files
+ under these directories. Select the files or directories you would
+ like to transfer (press *shift* or *control* to make multiple
+ selections) and click the blue highlighted arrow to start the
+ transfer.
+{{< figure src="/images/startTransfer.png" >}}
+
+3. Globus will display a message when your transfer has completed
+ (or in the unlikely event that it was unsuccessful), and you will
+ also receive an email. Select the 'refresh' icon to see your file
+ in the destination folder.
+{{< figure src="/images/transferComplete.png" >}}
+
+---
+
+
diff --git a/content/handling_data/data_transfer/high_speed_data_transfers.md b/content/handling_data/data_transfer/high_speed_data_transfers.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..922eb4b43902030f7a09eeb1f96332fea2bbe294 100644
--- a/content/handling_data/data_transfer/high_speed_data_transfers.md
+++ b/content/handling_data/data_transfer/high_speed_data_transfers.md
@@ -0,0 +1,28 @@
++++
+title = "High Speed Data Transfers"
+description = "How to transfer files directly from the transfer servers"
+weight = 10
++++
+
+Crane, Rhino, and Attic each have a dedicated transfer server with
+10 Gb/s connectivity that allows
+for faster data transfers than the login nodes. With [Globus
+Connect]({{< relref "globus_connect" >}}), users
+can take advantage of this connection speed when making large/cumbersome
+transfers.
+
+Those who prefer scp, sftp or
+rsync clients can also benefit from this high-speed connectivity by
+using these dedicated servers for data transfers:
+
+Cluster | Transfer server
+----------|----------------------
+Crane | `crane-xfer.unl.edu`
+Rhino | `rhino-xfer.unl.edu`
+Attic | `attic-xfer.unl.edu`
+
+{{% notice info %}}
+Because the transfer servers are login-disabled, third-party transfers
+between `crane-xfer`, and `attic-xfer` must be done via [Globus Connect]({{< relref "globus_connect" >}}).
+{{% /notice %}}
+
diff --git a/content/submitting_jobs/app_specific/submitting_an_openmp_job.md b/content/submitting_jobs/app_specific/submitting_an_openmp_job.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..a49e62292515d1763d8542414069240a8ff984c3 100644
--- a/content/submitting_jobs/app_specific/submitting_an_openmp_job.md
+++ b/content/submitting_jobs/app_specific/submitting_an_openmp_job.md
@@ -0,0 +1,42 @@
++++
+title = "Submitting an OpenMP Job"
+description = "How to submit an OpenMP job on HCC resources."
++++
+
+Submitting an OpenMP job is different from
+[Submitting an MPI Job]({{< relref "submitting_an_mpi_job" >}})
+since you must request multiple cores from a single node.
+
+{{% panel theme="info" header="OpenMP example submission" %}}
+{{< highlight batch >}}
+#!/bin/sh
+#SBATCH --ntasks-per-node=16 # 16 cores
+#SBATCH --nodes=1 # 1 node
+#SBATCH --mem-per-cpu=1024 # Minimum memory required per CPU (in megabytes)
+#SBATCH --time=03:15:00 # Run time in hh:mm:ss
+#SBATCH --error=/work/[groupname]/[username]/job.%J.err
+#SBATCH --output=/work/[groupname]/[username]/job.%J.out
+
+export OMP_NUM_THREADS=${SLURM_NTASKS_PER_NODE}
+./openmp-app.exe
+{{< /highlight >}}
+{{% /panel %}}
+
+Notice that we used `ntasks-per-node` to specify the number of cores we
+want on a single node. Additionally, we specify that we only want
+1 `node`.
+
+`OMP_NUM_THREADS` is required to limit the number of cores that OpenMP
+will use on the node. It is set to ${SLURM_NTASKS_PER_NODE} to
+automatically match the `ntasks-per-node` value (in this example 16).
+
+### Compiling
+
+Directions to compile OpenMP can be found on
+[Compiling an OpenMP Application]
+({{< relref "/applications/user_software/compiling_an_openmp_application" >}}).
+
+### Further Documentation
+
+Further OpenMP documentation can be found on LLNL's
+[OpenMP](https://computing.llnl.gov/tutorials/openMP) website.