POD Resources and Access

Available PODs

The table below describes the available BRCF PODs, servers and currently available groups. Unless otherwise noted, PODs authenticate using BRCF account credentials initialized by the user in the BRCF account management application (https://rctf-account-request.icmb.utexas.edu).

Multiple POD group membership

Depending on your affiliations, you may have access to more than one POD. For example, you may have active accounts on both the Lambowitz/CCBB POD and the GSAF POD.

You may also belong to more than one group on a given POD. For example, you may belong to both the Hofmann and GSAF groups on the GSAF POD. These POD groups control which shared Work and Scratch areas you can access.

To see what groups you are a member of, use the groups shell command from any POD compute server. For example:

$ groups
Hofmann GSAF

The first group in this list is your current group, which determines the group ownership for files you create. To change your current group so that new files are marked with a different group, use the newgrp shell command. For example:

$ groups
Hofmann GSAF

$ newgrp - GSAF
$ groups
GSAF Hofmann

You can also change the group assigned to existing files/directories using the chgrp command. For example, to make sure all files and directories under a particular directory are associated with a specific group, you would execute this command:

chgrp -R Hofmann <path_to_directory>

POD delegates

POD delegates act as local liaison to the BRCF for member organizations. Their responsibilities include:

• Communicate and help enforce BRCF policies among their colleagues
• Approve requests for user accounts
• Recommend user and group quotas
• Implement and monitor sub-directory organization in shared Work and Scratch areas
• Evaluate requests for additional software installations and communicate such to BRCF
  
  • may also perform test installations to evaluate new software functionality
• May have administrative rights on compute servers to assist their POD users with common issues (e.g. permissions)

POD access

By directive of UT's Information Security Office (ISO), starting April 1, 2019, access to BRCF POD resources (including ssh) will be blocked from outside the UT campus network. See UT VPN service setup for how to set up the UT Virtual Private Network (VPN) service to enable off-campus access or access from the Dell Medical School.

The table below lists the available POD resources and how they can be accessed. In addition, this remote_computing_software_download_instructions.pdf PDF provides detailed information about how to configure the UT VPN service, set up Duo 2-factor authenticaion, and installing software for remote SSH access in Windows.

POD compute server shell access

Compute servers can be accessed via ssh using either their formal BRCF name or their alias. For Mac and Linux users, ssh is available from any Terminal window. For Windows users, any SSH client program can be used, such as PuTTY (http://www.putty.org/).

ssh <brcf_account>@<compute_server>.ccbb.utexas.edu

# for example
ssh abattenh@cbrscomp01.ccbb.utexas.edu

ssh access is available from UT campus network addresses, with public key encryption (see below) or by using the UT VPN service.

POD storage server file transfer access

BRCF storage servers do not offer interactive shell (ssh) access. However, they provide direct file transfer capability via scp or rsync. Using the storage server as a file transfer target is useful when you have many files and/or large files, as it provides direct access to the shared storage.

See this FAQ for usage tips: Transferring data to/from PODs

Direct storage server file transfers are available from UT campus network addresses or using the UT VPN service.

Passwordless Access via SSH/SFTP

You can set up password-less access to the pod nodes via ssh from a specific, trusted machine (your office machine, laptop, another POD, TACC, etc). 

To set up password-less ssh access from any Linux-like environment (e.g. Mac Terminal, cygwin on Windows, Windows 10 Linux subsystem), follow the steps below. If you are using Windows PuTTY see this documentation: https://linux-audit.com/using-ssh-keys-instead-of-passwords/. 

1. On the machine and account you want to ssh/sftp from (e.g. your laptop), generate a SSH key pair if you don't already have one:

   mkdir -p ~/.ssh
   chmod 700 ~/.ssh
   cd ~/.ssh
   if [ ! -f id_rsa ]; then ssh-keygen -b 4096 -t rsa; fi

   Use the default answers for ssh-keygen, and do not specify a password. This creates a public/private key pair in your local ~/.ssh directory (id_rsa.pub and id_rsa, respectively).
   
   

2. Install your public key on the server you want to login to (e.g. any one of your POD compute nodes)
   1. If you are off campus and do not have access to the UT VPN service, Contact Us via email, so we can install it for you.
      • include your BRCF account name and attach your public key (~/.ssh/id_rsa.pub).

   2. If you are on campus or have access to the UT VPN service, you can use the ssh-copy-id command:

      ssh-copy-id user@hostname

      • If you are setting this up from off campus, you need to have the UT VPN service active for this command to work remotely.

      • If you are prompted to accept the SSH Host Key for the node you are connecting to, type "yes" to do so.

      • You will be asked for your password for the user@hostname, so enter it when prompted.

3. Login to the machine to make sure it is working properly:

   ssh user@hostname
   # requirement to use non-standard port 222 removed June 2022
   

   • If you are prompted for a password, then something went wrong with the setup. 

     • Most likely, it is file permissions on your local or remove home directory

       • You must not have group or world write access to your home directory or your ~/.ssh directory).

     • If you have multiple SSH keys (RSA, DSA, etc) on the machine you are connecting from then it could also be using the wrong key to connect.

Samba remote file system access

The Samba remote file system protocol allows you to mount POD storage from your desktop or laptop as if it were a local file system. Samba access is available from UT network addresses or using the UT VPN service. In addition, this remote_computing_software_download_instructions.pdf PDF provides detailed information about how to configure the UT VPN service, set up Duo 2-factor authentication, and installing software for remote SSH access in Windows.

Samba access to your Home directory and to shared Work areas is available on most PODs for most POD groups.

• The Samba server name is the POD's storage server name as listed above.
• The Samba share name for your Home directory is always users.
• The Samba share name for shared Work areas is the group name.
• Be sure to provide your BRCF account credentials to authenticate (by default your laptop or desktop account is uses)

For Mac users, Samba resource access syntax is of the form smb://<server_name>/<share_name>. For example:

• smb://gsafstor01.ccbb.utexas.edu/users – Samba share for an individual Home directory on the GSAF POD.
• smb://gsafstor01.ccbb.utexas.edu/GSAF – Samba share for an the GSAF group's shared Work directory on the GSAF POD.

For Windows users, Samba resource access syntax is of the form \\<server_name>\<share_name>. For example:

• \\gsafstor01.ccbb.utexas.edu\users – Samba share for an individual Home directory on the GSAF POD.
• \\gsafstor01.ccbb.utexas.edu\GSAF – Samba share for an the GSAF group's shared Work directory on the GSAF POD.

POD file systems

All of the POD compute servers have access to their own shared storage, where each user has an individual Home directory and shared Work and Scratch areas.

Home directory

Your Home directory on a POD is located under /stor/home. Home directories are meant for storing small files. All home directories have a 100 GB quota.

By default you are only allowed access to your own Home directory, although you may be able to view Home directory contents for other members of your group depending on the group's permissions policy.

Home directories are backed up weekly, and have snapshots enabled.

Home directory snapshots

Read-only snapshots are periodically taken of your home directory contents. Like Windows backups or Macintosh Time Machine, these backups only consume disk space for files that change (are updated or deleted), in which case the previous file state is "saved" in a snapshot.

Snapshots are stored in a .zfs/snapshot directory under your home directory. To see a list of the snapshots you currently have:

ls ~/.zfs/snapshot

To recover a changed or deleted file, first identify the snapshot it is in, then just copy the file from that snapshot directory to its desired location.

Home directory quotas

Your 100 GB Home directory includes snapshot data. These snapshot backups only consume disk space for files that change (are updated or deleted), in which case the previous file state is "saved" in the snapshot. Snapshots are taken frequently, so their data persists for several months even if the associated Home directory file has been deleted.

The main consequence of this snapshot behavior is that they can cause your 100 GB Home directory quota to be exceeded, even after non-snapshot files have been removed.

While you can view and copy files in your ~/.zfs/snapshot snapshot directories, you cannot write to them or delete them. Please contact us at rctf-support@utexas.edu to remove your snapshots if you exceed your Home directory quota.

Note that some Home directory sub-directories can become quite large, such as ~/.local/share/rstudio where session files can be very big and cause your Home directory quota to be exceeded. A solution is to copy directories like this to your Scratch area and symbolic link to them. For example:

mkdir -p /stor/scratch/BCG/abattenh/home_extra
rsync -avrP ~/.local/share/rstudio/ /stor/scratch/BCG/abattenh/home_extra/local_rstudio/
rm -rf ~/.local/share/rstudio
ln -sf /stor/scratch/BCG/abattenh/home_extra/local_rstudio/ ~/.local/share/rstudio

Another case involves using  R Studio Server or JupyterHub Server on a POD. Both these web-based applications by default use Home directories as the default working directory. This is not an issue as long as files created there are relatively small, but data directories for larger projects should be located in Work or Scratch. Navigation to these areas can be simplified using symbolic links as shown below.

Similarly, if large files are first copied to Home (e.g. when transferred from TACC), then moved to Work or Scratch, they may still take Home directory snapshot space if a snapshot is taken before they are moved (and snapshots are taken frequently). To avoid this issue, always transfer files directly to Work or Scratch. You can create a symbolic link to these areas in your Home directory to help with this. For example:

# change to your home directory where the symlinks will be created
cd 
ln -sf /stor/work/BCG bcg_work
ln -sf /stor/scratch/BCG bcg_scratch

# Then, use the symbolic link when copying data from TACC
rsync -avrW $SCRATCH/analysis/ abattenh@cbrsstor01.ccbb.utexas.edu:~/bcg_scratch/analysis/

Shared Work and Scratch areas

Shared Work and Scratch areas are available for each POD group under /stor/work/<GroupName> and /stor/scratch/<GroupName> (for example, /stor/work/Hofmann, /stor/scratch/Hofmann). These areas are accessible only by members of the named group. Users can find out which group or groups they belong to by typing the groups command on the command line.

These Work and Scratch areas are designed for storage of shared project artifacts, so they have no predefined structure (i.e. user directories are not automatically created). Group members may create any directory structure that is meaningful to the group's work.

Shared Work areas are backed up weekly. Scratch areas are not backed up. Both Work and Scratch areas may have quotas, depending on the POD (e.g. on the Rental or GSAF pod); such quotas are generally in the multi-terabyte range.

Because it has a large quota and is regularly backed up and archived, your group's Work area is where large research artifacts that need to be preserved should be located.

Scratch, on the other hand, can be used for artifacts that are transient or can easily be re-created (such as downloads from public databases).

See Manage storage areas by project activity for important guidelines for Work and Scratch area contents.

Weekly backups

All Home and Work directories are backed up weekly to a separate backup storage server (spinning disk). Backups take place sometime between Friday and Monday mornings and are currently not incremental backups.

Note that any directory in any file system tree named tmp, temp, or backups is not backed up. Directories with these names are intended for temporary files, especially large numbers of small temporary files. See "Cannot create tempfile" error and Avoid having too many small files.

Periodic and long-term archiving

Data on the backup server are periodically archived to TACC's Ranch tape archive roughly once a year. Current archives are as of:

• 2024-01 (many but not all PODs)
• 2022-01
• 2020-07

In addition, to avoid re-archiving the same directories multiple times, we maintain a "long term archives (LTA)" directory that contains data from projects that are no longer active. Such project data may have been transferred to a group's Scratch area to avoid consuming backup space, or may have been removed from POD storage entirely after archiving to avoid consuming storage server space.

Please Contact Us if you need something retrieved from tape archives.

Using POD resources wisely

Remember that PODs are shared resources, and it is important to be aware of how your work can affect others trying to use POD resources. Here are some tips for using POD resources wisely.

Storage management considerations

Manage storage areas by project activity

Shared POD storage servers are high capacity (~50 to ~250 TB), but space is not infinite! The same goes for backup storage, since the BRCF must have capacity to back up all POD Home and Work areas. The following guidelines will help you and your colleagues stay within storage limits.

There are several types of data activity that determine where the data should reside:

• Data that is active, such as project directories where new files are added and ongoing analysis is taking place.
  • This data belongs in your Work area where it is regularly backed up.
• Data that is no longer active, or is active but read-only) but needs to be accessible for reference, and needs to be preserved.
  
  • E.g. projects that are complete but that you refer to from time to time.
  • This data belongs in your Scratch area so that it does not consume backup space.
  • Please contact us at rctf-support@utexas.edu to request that a long-term archive of the data be made to tape.
    • We can also efficiently move the data from Work to Scratch for you since we can access the storage server directly.
• Data that is no longer active and does not need to be referenced, but needs to be preserved.
  
  • This data can be removed entirely so that it does not consume either storage server or backup server space.
• External/public data or downloaded software that needs to be accessible but does not need to be backed up or preserved.
  • This data always belongs in Scratch since it can be re-downloaded if necessary.
• Data that is no longer active, does not need to be referenced, and does not need to be preserved.
  • You can delete this data yourself, or contact us to remove the data for you (we can do this efficiently since we can access the storage server directly).

This table summarizes these guidelines.

Avoid having too many small files

While the ZFS file system we use is quite robust, we can experience issues in the weekly backup and periodic archiving process when there are too many small files in a directory tree.

What is too many? Ten million or more.

If the files are small, they don't take up much storage space. But the fact that there are so many causes the backup or archiving to run for a really long time. For weekly backups, this can mean that the previous week's backup is not done by the time the next one starts. For archiving, it means it can take weeks on end to archive a single directory that has many millions of small files.

Backing up gets even worse when a directory with many files is just moved or renamed. In this case the files need to be deleted from the old location and added to the new one – and both of these operations can be extremely long-running.















To see how many files (termed "inodes" in Unix) there are under a directory tree, use the df -i command. For example:

df -i /stor/work/MyGroup/my_dir

The results might look something like this:

Filesystem               Inodes     IUsed        IFree IUse% Mounted on
stor/work/MyGroup  103335902213  28864562 103307037651    1% /stor/work/MyGroup

The IUsed column (here 28864562) is the number of inodes (files plus directories) in the directory tree listed under Filesystem (here /stor/work/MyGroup). Note that the reported Filesystem may be different from the one you queried, depending on the structure of the ZFS file systems.

There are a several work-arounds for this issue.

1) Move the files to a temporary directory.
The backup process excludes any sub-directory anywhere in the file system directory tree named tmp, temp, or backups. So if there are files you don't care about, just rename the directory to, for example, tmp. There will be a one-time deletion of the directory under its previous name, but that would be it. 

2) Move the directories to a Scratch area.
Scratch areas are not backed up, so will not cause an issue. The directory can be accessed from your Work area via a symbolic link. Please Contact Us if you would like us to help move large directories of yours to Scratch (we can do it more efficiently with our direct access to the storage server).

3) Zip or Tar the directory
If these are important files you need to have backed up, ziping or taring the directory is the way to go. This converts a directory and all its contents into a single, larger file that can be backed up or archived efficiently. Please Contact Us if you would like us to help with this, since with our direct access to the storage server we can perform zip and tar operations much more efficiently than you can from a compute server.

If your analysis pipeline creates many small files as a matter of course, you should consider modifying the processing to create small files in a tmp directory then ziping or taring the as a final step.

Memory usage considerations

Using too much RAM can quickly make a compute server unusable. When a system's main random access memory (RAM) is filled and additional memory requests are made, "pages" of main memory will be written out to "swap" space on disk, then read back in when again needed. Since disk I/O is on the order of 1,000 times slower than RAM access, swapping can slow a system down considerably.

And in a pathological (but unfortunately not uncommon) pattern, a program (or programs) that need more memory than available can cause "thrashing" where swapping in and out of RAM is happening continuously. This will bring a computer to its knees, making it virtually impossible to do anything on it (slow logins, or logins timing out; any simple command just "hanging" for a long time or never returning). Note that when each processes a user starts itself spawns multiple threads, as described at Do not run too many processes, this situation can happen. We monitor system usage, and will intervene when we see this happen, by termininating the offending process(es) if possible, or by rebooting the compute server if not.

You can avoid causing a problem like this by following this advice:

Tips:

• Know the memory configuration of the compute server you're using
  • free -g will show you total RAM and swap in Gigabytes
• Before starting a memory intensive job, check the system's current memory status
  • free -g also shows used and available for both main memory and swap
• Know the memory requirements of your program.
  • Monitor the memory usage of one typical process while it is running using top (see https://www.booleanworld.com/guide-linux-top-command/) or htop
  • This is particularly important if you plan to run multiple instances of a program, since it will guide you in knowing how many such instances you should run.
• Use ulimit -H -m <max_ram> to limit the memory a given process can use.
  • e.g. to ensure a 25G memory limit is enforced and inherited by child processes:
    ( ulimit -H -m 25000000000 && exec <program or script> )
    • see https://ss64.com/bash/ulimit.html
  • or this, which uses cgroups to manage memory for the program and all child processes/threads:
    systemd-run  --user --scope -p MemoryMax=300G <program or script>
    • see https://manpages.debian.org/stretch/systemd/systemd-run.1.en.html
• Run memory intensive processes when system load is otherwise light (e.g. overnight)
• No single user should run programs that use excessive RAM
  
  • Less than 75% of total RAM if running when system load is otherwise light (e.g. overnight), and the programs are not expected to run for more than a few hours
  • Less than 25% of total RAM otherwise

Computational considerations

This section describes a number of computation-related considerations.

Multi-processing: cores vs hyperthreads

Many programs offer an option to divide their work among multiple processes, which can reduce the total clock time the program will run. The option may refer to "processes", "cores" or "threads", but actually target the available computing units on a server. Examples include: samtools sort --threads option; bowtie2 -p/--threads option; in R, library(doParallel); registerDoParallel(cores = NN) and the OMP_NUM_THREADS environment variable for OpenMP programs.

A "computing unit" is a server's cores and hyperthreads, and it is important to keep in mind the difference between the two. Cores are physical computing units, while hyperthreads are virtual computing units – kernel objects that "split" each core into two hyperthreads so that the single compute unit can be used by two processes.

The POD Resources and Access: AvailablePODs table describes the compute servers that are associated with each BRCF pod, along with their available cores and (hyper)threads. (Note that most servers are dual-CPU, meaning that total core count is double the per-CPU core count, so a dual 4-core CPU machine would have 8 cores.) You can also see the hyperthread and core counts on any server via:

cat /proc/cpuinfo | grep -c 'core id'           # actually the number of hyperthreads!
cat /proc/cpuinfo | grep 'siblings' | head -1   # the real number of physical cores

(Yes, the fact that 'core id' gives hyperthreads and 'siblings' the number of cores is confusing. But what do you expect -- this is Unix )

Since hyperthreads look like available computing units ("CPUs in OS displays), parallel processing options that detect "cores" usually really detect hyperthreads. Why does this matter? 

The bottom line:

• virtual Hyperthreads are useful if the work a process is doing periodically "yields", typically to perform input/output operations, since waiting for I/O allows the core to be used for other work. The majority of NGS tools fall into this category since they read/write sequencing and other data files.
• phycical Cores are best used when a program's work is compute-bound. When processing is compute bound -- as is typical of matrix-intensive machine learning algorithms -- hyperthreads actually degrade performance, because two compute-bound hyperthreads are competing for the same physical core, and there is OS-level overhead involved in process switching between the two.

So before you select a process/core/thread count for your program, consider whether it will perform significant I/O. If so, you can specify a higher count. If it is compute bound (e.g. machine learning), be sure to specify a count low enough to leave free hyperthreads for others to use.

Note that while you can't specify cores versus hyperthreads specifically, when you request some number of processes/corees/threads, the OS will allocate cores first, then hyperthreads.

Note also that the issue with machine learning (ML) workflows being incredibly compute bound is the main reason ML processing is best run on GPU-enabled servers, either at TACC or on one of the BRCF pods with GPUs (see BRCF GPU servers).

ps -ef | grep -v root | grep -v bash | grep -v sshd | grep -v screen | grep -v tmux | grep -v 'www-data'

Lower priority for large, long-running jobs

If you have one or more jobs that uses multiple threads, or does significant I/O, its execution can affect system responsiveness for other users.

To help avoid this, please use the renice tool to manipulate the priority of your tasks (a priority of 15 is a good choice). It's easy to do, and here's a quick tutorial: http://www.thegeekstuff.com/2013/08/nice-renice-command-examples/?utm_source=tuicool

For example, before you start any tasks, you can set the default priority to nice 15 as shown here. Anything you start from then on (from this shell) should inherit the nice 15 value.

renice +15 $$

Once you have tasks running, their priority can be changed for all of them by specifying your user name:

renice +15 -u `whoami`

or for a particular process id (PID):

renice +15 -p <some PID number>

Running processes unattended

While POD compute servers do not have a batch system, you can still run multiple tasks simultaneously in several different ways. 

For example, you can use terminal multiplexer tools like screen or tmux to create virtual terminal sessions that won't go away when you log off. Then, inside a screen  or  tmux  session you can create multiple sub-shells where you can run different commands.

You can also use the command line utility nohup to start processes in the background, again allowing you to log off and still have the process running.

 Here are some links on how to use these tools:

Input/Output considerations

Avoid heavy I/O load

Please be aware of the potential effects of the input/output (I/O) operations in your workflows.

Many common bioinformatics workflows are largely I/O bound; in other words, they do enough input/output that it is essentially the gating factor in execution time. This is in contrast to simulation or modeling type applications, which are essentially compute bound.

It is underappreciated that I/O is much more difficult to parallelize than compute. To add more compute power, one can generally just increase the number of processors, their speed, and optimize their CPU-to-memory architecture, which greatly affects compute-bound tasks.

I/O, on the other hand, is harder to parallelize. Large compute clusters such as TACC expose large single file system namespaces to users (e.g. Work, Scratch), but these are implemented using multiple redundant storage systems managed by a sophisticated parallel file system (Lustre, at TACC) to appear as one. Even so, file system outages at TACC caused by heavy I/O are not uncommon.

In the POD architecture, all compute servers share a common storage server, whose file system is accessed over a high-bandwidth local network (NFS over 10 Gbit ethernet). This means that heavy I/O to shared storage initiated from any compute server can negatively affect users on all compute servers.

For example, as few as three simultaneous invocations of gzip or samtools sort on large files can degrade system responsiveness for other users. If you notice that doing an ls or command completion on the command line seems to be taking forever, this can be a sign of an excessive I/O load (although very high compute loads can occasionally cause similar issues).

To gauge your program's I/O usage:

1. Run it on smaller datasets first
2. Check I/O effects by exercising tab-completion from the command line (see below)
   1. tab completion is directly impacted by I/O load, so if it slow there's too much I/O going on

ls /st                   # Typing this + Tab expands to /stor
ls /stor/sy              # Typing this + Tab expands to /stor/system
ls /stor/system/o        # Typing this + Tab expands to /stor/system/opt
ls /stor/system/opt/sam  # Typing this + Tab expands to /stor/system/opt/samtools (not uniquely)

# Typing this + Tab twice will list many possible completions:
ls /stor/system/opt/samtools/bam

Reduce the I/O priority of your processes

Similar to the way renice reduces the CPU priority of your processes (see above), ionice can reduce the I/O priority. This can be done for all your processes or for specific ones:

# lower I/O priority for process number <pid>
ionice -c 2 -n 7 -p <pid>

# lower I/O priority for all your processes
ionice -c 2 -n 7 -u <uid>

# and here's how to find your <uid> (user ID)
grep $USER /etc/passwd | awk -F ':' '{print $3}'

Transfer large files directly to the storage server

BRCF storage servers are just Linux servers, but ones you access from compute servers over a high-speed internal network. While they are not available for interactive shell (ssh) access; they provide direct file transfer capability via scp or rsync.

Using the storage server as a file transfer target is useful when you have many files and/or large files, as it provides direct access to the shared storage. Going through a compute server is also possible, but involves an extra step in the path – from the compute-server to its network-attached storage-server.

The solution is to target your POD's storage server directly using scp or rsync. When you do this, you are going directly to where the data is physically located, so you avoid extra network hops and do not burden heavily-used compute servers.

Please see this FAQ for more information: I'm having trouble transferring files to/from TACC.

Other available POD services

3rd party licensed software

We can, upon request, install 3rd party licensed software on your POD as long as an appropriate license is provided to us, and the software is compatible with our standard Ubuntu operating system. Please contact us at rctf-support@utexas.edu if you have such a request.

De-identified protected data

While our PODs are not HIPAA-compliant, we do support de-identified data that is considered sensitive/protected by the data provider, and we can provide appropriate information and protocols describing how we protect such data in data protection plan paperwork required by external data providers. These measures include:

• Protecting sensitive data directories using Unix group permissions, where group membership is controlled by the PI
• Disabling backups of sensitive data directories
• Disallowing Samba remote file system access to sensitive data directories
• Providing a group-specific temporary directory

Please contact us at rctf-support@utexas.edu if you would like to host protected data on your pod.

Data migration service

Upon request we can assist with transferring large datasets to your POD storage from other locations, such as external hard drives, servers at other institutions, or TACC. Please contact us at rctf-support@utexas.edu if you have such a request.

Temporary data transfer support

Since researchers often need to exchange large datasets with external collaborators, upon request, we can set up an area on your POD where an external collaborator can temporarily read/write data. Please contact us at rctf-support@utexas.edu if you have such a request, providing contact information for the external collaborator.

Instrument backup service

Many instruments have a Windows computer that controls the instrument and stores its data. Upon request we can set up automated and/or manual backups of data on Windows instrument-associated machines, using the Samba remote file system protocol to access a group-specific storage area on your POD. Please contact us at rctf-support@utexas.edu if you have such a request.

Educational and AMD GPU pods

The BRCF supports a dedicated Educational pod, reserved for official UT courses that have a computational component. The EDU pod provides the same RStudio Server and JupyterHub web-based applications as our standard Research pods, as well as the standard extensive set of command-line bioinformatics tools. We assist instructors in setting up courses and managing rosters, and troubleshoot issues that users encounter. Please contact us at rctf-support@utexas.edu if you would like to set up an EDU pod course.

We also support a special AMD GPU pod, which has 3 powerful servers with 8 AMD MI-100 GPUs each. This pod is available for acadmic courses that have a GPU computing component, and also for qualifying research groups. Please contact us at rctf-support@utexas.edu if you would like to set up an AMD GPU pod course or are interested in a Research allocation on it.

See this wiki page for further operational details on these Academic PODs

Online resourse for Workshops

We have provisioned a number of "Student" accounts on one of our pods that we make available for short-duration, non-academic workshops, such as those offered by the Center for Biomedical Research Support (CBRS). Please contact us at rctf-support@utexas.edu if you are interested in using this online resource to host a workshop.