Frontera User Guide
Last update: August 19, 2019 see revision history


  • 08/19/2019 Execute qlimits to display Frontera's queue configurations and charge rates.
  • 07/31/2019 The $WORK filesystem is not accessible on the Frontera compute nodes. Run your jobs out of $SCRATCH.
  • 07/22/2019 Queue limits are subject to change without notice.
  • 07/09/2019 You may now subscribe to Frontera User News.
  • 07/03/2019 Hyperthreading is not currently enabled on Frontera.
  • 06/20/2019 New users: read the Good Citizenship section.
  • 06/20/2019 Material in red represents temporary conditions, placeholders, or other content subject to change in the near future.
Figure 1. Frontera Art


Frontera is funded by the National Science Foundation (NSF) through award #1818253, Computing for the Endless Frontier. It is the largest cluster dedicated to open science in the United States and is the Texas Advanced Computing Center's latest flagship system. Frontera enters production in early summer 2019, building on the successes of the Stampede1 and Stampede2 systems.

Frontera provides a balanced set of capabilities that supports both capability and capacity simulation, data-intensive science, visualization, and data analysis, as well as emerging applications in AI and deep learning. Blue Waters and other cyberinfrastructure users in the open science community will find a familiar programming model and tools in a system that is productive today while serving as a bridge to the exascale future.

The design is anchored by Intel's top-of-the-line (at deployment) Xeon processor, Cascade Lake © (CLX). With a higher clock rate than other recent HPC processors, Intel's CLX processor delivers effective performance in the most commonly used and accessible programming model used in science applications today. Frontera's multi-tier storage system is designed to enable science at unprecedented scales with nearly 60 PB of Lustre-based storage, including 3 PB of flash storage for data-driven science applications that depend upon fast access to large amounts of data.

Frontera is also breaking new ground in its support for science applications. During the first six months of operation the system will provide support for users to run jobs using containers, immediately making tens of thousands of container-ready applications accessible on Frontera without the need for users to find and build their own versions.

Following the initial CPU-only rollout, the system will also provide users with access to the latest accelerator cards from NVIDIA with outstanding single-precision support especially targeted for machine-learning workloads. Later this summer a separate system will enter production to provide users with access to the latest double-precision HPC cards from NVIDIA, designed to serve more "traditional" science and engineering simulation needs.
Frontera's design also includes a totally new integration with web services, and will provide users with new options for data storage and access to emerging computer technologies. The award includes an innovative partnership with the three major commercial cloud providers, Google, Amazon and Microsoft, to provide users with additional high-integrity storage, sustainable archive options, and to keep the project regularly refreshed with novel computing technologies. Cloud integration is expected to be available to users in the second six months of operation.


Experienced HPC/TACC users will be very familiar with many of the topics presented in this guide. Here we'll highlight some sections for a quick start on Frontera.

  • Once you've confirmed that you're on a Frontera allocation, you can SSH to
  • Review the TACC info box displayed at login for your allocation availability and balances.
  • Consult the Frontera File Systems and Frontera Production Queues tables. These should be near identical to the structure used on other TACC systems but there are a few minor changes you will want to take note of.
  • Copy and modify any of the Sample Job Scripts for your own use. These scripts will also be helpful to show you how to modify any Jobs Scripts you are bringing over from other TACC systems so that they run efficiently on Frontera.
  • Read the Good Citizenship section. Frontera is a shared resource and this section covers practices and etiquette to keep your account in good standing and keep Frontera's systems running smoothly for all users.
  • Review the default modules with "module list". Make any changes needed for your code.
  • Start small. Run any jobs from other systems on a smaller scale in order to test the performance of your code on Frontera. You may find your code needs to be altered or recompiled in order to perform well and at scale on the new system.

User Guide Style Notes

This user guide is a work in progress and will be updated as Frontera installation and production continues.

login1$ Frontera login node
c123-456$ Frontera compute node
localhost$ local machine or laptop command line prompt
login1$ command-line examples
and command-line output
Command-line code snippets will appear in blue
#job script commands Job script snippets will appear in orange

Accessing the System

You must be added to a Frontera allocation in order to have access to Frontera. The ability to log on to the TACC User Portal does NOT signify access to Frontera or any TACC resource. You may monitor your allocations on the TACC User Portal. Please consult the allocations documentation for more information.

Multi-Factor Authentication

Access to all TACC systems now requires Multi-Factor Authentication (MFA). You can create an MFA pairing on the TACC User Portal. After login on the portal, go to your account profile (Home->Account Profile), then click the "Manage" button under "Multi-Factor Authentication" on the right side of the page. See Multi-Factor Authentication at TACC for further information.

Password Management

Use your TACC password for direct logins to TACC resources. You can change your TACC password through the TACC User Portal. Log into the portal, then select "Change Password" under the "HOME" tab. If you've forgotten your password, go to the TACC User Portal home page and select "Password Reset" under the Home tab.

Secure Shell (SSH)

The "ssh" command (SSH protocol) is the standard way to connect to Frontera. SSH also includes support for the file transfer utilities scp and sftp. Wikipedia is a good source of information on SSH. SSH is available within Linux and from the terminal app in the Mac OS. If you are using Windows, you will need an SSH client that supports the SSH-2 protocol: e.g. Bitvise, OpenSSH, PuTTY, or SecureCRT. Initiate a session using the ssh command or the equivalent; from the Linux command line the launch command looks like this:

localhost$ ssh

The above command will rotate connections across all available login nodes, login1-login4, and route your connection to one of them. To connect to a specific login node, use its full domain name:

localhost$ ssh

To connect with X11 support on Frontera (usually required for applications with graphical user interfaces), use the "-X" or "-Y" switch:

localhost$ ssh -X

To report a connection problem, execute the ssh command with the "-vvv" option and include the verbose output when submitting a help ticket.

Do not run the "ssh-keygen" command on Frontera. This command will create and configure a key pair that will interfere with the execution of job scripts in the batch system. If you do this by mistake, you can recover by renaming or deleting the .ssh directory located in your home directory; the system will automatically generate a new one for you when you next log into Frontera.

  1. execute "mv .ssh dot.ssh.old"
  2. log out
  3. log into Frontera again

After logging in again the system will generate a properly configured key pair.

Using Frontera

Regardless of your research workflow, you'll need to master Linux basics and a Linux-based text editor (e.g. emacs, nano, gedit, or vi/vim) to use the system properly. However, this user guide does not address these topics. There are numerous resources in a variety of formats that are available to help you learn Linux, including some listed on the TACC and training sites. If you encounter a term or concept in this user guide that is new to you, a quick internet search should help you resolve the matter quickly.

Linux Shell

The default login shell for your user account is Bash. To determine your current login shell, execute:

$ echo $SHELL

If you'd like to change your login shell to csh, sh, tcsh, or zsh, submit a ticket through the TACC portal. The "chsh" ("change shell") command will not work on TACC systems.

When you start a shell on Frontera, system-level startup files initialize your account-level environment and aliases before the system sources your own user-level startup scripts. You can use these startup scripts to customize your shell by defining your own environment variables, aliases, and functions. These scripts (e.g. .profile and .bashrc) are generally hidden files: so-called dotfiles that begin with a period, visible when you execute: "ls -a".

Before editing your startup files, however, it's worth taking the time to understand the basics of how your shell manages startup. Bash startup behavior is very different from the simpler csh behavior, for example. The Bash startup sequence varies depending on how you start the shell (e.g. using ssh to open a login shell, executing the "bash" command to begin an interactive shell, or launching a script to start a non-interactive shell). Moreover, Bash does not automatically source your .bashrc when you start a login shell by using ssh to connect to a node. Unless you have specialized needs, however, this is undoubtedly more flexibility than you want: you will probably want your environment to be the same regardless of how you start the shell. The easiest way to achieve this is to execute "source ~/.bashrc" from your ".profile", then put all your customizations in ".bashrc". The system-generated default startup scripts demonstrate this approach. We recommend that you use these default files as templates.

For more information see the Bash Users' Startup Files: Quick Start Guide and other online resources that explain shell startup. To recover the originals that appear in a newly created account, execute "/usr/local/startup_scripts/install_default_scripts".

Environment Variables

Your environment includes the environment variables and functions defined in your current shell: those initialized by the system, those you define or modify in your account-level startup scripts, and those defined or modified by the modules that you load to configure your software environment. Be sure to distinguish between an environment variable's name (e.g. HISTSIZE) and its value ($HISTSIZE). Understand as well that a sub-shell (e.g. a script) inherits environment variables from its parent, but does not inherit ordinary shell variables or aliases. Use export (in Bash) or setenv (in csh) to define an environment variable.

Execute the "env" command to see the environment variables that define the way your shell and child shells behave.

Pipe the results of env into grep to focus on specific environment variables. For example, to see all environment variables that contain the string GIT (in all caps), execute:

$ env | grep GIT

The environment variables PATH and LD_LIBRARY_PATH are especially important. PATH is a colon-separated list of directory paths that determines where the system looks for your executables. LD_LIBRARY_PATH is a similar list that determines where the system looks for shared libraries.

Account-Level Diagnostics

TACC's sanitytool module loads an account-level diagnostic package that detects common account-level issues and often walks you through the fixes. You should certainly run the package's sanitycheck utility when you encounter unexpected behavior. You may also want to run sanitycheck periodically as preventive maintenance. To run sanitytool's account-level diagnostics, execute the following commands:

login1$ module load sanitytool
login1$ sanitycheck

Execute "module help sanitytool" for more information.

Using Modules to Manage your Environment

Lmod, a module system developed and maintained at TACC, makes it easy to manage your environment so you have access to the software packages and versions that you need to conduct your research. This is especially important on a system like Frontera that serves thousands of users with an enormous range of needs. Loading a module amounts to choosing a specific package from among available alternatives:

$ module load intel          # load the default Intel compiler v19.0.4
$ module load intel/18.0.5   # load a specific version of Intel compiler

A module does its job by defining or modifying environment variables (and sometimes aliases and functions). For example, a module may prepend appropriate paths to $PATH and $LD_LIBRARY_PATH so that the system can find the executables and libraries associated with a given software package. The module creates the illusion that the system is installing software for your personal use. Unloading a module reverses these changes and creates the illusion that the system just uninstalled the software:

$ module load   ddt  # defines DDT-related env vars; modifies others
$ module unload ddt  # undoes changes made by load

The module system does more, however. When you load a given module, the module system can automatically replace or deactivate modules to ensure the packages you have loaded are compatible with each other. In the example below, the module system automatically unloads one compiler when you load another, and replaces Intel-compatible versions of IMPI and FFTW3 with versions compatible with gcc:

$ module load intel  # load default version of Intel compiler
$ module load fftw3  # load default version of fftw3
$ module load gcc    # change compiler

Lmod is automatically replacing "intel/19.0.4" with "gcc/9.1.0".

Inactive Modules:
  1) python2

Due to MODULEPATH changes, the following have been reloaded:
  1) fftw3/3.3.8     2) impi/19.0.4

On Frontera, modules generally adhere to a TACC naming convention when defining environment variables that are helpful for building and running software. For example, the "papi" module defines TACC_PAPI_BIN (the path to PAPI executables), TACC_PAPI_LIB (the path to PAPI libraries), TACC_PAPI_INC (the path to PAPI include files), and TACC_PAPI_DIR (top-level PAPI directory). After loading a module, here are some easy ways to observe its effects:

$ module show papi   # see what this module does to your environment
$ env | grep PAPI    # see env vars that contain the string PAPI
$ env | grep -i papi # case-insensitive search for 'papi' in environment

To see the modules you currently have loaded:

$ module list

To see all modules that you can load right now because they are compatible with the currently loaded modules:

$ module avail

To see all installed modules, even if they are not currently available because they are incompatible with your currently loaded modules:

$ module spider   # list all modules, even those not available to load

To filter your search:

$ module spider slep             # all modules with names containing 'slep'
$ module spider sundials/2.5.1   # additional details on a specific module

Among other things, the latter command will tell you which modules you need to load before the module is available to load. You might also search for modules that are tagged with a keyword related to your needs (though your success here depends on the diligence of the module writers). For example:

$ module keyword performance

You can save a collection of modules as a personal default collection that will load every time you log into Frontera. To do so, load the modules you want in your collection, then execute:

$ module save    # save the currently loaded collection of modules 

Two commands make it easy to return to a known, reproducible state:

$ module reset   # load the system default collection of modules
$ module restore # load your personal default collection of modules

On TACC systems, the command "module reset" is equivalent to "module purge; module load TACC". It's a safer, easier way to get to a known baseline state than issuing the two commands separately.

Help text is available for both individual modules and the module system itself:

$ module help swr     # show help text for software package swr
$ module help         # show help text for the module system itself

See Lmod's online documentation for more extensive documentation. The online documentation addresses the basics in more detail, but also covers several topics beyond the scope of the help text (e.g. writing and using your own module files).

It's safe to execute module commands in job scripts. In fact, this is a good way to write self-documenting, portable job scripts that produce reproducible results. If you use "module save" to define a personal default module collection, it's rarely necessary to execute module commands in shell startup scripts, and it can be tricky to do so safely. If you do wish to put module commands in your startup scripts, see Frontera's default startup scripts for a safe way to do so.

File System Quota Key Features
$HOME 25GB, 400,000 files Not intended for parallel or high-intensity file operations.
Backed up regularly.
Defaults: 1 stripe, 1MB stripe size.
Not purged.
$WORK 1TB, 3,000,000 files across all TACC systems,
regardless of where on the file system the files reside.
Not intended for high-intensity file operations or jobs involving very large files.
On the Global Shared File System that is mounted on most TACC systems.
Defaults: 1 stripe, 1MB stripe size.
Not backed up.
Not purged.
$SCRATCH1 no quota Overall capacity 44 PB.
Defaults: 1 stripe, 1MB stripe size.
Not backed up.
Subject to purge if access time* is more than 10 days old.

*The operating system updates a file's access time when that file is modified on a login or compute node. Reading or executing a file/script on a login node does not update the access time, but reading or executing on a compute node does update the access time. This approach helps us distinguish between routine management tasks (e.g. tar, scp) and production use. Use the command "ls -ul" to view access times.

Frontera mounts three Lustre file systems that are shared across all nodes: the home, work, and scratch file systems. Frontera will have a fourth file system, FLASH, supporting applications with very high bandwidth or IOPS requirements that will be an allocatable resource. Frontera's startup mechanisms define corresponding account-level environment variables $HOME, $SCRATCH, and $WORK that store the paths to directories that you own on each of these file systems. Consult the Frontera File Systems table above for the basic characteristics of these file systems, and the "Good Citizenship" sections for tips on file system etiquette.

Frontera's home and scratch file systems are mounted only on Frontera, but the work file system mounted on Frontera is the Global Shared File System hosted on Stockyard. This is the same work file system that is currently available on Lonestar5, Stampede2 and several other TACC resources.

The $STOCKYARD environment variable points to the highest-level directory that you own on the Global Shared File System. The definition of the $STOCKYARD environment variable is of course account-specific, but you will see the same value on all TACC systems that provide access to the Global Shared File System (see Figure 3). This directory is an excellent place to store files you want to access regularly from multiple TACC resources.

Figure 3. Account-level directories on the work file system (Global Shared File System hosted on Stockyard). Example for fictitious user bjones. All directories usable from all systems. Sub-directories (e.g. lonestar5, maverick2) exist only if you have allocations on the associated system.

Your account-specific $WORK environment variable varies from system to system and is a subdirectory of $STOCKYARD (Figure 3). The subdirectory name corresponds to the associated TACC resource. The $WORK environment variable on Frontera points to the $STOCKYARD/frontera subdirectory, a convenient location for files you use and jobs you run on Frontera. Remember, however, that all subdirectories contained in your $STOCKYARD directory are available to you from any system that mounts the file system. If you have accounts on both Frontera and Stampede2, for example, the $STOCKYARD/frontera directory is available from your Stampede2 account, and $STOCKYARD/stampede2 directory is available from your Frontera account. Your quota and reported usage on the Global Shared File System reflects all files that you own on Stockyard, regardless of their actual location on the file system.

Note that resource-specific subdirectories of $STOCKYARD are nothing more than convenient ways to manage your resource-specific files. You have access to any such subdirectory from any TACC resources. If you are logged into Frontera, for example, executing the alias "cdw" (equivalent to "cd $WORK") will take you to the resource-specific subdirectory $STOCKYARD/frontera. But you can access this directory from other TACC systems as well by executing "cd $STOCKYARD/frontera". These commands allow you to share files across TACC systems. In fact, several convenient account-level aliases make it even easier to navigate across the directories you own in the shared file systems:

Table 3. Built-in Account Level Aliases

Built-in Account Level Aliases
Alias Command
cd or cdh cd $HOME
cdw cd $WORK
cds cd $SCRATCH
cdy or cdg cd $STOCKYARD

Transferring Files: General


You can transfer files between Frontera and Linux-based systems using either scp or rsync. Both scp and rsync are available in the Mac Terminal app. Windows ssh clients typically include scp-based file transfer capabilities.

The Linux scp (secure copy) utility is a component of the OpenSSH suite. Assuming your Frontera username is bjones, a simple scp transfer that pushes a file named "myfile" from your local Linux system to Frontera $HOME would look like this:

localhost$ scp ./myfile  # note colon after net address

You can use wildcards, but you need to be careful about when and where you want wildcard expansion to occur. For example, to push all files ending in ".txt" from the current directory on your local machine to /work/01234/bjones/scripts on Frontera:

localhost$ scp *.txt

To delay wildcard expansion until reaching Frontera, use a backslash ("\") as an escape character before the wildcard. For example, to pull all files ending in ".txt" from /work/01234/bjones/scripts on Frontera to the current directory on your local system:

localhost$ scp\*.txt .

You can of course use shell or environment variables in your calls to scp. For example:

localhost$ destdir="/work/01234/bjones/frontera/data"
localhost$ scp ./myfile$destdir

You can also issue scp commands on your local client that use Frontera environment variables like $HOME, $WORK, and $SCRATCH. To do so, use a backslash ("\") as an escape character before the "$"; this ensures that expansion occurs after establishing the connection to Frontera:

localhost$ scp ./myfile\$WORK/data   # Note backslash

Avoid using scp for recursive ("-r") transfers of directories that contain nested directories of many small files:

localhost$ scp -r  ./mydata\$WORK  # DON'T DO THIS

Instead, use tar to create an archive of the directory, then transfer the directory as a single file:

localhost$ tar cvf ./mydata.tar mydata                                   # create archive
localhost$ scp     ./mydata.tar\$WORK  # transfer archive


The rsync (remote synchronization) utility is a great way to synchronize files that you maintain on more than one system: when you transfer files using rsync, the utility copies only the changed portions of individual files. As a result, rsync is especially efficient when you only need to update a small fraction of a large dataset. The basic syntax is similar to scp:

localhost$ rsync       mybigfile\$WORK/data
localhost$ rsync -avtr mybigdir\$WORK/data

The options on the second transfer are typical and appropriate when synching a directory: this is a recursive update ("-r") with verbose ("-v") feedback; the synchronization preserves time stamps ("-t") as well as symbolic links and other meta-data ("-a"). Because rsync only transfers changes, recursive updates with rsync may be less demanding than an equivalent recursive transfer with scp.

See Good Citizenship for additional important advice about striping the receiving directory when transferring large files; watching your quota on $HOME and $WORK; and limiting the number of simultaneous transfers. Remember also that $STOCKYARD (and your $WORK directory on each TACC resource) is available from several other TACC systems: there's no need for scp when both the source and destination involve subdirectories of $STOCKYARD. See Managing Your Files for more information about transfers on $STOCKYARD.

Sharing Files with Collaborators

If you wish to share files and data with collaborators in your project, see Sharing Project Files on TACC Systems for step-by-step instructions. Project managers or delegates can use Unix group permissions and commands to create read-only or read-write shared workspaces that function as data repositories and provide a common work area to all project members.

Striping Large Files

Before transferring large files to Frontera, or creating new large files, be sure to set an appropriate default stripe count on the receiving directory. To avoid exceeding your fair share of any given OST, a good rule of thumb is to allow at least one stripe for each 100GB in the file. For example, to set the default stripe count on the current directory to 30 (a plausible stripe count for a directory receiving a file approaching 3TB in size), execute:

$ lfs setstripe -c 30 $PWD

Note that an "lfs setstripe" command always sets both stripe count and stripe size, even if you explicitly specify only one or the other. Since the example above does not explicitly specify stripe size, the command will set the stripe size on the directory to Frontera's system default (1MB). In general there's no need to customize stripe size when creating or transferring files.

Remember that it's not possible to change the striping on a file that already exists. Moreover, the "mv" command has no effect on a file's striping if the source and destination directories are on the same file system. You can, of course, use the "cp" command to create a second copy with different striping; to do so, copy the file to a directory with the intended stripe parameters.

Building Software

The phrase "building software" is a common way to describe the process of producing a machine-readable executable file from source files written in C, Fortran, or some other programming language. In its simplest form, building software involves a simple, one-line call or short shell script that invokes a compiler. More typically, the process leverages the power of makefiles, so you can change a line or two in the source code, then rebuild in a systematic way only the components affected by the change. Increasingly, however, the build process is a sophisticated multi-step automated workflow managed by a special framework like autotools or cmake, intended to achieve a repeatable, maintainable, portable mechanism for installing software across a wide range of target platforms.

Basics of Building Software

This section of the user guide does nothing more than introduce the big ideas with simple one-line examples. You will undoubtedly want to explore these concepts more deeply using online resources. You will quickly outgrow the examples here. We recommend that you master the basics of makefiles as quickly as possible: even the simplest computational research project will benefit enormously from the power and flexibility of a makefile-based build process.

Intel Compilers

Intel is the recommended and default compiler suite on Frontera. Each Intel module also gives you direct access to mkl without loading an mkl module; see Intel MKL for more information. Here are simple examples that use the Intel compiler to build an executable from source code:

$ icc mycode.c                    # C source file; executable a.out
$ icc main.c calc.c analyze.c     # multiple source files
$ icc mycode.c     -o myexe       # C source file; executable myexe
$ icpc mycode.cpp  -o myexe       # C++ source file
$ ifort mycode.f90 -o myexe       # Fortran90 source file

Compiling a code that uses OpenMP would look like this:

$ icc -qopenmp mycode.c -o myexe  # OpenMP

See the published Intel documentation, available both online and in ${TACC_INTEL_DIR}/documentation, for information on optimization flags and other Intel compiler options.

GNU Compilers

The GNU foundation maintains a number of high quality compilers, including a compiler for C (gcc), C++ (g++), and Fortran (gfortran). The gcc compiler is the foundation underneath all three, and the term "gcc" often means the suite of these three GNU compilers.

Load a gcc module to access a recent version of the GNU compiler suite. Avoid using the GNU compilers that are available without a gcc module — those will be older versions based on the "system gcc" that comes as part of the Linux distribution.

Here are simple examples that use the GNU compilers to produce an executable from source code:

$ gcc mycode.c                    # C source file; executable a.out
$ gcc mycode.c          -o myexe  # C source file; executable myexe
$ g++ mycode.cpp        -o myexe  # C++ source file
$ gfortran mycode.f90   -o myexe  # Fortran90 source file
$ gcc -fopenmp mycode.c -o myexe  # OpenMP; GNU flag is different than Intel

Note that some compiler options are the same for both Intel and GNU (e.g. "-o"), while others are different (e.g. "-qopenmp" vs "-fopenmp"). Many options are available in one compiler suite but not the other. See the online GNU documentation for information on optimization flags and other GNU compiler options.

Include and Library Paths

Software often depends on pre-compiled binaries called libraries. When this is true, compiling usually requires using the "-I" option to specify paths to so-called header or include files that define interfaces to the procedures and data in those libraries. Similarly, linking often requires using the "-L" option to specify paths to the libraries themselves. Typical compile and link lines might look like this:

$ icc        -c main.c -I${WORK}/mylib/inc -I${TACC_HDF5_INC}                  # compile
$ icc main.o -o myexe  -L${WORK}/mylib/lib -L${TACC_HDF5_LIB} -lmylib -lhdf5   # link

On Frontera, both the hdf5 and phdf5 modules define the environment variables $TACC_HDF5_INC and $TACC_HDF5_LIB. Other module files define similar environment variables; see Using Modules for more information.

The details of the linking process vary, and order sometimes matters. Much depends on the type of library: static (.a suffix; library's binary code becomes part of executable image at link time) versus dynamically-linked shared (.so suffix; library's binary code is not part of executable; it's located and loaded into memory at run time). The link line can use rpath to store in the executable an explicit path to a shared library. In general, however, the LD_LIBRARY_PATH environment variable specifies the search path for dynamic libraries. For software installed at the system-level, TACC's modules generally modify LD_LIBRARY_PATH automatically. To see whether and how an executable named "myexe" resolves dependencies on dynamically linked libraries, execute "ldd myexe".

A separate section below addresses the Intel Math Kernel Library (MKL).

Compiling and Linking MPI Programs

Intel MPI (module impi) and MVAPICH2 (module mvapich2) are the two MPI libraries available on Frontera. After loading an impi or mvapich2 module, compile and/or link using an mpi wrapper (mpicc, mpicxx, mpif90) in place of the compiler:

$ mpicc    mycode.c   -o myexe   # C source, full build
$ mpicc -c mycode.c              # C source, compile without linking
$ mpicxx   mycode.cpp -o myexe   # C++ source, full build
$ mpif90   mycode.f90 -o myexe   # Fortran source, full build

These wrappers call the compiler with the options, include paths, and libraries necessary to produce an MPI executable using the MPI module you're using. To see the effect of a given wrapper, call it with the "-show" option:

$ mpicc -show  # Show compile line generated by call to mpicc; similarly for other wrappers

Building Third-Party Software in Your Own Account

You can discover already installed software using TACC's Software Search tool or execute "module spider" or "module avail" on the command-line.

You're welcome to download third-party research software and install it in your own account. In most cases you'll want to download the source code and build the software so it's compatible with the Frontera software environment. You can't use yum or any other installation process that requires elevated privileges, but this is almost never necessary. The key is to specify an installation directory for which you have write permissions. Details vary; you should consult the package's documentation and be prepared to experiment. When using the famous three-step autotools build process, the standard approach is to use the PREFIX environment variable to specify a non-default, user-owned installation directory at the time you execute configure or make:

$ export INSTALLDIR=$WORK/apps/t3pio
$ ./configure --prefix=$INSTALLDIR
$ make
$ make install

Other languages, frameworks, and build systems generally have equivalent mechanisms for installing software in user space. In most cases a web search like "Python Linux install local" will get you the information you need.

In Python, a local install will resemble one of the following examples:

$ pip install netCDF4     --user                    # install netCDF4 package to $HOME/.local
$ python3 install --user                   # install to $HOME/.local
$ pip3 install netCDF4     --prefix=$INSTALLDIR     # custom location; add to PYTHONPATH

Similarly in R:

$ module load Rstats            # load TACC's default R
$ R                             # launch R
> install.packages('devtools')  # R will prompt for install location

You may, of course, need to customize the build process in other ways. It's likely, for example, that you'll need to edit a makefile or other build artifacts to specify Frontera-specific include and library paths or other compiler settings. A good way to proceed is to write a shell script that implements the entire process: definitions of environment variables, module commands, and calls to the build utilities. Include echo statements with appropriate diagnostics. Run the script until you encounter an error. Research and fix the current problem. Document your experience in the script itself; including dead-ends, alternatives, and lessons learned. Re-run the script to get to the next error, then repeat until done. When you're finished, you'll have a repeatable process that you can archive until it's time to update the software or move to a new machine.

If you wish to share a software package with collaborators, you may need to modify file permissions. See Sharing Files with Collaborators for more information.

Intel Math Kernel Library (MKL)

The Intel Math Kernel Library (MKL) is a collection of highly optimized functions implementing some of the most important mathematical kernels used in computational science, including standardized interfaces to:

  • BLAS (Basic Linear Algebra Subroutines), a collection of low-level matrix and vector operations like matrix-matrix multiplication
  • LAPACK (Linear Algebra PACKage), which includes higher-level linear algebra algorithms like Gaussian Elimination
  • FFT (Fast Fourier Transform), including interfaces based on FFTW (Fastest Fourier Transform in the West)
  • ScaLAPACK (Scalable LAPACK), BLACS (Basic Linear Algebra Communication Subprograms), Cluster FFT, and other functionality that provide block-based distributed memory (multi-node) versions of selected LAPACK, BLAS, and FFT algorithms;
  • Vector Mathematics (VM) functions that implement highly optimized and vectorized versions of special functions like sine and square root.

MKL with Intel C, C++, and Fortran Compilers

There is no MKL module for the Intel compilers because you don't need one: the Intel compilers have built-in support for MKL. Unless you have specialized needs, there is no need to specify include paths and libraries explicitly. Instead, using MKL with the Intel modules requires nothing more than compiling and linking with the "-mkl" option.; e.g.

$ icc   -mkl mycode.c
$ ifort -mkl mycode.c

The "-mkl" switch is an abbreviated form of "-mkl=parallel", which links your code to the threaded version of MKL. To link to the unthreaded version, use "-mkl=sequential". A third option, "-mkl=cluster", which also links to the unthreaded libraries, is necessary and appropriate only when using ScaLAPACK or other distributed memory packages. For additional information, including advanced linking options, see the MKL documentation and Intel MKL Link Line Advisor.

MKL with GNU C, C++, and Fortran Compilers

When using a GNU compiler, load the MKL module before compiling or running your code, then specify explicitly the MKL libraries, library paths, and include paths your application needs. Consult the Intel MKL Link Line Advisor for details. A typical compile/link process on a TACC system will look like this:

$ module load gcc
$ module load mkl                         # available/needed only for GNU compilers
$ gcc -fopenmp -I$MKLROOT/include         \
         -Wl,-L${MKLROOT}/lib/intel64     \
         -lmkl_intel_lp64 -lmkl_core      \
         -lmkl_gnu_thread -lpthread       \
         -lm -ldl mycode.c

For your convenience the mkl module file also provides alternative TACC-defined variables like $TACC_MKL_INCLUDE (equivalent to $MKLROOT/include). Execute "module help mkl" for more information.

Using MKL as BLAS/LAPACK with Third-Party Software

When your third-party software requires BLAS or LAPACK, you can use MKL to supply this functionality. Replace generic instructions that include link options like "-lblas" or "-llapack" with the simpler MKL approach described above. There is no need to download and install alternatives like OpenBLAS.

Using MKL as BLAS/LAPACK with TACC's MATLAB, Python, and R Modules

TACC's MATLAB, Python, and R modules all use threaded (parallel) MKL as their underlying BLAS/LAPACK library. These means that even serial codes written in MATLAB, Python, or R may benefit from MKL's thread-based parallelism. This requires no action on your part other than specifying an appropriate max thread count for MKL; see the section below for more information.

Controlling Threading in MKL

Any code that calls MKL functions can potentially benefit from MKL's thread-based parallelism; this is true even if your code is not otherwise a parallel application. If you are linking to the threaded MKL (using "-mkl", "-mkl=parallel", or the equivalent explicit link line), you need only specify an appropriate value for the max number of threads available to MKL. You can do this with either of the two environment variables MKL_NUM_THREADS or OMP_NUM_THREADS. The environment variable MKL_NUM_THREADS specifies the max number of threads available to each instance of MKL, and has no effect on non-MKL code. If MKL_NUM_THREADS is undefined, MKL uses OMP_NUM_THREADS to determine the max number of threads available to MKL functions. In either case, MKL will attempt to choose an optimal thread count less than or equal to the specified value. Note that OMP_NUM_THREADS defaults to 1 on TACC systems; if you use the default value you will get no thread-based parallelism from MKL.

If you are running a single serial, unthreaded application (or an unthreaded MPI code involving a single MPI task per node) it is usually best to give MKL as much flexibility as possible by setting the max thread count to the total number of hardware threads on the node (112 on CLX). Of course things are more complicated if you are running more than one process on a node: e.g. multiple serial processes, threaded applications, hybrid MPI-threaded applications, or pure MPI codes running more than one MPI rank per node. See and related Intel resources for examples of how to manage threading when calling MKL from multiple processes.


When building software on Frontera, we recommend using the Intel compiler and Intel MPI stack. This will be the default in the early user period, but may change if we determine one of the other MPI stacks provides superior performance.

Architecture-Specific Flags

To compile for CLX only, include "-xCORE-AVX512" as a build option. The "-x" switch allows you to specify a target architecture. The CLX chips, as well as the Skylake chips (SKX) on Stampede2, support Intel's latest instruction set, CORE-AVX512. You should also consider specifying an optimization level using the "-O" flag:

$ icc   -xCORE-AVX512  -O3 mycode.c   -o myexe         # will run only on CLX/SKX
$ ifort  -xCORE-AVX512 -O3 mycode.f90 -o myexe         # will run only on CLX/SKX

It's best to avoid building with "-xHost" (a flag that means "optimize for the architecture on which I'm compiling now"). Although this will work on Frontera, since the Frontera login nodes are all CLX nodes, if you build on another system, your binary will be based on whatever architecture you built upon. This may not be the same as the architecture on which you will be running.

Also, you should not use the "-fast" flag for the Intel compiler. This flag sets the following options:

-ipo -O3 -no-prec-div -static -fp-model fast=2 -xHost

Frontera software libraries, including the MPI libraries, are installed as shared libraries in most cases. The "-static" flag included in "-fast" will cause the compile to fail at the link stage. If you'd like to use the other flags, you'll have to include each option individually.

For information on the performance implications of your choice of build flags, see the sections on Programming and Performance for CLX.

If you use GNU compilers, see GNU x86 Options for information regarding support for CLX.

Serial Jobs
MPI Job in Normal Queue
OpenMP Jobs
Hybrid (MPI + OpenMP) Job
Parametric / Array / HTC jobs


Running Jobs on the Frontera Compute Nodes

Frontera's job scheduler is the Slurm Workload Manager. Slurm commands enable you to submit, manage, monitor, and control your jobs. Jobs submitted to the scheduler are queued, then run on the compute nodes. Each job consumes Service Units (SUs) which are then charged to your allocation.

Job Accounting

Frontera's accounting system is based on node-hours: one unadjusted Service Unit (SU) represents a single compute node used for one hour (a node-hour). We then multiply by a charge rate that reflects supply and demand for the particular queue or the type of node you use. For any given job, the total cost in SUs is:

SUs billed (node-hrs) = ( # nodes ) x ( job duration in wall clock hours ) x ( charge rate per node-hour )

For example, a job that runs in the normal queue for two hours using four nodes, costs 8SUs:

4 nodes * 2 hours * 1.0 = 8SUs

while that same job running in the low queue will be charged only 4SUs:

4 nodes * 2 hours * 0.5 = 4SUs

The system tracks and charges for usage to a granularity of a few seconds of wall clock time. The system charges only for the resources you actually use, not those you request. In general, your queue wait time will be less if you request only the time you need: the scheduler will have an easier time finding a slot for the 2 hours you really need than for the 24 hours you request in your job script.

Principal Investigators can monitor allocation usage via the TACC User Portal under "Allocations->Projects and Allocations". Be aware that the figures shown on the portal may lag behind the most recent usage. Projects and allocation balances are also displayed upon command-line login.

To display a summary of your TACC project balances and disk quotas at any time, execute:

login1$ /usr/local/etc/taccinfo # Generally more current than balances displayed on the portals.

Frontera Production Queues

Frontera's Slurm current partitions (queues), maximum node limits and charge rates are summarized in the table below. Queues and limits are subject to change without notice.

Execute "qlimits" on Frontera for real-time information regarding limits on available queues. See Job Accounting to learn how jobs are charged to your allocation.

Be sure to request computing resources that are consistent with the type of application(s) you are running:

  • A serial (non-parallel) application can only make use of a single core on a single node, and will only see that node's memory.

  • A threaded program (e.g. one that uses OpenMP) employs a shared memory programming model and is also restricted to a single node, but the program's individual threads can run on multiple cores on that node.

  • An MPI (Message Passing Interface) program can exploit the distributed computing power of multiple nodes: it launches multiple copies of its executable (MPI tasks, each assigned unique IDs called ranks) that can communicate with each other across the network. The tasks on a given node, however, can only directly access the memory on that node. Depending on the program's memory requirements, it may not be possible to run a task on every core of every node assigned to your job. If it appears that your MPI job is running out of memory, try launching it with fewer tasks per node to increase the amount of memory available to individual tasks.

  • A popular type of parameter sweep (sometimes called high throughput computing) involves submitting a job that simultaneously runs many copies of one serial or threaded application, each with its own input parameters ("Single Program Multiple Data", or SPMD). The "launcher" tool is designed to make it easy to submit this type of job. For more information:

      $ module load launcher
      $ module help launcher

Table 5. Frontera Production Queues
Queue status as of August 19, 2019.

Queue Name Max Nodes per Job
(assoc'd cores)
Max Duration Charge Rate
(per node-hour)
development 40 nodes
(2,240 cores)
2 hrs 1 Service Unit (SU)
normal 512 nodes
(28,672 cores)
24 hrs 1 SU
large* 513-2048 nodes
(up to 114,688 cores)
24 hrs 1 SU

* Access to the large queue is restricted. To request more nodes than are available in the normal queue, submit a consulting (help desk) ticket through the TACC user portal. Include in your request reasonable evidence of your readiness to run under the conditions you're requesting. In most cases this should include your own strong or weak scaling results from Frontera.

Accessing the Compute Nodes

The login nodes are shared resources: at any given time, there are many users logged into each of these login nodes, each preparing to access the "back-end" compute nodes (Figure X. Login and Compute Nodes). What you do on the login nodes affects other users directly because you are competing for the same resources: memory and processing power. This is the reason you should not run your applications on the login nodes or otherwise abuse them. Think of the login nodes as a prep area where you can manage files and compile code before accessing the compute nodes to perform research computations. See Good Citizenship for more information.

Figure 2. Login and compute nodes

You can use your command-line prompt, or the "hostname" command, to discern whether you are on a login node or a compute node. The default prompt, or any custom prompt containing "\h", displays the short form of the hostname (e.g. c401-064). The hostname for a Frontera login node begins with the string "login" (e.g., while compute node hostnames begin with the character "c" (e.g.

While some workflows, tools, and applications hide the details, there are three basic ways to access the compute nodes:

  1. Submit a batch job using the sbatch command. This directs the scheduler to run the job unattended when there are resources available. Until your batch job begins it will wait in a queue. You do not need to remain connected while the job is waiting or executing. See Running Jobs for more information. Note that the scheduler does not start jobs on a first come, first served basis; it juggles many variables to keep the machine busy while balancing the competing needs of all users. The best way to minimize wait time is to request only the resources you really need: the scheduler will have an easier time finding a slot for the two hours you need than for the 24 hours you unnecessarily request.

  2. Begin an interactive session using ssh to connect to a compute node on which you are already running a job. This is a good way to open a second window into a node so that you can monitor a job while it runs.

  3. Begin an interactive session using idev or srun. This will log you into a compute node and give you a command prompt there, where you can issue commands and run code as if you were doing so on your personal machine. An interactive session is a great way to develop, test, and debug code. Both the srun and idev commands submit a new batch job on your behalf, providing interactive access once the job starts. You will need to remain logged in until the interactive session begins.

Submitting Batch Jobs with sbatch

Use Slurm's "sbatch" command to submit a batch job to one of the Frontera queues:

login1$ sbatch myjobscript

Here "myjobscript" is the name of a text file containing #SBATCH directives and shell commands that describe the particulars of the job you are submitting. The details of your job script's contents depend on the type of job you intend to run.

In your job script you (1) use #SBATCH directives to request computing resources (e.g. 10 nodes for 2 hrs); and then (2) use shell commands to specify what work you're going to do once your job begins. There are many possibilities: you might elect to launch a single application, or you might want to accomplish several steps in a workflow. You may even choose to launch more than one application at the same time. The details will vary, and there are many possibilities. But your own job script will probably include at least one launch line that is a variation of one of the examples described here.

See the customizable job script examples.

Your job will run in the environment it inherits at submission time; this environment includes the modules you have loaded and the current working directory. In most cases you should run your application(s) after loading the same modules that you used to build them. You can of course use your job submission script to modify this environment by defining new environment variables; changing the values of existing environment variables; loading or unloading modules; changing directory; or specifying relative or absolute paths to files. Do not use the Slurm "--export" option to manage your job's environment: doing so can interfere with the way the system propagates the inherited environment.

The Common sbatch Options table below describes some of the most common sbatch command options. Slurm directives begin with "#SBATCH"; most have a short form (e.g. "-N") and a long form (e.g. "--nodes"). You can pass options to sbatch using either the command line or job script; most users find that the job script is the easier approach. The first line of your job script must specify the interpreter that will parse non-Slurm commands; in most cases "#!/bin/bash" or "#!/bin/csh" is the right choice. Avoid "#!/bin/sh" (its startup behavior can lead to subtle problems on Frontera), and do not include comments or any other characters on this first line. All #SBATCH directives must precede all shell commands. Note also that certain #SBATCH options or combinations of options are mandatory, while others are not available on Frontera.

Table 6. Common sbatch Options

Option Argument Comments
-p queue_name Submits to queue (partition) designated by queue_name
-J job_name Job Name
-N total_nodes Required. Define the resources you need by specifying either:
(1) "-N" and "-n"; or
(2) "-N" and "--ntasks-per-node".
-n total_tasks This is total MPI tasks in this job. See "-N" above for a good way to use this option. When using this option in a non-MPI job, it is usually best to set it to the same value as "-N".
tasks_per_node This is MPI tasks per node. See "-N" above for a good way to use this option. When using this option in a non-MPI job, it is usually best to set --ntasks-per-node to 1.
-t hh:mm:ss Required. Wall clock time for job.
--mail-user= email_address Specify the email address to use for notifications.
--mail-type= begin, end, fail, or all Specify when user notifications are to be sent (one option per line).
-o output_file Direct job standard output to output_file (without -e option error goes to this file)
-e error_file Direct job error output to error_file
--dependency= jobid Specifies a dependency: this run will start only after the specified job (jobid) successfully finishes
-A projectnumber Charge job to the specified project/allocation number. This option is only necessary for logins associated with multiple projects.
N/A Not available. Use the launcher module for parameter sweeps and other collections of related serial jobs.
--mem N/A Not available. If you attempt to use this option, the scheduler will not accept your job.
--export= N/A Avoid this option on Frontera. Using it is rarely necessary and can interfere with the way the system propagates your environment.

By default, Slurm writes all console output to a file named "slurm-%j.out", where %j is the numerical job ID. To specify a different filename use the "-o" option. To save stdout (standard out) and stderr (standard error) to separate files, specify both "-o" and "-e".

Interactive Sessions with idev and srun

TACC's own idev utility is the best way to begin an interactive session on one or more compute nodes. idev submits a batch script requesting access to a compute node. Once the scheduler allocates a compute node, you are then automatically ssh'd to that node where you can begin any compute-intensive jobs.

To launch a thirty-minute session on a single node in the development queue, simply execute:

login1$ idev

You'll then see output that includes the following excerpts:

        Welcome to the Frontera Supercomputer          

-> After your idev job begins to run, a command prompt will appear,
-> and you can begin your interactive development session. 
-> We will report the job status every 4 seconds: (PD=pending, R=running).

->job status:  PD
->job status:  PD

The "job status" messages indicate that your interactive session is waiting in the queue. When your session begins, you'll see a command prompt on a compute node (in this case, the node with hostname c449-001). If this is the first time you launch idev, you may be prompted to choose a default project and a default number of tasks per node for future idev sessions.

For command-line options and other information, execute "idev --help". It's easy to tailor your submission request (e.g. shorter or longer duration) using Slurm-like syntax:

login1$ idev -p normal -N 2 -n 8 -m 150 # normal queue, 2 nodes, 8 total tasks, 150 minutes

You can also launch an interactive session with Slurm's srun command, though there's no clear reason to prefer srun to idev. A typical launch line would look like this:

login1$ srun --pty -N 2 -n 8 -t 2:30:00 -p normal /bin/bash -l # same conditions as above

Consult the idev documentation for further details.

Interactive Sessions using SSH

If you have a batch job or interactive session running on a compute node, you "own the node": you can connect via ssh to open a new interactive session on that node. This is an especially convenient way to monitor your applications' progress. One particularly helpful example: login to a compute node that you own, execute "top", then press the "1" key to see a display that allows you to monitor thread ("CPU") and memory use.

There are many ways to determine the nodes on which you are running a job, including feedback messages following your sbatch submission, the compute node command prompt in an idev session, and the squeue or showq utilities. The sequence of identifying your compute node then connecting to it would look like this:

login1$ squeue -u bjones
858811     development idv46796   bjones  R       0:39      1 c448-004
1ogin1$ ssh c448-004

Slurm Environment Variables

Be sure to distinguish between internal Slurm replacement symbols (e.g. "%j" described above) and Linux environment variables defined by Slurm (e.g. SLURM_JOBID). Execute "env | grep SLURM" from within your job script to see the full list of Slurm environment variables and their values. You can use Slurm replacement symbols like "%j" only to construct a Slurm filename pattern; they are not meaningful to your Linux shell. Conversely, you can use Slurm environment variables in the shell portion of your job script but not in an #SBATCH directive. For example, the following directive will not work the way you might think:

#SBATCH -o myMPI.o${SLURM_JOB_ID}   # incorrect

Instead, use the following directive:

#SBATCH -o myMPI.o%j     # "%j" expands to your job's numerical job ID

Similarly, you cannot use paths like $WORK or $SCRATCH in an #SBATCH directive.

For more information on this and other matters related to Slurm job submission, see the Slurm online documentation; the man pages for both Slurm itself ("man slurm") and its individual command (e.g. "man sbatch"); as well as numerous other online resources.

Launching Applications

The primary purpose of your job script is to launch your research application. How you do so depends on several factors, especially (1) the type of application (e.g. MPI, OpenMP, serial), and (2) what you're trying to accomplish (e.g. launch a single instance, complete several steps in a workflow, run several applications simultaneously within the same job). While there are many possibilities, your own job script will probably include a launch line that is a variation of one of the examples described in this section:

Launching One Serial Application

To launch a serial application, simply call the executable. Specify the path to the executable in either the PATH environment variable or in the call to the executable itself:

mycode.exe                   # executable in a directory listed in $PATH
$WORK/apps/myprov/mycode.exe # explicit full path to executable
./mycode.exe                 # executable in current directory
./mycode.exe -m -k 6 input1  # executable with notional input options

Launching One Multi-Threaded Application

07/03/19 Hyperthreading is not currently enabled on Frontera.

Launch a threaded application the same way. Be sure to specify the number of threads. Note that the default OpenMP thread count is 1.

export OMP_NUM_THREADS=56   # 56 total OpenMP threads (1 per CLX core)

Launching One MPI Application

To launch an MPI application, use the TACC-specific MPI launcher "ibrun", which is a Frontera-aware replacement for generic MPI launchers like mpirun and mpiexec. In most cases the only arguments you need are the name of your executable followed by any options your executable needs. When you call ibrun without other arguments, your Slurm #SBATCH directives will determine the number of ranks (MPI tasks) and number of nodes on which your program runs.

ibrun ./mycode.exe           # use ibrun instead of mpirun or mpiexec

Launching One Hybrid (MPI+Threads) Application

07/03/19 Hyperthreading is not currently enabled on Frontera.

When launching a single application you generally don't need to worry about affinity: both Intel MPI and MVAPICH2 will distribute and pin tasks and threads in a sensible way.

export OMP_NUM_THREADS=8    # 8 OpenMP threads per MPI rank
ibrun ./mycode.exe          # use ibrun instead of mpirun or mpiexec

More Than One Serial Application in the Same Job

TACC's "launcher" utility provides an easy way to launch more than one serial application in a single job. This is a great way to engage in a popular form of High Throughput Computing: running parameter sweeps (one serial application against many different input datasets) on several nodes simultaneously. The launcher utility will execute your specified list of independent serial commands, distributing the tasks evenly, pinning them to specific cores, and scheduling them to keep cores busy. Execute "module load launcher" followed by "module help launcher" for more information.

MPI Applications One at a Time

To run one MPI application after another (or any sequence of commands one at a time), simply list them in your job script in the order in which you'd like them to execute. When one application/command completes, the next one will begin.

module load git
module list
ibrun ./mycode.exe input1    # runs after completes
ibrun ./mycode.exe input2    # runs after previous MPI app completes

More than One MPI Application Running Concurrently

To run more than one MPI application simultaneously in the same job, you need to do several things:

  • use ampersands to launch each instance in the background;
  • include a wait command to pause the job script until the background tasks complete;
  • use ibrun's "-n" and "-o" switches to specify task counts and hostlist offsets respectively; and
  • include a call to the task_affinity script in your ibrun launch line.

If, for example, you use #SBATCH directives to request N=4 nodes and n=112 total MPI tasks, Slurm will generate a hostfile with 112 entries (28 entries for each of 4 nodes). The "-n" and "-o" switches, which must be used together, determine which hostfile entries ibrun uses to launch a given application; execute "ibrun --help" for more information. Don't forget the ampersands ("&") to launch the jobs in the background, and the "wait" command to pause the script until the background tasks complete:

ibrun -n 56 -o  0 task_affinity ./mycode.exe input1 &   # 56 tasks; offset by  0 entries in hostfile.
ibrun -n 56 -o 56 task_affinity ./mycode.exe input2 &   # 56 tasks; offset by 56 entries in hostfile.
wait                                                    # Required; else script will exit immediately.

The task_affinity script manages task placement and memory pinning when you call ibrun with the "-n, -o" switches (it's not necessary under any other circumstances).

More than One OpenMP Application Running Concurrently

07/03/19 Hyperthreading is not currently enabled on Frontera.

You can also run more than one OpenMP application simultaneously on a single node, but you will need to distribute and pin OpenMP threads appropriately. The most portable way to do this is with OpenMP Affinity.

An OpenMP executable sequentially assigns its N forked threads (thread number 0,…N-1) at a parallel region to the sequence of "places" listed in the OMP_PLACES environment variable. Each place is specified within braces ({}}. The sequence "{0,1},{2,3},{4,5}" has three places, and OpenMP thread numbers 0, 1, and 2 are assigned to the processor ids (proc-ids) 0,1 and 2,3 and 4,5, respectively. The hardware assigned to the proc-ids can be found in the /proc/cpuinfo file.

On CLX nodes the sequence of proc-ids on socket 0 are even: 0,2,4,…,108, 110 and on socket 1 they are oddly numbered: 1,3,5,…,109,111 Note, there are 56 cores on CLX, and since hyperthreading is turned on, the list of processors (proc-ids) goes from 0 to 111.

Specifically, the proc-id mapping to the cores for CLX is:

     Sockets  |------- Socket 0 ---------|-------- Socket 1 ---------|
     Core #    0   1   2,..., 25, 26, 27 |  0   1   2,..., 25, 26, 27
 proc-id 0     0   2   4,..., 50, 52, 54 |  1   3   5,..., 51, 53, 55
 proc-id 1    56  58  60,...,106,108,110 | 57  59  61,...,107,109,111

Hence, to bind OpenMP threads to a sequence of 3 cores on these systems, the places would be:

CLX socket 0:  export OMP_PLACES="{0,56},{2,58},{4,60}"
CLX socket 1:  export OMP_PLACES="{1,57},{3,59},{5,61}"

Interval notation can be used to express a sequences of places. The syntax is: {proc-ids},N,S, where N is the number of places to create from the base place ({proc-ids}) with a stride of S. Hence the above sequences could have been written:

CLX socket 0:  export OMP_PLACES="{0,56},3,2"
CLX socket 1:  export OMP_PLACES="{1,57},3,2"

In the example below two OpenMP programs are executed on a single node, each using 28 threads. The first program uses the cores on socket 0. It is put in the background, using the ampersand (&) character at the end of the line, so that the job script execution can continue to the second OpenMP program execution, which uses the cores on socket 1. It, too, is put in the background, and the job execution waits for both to finish with the wait command at the end.

env OMP_PLACES="{0,56},28,2" ./omp.exe &   #execution on socket 0 cores
env OMP_PLACES="{1,57},28,2" ./omp.exe &   #execution on socket 1 cores

Job Management

Several commands are available to help you plan and track your job submissions as well as check the status of the Slurm queues.

When interpreting queue and job status, remember that Frontera doesn't operate on a first-come-first-served basis. Instead, the sophisticated, tunable algorithms built into Slurm attempt to keep the system busy, while scheduling jobs in a way that is as fair as possible to everyone. At times this means leaving nodes idle ("draining the queue") to make room for a large job that would otherwise never run. It also means considering each user's "fair share", scheduling jobs so that those who haven't run jobs recently may have a slightly higher priority than those who have.

Monitoring Queue Status with sinfo and qlimits

To display resource limits for the Frontera queues, execute "qlimits". The result is real-time data; the corresponding information in this document's table of Frontera queues may lag behind the actual configuration that the qlimits utility displays.

Slurm's "sinfo" command allows you to monitor the status of the queues. If you execute sinfo without arguments, you'll see a list of every node in the system together with its status. To skip the node list and produce a tight, alphabetized summary of the available queues and their status, execute:

login1$ sinfo -S+P -o "%18P %8a %20F"    # compact summary of queue status

An excerpt from this command's output looks like this:

development*       up       41/70/1/112
normal             up       3685/8/3/3696

The AVAIL column displays the overall status of each queue (up or down), while the column labeled "NODES(A/I/O/T)" shows the number of nodes in each of several states ("Allocated", "Idle", "Other", and "Total"). Execute "man sinfo" for more information. Use caution when reading the generic documentation, however: some available fields are not meaningful or are misleading on Frontera (e.g. TIMELIMIT, displayed using the "%l" option).

Monitoring Job Status with squeue

Slurm's squeue command allows you to monitor jobs in the queues, whether pending (waiting) or currently running:

login1$ squeue             # show all jobs in all queues
login1$ squeue -u bjones   # show all jobs owned by bjones
login1$ man squeue         # more info

An excerpt from the default output looks like this:

170361      normal   spec12   bjones PD       0:00     32 (Resources)
170356      normal    mal2d slindsey PD       0:00     30 (Priority)
170204      normal   rr2-a2 tg123456 PD       0:00      1 (Dependency)
170250 development idv59074  aturing  R      29:30      1 c455-044
169669      normal  04-99a1  aturing CG    2:47:47      1 c425-003

The column labeled "ST" displays each job's status:

  • "PD" means "Pending" (waiting);
  • "R" means "Running";
  • "CG" means "Completing" (cleaning up after exiting the job script).

Pending jobs appear in order of decreasing priority. The last column includes a nodelist for running/completing jobs, or a reason for pending jobs. If you submit a job before a scheduled system maintenance period, and the job cannot complete before the maintenance begins, your job will run when the maintenance/reservation concludes. The squeue command will report "ReqNodeNotAvailable" ("Required Node Not Available"). The job will remain in the PD state until Frontera returns to production.

The default format for squeue now reports total nodes associated with a job rather than cores, tasks, or hardware threads. One reason for this change is clarity: the operating system sees each compute node's 56 hardware threads as "processors", and output based on that information can be ambiguous or otherwise difficult to interpret.

The default format lists all nodes assigned to displayed jobs; this can make the output difficult to read. A handy variation that suppresses the nodelist is:

login1$ squeue -o "%.10i %.12P %.12j %.9u %.2t %.9M %.6D"  # suppress nodelist

The "--start" option displays job start times, including very rough estimates for the expected start times of some pending jobs that are relatively high in the queue:

login1$ squeue --start -j 167635     # display estimated start time for job 167635

Monitoring Job Status with showq

TACC's "showq" utility mimics a tool that originated in the PBS project, and serves as a popular alternative to the Slurm "squeue" command:

login1$ showq            # show all jobs; default format
login1$ showq -u         # show your own jobs
login1$ showq -U bjones  # show jobs associated with user bjones
login1$ showq -h         # more info

The output groups jobs in four categories: ACTIVE, WAITING, BLOCKED, and COMPLETING/ERRORED. A BLOCKED job is one that cannot yet run due to temporary circumstances (e.g. a pending maintenance or other large reservation.).

If your waiting job cannot complete before a maintenance/reservation begins, showq will display its state as "**WaitNod**" ("Waiting for Nodes"). The job will remain in this state until Frontera returns to production.

The default format for showq now reports total nodes associated with a job rather than cores, tasks, or hardware threads. One reason for this change is clarity: the operating system sees each compute node's 112 hardware threads as "processors", and output based on that information can be ambiguous or otherwise difficult to interpret.

Other Job Management Commands (scancel, scontrol, and sacct)

It's not possible to add resources to a job (e.g. allow more time) once you've submitted the job to the queue.

To cancel a pending or running job, first determine its jobid, then use scancel:

login1$ squeue -u bjones    # one way to determine jobid
  170361      normal   spec12   bjones PD       0:00     32 (Resources)
login1$ scancel 170361      # cancel job

For detailed information about the configuration of a specific job, use scontrol:

login1$ scontrol show job=170361

To view some accounting data associated with your own jobs, use sacct:

login1$ sacct --starttime 2019-06-01  # show jobs that started on or after this date

Dependent Jobs using sbatch

You can use sbatch to help manage workflows that involve multiple steps: the "--dependency" option allows you to launch jobs that depend on the completion (or successful completion) of another job. For example you could use this technique to split into three jobs a workflow that requires you to (1) compile on a single node; then (2) compute on 40 nodes; then finally (3) post-process your results using 4 nodes.

login1$ sbatch --dependency=afterok:173210 myjobscript

For more information see the Slurm online documentation. Note that you can use $SLURM_JOBID from one job to find the jobid you'll need to construct the sbatch launch line for a subsequent one. But also remember that you can't use sbatch to submit a job from a compute node.

Programming and Performance

Programming and Performance: General

Programming for performance is a broad and rich topic. While there are no shortcuts, there are certainly some basic principles that are worth considering any time you write or modify code.

Timing and Profiling

Measure performance and experiment with both compiler and runtime options. This will help you gain insight into issues and opportunities, as well as recognize the performance impact of code changes and temporary system conditions.

Measuring performance can be as simple as prepending the shell keyword "time" or the command "perf stat" to your launch line. Both are simple to use and require no code changes. Typical calls look like this:

perf stat ./a.out    # report basic performance stats for a.out
time ./a.out         # report the time required to execute a.out
time ibrun ./a.out   # time an MPI code
ibrun time ./a.out   # crude timings for each MPI task (no rank info)

As your needs evolve you can add timing intrinsics to your source code to time specific loops or other sections of code. There are many such intrinsics available; some popular choices include gettimeofday, MPI_Wtime and omp_get_wtime. The resolution and overhead associated with each of these timers is on the order of a microsecond.

It can be helpful to compare results with different compiler and runtime options: e.g. with and without vectorization, threading, or Lustre striping. You may also want to learn to use profiling tools like Intel VTune Amplifier ("module load vtune") or GNU gprof.

Data Locality

Appreciate the high cost (performance penalty) of moving data from one node to another, from disk to RAM, and even from RAM to cache. Write your code to keep data as close to the computation as possible: e.g. in RAM when needed, and on the node that needs it. This means keeping in mind the capacity and characteristics of each level of the memory hierarchy when designing your code and planning your simulations.

When possible, best practice also calls for so-called "stride 1 access" – looping through large, contiguous blocks of data, touching items that are adjacent in memory as the loop proceeds. The goal here is to use "nearby" data that is already in cache rather than going back to main memory (a cache miss) in every loop iteration.

To achieve stride 1 access you need to understand how your program stores its data. Here C and C++ are different than (in fact the opposite of) Fortran. C and C++ are row-major: they store 2d arrays a row at a time, so elements a[3][4] and a[3][5] are adjacent in memory. Fortran, on the other hand, is column-major: it stores a column at a time, so elements a(4,3) and a(5,3) are adjacent in memory. Loops that achieve stride 1 access in the two languages look like this:

Fortran exampleC example
real*8 :: a(m,n), b(m,n), c(m,n)
! inner loop strides through col i
do i=1,n
  do j=1,m
  end do
end do
double a[m][n], b[m][n], c[m][n];
// inner loop strides through row i
for (i=0;i<m;i++){
  for (j=0;j<n;j++){


Give the compiler a chance to produce efficient, vectorized code. The compiler can do this best when your inner loops are simple (e.g. no complex logic and a straightforward matrix update like the ones in the examples above), long (many iterations), and avoid complex data structures (e.g. objects). See Intel's note on Programming Guidelines for Vectorization for a nice summary of the factors that affect the compiler's ability to vectorize loops.

It's often worthwhile to generate optimization and vectorization reports when using the Intel compiler. This will allow you to see exactly what the compiler did and did not do with each loop, together with reasons why.

Learning More

The literature on optimization is vast. Some places to begin a systematic study of optimization on Intel processors include: Intel's Modern Code resources; the Intel Optimization Reference Manual; and TACC training materials.

Programming and Performance: CLX

Hyperthreading. Hyperthreading is not enabled on Frontera.

Clock Speed. The published nominal clock speed of the Frontera CLX processors is 2.7GHz. But actual clock speed varies widely: it depends on the vector instruction set, number of active cores, and other factors affecting power requirements and temperature limits. At one extreme, a single serial application using the AVX2 instruction set may run at frequencies approaching 3.7GHz, because it's running on a single core (in fact a single hardware thread). At the other extreme, a large, fully-threaded MKL dgemm (a highly vectorized routine in which all cores operate at nearly full throttle) may run at 2.4GHz.

Vector Optimization and AVX2. In some cases, using the AVX2 instruction set may produce better performance than AVX512. This is largely because cores can run at higher clock speeds when executing AVX2 code. To compile for AVX2, replace the multi-architecture flags described above with the single flag "-xCORE-AVX2". When you use this flag you will be able to build and run on any Frontera node.

Vector Optimization and 512-Bit ZMM Registers. If your code can take advantage of wide 512-bit vector registers, you may want to try compiling for CLX with (for example):

-xCORE-AVX512 -qopt-zmm-usage=high

The "qopt-zmm-usage" flag affects the algorithms the compiler uses to decide whether to vectorize a given loop with 512 intrinsics (wide 512-bit registers) or AVX2 code (256-bit registers). When the flag is set to "-qopt-zmm-usage=low" (the default when compiling for the CLX using CORE-AVX512), the compiler will choose AVX2 code more often; this may or may not be the optimal approach for your application. The qopt-zmm-usage flag is available only on Intel compilers newer than 17.0.4. Do not use $TACC_VEC_FLAGS when specifying qopt-zmm-usage. This is because $TACC_VEC_FLAGS specifies CORE-AVX2 as the base architecture, and the compiler will ignore qopt-zmm-usage unless the base target is a variant of AVX512. See the recent Intel white paper, the compiler documentation, the compiler man pages, and the notes above for more information.

Task Affinity. If you run one MPI application at a time, the ibrun MPI launcher will spread each node's tasks evenly across an CLX node's two sockets, with consecutive tasks occupying the same socket when possible.

Hardware Thread Numbering. Execute "lscpu" or "lstopo" on an CLX node to see the numbering scheme for hardware threads. Note that hardware thread numbers alternate between the sockets: even numbered threads are on NUMA node 0, while odd numbered threads are on NUMA node 1.

File Operations: I/O Performance

This section includes general advice intended to help you achieve good performance during file operations. See Navigating the Shared File Systems for a brief overview of Frontera's Lustre file systems and the concept of striping. See TACC Training material for additional information on I/O performance.

Follow the advice in Good Citizenship to avoid stressing the file system.

Stripe for performance. If your application writes large files using MPI-based parallel I/O (including MPI-IO, parallel HDF5, and parallel netCDF, you should experiment with stripe counts larger than the default values (2 stripes on $SCRATCH, 1 stripe on $WORK). See Striping Large Files for the simplest way to set the stripe count on the directory in which you will create new output files. You may also want to try larger stripe sizes up to 16MB or even 32MB; execute "man lfs" for more information. If you write many small files you should probably leave the stripe count at its default value, especially if you write each file from a single process. Note that it's not possible to change the stripe parameters on files that already exist. This means that you should make decisions about striping when you create input files, not when you read them.

Aggregate file operations. Open and close files once. Read and write large, contiguous blocks of data at a time; this requires understanding how a given programming language uses memory to store arrays.

Be smart about your general strategy. When possible avoid an I/O strategy that requires each process to access its own files; such strategies don't scale well and are likely to stress a Lustre file system. A better approach is to use a single process to read and write files. Even better is genuinely parallel MPI-based I/O.

Use parallel I/O libraries. Leave the details to a high performance package like MPI-IO (built into MPI itself), parallel HDF5 ("module load phdf5"), and parallel netCDF ("module load pnetcdf").

When using the Intel Fortran compiler, compile with "-assume buffered_io". Equivalently, set the environment variable FORT_BUFFERED=TRUE. Doing otherwise can dramatically slow down access to variable length unformatted files. More generally, direct access in Fortran is typically faster than sequential access, and accessing a binary file is faster than ASCII.

Application Containers

Shortly after launch, Frontera will provide seamless, integrated support for the use of Singularity containers (both custom containers made by users and containers from standard repositories). The use of containers will greatly enhance the number of people who contribute to the Frontera software base, promote portability with other resources, and greatly expand the supported software catalog beyond that found on TACC's other HPC systems.

Frontera will support application containers from any specification-compliant science community (e.g. Biocontainers, with over 3,000 containers and counting, and the Nvidia GPU Cloud Library), opening this important resource for a wide range of new applications and new science communities. To make the experience seamless, our implementation injects mount points and environment variables into the container to match the HPC system environment – the $SCRATCH, $WORK, and $HOME filesystems all will be identical to what users see natively on any Frontera node.

Cloud Services Integration

Frontera's design also includes a totally new integration with cloud services, and will provide users with new options for data storage and access to emerging computing technologies.

For projects utilizing data of exceptional importance – such as may result from an especially difficult physical experiment or a long-running simulation that is impractical to repeat – users will have access to a cloud-based storage mirror that provides protection beyond the level already provided with TACC's redundant archive storage system. This capability relies upon the storage solutions of our cloud partners Microsoft, Google, and Amazon. For users who need this level of data protection, we will provide storage capacity during the term of Frontera's operation by awarding credits for users to store data with our cloud partners.

Users will also be able to access emerging computational capabilities (such as Tensor processors) by submitting jobs to special queues on Frontera that will run on specially-designated processors at Google, Microsoft, and Amazon. This will allow us to regularly refresh the project with novel computing technologies, while providing a real-world platform for users to explore the future of their science applications.

Cloud integration is expected to be available to users in the second six months of operation.

Cascade Lake (CLX) Compute Nodes

Frontera hosts 8,008 compute nodes contained in 91 racks.

Model:  Intel Xeon Platinum 8280 ("Cascade Lake")
Total cores per CLX node:  56 cores on two sockets (28 cores/socket)
Hardware threads per core:  2 Threading not currently enabled on Frontera.
Hardware threads per node:  56 x 2 = 112
Clock rate:  2.7GHz nominal
RAM:  192GB (2933 MT/s) DDR4
Cache:  32KB L1 data cache per core; 1MB L2 per core; 38.5 MB L3 per socket. Each socket can cache up to 66.5 MB (sum of L2 and L3 capacity).
Local storage:  144GB /tmp partition on a 240GB SSD.

Login Nodes

Frontera's four login nodes are Intel Xeon Platinum 8280 ("Cascade Lake") nodes with 56 cores and 192 GB of RAM. The login nodes are configured similarly to the compute nodes. However, since these nodes are shared, limits are enforced on memory usage and number of processes. Please use the login node for file management, compilation, and data movement. Any computing should be done within a batch job or an interactive session on compute nodes.

GPU Nodes

Coming Soon


The interconnect is based on Mellanox HDR technology with full HDR (200 Gb/s) connectivity between the switches and HDR100 (100 Gb/s) connectivity to the compute nodes. A fat tree topology employing six core switches connects the compute nodes and the $HOME and $SCRATCH filesystems. There are two 40-port leaf switches in each rack. Half of the nodes in a rack (44) connect to 22 downlinks of a leaf switch as pairs of HDR100 (100 Gb/s) links into HDR200 (200 Gb/s) ports of the leaf switch. The other 18 ports are uplinks to the six cores switches. The disparity in the number of uplinks and downlinks creates an oversubscription of 22/18.

Good Citizenship

You share Frontera with hundreds of other users, and what you do on the system affects others. Exercise good citizenship to ensure that your activity does not adversely impact the system and the research community with whom you share it.

Here are some rules of thumb.

Login Nodes

When you connect to Frontera you share the login node with dozens of other users.

  • You must avoid computationally intensive activity on login nodes. This means:

    • Don't run research applications on the login nodes; this includes frameworks like MATLAB and R. If you need interactive access, please use idev or srun to schedule a compute node.
    • Don't launch too many simultaneous processes: while it's fine to compile on a login node, "make -j 16" (which compiles on 16 cores) may impact other users.
    • That script you wrote to check job status should probably do so once every few minutes rather than several times a second.

It is imperative that you do not run jobs on the login nodes. Doing so is the fastest route to account suspension.

Shared Lustre File Systems

This section focuses on ways to avoid causing problems on $HOME, $WORK, and $SCRATCH. File Systems above is a brief overview of these file systems. Configuring Your Account covers environment variables and aliases that help you navigate the file systems.

  • Stripe the receiving directory before creating large files in the directory or transferring large files to the directory. See Striping Large Files for more information.

  • Don't run jobs in $HOME. The $HOME file system is for routine file management, not parallel jobs.

  • Run I/O intensive jobs in $SCRATCH rather than $WORK. If you stress $WORK, you affect every user on every TACC system.

  • Don't get greedy. If you know or suspect your workflow is I/O intensive, don't submit a pile of simultaneous jobs. Writing restart/snapshot files can stress the file system; avoid doing so too frequently.

  • Watch your file system quotas. If you're near your quota in $WORK and your job is repeatedly trying (and failing) to write to $WORK, you will stress the file system. If you're near your quota in $HOME, jobs run on any file system may fail, because all jobs write some data to the hidden $HOME/.slurm directory.

  • Avoid opening and closing files repeatedly in tight loops. Every open/close operation on the file system requires interaction with the MetaData Service (MDS). The MDS acts as a gatekeeper for access to files on Lustre's parallel file system. Overloading the MDS will affect other users on the system. If possible, open files once at the beginning of your program/workflow, then close them at the end.

  • Avoid storing many small files in a single directory, and avoid workflows that require many small files. A few hundred files in a single directory is probably fine; tens of thousands is almost certainly too many. If you must use many small files, group them in separate directories of manageable size.

Internal and External Networks

  • Avoid too many simultaneous file transfers. You share the network bandwidth with other users; don't use more than your fair share. Two or three concurrent scp sessions is probably fine. Twenty is probably not.

  • Avoid recursive file transfers, especially those involving many small files. Create a tar archive before transfers. This is especially true when transferring files to or from Ranch.

Submitting Jobs

  • When you submit a job to the scheduler, don't ask for more time than you really need. The scheduler will have an easier time finding a slot for the 2 hours you need than the 24 hours you request. This means shorter queue waits times for you and everybody else.

  • Test your submission scripts. Start small: make sure everything works on 2 nodes before you try 200. Work out submission bugs and kinks with 5 minute jobs that won't wait long in the queue and involve short, simple substitutes for your real workload: simple test problems; "hello world" codes; one-liners like "ibrun hostname"; or an ldd on your executable.

  • Respect memory limits and other system constraints. If your application needs more memory than is available, your job will fail, and may leave nodes in unusable states. Monitor your application's needs. Execute "module load remora" followed by "module help remora" for more information on a particularly handy monitoring tool.

Help Desk

You can submit a help ticket at any time via the TACC User Portal. Please review the following tips to help the consulting staff help you.

  • Describe your issue as precisely and completely as you can: what you did, what happened, verbatim error messages, other meaningful output. When appropriate, include the information a consultant would need to find your artifacts and understand your workflow: e.g. the directory containing your build and/or job script; the modules you were using; relevant job numbers; and recent changes in your workflow that could affect or explain the behavior you're observing.

  • Do your homework before submitting a help desk ticket. What does the user guide and other documentation say? Search the internet for key phrases in your error logs; that's probably what the consultants answering your ticket are going to do. What have you changed since the last time your job succeeded?

  • Be patient. It may take a business day for a consultant to get back to you, especially if your issue is complex. It might take an exchange or two before you and the consultant are on the same page. If the admins disable your account, it's not punitive. When the file system is in danger of crashing, or a login node hangs, they don't have time to notify you before taking action.

  • Subscribe to TACC User News. This is the best way to keep abreast of maintenance schedules, system outages, and other general interest items.

  • Have realistic expectations. Consultants can address system issues and answer questions about Frontera. But they can't teach parallel programming in a ticket, and may know nothing about the package you downloaded. They may offer general advice that will help you build, debug, optimize, or modify your code, but you shouldn't expect them to do these things for you.

Revision History

"Last Update" at the top of this document is the date of the most recent change to this document. This revision history is a list of non-trivial updates; it excludes routine items such as corrected typos and minor format changes.

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