Lonestar6 User Guide
Last update: November 22, 2021

This document is in progress and subject to frequent revisions.


  • Individual /scratch directories are still coming online. While LS6 remains in the early user period, feel free to run your jobs in the $WORK file system.
  • With Lonestar6 file systems fully available, Lonestar5 users may finish data migration from all three LS5 file systems. See Accessing Lonestar5 Files. (11/16/21)
  • All users: read the Good Citizenship section. Lonestar6 is a shared resource and your actions can impact other users. (10/18/2021)
  • You may now subscribe to Lonestar6 User News. Stay up-to-date on Lonestar6's status, scheduled maintenances and other notifications. (10/14/2021)

Introduction to Lonestar6

Lonestar6 provides a balanced set of resources to support simulation, data analysis, visualization, and machine learning. It is the next system in TACC's Lonestar series of high performance computing systems that are deployed specifically to support Texas researchers. Lonestar6 is funded through collaboration with TACC, the University of Texas System, Texas A&M University, Texas Tech University, and the University of North Texas, as well as a number of research centers and faculty at UT-Austin, including the Oden Institute for Computational Engineering & Sciences and the Center for Space Research.

The system employs Dell Servers with AMD's highly performant Epyc Milan processor, Mellanox's HDR Infiniband technology, and 8 PB of BeeGFS based storage on Dell storage hardware. Additionally, Lonestar6 supports GPU nodes utilizing NVIDIA's Ampere A100 GPUs to support machine learning workflows and other GPU-enabled applications. Lonestar6 will continue to support the TACC HPC environment, providing numerical libraries, parallel applications, programming tools, and performance monitoring capabilities to the user community.

Lonestar6 is available to researchers from all University of Texas System institutions and to our partners who purchased access: Texas A&M University, Texas Tech University and University of North Texas. Details on how to submit allocation requests will be provided here soon.

Dielectric liquid coolant cabinet

Accessing Lonestar5 Files

With Lonestar6 file systems fully available, Lonestar5 users should begin migrating older data from Lonestar5's /home and /scratch file systems.

  • Lonestar5's /scratch File System

    The old Lonestar5 /scratch file system has been re-mounted as "ls5-scratch" with read-only permissions on Lonestar6's login nodes. To access your old files, cd to the re-mounted file system. Construct the path to your files by concatenating the numerical portion of your $HOME path, plus your username.

      login1.ls6$ pwd
      login1.ls6$ cd /ls5-scratch/01158/bjones/
      login1.ls6$ ls
      login1.ls6$ cp -p myls5scratchfiles.tar.gz $SCRATCH
      login1.ls6$ cd $SCRATCH
      login1.ls6$ ls
      otherscratchdata/  myls5scratchfiles.tar.gz
  • Lonestar5's /home File System

    The Lonestar5 /home file system is not mounted on Lonestar6. To copy files between the Lonestar5 and Lonestar6 login nodes users may still logon to Lonestar5 (ls5.tacc.utexas.edu) and leverage $WORK as described below, or make use of the standard scp and rsync file transfer utilities,

Lonestar5's /work file system is Stockyard, mounted on all TACC resources. Lonestar5 users continue to have access to this data.

login1.ls6$ pwd
login1.ls6$ cd $WORK/../lonestar
login1.ls6$ ls

System Architecture

All Lonestar6 nodes run Rocky 8.4 and are managed with batch services through native Slurm 20.11.8. Global storage areas are supported by an NFS file system ($HOME), a BeeGFS parallel file system ($SCRATCH), and a Lustre parallel file system ($WORK). Inter-node communication is supported by a Mellanox HDF Infiniband network. Also, the TACC Ranch tape archival system is available from Lonestar6.

The system is composed of 560 compute nodes and 16 GPU nodes. The compute nodes are housed in 4 dielectric liquid coolant cabinets and ten air-cooled racks. The air cooled racks also contain the 16 GPU nodes. Each node has two AMD EPYC 7763 64-core processors (Milan) and 256 GB of DDR4 memory. Twenty four of the compute nodes are reserved for development and are accessible interactively for up to two hours. Each GPU node also contains two AMD EPYC 7763 64-core processes and two NVIDIA A100 GPUs each with 40 GB of high bandwidth memory (HBM2).

Compute Nodes

Lonestar6 hosts 560 compute nodes with 5 TFlops of peak performance per node and 256 GB of DRAM.

CPU:   2x AMD EPYC 7763 64-Core Processor ("Milan")
Total cores per node:   128 cores on two sockets (64 cores / socket )
Hardware threads per core:   1 per core
Hardware threads per node:   128 x 1 = 128
Clock rate:   2.45 GHz (Boost up to 3.5 GHz)
RAM:   256 GB (3200 MT/s) DDR4
Cache:   32KB L1 data cache per core
512KB L2 per core
32 MB L3 per core complex
(1 core complex contains 8 cores)
256 MB L3 total (8 core complexes )
Each socket can cache up to 288 MB
(sum of L2 and L3 capacity)
Local storage:  144GB /tmp partition on a 288GB SSD.

Login Nodes

Lonestar6's three login nodes, login1, login2, and login3, contain the same hardware and 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 nodes only for file management, compilation, and data movement. Any and all computing should be done within a batch job or an interactive session on the compute nodes.

GPU Nodes

Lonestar6 hosts 16 GPU nodes that are configured identically to the compute nodes with the addition of 2 NVIDIA A100 GPUs, one for each socket. Each A100 gpu has a peak performance of 9.7 TFlops in double precision and 312 TFlops in FP16 precision using the Tensor Cores.

(1 per socket )
GPU Memory:  40 GB HBM2
CPU:   2x AMD EPYC 7763 64-Core Processor ("Milan")
Total cores per node:   128 cores on two sockets (64 cores / socket )
Hardware threads per core:   1 per core
Hardware threads per node:   128 x 1 = 128
Clock rate:   2.45 GHz
RAM:   256 GB
Cache:   32KB L1 data cache per core
512KB L2 per core
32 MB L3 per core complex
(1 core complex contains 8 cores)
256 MB L3 total (8 core complexes )
Each socket can cache up to 288 MB
(sum of L2 and L3 capacity)
Local storage:   144GB /tmp partition on a 288GB SSD.


The interconnect is based on Mellanox HDR technology with full HDR (200 Gb/s) connectivity between the switches and the compute nodes. A fat tree topology employing sixteen core switches connects the compute nodes and the $SCRATCH file systems. There is an oversubscription of 24/16.

File System Quota Key Features
$HOME 10 GB 200,000 files
Not intended for parallel or high-intensity file operations.
NFS file system
Backed up regularly.
Overall capacity 7 TB
Not purged.
3,000,000 files
Across all TACC systems
Not intended for high-intensity file operations or jobs involving very large files.
Lustre file system
On the Global Shared File System that is mounted on most TACC systems.
See Stockyard system description for more information.
Defaults: 1 stripe, 1MB stripe size
Not backed up.
Not purged.
$SCRATCH none Overall capacity 8 PB
Defaults: 4 targets, 512 KB chunk size
Not backed up
Files are subject to purge if access time* is more than 10 days old.*
/tmp on nodes 288 GB Data purged at the end of each job.
Access is local to the node.
Data in /tmp is not shared across nodes.

Scratch Purge Policy

The $SCRATCH file system, as its name indicates, is a temporary storage space. Files that have not been accessed* in ten days are subject to purge. Deliberately modifying file access time (using any method, tool, or program) for the purpose of circumventing purge policies is prohibited.


Secure Shell (SSH)

The "ssh" command (SSH protocol) is the standard way to connect to Lonestar6 (ls6.tacc.utexas.edu). 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 username@ls6.tacc.utexas.edu

The above command will rotate connections across all available login nodes, login1-login3, and route your connection to one of them. To connect to a specific login node, use its full domain name:

localhost$ ssh username@login2.ls6.tacc.utexas.edu

To connect with X11 support on Lonestar6 (usually required for applications with graphical user interfaces), use the "-X" or "-Y" switch:

localhost$ ssh -X username@ls6.tacc.utexas.edu

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 Lonestar6. 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 Lonestar6.

  1. execute "mv .ssh dot.ssh.old"
  2. log out
  3. log into Lonestar6 again

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

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.

Check your Allocation Status

You must be added to a Lonestar6 allocation in order to have access/login to Lonestar6. The ability to log on to the TACC User Portal does NOT signify access to Lonestar6 or any TACC resource. Submit Lonestar6 allocations requests via TACC's Resource Allocation System. Continue to manage your allocation's users via the TACC User Portal.

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 User Portal 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.


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 Lonestar6, 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.

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 Lonestar6 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 v19.1.14
$ module load intel/19.1.1   # load a specific version of the 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   netcdf  # defines DDT-related env vars; modifies others
$ module unload netcdf  # 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 Lonestar6, 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 netcdf   # see what this module does to your environment
$ env | grep NETCDF    # see env vars that contain the string PAPI
$ env | grep -i netcdf # 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 netcdf             # all modules with names containing 'slep'
$ module spider netcdf/3.6.3       # 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 Lonestar6. 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 Lonestar6's default startup scripts for a safe way to do so.


You share Lonestar6 with many, sometimes hundreds, of other users, and what you do on the system affects others. All users must follow a set of good practices which entail limiting activities that may impact the system for other users. Exercise good citizenship to ensure that your activity does not adversely impact the system and the research community with whom you share it.

TACC staff has developed the following guidelines to good citizenship on Lonestar6. Please familiarize yourself especially with the first two mandates. The next sections discuss best practices on limiting and minimizing I/O activity and file transfers. And finally, we provide job submission tips when constructing job scripts to help minimize wait times in the queues.

Do Not Run Jobs on the Login Nodes

Lonestar6's few login nodes are shared among all users. Dozens, (sometimes hundreds) of users may be logged on at one time accessing the file systems. Think of the login nodes as a prep area, where users may edit and manage files, compile code, perform file management, issue transfers, submit new and track existing batch jobs etc. The login nodes provide an interface to the "back-end" compute nodes.

The compute nodes are where actual computations occur and where research is done. Hundreds of jobs may be running on all compute nodes, with hundreds more queued up to run. All batch jobs and executables, as well as development and debugging sessions, must be run on the compute nodes. To access compute nodes on TACC resources, one must either submit a job to a batch queue or initiate an interactive session using the idev utility.

A single user running computationally expensive or disk intensive task/s will negatively impact performance for other users. Running jobs on the login nodes is one of the fastest routes to account suspension. Instead, run on the compute nodes via an interactive session (idev) or by submitting a batch job.

Do not run jobs or perform intensive computational activity on the login nodes or the shared file systems.
Your account may be suspended and you will lose access to the queues if your jobs are impacting other users.

Dos & Don'ts on the Login Nodes

  • Do not run research applications on the login nodes; this includes frameworks like MATLAB and R, as well as computationally or I/O intensive Python scripts. If you need interactive access, use the idev utility or Slurm's srun to schedule one or more compute nodes.

    DO THIS: Start an interactive session on a compute node and run Matlab.

      login1$ idev
      nid00181$ matlab

    DO NOT DO THIS: Run Matlab or other software packages on a login node

    login1$ matlab
  • Do not launch too many simultaneous processes; while it's fine to compile on a login node, a command like "make -j 16" (which compiles on 16 cores) may impact other users.

    DO THIS: build and submit a batch job. All batch jobs run on the compute nodes.

      login1$ make mytarget
      login1$ sbatch myjobscript

    DO NOT DO THIS: Invoke multiple build sessions.

    login1$ make -j 12

    DO NOT DO THIS: Run an executable on a login node.

      login1$ ./myprogram
  • That script you wrote to poll job status should probably do so once every few minutes rather than several times a second.

Do Not Stress the Shared File Systems

The TACC Global Shared File System, Stockyard, is mounted on most TACC HPC resources as the /work ($WORK) directory. This file system is accessible to all TACC users, and therefore experiences a lot of I/O activity (reading and writing to disk, opening and closing files) as users run their jobs, read and generate data including intermediate and checkpointing files. As TACC adds more users, the stress on the $WORK file system is increasing to the extent that TACC staff is now recommending new job submission guidelines in order to reduce stress and I/O on Stockyard.

TACC staff now recommends that you run your jobs out of the $SCRATCH file system instead of the global $WORK file system.

To run your jobs out $SCRATCH:

  • Copy or move all job input files to $SCRATCH
  • Make sure your job script directs all output to $SCRATCH
  • Once your job is finished, move your output files to $WORK to avoid any data purges.

Compute nodes should not reference $WORK unless it's to stage data in/out only before/after jobs.

Consider that $HOME and $WORK are for storage and keeping track of important items. Actual job activity, reading and writing to disk, should be offloaded to your resource's $SCRATCH file system (see Table. File System Usage Recommendations. You can start a job from anywhere but the actual work of the job should occur only on the $SCRATCH partition. You can save original items to $HOME or $WORK so that you can copy them over to $SCRATCH if you need to re-generate results.

More File System Tips

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

  • Watch all 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 that 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 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.

  • TACC resources, with a few exceptions, mount three file systems: /home, /work and /scratch. Please follow each file system's recommended usage.

File System Best Storage Practices Best Activities
$HOME cron jobs
small scripts
environment settings
compiling, editing
$WORK store software installations
original datasets that can't be reproduced
job scripts and templates
staging datasets
$SCRATCH Temporary Storage
I/O files
job files
temporary datasets
all job I/O activity
see TACC's Scratch File System Purge Policy.

Limit Input/Output (I/O) Activity

In addition to the file system tips above, it's important that your jobs limit all I/O activity. This section focuses on ways to avoid causing problems on each resources' shared file systems.

  • Limit I/O intensive sessions (lots of reads and writes to disk, rapidly opening or closing many files)

  • 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.

  • 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. Also, use the hdf5 or netcdf libraries to generate a single restart file in parallel, rather than generating files from each process separately.

If you know your jobs will require significant I/O, please submit a support ticket and an HPC consultant will work with you. See also Managing I/O on TACC Resources for additional information.

File Transfer Guidelines

In order to not stress both internal and external networks, be mindful of the following guidelines:

  • When creating or transferring large files to Stockyard ($WORK) or the $SCRATCH file systems, be sure to stripe the receiving directories appropriately. See Striping Large Files in the Stampede2 User Guide for more information.

  • 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.

Job Submission Tips

  • Request Only the Resources You Need Make sure your job scripts request only the resources that are needed for that job. Don't ask for more time or more nodes than you really need. The scheduler will have an easier time finding a slot for a job requesting 2 nodes for 2 hours, than for a job requesting 4 nodes for 24 hours. 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 20. 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. Use TACC's Remora tool to monitor your application's needs.

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.

The 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 Lonestar6. 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:

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.

Compiling and Linking as Separate Steps

Building an executable requires two separate steps: (1) compiling (generating a binary object file associated with each source file); and (2) linking (combining those object files into a single executable file that also specifies the libraries that executable needs). The examples in the previous section accomplish these two steps in a single call to the compiler. When building more sophisticated applications or libraries, however, it is often necessary or helpful to accomplish these two steps separately.

Use the -c ("compile") flag to produce object files from source files:

$ icc -c main.c calc.c results.c

Barring errors, this command will produce object files main.o, calc.o, and results.o. Syntax for other compilers Intel and GNU compilers is similar.

You can now link the object files to produce an executable file:

$ icc main.o calc.o results.o -o myexe

The compiler calls a linker utility (usually /bin/ld) to accomplish this task. Again, syntax for other compilers is similar.

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 Lonestar6, 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 Lonestar6. 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

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 Lonestar6 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:

$ pip3 install netCDF4      --user                  # install netCDF4 package to $HOME/.local
$ python3 setup.py 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 Lonestar6-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 (128 on AMD Milan). 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 http://software.intel.com/en-us/articles/recommended-settings-for-calling-intel-mkl-routines-from-multi-threaded-applications and related Intel resources for examples of how to manage threading when calling MKL from multiple processes.

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
$SCRATCH/apps/mydir/mycode.exe          # explicit full path to executable
./mycode.exe                            # executable in current directory
./mycode.exe -m -k 6 input1             # executable with notional input options

Parametric Sweep / HTC jobs

Consult the Launcher at TACC documentation for instructions on running parameter sweep and other High Throughput Computing workflows.

Launching One Multi-Threaded Application

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=128      # 128 total OpenMP threads (1 per core)

Launching One MPI Application

To launch an MPI application, use the TACC-specific MPI launcher ibrun, which is a Lonestar6-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 arguments 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.

#SBATCH -N 4                
#SBATCH -n 512

# ibrun uses the $SBATCH directives to properly allocate nodes and tasks
ibrun ./mycode.exe              

To use ibrun interactively, say within an idev session, you can specify:

login1$ idev -N 2 -n 100                 
c309-005$ ibrun ./mycode.exe

Launching One Hybrid (MPI+Threads) Application

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

As a practical guideline, the product of $OMP_NUM_THREADS and the maximum number of MPI processes per node should not be greater than total number of cores available per node (128 cores in the development/normal/large queues).

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.

ibrun ./mycode.exe input1    # runs after preprocess.sh 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=256 total MPI tasks, Slurm will generate a hostfile with 256 entries (64 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:

# 128 tasks; offset by  0 entries in hostfile.
ibrun -n 128 -o  0 task_affinity ./mycode.exe input1 &   

# 128 tasks; offset by 128 entries in hostfile.
ibrun -n 128 -o 128 task_affinity ./mycode.exe input2 &   

# 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

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.

The sequence of proc-ids on socket 0 and socket 1 are sequentially numbered.

On socket 0:


and on socket 1:


Note, hardware threads are not enabled on Lonestar6. So, there are no core ids greater than 127.

The proc-id mapping to the cores for Milan is:

|------- Socket 0 ------------|-------- Socket 1 -------------|
#   0   1   2,..., 61, 62, 63 |  0   1   2,...,  61,  62,  63 |
0   0   1   2,..., 61, 62, 63 | 64  65  66,..., 125, 126, 127 |

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

socket 0:  export OMP_PLACES="{0},{1},{2}"
socket 1:  export OMP_PLACES="{64},{65},{66}"

Under the NUMA covers, each AMD chip is actually composed of 8 "chiplets" which share a 32 MB L3 cache. To place each thread on its own chiplet for an 8 thread OpenMP program, you would use this command:

socket 0:  export OMP_PLACES="{0},{8},{16},{24},{32},{40},{48},{56}"
socket 1:  export OMP_PLACES="{64},{72},{80},{88},{96},{104},{112},{120}"

Interval notation can be used to express a sequence 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:

socket 0:  export OMP_PLACES="{0},8,8"
socket 1:  export OMP_PLACES="{64},8,8"

In the example below two OpenMP programs are executed on a single node, each using 64 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},64,1" ./omp.exe &    #execution on socket 0 cores
env OMP_PLACES="{64},64,1" ./omp.exe &   #execution on socket 1 cores

Running Jobs on Lonestar6

This section provides an overview of how compute jobs are charged to allocations and describes the Simple Linux Utility for Resource Management (Slurm) batch environment, Lonestar6 queue structure, lists basic Slurm job control and monitoring commands along with options.

Job Accounting

Like all TACC systems, Lonestar6'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). For any given job, the total cost in SUs is the use of one compute node for one hour of wall clock time plus any charges or discounts for the use of specialized queues, e.g. Frontera's flex queue, Stampede2's development queue, and Longhorn's v100 queue. The queue charge rates are determined by the supply and demand for that particular queue or type of node used and are subject to change.

Lonestar6 SUs billed = (# nodes) x (job duration in wall clock hours) x (charge rate per node-hour)

The Slurm scheduler 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. If your job finishes early and exits properly, Slurm will release the nodes back into the pool of available nodes. Your job will only be charged for as long as you are using the nodes.

TACC does not implement node-sharing on any compute resource. Each Lonestar6 node can be assigned to only one user at a time; hence a complete node is dedicated to a user's job and accrues wall-clock time for all the node's cores whether or not all cores are used.

Tip: Your queue wait times will be less if you request only the time you need: the scheduler will have a much easier time finding a slot for the 2 hours you really need than say, for the 12 hours requested 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.

Table. Lonestar6 Production Queues

Queue limits are subject to change without notice.

Queue Name Min/Max Nodes per Job
(assoc'd cores)*
Max Job Duration Max Nodes
per User
Max Jobs
per User
Charge Rate
(per node-hour)
normal 1/64 nodes
(8192 cores)
48 hours 96 15 1 SU
large* 65/256 nodes
(65536 cores)
48 hours 256 1 1 SU
development 4 nodes
(512 cores)
2 hours 6 1 1 SU
gpu_a100 4 nodes
(512 cores)
48 hours 6 2 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 Lonestar6.

Serial Codes
MPI Jobs
Hybrid (MPI + OpenMP) Job
OpenMP Jobs

Customizing your Job Script

Copy and customize the following scripts to specify and refine your job's requirements.

  • specify the maximum run time with the -t option.
  • specify number of nodes needed with the -N option
  • specify tasks per node with the -n option
  • specify the project to be charged with the -A option.

In general, the fewer resources (nodes) you specify in your batch script, the less time your job will wait in the queue. See 4. Request Only the Resources You Need in the Citizenship section.

Consult Table 6 in the Stampede2 User Guide for a listing of common Slurm #SBATCH options.

Job Management

In this section, we present several Slurm commands and other utilities that 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 Lonestar6 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.

TACC's qlimits command

To display resource limits for the Lonestar queues, execute: STTYLEREDqlimits. The result is real-time data; the corresponding information in this document's table of Lonestar6 queues may lag behind the actual configuration that the qlimits utility displays.

Slurm's sinfo command

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 might look like this:

login1$ sinfo -S+P -o "%18P %8a %20F"
PARTITION          AVAIL    NODES(A/I/O/T)    
development        up       0/8/0/8
v100               up       44/43/1/96          
v100-lm            up       0/8/0/8

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", "Offline", 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 Lonestar6 (e.g. TIMELIMIT, displayed using the %l option).

Slurm's squeue command

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 might look like this:

25781 development idv72397   bjones CG       9:36      2 c001-011,012
25918 development ppm_4828   bjones PD       0:00     20 (Resources)
25915 development MV2-test    siliu PD       0:00     14 (Priority)
25589        v100   aatest slindsey PD       0:00      8 (Dependency)
25949 development psdns_la sniffjck PD       0:00      2 (Priority)
25618        v100   SP256U   connor PD       0:00      1 (Dependency)
25944        v100  MoTi_hi   wchung  R      35:13      1 c005-003
25945        v100 WTi_hi_e   wchung  R      27:11      1 c006-001
25606        v100   trainA   jackhu  R   23:28:28      1 c008-012

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 Lonestar6 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

TACC's showq utility

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 Lonestar6 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        v100   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.

Help Desk

TACC Consulting operates from 8am to 5pm CST, Monday through Friday, except for holidays. You can submit a help desk ticket at any time via the TACC User Portal with "Lonestar6" in the Resource field. Help the consulting staff help you by following these best practices when submitting tickets.

  • 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?

  • 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.

  • Subscribe to Lonestar6 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 Lonestar6. 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.

  • 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.