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Pending decommission: August 30th, 2019. Please migrate to NOTS. Contact us here if you need help migrating to NOTS.








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Prerequisite for Using This System

All of the clusters that make up our shared computing resources run the Linux operating system (not Windows or MacOS operating systems).  In order to effectively utilize the clusters you must have knowledge of Linux, how to navigate the filesystem, how to create, edit, rename, and delete files, and how to run basic commands and write small scripts.  If you need assistance in this area please review the tutorials that are available on our web site.

Logging Into the Cluster

The cluster login nodes can be accessed through Secure Shell from any machine on the Rice campus network.  You will need an active NetID and password in order to login (unless otherwise instructed).

You must apply for an account.

If you do not have an account on the Shared Computing Resources then you should apply for one. You will need a faculty sponsor for your account who is willing to pay the access fee.

If you need off-campus access, please visit our Off-Campus Access Guide.

To login to the system from a Linux or Unix machine, use the ssh command:

Substitute the actual host name of the cluster in place of above.  For example, or, depending on which cluster you want to access.

To transfer files into the cluster from a Linux or Unix machine, use the scp command:

Substitute the actual host name of the cluster in place of  above.  For example, or, depending on which cluster you want to access.

For more information about using Secure Shell, please see our Using SSH to Login and Copy Files Guide.

Login Nodes

Once you are logged in to the system, you are logged into one of several login nodes as shown in the diagram below. These nodes are intended for users to compile software, prepare data files, and submit jobs to the job queue. They are not intended for running compute jobs. Please run all compute jobs in one of the job queues described later in this document.


Diagram courtesy of Chris Hunter, Rice University

Do not run compute jobs on login nodes

Cluster login nodes are multi-user access points intended for users to compile software, copy and prepare data files, and submit jobs to the job queue. Any user running intensive computational tasks directly on the login node risks disciplinary action up to and including the loss of their access privileges.

Data and Quotas

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Data Backups Are the Responsibility of Each User

Backing up and archiving data remains the sole responsibility of the end user. At this point in time shared computing does not offer these services in any automated way. We strongly encourage all users to take full advantage of any storage services to prevent accidental loss or deletion of critical data by contacting us for advice on best practices in data management. We welcome any suggestions for offering a higher level of data security as we move forward with shared computing at Rice.

Research Data Compliance

Due to recent changes in NSF, NIH, DOD, and other government granting agencies, Research Data Management has become an important area of growth for Rice and is a critical factor in both conducting and funding research. The onus of maintaining and preserving research data generated by funded research is placed squarely upon the research faculty, post docs, and graduate students conducting the research. It is imperative that you are aware of your compliance responsibilities so as not to jeopardize the ability of Rice University to receive federal funding. We will help to provide you the information and assistance you need, but the best place to start is the campus research data management website.

To see your current quota and your disk usage for your home directory, run this command:

To see the quota and usage for the $PROJECTS directories for all groups that you belong to, run this command:

To see the quota and usage for the $WORK directories on DAVinCI, and PO for the primary group to which you belong, run this command:


The clustered file system $SHARED_SCRATCH provides fast, high-bandwidth I/O for running jobs. Though not limited by quotas, $SHARED_SCRATCH is intended for in-flight data being used as input and output for running jobs, and may be periodically cleaned through voluntary and involuntary means as use and abuse dictate.

Volatility of $SHARED_SCRATCH

The $SHARED_SCRATCH filesystem is designed for speed rather than data integrity and therefore may be subject to catastrophic data loss! It is designed for input and output files of running jobs, not persistent storage of data and software.

When dealing with $SHARED_SCRATCH always copy your data in. A "cp" will update the access time on files whereas a move "mv" will preserve the access time. This is important as our periodic cleaning mechanism may purge files where the access time is maintained via the "mv" command.

Avoid I/O over NFS

$HOME and $PROJECTS should not be used for job I/O. Jobs found to be using $HOME and $PROJECTS for job I/O are subject to termination without notice.

Use Variables Everywhere!

NOTE: The physical paths for the above file systems are subject to change. You should always access the filesystems using environment variables, especially in job scripts.

For information on how to use $PROJECTS, please see our FAQ.


Environment and Shells

The default shell on all the CRC clusters is bash. Other popular shells are available. To have your account's default shell changed from bash to one of these, please file a help request and specify the cluster, username, and desired shell in the ticket. Once your shell is changed this is reflective on all clusters with which you have access. Any active login sessions when your shell is changed will need to be terminated to effect change.

Due to the nature of high performance applications and the batch scheduling system used on CRC clusters, managing your shell environment variables properly is vital.

Customizing Your Environment With the Module Command

Each user can customize their environment using the module command. This command lets you select software and will source the appropriate paths and libraries. All the requested user applications are located under the /opt/apps directory.

To list what applications are available, use the spider sub command:

To see a description of a specific package, use the spider sub command again:

To load the module for OpenMPI built with the GCC compilers, for example, use the load sub command:

To see a list of modules that you have loaded, use this command:

To change to the Intel compiler build of OpenMPI use the swap sub command:

To unload all of your modules, use this command:

To make sure a set of modules are loaded automatically at login, use the module save sub command:

Sometimes a module will not load without explicit dependencies. The following outlines this "error" and what to do.

The Job Scheduler

The batch job scheduling system implemented on this system uses SLURM. SLURM is responsible for resource management, job scheduling, and monitoring. 

Fairshare Scheduling Policy

We implement the SLURM Fairshare feature to provide a fair utilization of the available resources.  This is accomplished by allowing historical resource utilization information to be incorporated into job feasibility and priority decisions. This is normally the most significant component of a job's priority, which ultimately defines the position of the job on a queue. We do not use a FIFO (First-In-First-Out) scheduler.  Your jobs' priority will be determined by your utilization over the past seven days (sliding window), with high utilization resulting in lower priority for new jobs.

Backfill Scheduling Policy

This is a scheduling optimization which allows SLURM to make better use of available resources by running jobs out of order. Using job data such as walltime and resources requested, the scheduler can start other, lower-priority jobs so long as they do not delay the highest priority jobs.  Because of the way it works, essentially filling in holes in node space, backfill tends to favor smaller and shorter running jobs more than larger and longer running ones.

Accurate Walltime Improves Scheduling of Jobs

It is important to specify an accurate walltime for your job in your SLURM submission script.  Selecting the default walltime for jobs that are known to run for less time may result in the job being delayed by the scheduler due to an overestimation of the time the job needs to run.

Automatic Queue Routing

Each of our compute resources has a pre-defined default queue.  If you submit your job without specifying a queue, your job will be automatically routed to the default queue.  Therefore, be aware of which queue you intend for the job to run in and specify this queue in your SLURM batch script.

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Determining Partition Status

A good way to obtain the status of all partitions and their current usage is to run the following SLURM command:

Here is a brief description of the relevant fields:

PARTITION: Name of a partition. Node that the suffix "*" identifies the default partition.
AVAIL: Partition state: up or down.
TIMELIMIT:  Maximum time limit for an user job in days-hours:minutes:seconds.
NODES: Count of nodes with this particular configuration by node state in the form "[A]vailable/[I]dle/[O]ther/[T]otal
STATE: State of the nodes.
NODELIST: Names of nodes associated with this configuration/partition.

See the manpage for sinfo for more information

Submitting Jobs with SLURM

Once you have an executable program and are ready to run it on the compute nodes, you must create a job script that performs the following functions:

  • Use job batch options to request the resources that will be needed (i.e. number of processors, run time, etc.), and
  • Use commands to prepare for execution of the executable (i.e. cd to working directory, source shell environment files, copy input data to a scratch location, copy needed output off of scratch location, clean up scratch files, etc).

After the job script has been constructed you must submit it to the job scheduler for execution. The remainder of this section will describe the anatomy of a job script and how to submit and monitor jobs.

Please note script options are being provided using the long options and not the short options for readability and consistency e.g. --nodes versus -N.


SLURM Batch Script Options

All jobs must be submitted via a SLURM batch script or invoking sbatch at the command line . See the table below for SLURM submission options.



#SBATCH --account=AccountName

#SBATCH --partition=PartitionName

Required: You need to specify both the name of the account and partition to schedule jobs especially to use a condo on the cluster.

Use the command sacctmgr show assoc user=netID to show which accounts and partitions with which you have access.

#SBATCH --job-name=YourJobName

Required:  Assigns a job name.  The default is the name of SLURM job script.

#SBATCH --ntasks=2 Recommended: The number of tasks per job. Usually used for MPI jobs.

You can get further explanation here .

#SBATCH --nodes=2

Recommended: The number of nodes requested.
You can get further explanation
here .

#SBATCH --ntasks-per-node=2

Recommended: The number of tasks per node. Usually used in combination with --nodes for MPI jobs.
You can get further explanation
here .

#SBATCH --cpus-per-task=4Recommended:  The number processes per task. Usually used for OpenMP or multi-threaded jobs.

#SBATCH --partition=PartitionName

Recommended:  Specify the name of the Partition (queue) to use. Use this to specify the default partition or a special partition i.e. non-condo partiton with which you have access.

#SBATCH --time=08:00:00

Recommended:  The maximum run time needed for this job to run, in days-hh:mm:ss.  If not specified, the default run time will be chosen automatically.

#SBATCH --mem-per-cpu=1024M

Optional: The maximum amount of physical memory used by any single process of the job ([M]ega|[G]iga|[T]era)Bytes.

The value of "mem-per-cpu" multiplied by "tasks" (mem X tasks) should not exceed the amount of memory on a node.

 See our FAQ for more details. 

#SBATCH --export=ALLRequired:  Exports all environment variables to the job.  See our FAQ for details.
#SBATCH --mail-user=netID@rice.eduRecommended:  Email address for job status messages. Replace netID with your netID at Rice.
#SBATCH --mail-type=ALLRecommended:  SLURM will notify the user via email when the job reaches the following states BEGIN, END, FAIL or REQUEUE.
#SBATCH --nodes=1 --exclusiveOptional:  Using both of these options will give your job exclusive access to a node such that no other jobs can share the node. 
This combination of arguments will assign eight tasks to your job and will give it exclusive access to all of the resources
(i.e. memory) of the entire node without interference from other jobs.  Please see our FAQ for more details on exclusive access.

#SBATCH --output=mypath

Optional:  The full path for the standard output (stdout) and standard error (stderr) "slurm-%j.out" file, where the "%j" is replaced by the job ID.  Current working directory is the default.

#SBATCH --error=mypath

Optional:  The full path for the standard error (stderr) "slurm-%j.out" files. Use this only when you want to separate (stderr) from (stdout). Current working directory is the default.

Serial Job Script

A job script may consist of SLURM directives, comments and executable statements. A SLURM directive provides a way of specifying job attributes in addition to the command line options. For example, we could create a myjob.slurm script this way:


This example script will submit a job to the default partition using 1 processor and 1GB of memory per processor, with a maximum run time of 30 minutes.

Definition of --ntasks-per-node

For the clusters the  --ntasks-per-node  option means  tasks per node.

Accurate run time value is strongly recommended

It is important to specify an accurate run time for your job in your SLURM submission script.  Selecting eight hours for jobs that are known to run for much less time may result in the job being delayed by the scheduler due to an overestimation of the time the job needs to run.

How to specify mem

The --mem value represents memory per processor core.  If your --mem value multiplied by the number of tasks (--ntasks-per-node) exceeds the amount of memory per node, your job will not run.  If your job is going to use the entire node, then you should use the --exclusive option instead of the --mem or --ntasks-per-node options (See Here).  It is good practice to specify the --mem option if you are going to be using less than an entire node and thus sharing the node with other jobs.

If you need to debug your program and want to run in interactive mode, the same request above could be constructed like this (via the srun command):

For more details on interactive jobs, please see our FAQ on this topic.

SLURM Environment Variables in Job Scripts

When you submit a job, it will inherit several environment variables that are automatically set by SLURM. These environment variables can be useful in your job submission scripts as seen in the examples above. A summary of the most important variables are presented in the table below.

Variable Name



Location of shared scratch space.  See our FAQ for more details.

$LOCAL_SCRATCHLocation of local scratch space on each node.


Environment variable containing a list of all nodes assigned to the job.


Path from where the job was submitted.

Job Launcher (srun)

For jobs that need two or more processors and are compiled with MPI libraries, you must use srun to launch your job.  The job launcher's purpose is to spawn copies of your executable across the resources allocated to your job. We currently support srun for this task and do not support the mpirun or mpiexec launchers. By default srun only needs your executable, the rest of the information will be extracted from SLURM.

The following is an example of how to use srun inside your SLURM batch script. This example will run myMPIprogram as a parallel MPI code on all of the processors allocated to your job by SLURM:


This example script will submit a job to the default partition using 24 processor cores and 1GB of memory per processor core, with a maximum run time of 30 minutes.

Your Program must use MPI

The above example assumes that myMPIprogram is a program designed to be parallel (using MPI). If your program has not been parallelized then running on more than one processor will not improve performance and will result in wasted processor time and could result in multiple copies of your program being executed.

The following example will run myMPIprogram on only four processors even if your batch script requested more than four.

To ensure that your job will be able to access an mpi runtime, you must load an mpi module before submitting your job as follows:

Submitting and Monitoring Jobs

Once your job script is ready, use sbatch to submit it as follows:

This will return a jobID number while the output and error stream of the job will be saved to one file inside the directory where the job was submitted, unless you specified otherwise.

The status of the job can be obtained using SLURM commands.  See the table below for a list of commands:




Show a detailed list of all submitted jobs.

squeue -j jobID

Show a detailed description of the job given by jobID.

squeue -- start -j jobID

Gives an estimate of the expected start time of the job given by jobID.

There are variations to these commands that can also be useful.  They are described below:



squeue -l

Show a list of all running jobs.

squeue -u username

Show a list of all jobs in queue owned by the user specified by username.

scontrol show job jobID

To get a verbose description of the job given by jobID. The output can be used as a template when you are attempting to modify a job.

There are many different states that a job can be after submission: BOOT_FAIL (BF), CANCELLED (CA), COMPLETED (CD), CONFIGURING (CF), COMPLETING (CG), FAILED (F), NODE_FAIL (NF), PENDING (PD), PREEMPTED (PR), RUNNING (R), SUSPENDED (S), TIMEOUT (TO), or SPECIAL_EXIT (SE). The squeue command with no arguments will list all jobs in their current state.  The most common states are described below.

Running (R): These are jobs that are running.

Pending (PD): These jobs are eligible to run but there is simply not enough resources to allocate to them at this time.

Deleting Jobs

A job can be deleted by using the scancel command as follows:

Compiling and Optimizing

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Using the GPGPUs

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Getting Help

Request Help with the Center for Research Computing Resources


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