Introduction

NOTS (Night Owls Time-Sharing Service history of name) is a batch scheduled HPC/HTC cluster running on the Rice Big Research Data (BiRD) cloud infrastructure. The system consists of 298 dual socket compute blades housed within HPE s6500, HPE Apollo 2000, and Dell PowerEdge C6400 chassis. All the nodes are interconnected with 10 or 25 Gigabit Ethernet network. In addition, the Apollo and C6400 chassis are connected with high speed Omni-Path for message passing applications. There is a 300TB VAST filesystem attached to the compute nodes via Ethernet. The system can support various work loads including single node, parallel, large memory multithreaded, and GPU jobs.

NSF Citation

If you use NOTS to support your research activities, you are required to acknowledge (in publications, on your project web pages, …) the National Science Foundation grant that was used in part to fund the procurement of this system. An example acknowledgement that can be used follows. Feel free to modify wording for your specific needs but please keep the essential information:

This work was supported in part by the Big-Data Private-Cloud Research Cyberinfrastructure MRI-award funded by NSF under grant CNS-1338099 and by Rice University's Center for Research Computing (CRC).

Node Configurations

Compute

GPU

Prerequisites for Using the Shared Research Computing Resources

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).

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:

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

For more information about using Secure Shell, please see our Using SSH to Login and Copy Files on the campus network 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

Data and Quotas

A summary of all filesystems available to all users is presented in the following table:

File Systems, Quotas, and Data Handling

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 NOTS 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.

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:

$ module spider

--------------------------------------------------------------------
The following is a list of the modules currently available:
--------------------------------------------------------------------
  4ti2: 4ti2/1.6.9
    A software package for algebraic, geometric and combinatorial problems on linear spaces

  ACTC: ACTC/1.1
    ACTC converts independent triangles into triangle strips or fans.

  AFNI: AFNI/20150603
    Free software for analysis and display of FMRI data - Homepage: http://afni.nimh.nih.gov

  ...
--------------------------------------------------------------------

To learn more about a package execute:

   $ module spider Foo

where "Foo" is the name of a module.

To find detailed information about a particular package you
must specify the version if there is more than one version:

   $ module spider Foo/11.1

--------------------------------------------------------------------

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

$ module spider OpenMPI/4.1.1

-----------------------------------
  OpenMPI: OpenMPI/4.1.1
-----------------------------------
    Description:
      The Open MPI Project is an open source MPI-3 implementation.

    You will need to load all module(s) on any one of the lines below before the "OpenMPI/4.1.1" module is available to load.

      GCC/10.3.0

    Help:

      Description
      ===========

      The Open MPI Project is an open source MPI-3 implementation.      

      More information
      ================
       - Homepage: https://www.open-mpi.org/

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:

$ module load Boost
Lmod has detected the following error:  These module(s) exist but cannot be loaded as requested: "Boost"
   Try: "module spider Boost" to see how to load the module(s).

$ module spider Boost

-------------------------------------------
  Boost:
---------------------------------------------
    Description:
      Boost provides free peer-reviewed portable C++ source libraries.

     Versions:
        Boost/1.58.0
        Boost/1.61.0
        Boost/1.63.0
        Boost/1.64.0
        Boost/1.66.0
        Boost/1.67.0
        Boost/1.68.0
        Boost/1.69.0
        Boost/1.70.0
        Boost/1.71.0
        Boost/1.72.0

-----------------------------------------
  For detailed information about a specific "Boost" module (including how to load the modules) use the module's full name.
  For example:

     $ module spider Boost/1.72.0
----------------------------------------

$ module load Boost/1.72.0
Lmod has detected the following error:  These module(s) exist but cannot be loaded as requested: "Boost/1.72.0"
   Try: "module spider Boost/1.72.0" to see how to load the module(s).

$ module spider Boost/1.72.0

---------------------------------------
  Boost: Boost/1.72.0
---------------------------------------
    Description:
      Boost provides free peer-reviewed portable C++ source libraries.

    You will need to load all module(s) on any one of the lines below before the "Boost/1.72.0" module is available to load.

      GCC/9.3.0  CUDA/11.0.182  OpenMPI/4.0.3
      GCC/9.3.0  OpenMPI/4.0.3
      iccifort/2020.1.217  CUDA/11.0.182  OpenMPI/4.0.3
      iccifort/2020.1.217  impi/2019.7.217 

    Help:

      Description
      ===========

      Boost provides free peer-reviewed portable C++ source libraries. 

      More information
      ================
       - Homepage: https://www.boost.org/

$ module load GCC/9.3.0  OpenMPI/4.0.3 

$ module load Boost/1.72.0

Available Partitions and System Load

The definition of the queues are as follows:

commons - the general shared computing pool. 

interactive - intended for short jobs, primarily for the purposes of debugging and running interactive jobs.  See our FAQ for information on interactive jobs.

scavenge - intended for jobs that run for 4 hours or less, taking advantage of idle condo resources and possibly reducing your wait time. 

Use the following command to determine the partitions with which you have access. Please note in the output the Account column information needs to be provided to your batch script in addition to the partition information.

Advanced usage: Use the following command to determine raw qos information for NOTS to see cluster characteristics. This information is presented in the above table to simplify the output.

Determining Partition Status

An old 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

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.

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.

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.

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.

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.

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:

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

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

Several programming models are supported on this system. Programs that are sequential, parallel (within a node) or distributed can be run. Sequential programs require one processor to run. Parallel and distributed programs utilize multiple processors concurrently. Parallel programs are a subset of distributed programs. Generally speaking, distributed computing involve parametric sweeps, task farming, etc. Message passing, threaded applications generally fit under the scope of parallel computing.  SPMD (single process, multiple data) is one of the most popular method of parallelism, where a single executable works on its own data. 

The supported compilers on this system are Intel and GCC. The MPI implementations from OpenMPI and Intel are available for both compilers and can be loaded upon demand using the module command. The preferred compiler for this system is Intel.

Compiling Serial Code

To compile serial code you must first load the appropriate compiler environment module .  To load the Intel compiler, execute this command:

Once the environment is set, you can compile your program with one of the following (using Intel compiler as an example):

When invoked as described above, the compiler will perform the preprocessing, compilation, assembly and linking stages in a single step. The output file (or executable) is specified by executablename and the source code file is specificed by sourcecode.f77, for example. Omitting the -o executablename option will result in the executable being named a.out by default. For additional instructions and advanced options please view the online manual pages for each compiler (i.e. execute the command man ifort ).

Compiling Parallel Code

To compile a parallel version of your code that has MPI library calls, use the appropriate MPI library. Again, use module command to load the appropriate compiler environment as follows (Intel versions highly recommended):

To compile your code you will have use the MPI compiler wrappers that are currently in your default path. The MPI wrappers are responsible for invoking the compiler, linking your program with the MPI library and setting the MPI include files.

Once the environment is set, you can compile your program with one of the following:

When invoked as described above, the compiler will perform the preprocessing, compilation, assembly and linking stages in a single step. The output file (or executable) is specified by executablename and the source code file is specificed by mpi_sourcecode.f77, for example. Omitting the -o executablename option will result in the executable being named a.out by default. For additional instructions and advanced options please view the online manual pages for each compiler (i.e. execute the command man mpif77 ).

GNU Compiler

The GNU compiler is installed as part of the Red Hat Enterprise Linux distribution. Use man gcc to view the online manual for the C and C++ compiler, and man gfortran to view the online manual for the Fortran compiler.

Examples

Getting Help

The CRC Can Help!

Request Help with the Center for Research Computing Resources