Niagara Quickstart

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Installed Jan 2018
Operating System CentOS 7.4
Number of Nodes 1500 nodes (60,000 cores)
Interconnect Mellanox Dragonfly+
Ram/Node 188 GiB / 202 GB
Cores/Node 40 (80 hyperthreads)
Login/Devel Node
Vendor Compilers icc (C) ifort (fortran) icpc (C++)
Queue Submission Slurm


The Niagara cluster is a large cluster of 1500 Lenovo SD530 servers each with 40 Intel "Skylake" cores at 2.4 GHz. The peak performance of the cluster is 3.02 PFlops delivered / 4.6 PFlops theoretical. It was the 53rd fastest supercomputer on the TOP500 list of June 2018, and is currently number 59 on the current list.

Each node of the cluster has 188 GiB / 202 GB RAM per node (at least 4 GiB/core for user jobs). Being designed for large parallel workloads, it has a fast interconnect consisting of EDR InfiniBand in a Dragonfly+ topology with Adaptive Routing. The compute nodes are accessed through a queueing system that allows jobs with a minimum of 15 minutes and a maximum of 24 hours and favours large jobs.

More detailed hardware characteristics of the Niagara supercomputer can be found on this page.

Getting started on Niagara

Access to Niagara is not enabled automatically for everyone with a Compute Canada account, but anyone with an active Compute Canada account can get their access enabled.

If you do not have access yet (e.g. because you are new to SciNet or belong to a group whose primary PI does not have an allocation as granted in the annual Compute Canada RAC), follow the old route of requesting a SciNet Consortium Account on the CCDB site to gain access to Niagara. After clicking the "Apply" button, it usually takes only one or two business days for access to be granted.

Please read this document carefully. The FAQ is also a useful resource. If at any time you require assistance, or if something is unclear, please do not hesitate to contact us.

Logging in

Niagara runs CentOS 7, which is a type of Linux. You will need to be familiar with Linux systems to work on Niagara. If you are not it will be worth your time to review our Introduction to Linux Shell class.

As with all SciNet and CC (Compute Canada) compute systems, access to Niagara is done via SSH (secure shell) only. Open a terminal window (e.g. Connecting with PuTTY on Windows or Connecting with MobaXTerm), then SSH into the Niagara login nodes with your CC credentials:

$ ssh -Y


$ ssh -Y
  • The Niagara login nodes are where you develop, edit, compile, prepare and submit jobs.
  • These login nodes are not part of the Niagara compute cluster, but have the same architecture, operating system, and software stack.
  • The optional -Y is needed to open windows from the Niagara command-line onto your local X server.
  • You can only connect 4 times in a 2-minute window to the login nodes.
  • To run on Niagara's compute nodes, you must submit a batch job.

If you cannot log in, be sure to first check the System Status on this site's front page.

Your various directories

By virtue of your access to Niagara you are granted storage space on the system. There are several directories available to you, each indicated by an associated environment variable.

home and scratch

You have a home and scratch directory on the system, the paths to which are stored in the environment variables $HOME and $SCRATCH. The locations are of the form


where groupname is the name of your PI's group, and myccusername is your CC username. For example:

 nia-login07:~$ pwd
 nia-login07:~$ cd $SCRATCH
 nia-login07:rzon$ pwd

NOTE: home is read-only on compute nodes.

project and archive/nearline

Users from groups with RAC storage allocation will also have a project directory and possible an archive (a.k.a. "nearline") directory, the paths to which are stored in the environment variables $PROJECT and $ARCHIVE. They follow the naming convention:


NOTE: Currently archive space is available only via HPSS, and is not accessible on the Niagara login, compute, or datamover nodes.

IMPORTANT: Future-proof your scripts

When writing your scripts, use the environment variables ($HOME, $SCRATCH, $PROJECT, $ARCHIVE) instead of the actual paths! The paths may change in the future.

Storage and quotas

You should familiarize yourself with the various file systems, what purpose they serve, and how to properly use them. This table summarizes the various file systems. See the Data Management page for more details.

location quota block size expiration time backed up on login nodes on compute nodes
$HOME 100 GB / 250,000 files per user 1 MB yes yes read-only
$SCRATCH 25 TB / 6,000,000 file per user 16 MB 2 months no yes yes
50-500TB per group depending on group size
$PROJECT by group allocation 16 MB yes yes yes
$ARCHIVE by group (nearline) allocation dual-copy no no
$BBUFFER 10 TB per user 1 MB very short no yes yes

Moving data to Niagara

If you need to move data to Niagara for analysis, or when you need to move data off of Niagara, use the following guidelines:

  • If your data is less than 10GB, move the data using the login nodes.
  • If your data is greater than 10GB, move the data using the datamover nodes and .

Details of how to use the datamover nodes can be found on the Data Management page.

Loading software modules

You have two options for running code on Niagara: use existing software, or compile your own. This section focuses on the former.

Other than essentials, all installed software is made available using module commands. These modules set environment variables (PATH, etc.), allowing multiple, conflicting versions of a given package to be available. A detailed explanation of the module system can be found on the modules page.

Common module subcommands are:

  • module load <module-name>: load the default version of a particular software.
  • module load <module-name>/<module-version>: load a specific version of a particular software.
  • module purge: unload all currently loaded modules.
  • module spider (or module spider <module-name>): list available software packages.
  • module avail: list loadable software packages.
  • module list: list loaded modules.

Along with modifying common environment variables, such as PATH, and LD_LIBRARY_PATH, these modules also create a SCINET_MODULENAME_ROOT environment variable, which can be used to access commonly needed software directories, such as /include and /lib.

There are handy abbreviations for the module commands. ml is the same as module list, and ml <module-name> is the same as module load <module-name>.

Software stacks: NiaEnv and CCEnv

On Niagara, there are two available software stacks:

  1. A Niagara software stack tuned and compiled for this machine. This stack is available by default, but if not, can be reloaded with

    module load NiaEnv

    This loads the 2018a 'epoch' (set of modules). A newer epoch is available by loading NiaEnv/2019b. This epoch will become the default later in 2019.
  2. The same software stack available on Compute Canada's General Purpose clusters Graham and Cedar:

    module load CCEnv

    Or, if you want the same default modules loaded as on Béluga, then do

    module load CCEnv StdEnv
    or, if you want the same default modules loaded as on Cedar and Graham, do
    module load CCEnv arch/avx2 StdEnv

No modules are loaded by default (except NiaEnv, which does not do anything except make other modules visible).

Tips for loading software

  • We advise against loading modules in your .bashrc. This can lead to very confusing behaviour under certain circumstances. Our guidelines for .bashrc files can be found here.
  • Instead, load modules by hand when needed, or by sourcing a separate script.
  • Load run-specific modules inside your job submission script.
  • Short names give default versions; e.g. intelintel/2018.2. It is usually better to be explicit about the versions, for future reproducibility.
  • Modules often require other modules to be loaded first. Solve these dependencies by using module spider.

Available compilers and interpreters

  • For most compiled software, one should use the Intel compilers (icc for C, icpc for C++, and ifort for Fortran). Loading an intel module makes these available.
  • The GNU compiler suite (gcc, g++, gfortran) is also available, if you load one of the gcc modules.
  • To compile mpi code, you must additionally load an openmpi or intelmpi module.
  • Open source interpreted, interactive software is also available:

Please visit the corresponding page for details on using these tools. For information on running MATLAB applications on Niagara, visit this page.

Using Commercial Software

May I use commercial software on Niagara?

  • Possibly, but you have to bring your own license for it. You can connect to an external license server using ssh tunneling.
  • SciNet and Compute Canada have an extremely large and broad user base of thousands of users, so we cannot provide licenses for everyone's favorite software.
  • Thus, the only freely available commercial software installed on Niagara is software that can benefit everyone: Compilers, math libraries and debuggers.
  • That means no MATLAB, Gaussian, IDL,
  • Open source alternatives like Octave, Python, and R are available.
  • We are happy to help you to install commercial software for which you have a license.
  • In some cases, if you have a license, you can use software in the Compute Canada stack.

The list of commercial software which is installed on Niagara, for which you will need a license to use, can be found on the commercial software page.

Compiling on Niagara: Example

Suppose one wants to compile an application from two c source files, appl.c and module.c, which use the Math Kernel Library. This is an example of how this would be done:

nia-login07:~$ module list
Currently Loaded Modules:
  1) NiaEnv/2018a (S)
   S:  Module is Sticky, requires --force to unload or purge

nia-login07:~$ module load intel/2018.2

nia-login07:~$ ls
appl.c module.c

nia-login07:~$ icc -c -O3 -xHost -o appl.o appl.c
nia-login07:~$ icc -c -O3 -xHost -o module.o module.c
nia-login07:~$ icc  -o appl module.o appl.o -mkl

nia-login07:~$ ./appl


  • The optimization flags -O3 -xHost allow the Intel compiler to use instructions specific to the architecture CPU that is present (instead of for more generic x86_64 CPUs).
  • Linking with the Intel Math Kernel Library (MKL) is easy when using the intel compiler, it just requires the -mkl flags.
  • If compiling with gcc, the optimization flags would be -O3 -march=native. For the way to link with the MKL, it is suggested to use the MKL link line advisor.

Testing and Debugging

You really should test your code before you submit it to the cluster to know if your code is correct and what kind of resources you need.

  • Small test jobs can be run on the login nodes. Rule of thumb: tests should run no more than a couple of minutes, taking at most about 1-2GB of memory, and use no more than a couple of cores.
  • You can run the DDT debugger on the login nodes after module load ddt.
  • Short tests that do not fit on a login node, or for which you need a dedicated node, request an interactive debug job with the debug command:
nia-login07:~$ debugjob N

where N is the number of nodes, If N=1, this gives an interactive session one 1 hour, when N=4 (the maximum), it gives you 22 minutes. Finally, if your debugjob process takes more than 1 hour, you can request an interactive job from the regular queue using the salloc command. Note, however, that this may take some time to run, since it will be part of the regular queue, and will be run when the scheduler decides.

nia-login07:~$ salloc --nodes N --time=M:00:00 --x11

where N is again the number of nodes, and M is the number of hours you wish the job to run. The --x11 is required if you need to use graphics while testing your code through salloc, e.g. when using a debugger such as DDT or DDD, See the Testing with graphics page for the options in that case.

Submitting jobs

Once you have compiled and tested your code or workflow on the Niagara login nodes, and confirmed that it behaves correctly, you are ready to submit jobs to the cluster. Your jobs will run on some of Niagara's 1500 compute nodes. When and where your job runs is determined by the scheduler.

Niagara uses SLURM as its job scheduler. More-advanced details of how to interact with the scheduler can be found on the Slurm page.

You submit jobs from a login node by passing a script to the sbatch command:

nia-login07:scratch$ sbatch

This puts the job in the queue. It will run on the compute nodes in due course.

In most cases, you should not submit from your $HOME directory, but rather, from your $SCRATCH directory, so that the output of your compute job can be written out (as mentioned above, $HOME is read-only on the compute nodes).

Jobs will run under your group's RRG allocation, or, if the your group has none, under a RAS allocation (previously called `default' allocation).

Some example job scripts can be found below.

Keep in mind:

  • Scheduling is by node, so in multiples of 40 cores.
  • Your job's maximum walltime is 24 hours.
  • Jobs must write their output to your scratch or project directory (home is read-only on compute nodes).
  • Compute nodes have no internet access.
  • Move your data to Niagara before you submit your job.

Scheduling by Node

On many systems that use SLURM, the scheduler will deduce from the specifications of the number of tasks and the number of cpus-per-node what resources should be allocated. On Niagara things are a bit different.

  • All job resource requests on Niagara are scheduled as a multiple of nodes.
  • The nodes that your jobs run on are exclusively yours, for as long as the job is running on them.
    • No other users are running anything on them.
    • You can SSH into them to see how things are going.
  • Whatever your requests to the scheduler, it will always be translated into a multiple of nodes allocated to your job.
  • Memory requests to the scheduler are of no use. Your job always gets N x 202GB of RAM, where N is the number of nodes and 202GB is the amount of memory on the node.
  • If you run serial jobs you must still use all 40 cores on the node. Visit the serial jobs page for examples of how to do this.
  • Since there are 40 cores per node, your job should use N x 40 cores. If you do not, we will contact you to help you optimize your workflow. Or you can contact us to get assistance.


There are limits to the size and duration of your jobs, the number of jobs you can run and the number of jobs you can have queued. It matters whether a user is part of a group with a Resources for Research Group allocation or not. It also matters in which 'partition' the job runs. 'Partitions' are SLURM-speak for use cases. You specify the partition with the -p parameter to sbatch or salloc, but if you do not specify one, your job will run in the compute partition, which is the most common case.

Usage Partition Limit on Running jobs Limit on Submitted jobs (incl. running) Min. size of jobs Max. size of jobs Min. walltime Max. walltime
Compute jobs compute 50 1000 1 node (40 cores) default: 20 nodes (800 cores)
with allocation: 1000 nodes (40000 cores)
15 minutes 24 hours
Testing or troubleshooting debug 1 1 1 node (40 cores) 4 nodes (160 cores) N/A 1 hour
Archiving or retrieving data in HPSS archivelong 2 per user (5 in total) 10 per user N/A N/A 15 minutes 72 hours
Inspecting archived data, small archival actions in HPSS archiveshort vfsshort 2 per user 10 per user N/A N/A 15 minutes 1 hour

Even if you respect these limits, your jobs will still have to wait in the queue. The waiting time depends on many factors such as your group's allocation amount, how much allocation has been used in the recent past, the number of requested nodes and walltime, and how many other jobs are waiting in the queue.

File Input/Output Tips

It is important to understand the file systems, so as to perform your file I/O (Input/Output) responsibly. Refer to the Data Management page for details about the file systems.

  • Your files can be seen on all Niagara login and compute nodes.
  • $HOME, $SCRATCH, and $PROJECT all use the parallel file system called GPFS.
  • GPFS is a high-performance file system which provides rapid reads and writes to large data sets in parallel from many nodes.
  • Accessing data sets which consist of many, small files leads to poor performance on GPFS.
  • Avoid reading and writing lots of small amounts of data to disk. Many small files on the system waste space and are slower to access, read and write. If you must write many small files, use ramdisk.
  • Write data out in a binary format. This is faster and takes less space.
  • The Burst Buffer is another option for I/O heavy-jobs and for speeding up checkpoints.

Example submission script (MPI)

#SBATCH --nodes=2
#SBATCH --ntasks-per-node=40
#SBATCH --time=1:00:00
#SBATCH --job-name=mpi_job
#SBATCH --output=mpi_output_%j.txt
#SBATCH --mail-type=FAIL


module load intel/2018.2
module load openmpi/3.1.0

mpirun ./mpi_example
# or "srun ./mpi_example"

Submit this script from your scratch directory with the command:

   nia-login07:scratch$ sbatch
  • First line indicates that this is a bash script.
  • Lines starting with #SBATCH go to SLURM.
  • sbatch reads these lines as a job request (which it gives the name mpi_job)
  • In this case, SLURM looks for 2 nodes each running 40 tasks (for a total of 80 tasks), for 1 hour
  • Note that the mpifun flag "--ppn" (processors per node) is ignored.
  • Once it found such a node, it runs the script:
    • Change to the submission directory;
    • Loads modules;
    • Runs the mpi_example application (SLURM will inform mpirun or srun on how many processes to run).
  • To use hyperthreading, just change --ntasks-per-node=40 to --ntasks-per-node=80, and add --bind-to none to the mpirun command (the latter is necessary for OpenMPI only, not when using IntelMPI).

Example submission script (OpenMP)

#SBATCH --nodes=1
#SBATCH --cpus-per-task=40
#SBATCH --time=1:00:00
#SBATCH --job-name=openmp_job
#SBATCH --output=openmp_output_%j.txt
#SBATCH --mail-type=FAIL


module load intel/2018.2


# or "srun ./openmp_example".

Submit this script from your scratch directory with the command:

   nia-login07:~$ sbatch
  • First line indicates that this is a bash script.
  • Lines starting with #SBATCH go to SLURM.
  • sbatch reads these lines as a job request (which it gives the name openmp_job) .
  • In this case, SLURM looks for one node with 40 cores to be run inside one task, for 1 hour.
  • Once it found such a node, it runs the script:
    • Change to the submission directory;
    • Loads modules;
    • Sets an environment variable;
    • Runs the openmp_example application.
  • To use hyperthreading, just change --cpus-per-task=40 to --cpus-per-task=80.

Monitoring queued jobs

Once the job is incorporated into the queue, there are some command you can use to monitor its progress.

  • squeue or sqc (a caching version of squeue) to show the job queue (squeue -u $USER for just your jobs);

  • squeue -j JOBID to get information on a specific job

    (alternatively, scontrol show job JOBID, which is more verbose).

  • squeue --start -j JOBID to get an estimate for when a job will run; these tend not to be very accurate predictions.

  • scancel -i JOBID to cancel the job.

  • jobperf JOBID to get an instantaneous view of the cpu and memory usage of the nodes of the job while it is running.

  • sacct to get information on your recent jobs.

Further instructions for monitoring your jobs can be found on the Slurm page. The my.SciNet site is also a very useful tool for monitoring your current and past usage.


Information about how to use visualization tools on Niagara is available on Visualization page.