|Operating System||CentOS 7.4|
|Number of Nodes||1500 nodes (60,000 cores)|
|Ram/Node||188 GiB / 202 GB|
|Cores/Node||40 (80 hyperthreads)|
|Vendor Compilers||icc (C) ifort (fortran) icpc (C++)|
- 1 Specifications
- 2 Using Niagara: Logging in
- 3 Locating your directories
- 4 Data Management
- 5 Loading Software Modules
- 6 Running Commercial Software
- 7 Compiling on Niagara: Example
- 8 Testing
- 9 Submitting jobs
- 10 Monitoring queued jobs
- 11 Visualization
- 12 Further information
The Niagara cluster is a large cluster of 1500 Lenovo SD350 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 (would've been #42 on the TOP500 in Nov 2017). Each node 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 12 or 24 hours and favours large jobs.
- See the "Intro to Niagara" recording
Using Niagara: Logging in
As with all SciNet and CC (Compute Canada) compute systems, access to Niagara is via ssh (secure shell) only.
To access SciNet systems, first open a terminal window (e.g. MobaXTerm on Windows).
Then ssh into the Niagara login nodes with your CC credentials:
$ ssh -Y MYCCUSERNAME@niagara.scinet.utoronto.ca
$ ssh -Y MYCCUSERNAME@niagara.computecanada.ca
- 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
-Yis needed to open windows from the Niagara command-line onto your local X server.
- To run on Niagara's compute nodes, you must submit a batch job.
Locating your directories
home and scratch
You have a home and scratch directory on the system, whose locations will be given in the form
nia-login07:~$ pwd /home/s/scinet/rzon nia-login07:~$ cd $SCRATCH nia-login07:rzon$ pwd /scratch/s/scinet/rzon
NOTE: home is read-only on compute nodes.
project and archive
Users from groups with RAC storage allocation will also have a project and/or archive directory.
NOTE: Currently archive space is available only via HPSS
IMPORTANT: Future-proof your scripts
Use the environment variables (HOME, SCRATCH, PROJECT, ARCHIVE) instead of the actual paths! The paths may change in the future.
Migration to Niagara
Migration for Existing Users of the GPC
Niagara is replacing the General Purpose Cluster (GPC) and the Tightly Coupled Cluster (TCS) at SciNet. The TCS was decommissioned last fall, and the compute nodes of the GPC were decommissioned on April 21, 2018, while the storage attached to the GPC will be decommissioned on May 30, 2018.
Active GPC Users got access to the new system, Niagara, on April 9, 2018.
Users' home and project folder were last copied over from the GPC to Niagara on April 5th, 2018, except for files whose name start with a period that were in their home directories (these files were never synced).
It is the user's responsibility to copy over data generated on the GPC after April 5th, 2018.
Data stored in scratch has also not been transfered automatically. Users are to clean up their scratch space on the GPC as much as possible (remember it's temporary data!). Then they can transfer what they need using datamover nodes.
To enable this transfer, there will be a short period during which you can have access to Niagara as well as to the GPC storage resources. This period will end on May 30, 2018.
To copy substantial amounts of data (i.e.,more than 10 GB), please use the datamovers of both the GPC (called gpc-logindm01 and gpc-logindm02) and the Niagara datamovers (called nia-dm1 and nia-dm2). For instance, to copy a directory abc from your GPC scratch to your Niagara scratch directory, you can do the following:
$ ssh CCUSERNAME@niagara.computecanada.ca $ ssh nia-dm1 $ scp -r SCINETUSERNAME@gpc-logindm01:\$SCRATCH/abc $SCRATCH/abc
For many of you, CCUSERNAME amd SCINETUSERNAME will be the same. Make sure you use the slash (\) before the first $SCRATCH; it cause the value of scratch on the remote node (i.e., here, gpc-logindm01) to be used. Note that the gpc-logindm01 will ask for your SciNet password.
You can also go the other way:
$ ssh SCINETUSERNAME@login.scinet.utoronto.ca $ ssh gpc-logindm01 $ scp -r $SCRATCH/abc CCUSERNAME@nia-dm1:\$SCRATCH/abc
Again, pay attention to the slash in front of the last occurrence of $SCRATCH.
If you are using rsync, we advice to refrain from using the -a flags, and if using cp, refrain from using the -a and -p flags.
For Non-GPC Users
Those of you new to SciNet, but with 2018 RAC allocations on Niagara, will have your accounts created and ready for you to login.
New, non-RAC users: we are still working out the procedure to get access. If you can't wait, for now, you can follow the old route of requesting a SciNet Consortium Account on the CCDB site.
Move amounts less than 10GB through the login nodes.
- Only Niagara login nodes visible from outside SciNet.
- Use scp or rsync to niagara.scinet.utoronto.ca or niagara.computecanada.ca (no difference).
- This will time out for amounts larger than about 10GB.
Move amounts larger than 10GB through the datamover nodes.
- From a Niagara login node, ssh to
- Transfers must originate from this datamover.
- The other side (e.g. your machine) must be reachable from the outside.
- If you do this often, consider using Globus, a web-based tool for data transfer.
Moving data to HPSS/Archive/Nearline using the scheduler.
- HPSS is a tape-based storage solution, and is SciNet's nearline a.k.a. archive facility.
- Storage space on HPSS is allocated through the annual Compute Canada RAC allocation.
Storage and quotas
|location||quota||block size||expiration time||backed up||on login||on compute|
|$HOME||100 GB per user||1 MB||yes||yes||read-only|
|$SCRATCH||25 TB per user (dynamic per group)||16 MB||2 months||no||yes||yes|
|up to 4 users per group||50TB|
|up to 11 users per group||125TB|
|up to 28 users per group||250TB|
|up to 60 users per group||400TB|
|above 60 users per group||500TB|
|$PROJECT||by group allocation||16 MB||yes||yes||yes|
|$ARCHIVE||by group allocation||dual-copy||no||no|
|$BBUFFER||?||1 MB||very short||no||?||?|
- Inode vs. Space quota (PROJECT and SCRATCH)
- dynamic quota per group (SCRATCH)
- Compute nodes do not have local storage.
- Archive space is on HPSS.
- Backup means a recent snapshot, not a replica of all data or version that ever was.
$BBUFFERstands for the Burst Buffer, a faster parallel storage tier for temporary data.
File/Ownership Management (ACL)
- By default, at SciNet, users within the same group already have read permission to each other's files (not write)
- You may use access control list (ACL) to allow your supervisor (or another user within your group) to manage files for you (i.e., create, move, rename, delete), while still retaining your access and permission as the original owner of the files/directories. You may also let users in other groups or whole other groups access your files using this same mechanism.
- NOTE 1: In the case of PROJECT, your group's supervisor will need to set proper ACL to the /project/G/GROUP level in order to let users from other groups access your files.
- NOTE 2: ACLs won't let you give away permission to files or directories that do not belong to you.
- NOTE 3: We highly recommend that you never give write permission to other users on the top level of your home directory (/home/G/GROUP/[owner]), since that would seriously compromise your privacy, in addition to disable ssh key authentication, among other things. If necessary, make specific sub-directories under your home directory so that other users can manipulate/access files from those.
- You may use gpfs' native mmputacl and mmgetacl commands. The advantages are that you can set "control" permission and that POSIX or NFS v4 style ACL are supported. You will need first to create a /tmp/supervisor.acl file with the following contents:
user::rwxc group::---- other::---- mask::rwxc user:[owner]:rwxc user:[supervisor]:rwxc
Then issue the following 2 commands:
1) $ mmputacl -i /tmp/supervisor.acl /project/g/group/[owner] 2) $ mmputacl -d -i /tmp/supervisor.acl /project/g/group/[owner] (every *new* file/directory inside [owner] will inherit [supervisor] ownership by default as well as [owner] ownership, ie, ownership of both by default, for files/directories created by [supervisor]) $ mmgetacl /project/g/group/[owner] (to determine the current ACL attributes) $ mmdelacl -d /project/g/group/[owner] (to remove any previously set ACL) $ mmeditacl /project/g/group/[owner] (to create or change a GPFS access control list) (for this command to work set the EDITOR environment variable: export EDITOR=/usr/bin/vi)
- There is no option to recursively add or remove ACL attributes using a gpfs built-in command to existing files. You'll need to use the -i option as above for each file or directory individually. Here is a sample bash script you may use for that purpose
- mmputacl/setfacl will not overwrite the original linux group permissions for a directory when copied to another directory already with ACLs, hence the "#effective:r-x" note you may see from time to time with mmgetacf/getfacl. If you want to give rwx permissions to everyone in your group you should simply rely on the plain unix 'chmod g+rwx' command. You may do that before or after copying the original material to another folder with the ACLs.
Recursive ACL script
You may use/adapt this sample bash script to recursively add or remove ACL attributes using gpfs built-in commands
Courtesy of Agata Disks (http://csngwinfo.in2p3.fr/mediawiki/index.php/GPFS_ACL)
Scratch Disk Purging Policy
In order to ensure that there is always significant space available for running jobs we automatically delete files in /scratch that have not been accessed or modified for more than 2 months by the actual deletion day on the 15th of each month. Note that we recently changed the cut out reference to the MostRecentOf(atime,ctime). This policy is subject to revision depending on its effectiveness. More details about the purging process and how users can check if their files will be deleted follows. If you have files scheduled for deletion you should move them to a more permanent locations such as your departmental server or your /project space (for PIs who have either been allocated disk space by the RAC or have bought diskspace).
On the first of each month, a list of files scheduled for purging is produced, and an email notification is sent to each user on that list. Furthermore, at/or about the 12th of each month a 2nd scan produces a more current assessment and another email notification is sent. This way users can double check that they have indeed taken care of all the files they needed to relocate before the purging deadline. Those files will be automatically deleted on the 15th of the same month unless they have been accessed or relocated in the interim. If you have files scheduled for deletion then they will be listed in a file in /scratch/t/todelete/current, which has your userid and groupid in the filename. For example, if user xxyz wants to check if they have files scheduled for deletion they can issue the following command on a system which mounts /scratch (e.g. a scinet login node): ls -1 /scratch/t/todelete/current |grep xxyz. In the example below, the name of this file indicates that user xxyz is part of group abc, has 9,560 files scheduled for deletion and they take up 1.0TB of space:
[xxyz@scinet04 ~]$ ls -1 /scratch/t/todelete/current |grep xxyz -rw-r----- 1 xxyz root 1733059 Jan 17 11:46 3110001___xxyz_______abc_________1.00T_____9560files
The file itself contains a list of all files scheduled for deletion (in the last column) and can be viewed with standard commands like more/less/cat - e.g. more /scratch/t/todelete/current/3110001___xxyz_______abc_________1.00T_____9560files
Similarly, you can also verify all other users on your group by using the ls command with grep on your group. For example: ls -1 /scratch/t/todelete/current |grep abc. That will list all other users in the same group that xxyz is part of, and have files to be purged on the 15th. Members of the same group have access to each other's contents.
NOTE: Preparing these assessments takes several hours. If you change the access/modification time of a file in the interim, that will not be detected until the next cycle. A way for you to get immediate feedback is to use the 'ls -lu' command on the file to verify the atime and 'ls -la' for the mtime. If the file atime/ctime has been updated in the meantime, coming the purging date on the 15th it will no longer be deleted.
How much Disk Space Do I have left?
The /scinet/niagara/bin/diskUsage command, available on the login nodes and datamovers, provides information in a number of ways on the home, scratch, project and archive file systems. For instance, how much disk space is being used by yourself and your group (with the -a option), or how much your usage has changed over a certain period ("delta information") or you may generate plots of your usage over time. Please see the usage help below for more details.
Usage: diskUsage [-h|-?| [-a] [-u <user>] [-de|-plot] -h|-?: help -a: list usages of all members on the group -u <user>: as another user on your group -de: include delta information -plot: create plots of disk usages
Did you know that you can check which of your directories have more than 1000 files with the /scinet/niagara/bin/topUserDirOver1000list command and which have more than 1GB of material with the /scinet/niagara/bin/topUserDirOver1GBlist command?
- information on usage and quota is only updated every 3 hours!
- $HOME, $SCRATCH, and $PROJECT all use the parallel file system called GPFS.
- Your files can be seen on all Niagara login and compute nodes.
- GPFS is a high-performance file system which provides rapid reads and writes to large data sets in parallel from many nodes.
- But accessing data sets which consist of many, small files leads to poor performance.
- Avoid reading and writing lots of small amounts of data to disk.
- Many small files on the system would waste space and would be slower to access, read and write.
- Write data out in binary. Faster and takes less space.
- The Burst Buffer is better for i/o heavy jobs and to speed up checkpoints.
Loading Software Modules
Other than essentials, all installed software is made available using module commands. These modules set environment variables (PATH, etc.) This allows multiple, conflicting versions of a given package to be available. module spider shows the available software.
nia-login07:~$ module spider --------------------------------------------------- The following is a list of the modules currently av --------------------------------------------------- CCEnv: CCEnv NiaEnv: NiaEnv/2018a anaconda2: anaconda2/5.1.0 anaconda3: anaconda3/5.1.0 autotools: autotools/2017 autoconf, automake, and libtool boost: boost/1.66.0 cfitsio: cfitsio/3.430 cmake: cmake/3.10.2 cmake/3.10.3 ...
Common module subcommands are:
module load <module-name>
use particular software
remove currently loaded modules
module spider <module-name>)
list available software packages
list loadable software packages
list loaded modules
On Niagara, there are really two software stacks:
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
module load CCEnv
If you want the same default modules loaded as on Cedar and Graham, then afterwards also
module load StdEnv.
*Env modules are sticky; remove them by
Tips for loading software
We advise against loading modules in your .bashrc.
This could lead to very confusing behaviour under certain circumstances.
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.
It is usually better to be explicit about the versions, for future reproducibility.
ml → module list ml NAME → module load NAME # if NAME is an existing module ml X → module X
- Modules sometimes require other modules to be loaded first.
Solve these dependencies by using
Oddly named, the module subcommand spider is the search-and-advice facility for modules.
Suppose one wanted to load the openmpi module. Upon trying to load the module, one may get the following message:
nia-login07:~$ module load openmpi Lmod has detected the error: These module(s) exist but cannot be loaded as requested: "openmpi" Try: "module spider openmpi" to see how to load the module(s).
So while that fails, following the advice that the command outputs, the next command would be:
nia-login07:~$ module spider openmpi ------------------------------------------------------------------------------------------------------ openmpi: ------------------------------------------------------------------------------------------------------ Versions: openmpi/2.1.3 openmpi/3.0.1 openmpi/3.1.0rc3 ------------------------------------------------------------------------------------------------------ For detailed information about a specific "openmpi" module (including how to load the modules) use the module s full name. For example: $ module spider openmpi/3.1.0rc3 ------------------------------------------------------------------------------------------------------
So this gives just more detailed suggestions on using the spider command. Following the advice again, one would type:
nia-login07:~$ module spider openmpi/3.1.0rc3 ------------------------------------------------------------------------------------------------------ openmpi: openmpi/3.1.0rc3 ------------------------------------------------------------------------------------------------------ You will need to load all module(s) on any one of the lines below before the "openmpi/3.1.0rc3" module is available to load. NiaEnv/2018a gcc/7.3.0 NiaEnv/2018a intel/2018.2
These are concrete instructions on how to load this particular openmpi module. Following these leads to a successful loading of the module.
nia-login07:~$ module load NiaEnv/2018a intel/2018.2 # note: NiaEnv is usually already loaded nia-login07:~$ module load openmpi/3.1.0rc3
nia-login07:~$ module list Currently Loaded Modules: 1) NiaEnv/2018a (S) 2) intel/2018.2 3) openmpi/3.1.0.rc3 Where: S: Module is Sticky, requires --force to unload or purge
Running Commercial Software
- Possibly, but you have to bring your own license for it.
- 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 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, 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.
Compiling on Niagara: Example
Suppose one want to compile an application from two c source files, appl.c and module.c, which use the Gnu Scientific Library (GSL). This is an example of how this would be done:
nia-login07:~$ module list Currently Loaded Modules: 1) NiaEnv/2018a (S) Where: S: Module is Sticky, requires --force to unload or purge nia-login07:~$ module load intel/2018.2 gsl/2.4 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 -lgsl -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).
- The GSL requires a cblas implementation, for is contained in the Intel Math Kernel Library (MKL). Linking with this library 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.
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: couple of minutes, taking at most about 1-2GB of memory, couple of cores.
You can run the 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 salloc command
nia-login07:~$ salloc -pdebug --nodes N --time=1:00:00
where N is the number of nodes. The duration of your interactive debug session can be at most one hour, can use at most 4 nodes, and each user can only have one such session at a time.
Alternatively, on Niagara, you can use the 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 give you 30 minutes.
Niagara uses SLURM as its job scheduler.
You submit jobs from a login node by passing a script to the sbatch command:
nia-login07:~$ sbatch jobscript.sh
This puts the job in the queue. It will run on the compute nodes in due course.
Jobs will run under their group's RRG allocation, or, if the group has none, under a RAS allocation (previously called `default' allocation).
Keep in mind:
Scheduling is by node, so in multiples of 40-cores.
Maximum walltime is 24 hours.
Jobs must write to your scratch or project directory (home is read-only on compute nodes).
Compute nodes have no internet access.
Download data you need beforehand on a login node.
Scheduling by Node
All job resource requests on Niagara are scheduled as a multiple of nodes.
- The nodes that your jobs run on are exclusively yours.
- 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.
Memory requests to the scheduler are of no use.
Your job gets N x 202GB of RAM if N is the number of nodes.
You should use all 40 cores on each of the nodes that your job uses.
You will be contacted if you don't, and we will help you get more science done.
Hyperthreading: Logical CPUs vs. cores
- Hyperthreading, a technology that leverages more of the physical hardware by pretending there are twice as many logical cores than real ones, is enabled on Niagara.
- So the OS and scheduler see 80 logical cores.
- 80 logical cores vs. 40 real cores typically gives about a 5-10% speedup (YMMV).
Because Niagara is scheduled by node, hyperthreading is actually fairly easy to use:
Ask for a certain number of nodes N for your jobs.
You know that you get 40xN cores, so you will use (at least) a total of 40xN mpi processes or threads.
(mpirun, srun, and the OS will automaticallly spread these over the real cores)
But you should also test if running 80xN mpi processes or threads gives you any speedup.
Regardless, your usage will be counted as 40xNx(walltime in years).
There are limits to the size and durection 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 Resarch Group allocation or not. It also matters in which 'partition' the jobs 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||Running jobs||Submitted jobs (incl. running)||Min. size of jobs||Max. size of jobs||Min. walltime||Max. walltime|
|Compute jobs with an allocation||compute||50||200||1 node (40 cores)||1000 nodes (40000 cores)||15 minutes||24 hours|
|Compute jobs without allocation ("default")||compute||50||200||1 node (40 cores)||20 nodes (800 cores)||15 minutes||12 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 (max 5 total)||10 per user||N/A||N/A||15 minutes||72 hours|
|Inspecting archived data, small archival actions in HPSS||archiveshort||2 per user||10 per user||N/A||N/A||15 minutes||1 hour|
Within these limits, jobs will still have to wait in the queue. The waiting time depends on many factors such as the allocation amount, how much allocation was used in the recent past, the number of nodes and the walltime, and how many other jobs are waiting in the queue.
Passing Variables to Job's submission scripts
It is possible to pass values through environment variables into your SLURM submission scripts. For doing so with already defined variables in your shell, just add the following directive in the submission script,
and you will have access to any predefined environment variable.
A better way is to specify explicitly which variables you want to pass into the submision script,
sbatch --export=i=15,j='test' jobscript.sbatch
You can even set the job name and output files using environment variables, eg.
i="simulation" j=14 sbatch --job-name=$i.$j.run --output=$i.$j.out --export=i=$i,j=$j jobscript.sbatch
Command line arguments
Command line arguments can also be used in the same way as command line argument for shell scripts. The only important detail is that they are considered after the script name, ie. it is better to place all your sbatch flags before the script name, ie.
sbatch -p debug jobscript.sbatch FirstArgument SecondArgument ...
then in your submission script you can access those values by referring to
$1, $2, ...
Email notification works, but you need to add the email address and type of notification you may want to receive in your submission script, eg.
#SBATCH --mail-user=YOUR.email.ADDRESS #SBATCH --mail-type=ALL
Example submission script (OpenMP)
#!/bin/bash #SBATCH --nodes=1 #SBATCH --cpus-per-task=40 #SBATCH --time=1:00:00 #SBATCH --job-name openmp_job #SBATCH --output=openmp_output_%j.txt cd $SLURM_SUBMIT_DIR module load intel/2018.2 export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK ./openmp_example # or "srun ./openmp_example".
Submit this script with the command:
nia-login07:~$ sbatch openmp_job.sh
- First line indicates that this is a bash script.
- Lines starting with
#SBATCHgo to SLURM.
- sbatch reads these lines as a job request (which it gives the name
- 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
- To use hyperthreading, just change
Example submission script (MPI)
#!/bin/bash #SBATCH --nodes=8 #SBATCH --ntasks=320 #SBATCH --time=1:00:00 #SBATCH --job-name mpi_job #SBATCH --output=mpi_output_%j.txt cd $SLURM_SUBMIT_DIR module load intel/2018.2 module load openmpi/3.1.0rc3 mpirun ./mpi_example # or "srun ./mpi_example"
Submit this script with the command:
nia-login07:~$ sbatch mpi_job.sh
First line indicates that this is a bash script.
Lines starting with
#SBATCHgo to SLURM.
sbatch reads these lines as a job request (which it gives the name
In this case, SLURM looks for 8 nodes with 40 cores on which to run 320 tasks, for 1 hour.
Once it found such a node, it runs the script:
- Change to the submission directory;
- Loads modules;
- Runs the
- To use hyperthreading, just change --ntasks=320 to --ntasks=640, and add --bind-to none to the mpirun command (the latter is necessary for OpenMPI only, not when using IntelMPI).
Monitoring queued jobs
Once the job is incorporated into the queue, there are some command you can use to monitor its progress.
qsumto show the job queue (
squeue -u $USERfor just your jobs);
squeue -j JOBIDto get information on a specific job
scontrol show job JOBID, which is more verbose).
squeue --start -j JOBIDto get an estimate for when a job will run; these tend not to be very accurate predictions.
scancel -i JOBIDto cancel the job.
sinfo -pcomputeto look at available nodes.
jobperf JOBIDto get an instantaneous view of the cpu and memory usage of the nodes of the job while it is running.
sacctto get information on your recent jobs.
More utilities like those that were available on the GPC are under development.
Information about how to use visualization tools on Niagara is available on Visualization page.
- SciNet: https://www.scinet.utoronto.ca
- Niagara: https://docs.computecanada.ca/wiki/niagara
- System Status: https://docs.scinet.utoronto.ca/index.php/Main_Page
- Training: https://support.scinet.utoronto.ca/education