What would you like to get out of this workshop?
learn more how to use HPC system for running AI analyses
Confidence in using HPC facilities
Other clusters than Saga, such as Colossus on TSD
More about #SBATCH parameters and how to estimate memory, time, CPU etc in a script
Are you able to access Saga? Add your “o” to your option
maybe: had to reset password
do we need to reset password again if we participated in the previous course?
I am not sure, but try to log in and check that it works
Are you a member of the nn9987k project on Saga when you type cost?
if no, where should people write to?
Can not login to Saga?
Do not have access to project after login The link to change password is : https://www.metacenter.no/user/
how long does it take before new password is working after reset?
15 minutes or less (as far as I know)
When are the recordings uploaded?
usually after a week or two
we will send out an email when it is ready
Do you know what GPFS is
Do you know what S3 storage is
Do you know what Infiniband is?
Comment: restaurant equivalent to Slurm backfill: “the table is reserved from 21:00 on but you can have it until then for a short dinner” :+1:
When working with creating models I need to work more interactively for debugging and all that. How can that be done? I have been using Jupyter on the UiO system earlier. Spyder would even be better. is this possible?
great question. for these situations it is impractical to submit a job and wait few hours for it to start. for this you can submit an interactive job where you get an interactive allocation on a node.
more info here: https://documentation.sigma2.no/jobs/interactive_jobs.html
inside an interactive allocation one can then start an editor or run debug/test jobs until the interactive allocation stops
Can Jupyter be connected to this HPC-resources?
here you mean: it would be running in the browser on your computer and connect to a jupyter server on the cluster?
Yes, prototypes with small amount of test data
it is possible to run jupyter on the computer and connect it to a jupyter server via SSH. it is pity that we do not have good documentation on how to do that where I could point you to.
These pages might answer parts of this questions; https://apps.sigma2.no
What is mpirun? vs srun
For most practical work choose the one you like, if you need the options mpirun offer you might use mpirun, but avoid the -np option, this is set by SLURM. The performance using mpirun or srun is the same. For large jobs with a huge number of mpi ranks srun might yield a faster startup. Both mpirun and srun can start any script, just multiple instances of it. When there is no SLURM e.g. interactive work you need to use mpirun, so if you want you can always use mpirun.
but when do you use it(either of them)? I probably missed the intro to it
more info here: https://documentation.sigma2.no/jobs/guides/running_mpi_jobs.html
How to estimate number of tasks/processes? Is it always just one task unless you do parallell work in the job?
we will have a session on this good question tomorrow. summary of the session: it typically requires calibration and trying few configurations to know which is the optimal number for that type of job. once you know, then you can keep that setting for the series of jobs.
Is random access also bad for solid state disks?
Random access is always bad, but how bad depends on the media. Random write is better than random read. (For quite obvious reasons?!)
Does $SCRATCH or $LOCALSCRATCH exist also on Colossus? Or what is the equivalent storage location there?
I do not see anything related to a $LOCALSCRATCH here: https://www.uio.no/english/services/it/research/sensitive-data/help/hpc/job-scripts.html#Work_Directory, but $SCRATCH seems it is on a fast system, so might be equivalent in performance as the $LOCALSCRATCH on the other systems
When is it useful and/or necessary to use MPI programs? Is it commonly used for a single person performing “normal” data analysis?
Any large scale program is MPI based, it can run with distributed memory. There is no limit of how many nodes you can run on. Take Betzy, at 1341 nodes and 172k cores one can (I have) run jobs using over 100k MPI ranks.
it is one out of many mechanisms to make use of several cores. but it is not the only one. can you describe a bit more how your data analysis is? because whether it is useful/necessary, it “depends” what language/tool/library
I usually use several bash scripts to do serial calculations. Bioinformatics
and the bash scripts call python or R scripts/tools?
yes, a mix of python and R, loading modules
in this case, often mpirun is not so interesting but one parallelizes rather by splitting the problem into independent batches and one can parallelize using slurm jobarrays or similar. but some R and Python packages use OpenMP or MPI underneath and it can be difficult to know/see from the outside. Tomorrow we will have a session on this. Example for a Python package which can use OpenMP under the hood: https://documentation.sigma2.no/jobs/choosing-number-of-cores.html#code-may-call-a-library-which-is-shared-memory-parallelized
High volume production is also parallel work, but falls into the class of “naturally parallel” and hence gets little attention.
Path where to find the demonstration project:
Please name one thing that you liked about today
What should we change/improve for tomorrow or next time?