ohpc VS slurm

We have plenty of licensed RHEL, but in isolated environments the hurdle of connecting to a Satellite server or their subscription hub on the internet is too high -- at least with Rocky and the ilk available. For this set up, the licensing model doesn't match reality, at least not easily.

Are we really going to build out compatible configuration management, monitoring, logging, etc? -- it's not a seamless transition. How much time do we have to put towards this?

And yes -- there is software compatibility issues. Look at the OpenHPC software distribution, it's designed for SUSE or Enterprise Linux: https://github.com/openhpc/ohpc/wiki/2.X

I recommend you just stick with HPC centric tools are workflows. Your scientists aren’t going to learn k8s as you said. SLURM is the scheduler you want and if you’re new to HPC, I recommend taking a look at https://openhpc.community

the general consensus is that pam_slurm_adopt is the better module (that's just one dude's opinion but his citations are good) - the advantage is that not only will it gatekeep SSH access, it'll also drop their SSH session into the cgroups that are constraining the user's resource limits, which also means their CPU usage will show up in sacct for the job (if the user has multiple jobs running on a node their ssh session may get dropped into the wrong one, no help for that)

Check this: https://openhpc.community/ (this helped me a lot when I started. I'm no longer the admin of such systems)

Overall, if you're already in a RedHat-based environment, an installation of OpenHPC is pretty straightforward. Their reference implementation assumes you have a head node for the scheduler that all other nodes NAT through, but that's not a 100% requirement as much as a common setup. It also assumes you can reformat the compute nodes and dedicate them to HPC work, so if you need to keep the systems available as normal workstations, you'll need to deviate a bit. You could also use the OpenHPC instructions as a guide for what packages to install, but it may take longer to get everything right.

https://github.com/openhpc/ohpc/wiki/1.3.X Newer versions of OpenHPC don't seem to releasing XCat guides anymore unfortunately.

I'm trying to have Slurm automatically switch partitions to a specific one via the job_sutmit.lua plugin whenever our users request strictly more than 8 cpus. But trying to extract or calculate ahead of time how many cpus will be allocated or requested isn't trivial (to me). Are there attributes in job_submit that could help out with this task? For example, I don't see any job->desc.ntasks attribute in https://github.com/SchedMD/slurm/blob/master/src/plugins/job_submit/lua/job_submit_lua.c. Any information or documentation on how to leverage job_submit.lua would be appreciated.

Do you have a reason to use kubernetes besides it’s the $CURRENT tech? Why not stick with what you’re already familiar with (batch job managers) and use SLURM, a workload and resource manager, like many others in HPC? Do the researchers need to schedule against Nvidia GPU resources now or in the future? Nvidia themselves recommend SLURM.

Quantum computing and supercomputers are two different things. Quantum computers are currently an area of research, there isn't a version ready for use apart from some prototypes, and it will probably stay that way for while. Also, quantum computing will most likely not be a completly new architecture, that all of the chips we use will adopt, but an addition to current chipsets for some important but special tasks. Supercomputers, or HPC (High performance clusters) are classic computers, just that they are huge. They use derivatives of "off-the-shelf", but high end, hardware. There is a lot of interesting work in designing such systems, a lot of challenging problems in distributed systems theory, but they aren't a complete detached industry. Using them for work, not designing them, doesn't require a EECS type degree, they guy who sit's next to me in the office, uses a supercomputer to predict protein folding, he is by training a doctor and now does computational microbiology. The applications for massive compute power (often times "just brute force the solution instead of spending years in the lab") are almost endless, but to use them it's not that important to understand the full details of how they are constructed, domain knowledge in the application domain is much more important. If you know how your cluster is structured, and knowledge of slurm etc. will enable you to use the supercomputer just fine, again, they aren't that different from regular computers, just that you workstation might have 1 CPU and your supercomputer has 500. Hiding this complexity is done by slurm or any other resource manager. It's open source as well :) https://github.com/SchedMD/slurm

SLURM (distributed by OpenHPC) If you have shared storage then this is the industry standard solution that is both open source and free (extremely popular in the top 500 list). You can pair this with a high speed network or not depending on your research workloads.