spack VS slurm

> Are we talking about the same autotools?

Yes. Instead of figuring out how to do something particular with every single software package, I can do a --with-foo or --without-bar or --prefix=/opt/baz-1.2.3, and be fairly confident that it will work the way I want.

Certainly with package managers or (FreeBSD) Ports a lot is taken care of behind the scenes, but the above would also help the package/port maintainers as well. Lately I've been using Spack for special-needs compiles, but maintainer ease also helps there, but there are still cases one a 'fully manual' compile is still done.

> Suffice it to say, I prefer to work with handwritten makefiles.

Having everyone 'roll their own' system would probably be worse, because any "mysteriously failure" then has to be debugged specially for each project.

Have you tried Spack?

* https://spack.io

* https://spack.readthedocs.io/en/latest/

Well, good luck with that, cause it's broken.

Previous release miscompiled Python [1]

Current release miscompiles bison [2]

[1] https://github.com/spack/spack/issues/38724

[2] https://github.com/spack/spack/issues/37172#issuecomment-181...

gh is available via Homebrew, MacPorts, Conda, Spack, Webi, and as a…

> I can't count the number of times I've seen people say "md5 is fine for use case xyz" where in some counterintuitive way it wasn't fine.

I can count many more times that people told me that md5 was "broken" for file verification when, in fact, it never has been.

My main gripe with the article is that it portrays the entire legal profession as "backwards" and "deeply negligent" when they're not actually doing anything unsafe -- or even likely to be unsafe. And "tech" knows better. Much of tech, it would seem, has no idea about the use cases and why one might be safe or not. They just know something's "broken" -- so, clearly, we should update.

> Just use a safe one, even if you think you "don't need it".

Here's me switching 5,700 or so hashes from md5 to sha256 in 2019: https://github.com/spack/spack/pull/13185

Did I need it? No. Am I "compliant"? Yes.

Really, though, the main tangible benefit was that it saved me having to respond to questions and uninformed criticism from people unnecessarily worried about md5 checksums.

[1] https://github.com/spack/spack/blob/develop/var/spack/repos/...

In Spack [1] we can express all these constraints for the dependency solver, and we also try to always re-cythonize sources. The latter is because bundled cythonized files are sometimes forward incompatible with Python, so it's better to just regenerate those with an up to date cython.

[1] https://github.com/spack/spack/

You want to look at the tools used for HPC systems, these are generally very well tried and tested and can be setup for single machine usage. Remote access - we use ssh, but web interfaces such as Open On Demand exist - https://openondemand.org/. For managing Jobs, Slurm is currently the most popular option - https://slurm.schedmd.com/documentation.html. For a module system (to load software and libraries per user), Spack is a great - https://spack.io/. You might also want to consider containerisation options, https://apptainer.org/ is a good option.

git clone https://github.com/spack/spack.git ./spack/bin/spack install gcc

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.