131–140 of 281 posts
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Don’t worry, we do incremental backups during weekdays and a full backup on Sunday. We use 2 tapes only, so one is always outside of the building. But you know, we saved money.
A bit off topic, but I gotta say you guys are a riot! If there was a comedy tour for IT/Programmer types, I'd pay to see you guys in it. Best thing about your stuff is that it's literally all funny precisely because it's all true.
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It's good to learn how not to be blocked on long-running calculations. On the other hand, if transitioning to a bursty cloud model means you can do your full run in hours instead of weeks, that has real impact on how many iterations you can do and often does appreciably affect velocity.
It can, if you have the technical ability to write code that can leverage the scale-out that most bursty-cloud solutions entail. Coding for clustering can be pretty challenging, and I would generally recommend a user target a single large system with job that takes a week over trying to adapt that job to a clustered solution of 100 smaller systems that can complete it in 8 hours.
This rings true for me. I have a federal grant that prohibits me from using its funds for capital acquisitions: i.e. servers. But I can spend it on AWS at massive cost for minimal added utility for my use case. Even though it would be a far better use of taxpayer funds to buy the servers, I have to rent them instead.
> prohibits me from using its funds for capital acquisitions What is a legitimate reason for this restriction?
There are other grant mechanisms for large capital expenditures.
The problem is the thresholds haven't shifted in a long time, so you can easily trigger it with a nice workstation. But then, the budget for a modular NIH R01 was set in 1999, so thats hardly a unique problem.
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> prohibits me from using its funds for capital acquisitions What is a legitimate reason for this restriction?
I can think of a few ways to abuse it while still spinning it as "for research". The obvious one is to buy a $9999 gaming machine with several of whatever the fanciest GPU on the market is at the time, and say you're doing machine learning. So my guess is it's an overly broad patch for that sort of thing.
I’ve also been skeptical of the commercial cloud for scientific computing workflows. I don’t think this cost benefit analysis mentions it, but the commercial cloud makes even less sense when you take into account brick and mortar considerations. In other words, if your company/institution has already paid for the machine rooms, sys admins, networks, the physical buildings, the commercial cloud is even less appealing.…
It's often not that they're "not comfortable"; it's that we're often flat-out not allowed to.
I imagine what makes this especially hard is you have (at least) three parties in play here: - the people doing the research - the institution's IT services group - the administrator who writes the checks And in my experience, "actual knowledge of what must be done and what it will or could cost" can vary greatly across these three groups; frequently in very unintuitive ways.
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I remember the first time a server caught fire in the closet we kept the rack in. Backups were kept on a server right below the one on fire. But, y'know, we saved money.
Don’t worry, we do incremental backups during weekdays and a full backup on Sunday. We use 2 tapes only, so one is always outside of the building. But you know, we saved money.
Having worked for 2 of the largest cloud providers (1 of them beimg the largest) i have to say "The Cloud" just doesnt makes sense (maybe with the exception of cloud storage) yet for most use cases, this including start ups, small and, mid size companies its just way to expensive for the benefits it provides, it moves your hardware acquisitions /maintainance cost to development costs, you just think better/cheaper be…
Building on AWS has provided scale, security, and redundancy at a substantially lower cost than doing any on-prem solution (except for a shitty one strung together with lowendbox machines).
The combined AWS bill for the three startups is less than the cost of an F5, even on a non-inflation adjusted basis.
The cloud doesn't mean that you can be totally clueless. I've had experience in HA/scalability/redundancy/deployment/development/networking/etc. It means that if you do know what you're doing you can deliver a scalable HA solution at a ridiculously lower price point than a DIY solution using bare iron and colo.
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I've been running a group server (basically a shared workstation) for 5 years and it's been great. Way cheaper than cloud, no worrying about national rules on where data can be stored, no waiting in a SLURM batch queue, Jupyter notebooks on tap for everyone. A single $~6k outlay (we don't need GPUs which helps). Classic big workstations are way more capable than people think - but at the same time it's hard to justif…
Aren't you talking about 1 server when this is talking about HPC?