AWS doesn't make sense for scientific computing
noahlebovic.com
AWS doesn't make sense for scientific computing
1–10 of 281 posts
Re: AWS doesn't make sense for scientific computing
#2This 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.
Re: AWS doesn't make sense for scientific computing
#3using on-demand for latency insensitive work, especially when you’re also very cost sensitive, isn’t the right choice. spot instances will get you somewhere in the realm of the hetzner/on-prem numbers.
Re: AWS doesn't make sense for scientific computing
#4using on-demand for latency insensitive work, especially when you’re also very cost sensitive, isn’t the right choice. spot instances will get you somewhere in the realm of the hetzner/on-prem numbers.
But, as the article points out, you are still paying a lot of money for features that you don't need for scientific computing.
Also, AWS is notoriously easy to undercut with on-prem hardware, especially if your budget is large and your uptime requirements aren't - you'll save a few hundred thousand a year alone by not having to hire expert engineers for on-call duty and extreme reliability.
Re: AWS doesn't make sense for scientific computing
#5I completely agree for most cases. In many scientific computing applications, compute time isn’t the factor you prioritize in the good/fast/cheap triad. Instead, you often need to do things as cheaply as possible. And your data access isn’t always predictable, so you need to keep results around for an extended period of time. This makes storage costs a major factor. For us, this alone was enough to move workloads away from cloud and onto local resources.
Re: AWS doesn't make sense for scientific computing
#6Sure? I mean, if you have:
1) A large enough queue of tasks
2) Users/donstream willing to wait
using your own infrastructure always wins (alsuming free labor) since you can load your own infrastructure to ~95% pretty much 24/7 which is unbeatable.
Re: AWS doesn't make sense for scientific computing
#7This is sort of a confusing article because it assumes the premise of "you have a fixed hardware profile" and then argues within that context ("Most scientific computing runs on queues. These queues can be months long for the biggest supercomputers".) Of course if you're getting 100% utilization then you'll find better raw pricing (and this article conveniently leaves out staffing costs), but this model misses one of the most powerful parts of cloud providers: autoscaling. Why would you want to waste scientist time by making them wait in a queue when you can just instead autoscale as high as needed? Giving scientists a tight iteration loop will likely be the biggest cost reduction and also the biggest benefit. And if you're doing that on prem then you need to provision for the peak load, which drives your utilization down and makes on prem far less cost effective.
Re: AWS doesn't make sense for scientific computing
#8The general rule of thumb in the HPC world is if you can keep a system computing for more than 40% of the time, it will be cheaper to buy.
Re: AWS doesn't make sense for scientific computing
#9using on-demand for latency insensitive work, especially when you’re also very cost sensitive, isn’t the right choice. spot instances will get you somewhere in the realm of the hetzner/on-prem numbers.
Even spot instances on AWS are still over 2x more expensive per month than Hetzner. The cheapest c5a.24xlarge spot instance right now is $1.5546/hr in us-east-1c. That's $1134.86/mo, excluding data transfer costs. If you transfer out 10 TB over the course of a month, that's another $921.60/mo – or now 4x more expensive than Hetzner.
Using the estimate from the article, spot instances are still over 8x more expensive than on-prem for scientific computing.
Re: AWS doesn't make sense for scientific computing
#10This is sort of a confusing article because it assumes the premise of "you have a fixed hardware profile" and then argues within that context ("Most scientific computing runs on queues. These queues can be months long for the biggest supercomputers".) Of course if you're getting 100% utilization then you'll find better raw pricing (and this article conveniently leaves out staffing costs), but this model misses one of…
Generally, scientists aren't blocked while they are waiting on a computational queue. The results of a computation are needed eventually, but there is lots of other work that can be done that doesn't depend on a specific calculation.