At $previous_job we shifted a large workload from Intel to Graviton which was projected to save ~$1.7m annually while keeping roughly equivalent performance (after some tuning).
Did it?
All combined the hype is quite real. Heck, even moving an intel based service to newer nitro based EC2 instances resulted in a drastic performance improvement. Moved from m5.24xlarge --> m6g.8xlarge with better service performance and improved latency characteristics. Intel is in trouble in my opinion.
I wonder if this is actually an Intel issue or if there are some other optimizations at play, such as in the virtualization layer.
Because at one point I wanted to try Jetbrains' new "gateway" product, which basically runs a remote IDE and only shows the GUI locally. I was curious on one hand, but I also wanted a machine with a bit more oomph for my occasional compilation needs (rust on Linux, fwiw). I was really unimpressed, the c6i was comparable to my local slim laptop running an 11th gen i7u part. My similar slim AMD 5650U laptop is actually faster. IIRC, the c6i.metal wasn't particularly faster on this kind of single threaded work.
On intel aws, you pay per HyperThread. On Graviton, you pay per core.
But on this kind of workload and with modern schedulers, HT bump is rather limited. So in practice you are paying twice the price for the same number of cores.
This is the biggest contributing factor to that difference and i keep being surprised noone mention it.
You can disable hyperthreading on the x64 instances at the cost of halving the number of cores you have available in the instance that you paid for.
Is it? The phone you use probably uses ARM. If you buy a mac now, it's probably gonna be ARM. It's very much different from the calxeda days!
All of this means that a lot can go wrong when running code on those instances, resulting in lower performance. It is either advised to run separate processes on each NUMA domain, or use NUMA aware code (which Java almost never is). In addition, the code (or the system) should be highly scalable to multiple CPU cores.
In addition, the cores are old enough to suffer from Spectre/Meltdown related patches/workarounds, decreasing especially syscall performance.
I did slightly misspeak on the instance move having seen your reply. We moved from m5.24xlarge to m6i.16xlarge. Sorry for the confusion.
That said, you shared some interesting information. I'd love to read up more on this, any specific place I can dig in a bit deeper regarding the finer specifics of these instance types and architecture?
As for getting information on AWS instances, the best way in my opinion is just to spin up the instance and look up which exact CPU model it uses. Then you can go for example to WikiChip (https://en.wikichip.org/wiki/WikiChip) to see more information about the CPU. Other good sources include Anandtech (for example https://www.anandtech.com/show/15578/cloud-clash-amazon-grav...) and Chips and Cheese (for example https://chipsandcheese.com/2022/05/29/graviton-3-first-impre...).
Things like NUMA configuration can be inspected with tools like numactl.
The 16xlarge version is also a 32 core single socket CPU, meaning there should be no issues with NUMA. I would expect it to be much better than m5.24xlarge in most applications when taking the much faster single-threaded performance into account. Of course, nothing beats benchmarking and measuring yourself though.
I have personally seen issues with NUMA systems and code that theoretically parallelizes very well. Any synchronized mutable state becomes an issue with these kinds of systems. For example, I have had an issue where third party code would use the C "rand" function for randomness. Even though this was not used in a hot code path, on m5.24xlarge >90% of the execution time would be spent on just on the lock guarding the internal random state. On a "normal" system with fewer cores this never showed up while profiling.
Did you replace 5m of EC2 with 3.3m of EC2 and save 1.7m (impressive) or did you replace 50m of EC2 with 48.3m of EC2 (not really so impressive)?
I'm failing to comprehend how that's not impressive. Bean counters would still love this type of savings.
On the other hand, if the larger organization hired an AWS specialist, as many do, the optimization might be "free" because the specialist wouldn't have been effective outside of their area.
Understanding you saved $1M/yr means you're closer to profitability (I understand in the VC world all you really care about is % growth, but that's up for debate if that's how everything should be) or able to hire more engineers.