Amazon's cloud unit readies more powerful data center chip – sources
reuters.com
reuters.com
I use AWS's existing a1 generation for map-reduce jobs on the Common Crawl. They're slower in absolute terms than the Intel-based c5 generation, but are cheaper on a $/GB-processed basis. This is despite my runtime of choice (the JVM) being much more heavily optimized for x86 than for ARM. I can imagine there's a lot of headroom for improvements both at the hardware level and at the software optimization level as adoption grows.
If you’re a cloud company, you absolutely, 100% do not want to depend on an Intel or AMD. If anything, as CEO, this is probably one of the what if scenarios that would keep you up at night.
Intel make roughly $20B from Datacenter this year, and the estimate was nearly 50% goes to the HyperScaler, which is AWS, Azure, Google, Alibaba etc.
And AWS owns more than 50% of the HyperScaler market, so that is at least 25% of Intel's DC revenue going to Amazon, roughly $5B per year.
As a developer, it’s never been easier for me to be cloud, OS, and CPU-architecture agnostic.
I’m sure AWS sees the writing on the wall, and I don’t see any other thing happening now other than a race to the bottom.
https://kinvolk.io/blog/2019/11/comparative-benchmark-of-amp...
This new chip would improve over AWS Graviton.
Try using ARM based server and you quickly realize the pain.
Besides, other Hyperscalers won't use Amazon's Chips. So no economy of scale.
Do you remember Amazon Phone ?
Compatibility issues?
Performance issues?
Something else?
Many open source projects don’t even have build tooling or testing on anything but x64.
That’s a lot of pain, even discounting buggy and poorly tested drivers.
As for which libraries, I’m too lazy to do a detailed search. All I can report anecdotally is that we very briefly tried ARM at dayjob on our codebase about a year ago and it seemed everything was immediately broken. Especially interop between high level languages and C libraries. The team tasked with it said almost imm fiat would “way too hard, let’s quit this experiment”.