Marcos Ortiz

AWS Graviton Weekly # 13: AWS re:Invent 2022 Week - November 25th, 2022 to December, 1st, 2022

Published about 1 year ago • 5 min read

Issue # 13: November 25th, 2022 to December 1st, 2022

Hey Reader

Welcome to Issue # 13 of AWS Graviton Weekly, which will be focused on sharing everything that happened in the past week related to AWS Graviton: from November 25th, 2022 to December 1st, 2022.

We are in the AWS re:Invent 2022 Week, so, I will focus in the main announcements, news and everything related to AWS Silicon in general, not only Graviton.

There were a lot of exciting stuff announced in the event, so feel free to grab a pen, a good cup of ☕ (I'm Cuban, so coffee is my "fuel") and enjoy all the resources I will share here.

But before sharing everything, I have one BIG QUESTION for you:

Which was your favorite announcements from re:Invent 2022?

It doesn't have to be related to Graviton. Reply to this email with the answer. I would love to hear all about it.


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Here are all the announcements from re:Invent 2022 related to AWS Silicon

Peter DeSantis's keynote

First, you need to watch the keynote from Peter DeSantis (Senior Vice President of AWS Utility Computing).

Why? Because he shared some incredible stuff there related to AWS Silicon:

  • First, he announced the new generation of AWS Nitro v5 (link to the exact moment in the YouTube video), which is a massive jump in performance from the previous generation: 2x transistors, 50% faster DRAM speed and a PCIe adapter that provides about twice the bandwith. What this means exactly? This new card is able to support 60% higher packet per second rate with a 30% reduction in packet latency and with 40% better performance per watt.

  • Then, he continued with the announcement of a new instance type called C7gn, a new network optimized EC2 instance, which is the first instance based in the Nitro v5 system and Graviton. The specs are simply insane: 200 Gbps per second of networking bandwitdth and up to 50% better packet processing performance (compared to C6gn instances). If you want to know more details, I encourage you to read this post from Jeff Barr here.

  • And then, he shared the coolest stuff of the night: AWS built a new Graviton chip called Graviton3E, a variant of the Graviton3 processor but it's specifically optimized to do more floating points and vector math, which is perfect for HPC applications. Compared to Graviton3 based instances, it does 35% higher performance on HPL (a computational benchmark for lineal algebra), 12% on Gromacs and 30% better on common financial options models

  • To go even further, he announced another new instance called HPC7g, the lastest generation of EC2 instance for compute-intensive HPC workloads, powered by Graviton3E

  • Then, he talked slightly about the AWS Trn1 instances, which is powered by up to 16 AWS Trainium accelerators purpose built to accelerate DL training, 512 GB of memory and 800 Gbps of networking bandwidth.

There are more cool and exciting stuff in the video. So, feel free to watch it entirely. You will love it.

Dave Brown's Leadership session

Another interesting session was Dave Brown's session (VP of EC2) where he shared some amazing stuff as well related to Compute at AWS.

But to be honest, I encourage you to watch the entire talk because it's very interesting and refreshing, especially after watching Dave talking about many customers like Sprinklr, Formula 1 and DirecTV using Graviton processors to innovate every single day.

Now, I will share all the talks related to Graviton below.

Note: there are a lot of recordings that are not available yet in the AWS Events YouTube channel, so my advice? Subscribe to it and make sure to click in the 🔔 to get notified when a new video is uploaded.

AWS re:Invent 2022 Graviton sessions

Again: there are a lot of videos that have not being uploaded yet to the channel. So, subscribe here.


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Marcos Ortiz

I'm a Data Engineer by day at Riot Games (via X-Team ) and by night, I curate the last news/product announcements/resources about AWS Silicon (Graviton, AWS Nitro, Inferentia, and Trainium).

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