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  1. DZone
  2. Software Design and Architecture
  3. Cloud Architecture
  4. Everything You Need to Know About AWS Graviton2

Everything You Need to Know About AWS Graviton2

Here's everything you need to know about AWS Graviton2, including how it compares to other processors and what migrating your workload entails.

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Ned Hallett user avatar
Ned Hallett
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Jan. 03, 22 · Analysis
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Graviton2 processors are a type of server processor released by AWS in 2020. They’re a successor to the first generation of Graviton processors (simply called Graviton).

They’re custom-built by AWS using 64-bit Arm architecture, with the goal of offering better performance across EC2 instances vs competitors. 

What’s Improved?

Compared to the previous generation, Graviton2 boasts:

  • 7 times the performance 
  • 4 times the number of computing cores 
  • 2 times larger caches 
  • 5 times faster memory 
  • 40 percent overall better price-performance 

This is a sizeable leap, but it’s how they stack up against alternatives that’ll be the clincher for many.

How Does Graviton2 Compare to Processors from Other Providers?

Comparing Graviton2 performance to other processors is somewhat tricky.

Factors such as instance type, workload, and pricing plan all have an impact on whether you’re truly getting more or less for your money.

For this reason, many choose common instance types and compare them directly for a representative comparison.

This is the same logic as comparing specific makes of cars on a 100km journey to answer the question, ‘which is more fuel-efficient, Honda or Citroen?’

One study, comparing four representative instance types — Graviton2 (c6g), Intel (m5zn), Intel (m5zn) AMD (c5a) — found that Graviton2 had the best performance spend when CPU utilization was maxed out. 

The break-even point (where the CPU usage declined to the point that the Graviton2 processor was equal in price-performance to other processors) was around 70%.

So, from this study, it’s safe to say that Graviton2’s impressive stats really start to kick in with CPU-hungry workloads.

Another comparison, this time looking at m5d (x86) intel and m6gd (Graviton2), found that Graviton reached 15 to 25 percent better price-performance vs Intel for various EC2 workloads.

In every case, the nature of workloads and instances used will be a relevant factor in whether or not migrating to Graviton2 makes sense.

But in the event that it is the right decision, what’re the options?

Migrating to Graviton2 

The good news is Graviton2 have a significantly larger ecosystem than the first generation, including:

  • For OS: Amazon Linux 2, Ubuntu 16.04, 18.04, and newer; Red Hat Enterprise Linux 7.6 and 8.0, SUSE, Fedora, Debian, FreeBSD, NetBSD, Amazon Corretto distribution of OpenJDK
  • Container-wise:  Docker Desktop, Amazon ECS, Amazon EKS, for containers
  • Tool-wise: Amazon CloudWatch, AWS Systems Manager, AWS CodeCommit, Cloud9, CodePipeline, and Amazon Inspector
  • Source control: Code Suite, Jenkins

So Linux-based workloads utilizing a lot of open-source technologies should have little trouble switching to Graviton2.

AWS’ own Graviton Challenge also provides a great springboard for individual devs interested in exploring migration.

And if it looks as though your current workloads aren’t supported, you can reach out to the Graviton Challenge dedicated Slack channel.

One point to note, however, is that Graviton2-based instances offer zero compatibility with Windows right now.

What Kind of Workloads do Graviton2 Instances Suit Best?

As we’ve established, Graviton2’s price performance comes into its own with high-CPU workloads. 

This, amongst other things, makes Graviton2 especially popular for:

  • CPU-based machine learning inference
  • Video encoding 
  • Electronic design automation
  • Gaming
  • Microservices
AWS

Published at DZone with permission of Ned Hallett. See the original article here.

Opinions expressed by DZone contributors are their own.

Related

  • AWS Managed Database Observability: Monitoring DynamoDB, ElastiCache, and Redshift Beyond CloudWatch
  • Architecting Petabyte-Scale Hyperspectral Pipelines on AWS
  • Why SAP S/4HANA Landscape Design Impacts Cloud TCO More Than Compute Costs
  • We Went Multi-Cloud and Almost Drowned: Lessons From Running Across AWS, GCP, and Azure

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