AWS Graviton Weekly # 36

published7 months ago
10 min read

Issue # 36: May 5th, 2023 to May 12th, 2023

[Read the browser version right here]

In Partnership with the Interesting Data Gigs Talent Collective

Hey Reader

Welcome to Issue # 36 of AWS Graviton Weekly, which will be focused on sharing everything that happened in the past week related to AWS Silicon: from May, 5th, 2023 to May, 12th, 2023

The recommended resources for this week?

  • The launch of Cedar, an Open Source language for access control
  • The support of Graviton3 R7g instances for Amazon Aurora and PostgreSQL, and the launch of I4g instances
  • An online event you can't miss if you want to port code to Graviton
  • An interesting post from the Coiled team about some performance tests for Dask on Graviton3 instances
  • and more cool stuff

Enjoy the content of this week

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Introducing Cedar, an open-source language for access control

Today, AWS open-sourced the Cedar policy language and authorization engine.
You can use Cedar to express fine-grained permissions as easy-to-understand policies enforced in your applications, and you can decouple access control from your application logic.
Cedar supports common authorization models such as role-based access control and attribute-based access control. It follows a new verification-guided development process to give you high assurance of Cedar’s correctness and security: AWS formally models Cedar's authorization engine and other tools, proves safety and correctness properties about them using automated reasoning, and rigorously tests that the model matches the Rust implementation.

Learn more:

Introducing Amazon EC2 I4g storage-optimized instances

Amazon Elastic Compute Cloud (EC2) I4g storage-optimized instances powered by AWS Graviton2 processors are now generally available. I4g instances deliver the best compute price performance for a storage-optimized instance versus comparable x86-based storage optimized instances, and the best storage performance per TB for a Graviton-based storage instance.
Based on AWS Nitro SSDs that are custom built by AWS and reduce both latency and latency variability, I4g instances are optimized for workloads that perform a high mix of random read/write and require very low I/O latency, such as transactional databases (Amazon DynamoDB, MySQL, and PostgreSQL) and real-time analytics such as Apache Spark.

Learn more:

Amazon Aurora MySQL and PostgreSQL support for Graviton3 based R7g instance family

AWS Graviton3-based R7g database instances are now generally available for Amazon Aurora with PostgreSQL compatibility and Amazon Aurora with MySQL compatibility in US East (N. Virginia, Ohio), US West (Oregon), and Europe (Ireland) regions.
Graviton3 instances provide up to 30% performance improvement and up to 20% price-performance improvement over Graviton2 instances for Aurora depending on database engine, version, and workload.
AWS Graviton3 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. The Graviton3 processors offer several improvements over the second-generation Graviton processors. Graviton3-based R7g are the first AWS database instances to feature the latest DDR5 memory, which provides 50% more memory bandwidth compared to DDR4, enabling high-speed access to data in memory. R7g database instances offer up to 30Gbps enhanced networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).
AWS Graviton3 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. The Graviton3 processors offer several improvements over the second-generation Graviton processors.
Graviton3-based R7g are the first AWS database instances to feature the latest DDR5 memory, which provides 50% more memory bandwidth compared to DDR4, enabling high-speed access to data in memory. R7g database instances offer up to 30Gbps enhanced networking bandwidth and up to 20 Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).

Learn more:

AWS announces Amazon Aurora I/O-Optimized

Today, we are announcing the general availability of Amazon Aurora I/O-Optimized, a new configuration that provides improved price performance and predictable pricing for customers with I/O-intensive applications.
Aurora I/O-Optimized offers improved performance, increasing throughput and reducing latency for customers’ most demanding workloads. With Aurora I/O-Optimized, there are zero charges for read and write I/O operations—you only pay for your database instances and storage usage, making it easy to predict your database spend up front. Aurora I/O-Optimized offers up to 40% cost savings for I/O-intensive applications where I/O charges exceed 25% of the total Aurora database spend.

Learn more:


.NET Workflows for arm64 with Amazon CodeCatalyst: Part 1, by Kirk Davis (Principal Solutions Architect for Microsoft Workloads on AWS)

AWS Graviton arm64 processors offer compelling price-performance for .NET workloads in the cloud. In this post, you learned how to create a CodeCatalyst workflow for Lambda functions that builds the code and runs unit tests using Graviton compute in CodeCatalyst, and then deploy to Lambda on Graviton. Get started building your own .NET workflows in Amazon CodeCatalyst today, and learn more about CodeCatalyst workflows and blueprints in the documentation. In part 2 of this blog, to be published soon, we’ll deploy an ASP.NET Core application to ECS Fargate on Graviton, with the build and test running on arm64 compute..

Learn more:

New Storage-Optimized Amazon EC2 I4g Instances: Graviton Processors and AWS Nitro SSDs, by Jeff Barr (Chief Evangelist for AWS)

Today we are launching I4g instances powered by AWS Graviton2 processors that deliver up to 15% better compute performance than our other storage-optimized instances.
With up to 64 vCPUs, 512 GiB of memory, and 15 TB of NVMe storage, one of the six instance sizes is bound to be a great fit for your storage-intensive workloads: relational and non-relational databases, search engines, file systems, in-memory analytics, batch processing, streaming, and so forth. These workloads are generally very sensitive to I/O latency, and require plenty of random read/write IOPS along with high CPU performance.

Learn more:

Reduce Amazon SageMaker inference cost with AWS Graviton, by Sunita Nadampalli (Software Development Manager at Amazon), Jaymin Desai (Software Engineer at AWS AI), Lauren Mullennex (Senior AI/ML Specialist Solution Architect at AWS), Mohan Gandhi (Senior Software Engineer at AWS), Qingwei Li (Senior AI/ML Specialist Solution Architect at AWS), Wayne Toh (EC2 Graviton Specialist at AWS), and Mike Schneider (System Development Engineer II at AWS)

AWS measured up to 50% cost savings for PyTorch, TensorFlow, XGBoost, and scikit-learn model inference with AWS Graviton3-based EC2 C7g instances relative to comparable EC2 instances on Amazon SageMaker.
You can migrate your existing inference use cases or deploy new ML models on AWS Graviton by following the steps provided in this post.
You can also refer to the AWS Graviton Technical Guide, which provides the list of optimized libraries and best practices that will help you achieve cost benefits with AWS Graviton instances across different workloads.
If you find use cases where similar performance gains are not observed on AWS Graviton, please reach out us. We will continue to add more performance improvements to make AWS Graviton the most cost-effective and efficient general-purpose processor for ML inference.

Learn more:

Running Golang on ARM Lambdas, by James Farrell (Senior Software Engineer I at NS1)

In 2018, AWS introduced its initial lineup of Graviton processors, and since then, they have continued to evolve and improve. The latest iteration of Graviton processors showcases impressive enhancements, including “25% improved compute performance, up to double the floating-point performance, and up to double the speed for cryptographic workloads when compared to the previous generation…”
You can note they don’t compare to Intel or AMD. Thats because in a brute force workload usually the x86 processors win out. But from my experience there are many workloads where the ARM based processors are comparable and when considering the 20% cost reduction, it becomes a compelling argument. Nik Krichko wrote a great blog post benchmarking the three architectures.

Learn more:

Using Open Source Cedar to Write and Enforce Custom Authorization Policies, by Mike Hicks (Senior Principal Scientist at Amazon Web Services, and Professor Emeritus at the University of Maryland)

Cedar is the authorization policy language used by customers of the Amazon Verified Permissions and AWS Verified Access managed services. With the release of the Cedar SDK on GitHub, we provide transparency into Cedar’s development, invite community contributions, and hope to build trust in Cedar’s security.

Learn more:

How Canva saves over $3 million annually in Amazon S3 costs, by Joshua Smith (Engineering Lead for the Core Data team at Canva)

Canva saves roughly $300,000 per month ($3.6 million annually) thanks to these changes, and given the ever-growing amount of user-generated data we store, these savings continue to grow over time. It’s very important to remember that these savings required us to first understand the access patterns for our data, as well as a one-off spend of $1.6 million to transition roughly 80 billion objects. Overall this meant that we saw a positive ROI after only a few months of making the transition, which is still pretty fantastic!

Learn more:

How well does Dask run on Graviton? by Nat Tabris (Staff Software Engineer at Coiled) and Sarah Johnson (Documentation Engineer at Coiled)

ARM-based processors are known for matching performance of x86-based instance types at a lower cost, since they consume far less energy for the same performance. It’s not surprising then that some companies, like Honeycomb, are switching their entire infrastructure to ARM.
We ran a number of Dask workloads on both x86- and ARM-based instance types and found costs were typically 20–30% lower when using ARM.
We also tested out the latest generation of Amazon’s ARM-based processors Graviton3 instance types and looked at performance for a compute-heavy workload using Dask and XGBoost.

Learn more:


[VIDEO] AWS Made Easy Livestream - Ep 48 - Cloud Intelligence Dashboards with Meredith Holborn (Technical Account Manager at AWS Enterprise Support), Rahul Subramaniam (CEO and founder at CloudFix) and Stephen Barr (Principal Architect and Technical Evangelist at CloudFix )



Graviton Essentials - Developer Day

Wednesday, May 31 2023 | 8:30AM - 5:00PM PDTAmazon SJC18, 2100 University Avenue, East Palo Alto, CA94303

About the event

AWS presents the Graviton Essentials Developer Day. Graviton Essentials Developer Day is a free in-person immersion event where AWS Graviton experts deliver technically-focused Graviton-based Amazon EC2 training. This full-day event helps attendees learn best practices to accelerate migration and development of their workloads on Graviton-based instances. Attendees can expect to leave Graviton Essentials Developer Day feeling confident they will know how to achieve performance gains and cost reductions from using Graviton-based instances. Sessions will cover topics such as Introduction to Graviton, programming language and focused deep dives, testing and optimization techniques, and workload deployments. Space is limited, so sign up today.

Presenters & Hosts

AWS Graviton Specialist Solution Architects

About Graviton: AWS Graviton processors are custom Arm-based processors designed by Amazon Web Services (AWS). Graviton-based instances deliver up to 20% lower cost, up to 40% better price performance, and up to 60% less energy consumption than comparable x86-based instances. With Graviton-based instances, you can:

1. Increase and scale your application performance to enhance your customer experience

2. Reduce your operational cost
3. Support running workloads sustainably in the cloud

Who should attend

  • Software Engineers
  • Cloud Architects
  • DevOps Engineers
  • Platform Engineers

What to expect

  • Introduction to Graviton
  • Programming language & focused deep dives
  • Testing & optimization techniques
  • Workload deployments
  • Hands-on working sessions
  • Interaction with AWS Graviton SMEs
  • Light breakfast, lunch, and happy hour


  • Participants need to supply their own laptop and power supply
  • Some coding knowledge

No prior Graviton experience is necessary

AWS on Air: Porting Advisor for AWS Graviton: May 16th, 2:00 PM PST / 5:00 PM EST


  • Art Baudo (Principal Product Marketing Manager at AWS)
  • Vishal Manan (Sr. Solution Architect, Graviton at AWS)
  • Ryan Doty (Solution Architect at AWS)

AWS Migration Meetup: June 7, 4 PM CEST | Online

Join Migration Meetup by N-iX on June 7th to discuss cases about migration and data engineering on AWS for business growth!

We’ll deep dive into how the Migration Acceleration Program helps organizations achieving their business goals and how to start the migration journey! And focus on AWS tools and services you need to easily collect, store, process, and analyze data in real-time, enabling you to make informed decisions faster than ever before.

Free online event.

All Talks will be in English


From the ARM Ecosystem

Social Media post of the week

The post came from Ronen Laviv (Enterprise Account Manager at AWS)


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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