This repository provides technical guidance for users and developers using instances using Ampere Computing processors. While it calls out specific features of the Graviton processors themselves, this repository is also generally useful for anyone running code on Arm-based systems.

• Transitioning to Ampere processors
• Optimizing for Ampere processors
• Recent software updates relevant to Ampere processors
• Language-specific considerations
  • C/C++
  • Go
  • Java
  • .NET
  • Python
  • Rust
• Containers on Ampere processors
• Headless website testing with Chrome and Puppeteer on Ampere processors
• Operating Systems support
• DPDK, SPDK, and other datapath software
• PyTorch
• R
• TensorFlow
• Spark on Graviton
• Known issues and workarounds
• Ampere Performance Runbook
• Additional resources
• How To Resources

If you are new to Ampere processors and want to understand how to identify target workloads, how to plan a transition project, how to test your workloads on any Ampere based instances, and finally how to deploy in production, please read the key considerations to take into account when transitioning workloads to Ampere instances.

Please refer to optimizing for general debugging and profiling information. For detailed checklists on optimizing and debugging performance on Graviton, see our performance runbook.

Different architectures and systems have differing capabilities, which means some tools you might be familiar with on one architecture don't have equivalent on AWS Graviton. Documented Monitoring Tools with some of these utilities.

There is a huge amount of activity in the Arm software ecosystem and improvements are being made on a daily basis. As a general rule later versions of compilers, language runtimes, and applications should be used whenever possible. The table below includes known recent changes to popular packages that improve performance (if you know of others please let us know).

You can run Docker, Kubernetes, Amazon ECS, and Amazon EKS on Graviton. Amazon ECR supports multi-arch containers. Please refer to containers for information about running container-based workloads on Graviton.

AWS Lambda now allows you to configure new and existing functions to run on Arm-based AWS Graviton2 processors in addition to x86-based functions. Using this processor architecture option allows you to get up to 34% better price performance. Duration charges are 20 percent lower than the current pricing for x86 with millisecond granularity. This also applies to duration charges when using Provisioned Concurrency. Compute Savings Plans supports Lambda functions powered by Graviton2.

The Lambda page highlights some of the migration considerations and also provides some simple to deploy demos you can use to explore how to build and migrate to Lambda functions using Arm/Graviton2.

Please check os.md for more information about which operating system to run on Graviton based instances.

Postgres performance can be heavily impacted by not using LSE. Today, postgres binaries from distributions (e.g. Ubuntu) are not built with -moutline-atomics or -march=armv8.2-a which would enable LSE. Note: Amazon RDS for PostgreSQL isn't impacted by this.

In November 2021 PostgreSQL started to distribute Ubuntu 20.04 packages optimized with -moutline-atomics. For Ubuntu 20.04, we recommend using the PostgreSQL PPA instead of the packages distributed by Ubuntu Focal. Please follow the instructions to set up the PostgreSQL PPA.

The default installation of pip on some Linux distributions is old (<19.3) to install binary wheel packages released for Graviton. To work around this, it is recommended to upgrade your pip installation using:

sudo python3 -m pip install --upgrade pip

The Bazel build tool now releases a pre-built binary for arm64. As of October 2020, this is not available in their custom Debian repo, and Bazel does not officially provide an RPM. Instead, we recommend using the Bazelisk installer, which will replace your bazel command and keep bazel up to date.

Below is an example using the latest Arm binary release of Bazelisk as of October 2020:

wget https://github.com/bazelbuild/bazelisk/releases/download/v1.7.1/bazelisk-linux-arm64
chmod +x bazelisk-linux-arm64
sudo mv bazelisk-linux-arm64 /usr/local/bin/bazel
bazel

Bazelisk itself should not require further updates, as its only purpose is to keep Bazel updated.

Linux distributions, in general, use the original zlib without any optimizations. zlib-cloudflare has been updated to provide better and faster compression on Arm and x86. To use zlib-cloudflare:

git clone https://github.com/cloudflare/zlib.git
cd zlib
./configure --prefix=$HOME
make
make install

Make sure to have the full path to your lib at $HOME/lib in /etc/ld.so.conf and run ldconfig.

For users of OpenJDK, which is dynamically linked to the system zlib, you can set LD_LIBRARY_PATH to point to the directory where your newly built version of zlib-cloudflare is located or load that library with LD_PRELOAD.

You can check the libz that JDK is dynamically linked against with:

$ ldd /Java/jdk-11.0.8/lib/libzip.so | grep libz
libz.so.1 => /lib/x86_64-linux-gnu/libz.so.1 (0x00007ffff7783000)

Currently, users of Amazon Corretto cannot link against zlib-cloudflare.

• AWS Graviton
• Neoverse N1 Software Optimization Guide
• Armv8 reference manual
• Package repository search tool

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