Write Neon intrinsics using GitHub Copilot to improve Adler32 performance
Introduction
About Neon and Adler32
Create a C Version of Adler32
Create a test program
Create a Makefile
Build and run the test program
Create a Neon version of Adler32
Compare the Neon version to the standard C version of Adler32
Debug the Neon version to match the standard C version
Summarize the project with a README.md file
Other ideas for GitHub Copilot
Next Steps

  Log an issue

  Fork and edit

  Discuss on Discord

About this Learning Path

Skill level:  Introductory
Reading time:  45 min
Last updated:  02 Jul 2026
Skill level:
  Introductory
Reading time:
  45 min
Last updated:
  02 Jul 2026
Author:Jason Andrews, Arm
Arm IP:Neoverse Cortex-A
Tags: Performance and Architecture Linux GCC Runbook
Author:
  • Jason Andrews, Arm
Arm IP:
Neoverse Cortex-A
Tags:
Performance and Architecture Linux GCC Runbook

Who is this for?

This is an introductory topic for C/C++ developers who are interested in using GitHub Copilot to improve performance using Neon intrinsics.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Use GitHub Copilot to write Neon intrinsics that accelerate the Adler32 checksum algorithm.

Prerequisites

Before starting, you will need the following:

  • An Arm computer running Linux with the GNU compiler (gcc) installed.
  • Visual Studio Code with the GitHub Copilot extension installed.

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll use GitHub Copilot in Visual Studio Code to set up an Adler32 checksum project on an Arm Linux system, establish a C baseline, and measure results. First, you’ll generate the C implementation, create a test program that validates correctness and records timing for multiple input sizes, and ask Copilot to produce a GCC-based Makefile tuned for a Neoverse N1 target. Then, you’ll build and run the project, verify outputs, and collect timing data. With a working harness and repeatable measurements, you’re positioned to introduce Arm Neon intrinsics for the Adler32 hot path and compare behavior against the baseline C version.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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What files should exist before building?
You should have the C implementation (adler32-simple.c), a test program (adler32-test.c), and a Makefile generated by GitHub Copilot. If a file is missing or named differently, regenerate it using the provided prompts.
Which GitHub Copilot mode should I use to generate the code and Makefile?
Use the Copilot Agent session in VS Code and paste the prompts from the steps. Results vary by model, so minor differences in code or target names are expected.
How do I run the tests and verify correctness?
Use the Copilot-generated Makefile to build and execute the test program. The run should confirm correct checksum results for all listed data sizes before you proceed.
What should I check if the GCC build fails on my Arm system?
Confirm that GCC is installed and that the Makefile’s architecture-specific flags are supported. If a flag is not recognized for your CPU, adjust or remove it and rebuild.
What output should I expect when measuring performance?
The test program measures timing around Adler32 calls on random inputs of 1 KB, 10 KB, 100 KB, 1 MB, and 10 MB. You should see per-size timings that you can reuse when evaluating a Neon-based implementation.
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