Who is this for?
This is an introductory topic for C/C++ developers who want to learn how to vectorize code using Arm SVE intrinsics, guided by an AI coding assistant connected to the Arm MCP server.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Optimize C code by learning from an AI assistant
- Establish a reproducible performance baseline for a scalar Adler-32 implementation written in C
- Apply the NMAX technique to defer modulo operations and improve scalar throughput
- Implement an SVE version of Adler-32 using svwhilelt, svdot, and svaddv
- Validate correctness and measure the performance improvement of the SVE implementation
Prerequisites
Before starting, you will need the following:
- An AI coding assistant configured with the Arm MCP server, such as Kiro CLI, GitHub Copilot, or Gemini CLI. For setup instructions, see the
Arm MCP server Learning Path
.
- An Arm Neoverse server running Ubuntu 26.04 with SVE support (for example, AWS Graviton3 or later, Google Axion, or Microsoft Cobalt 100)
- Basic familiarity with C programming