Identify code hotspots using Arm Performix through the Arm MCP Server

About this Learning Path

Who is this for?

This is an advanced topic for developers who want to use AI-powered tools to automate performance profiling and optimization of C++ applications on Arm Neoverse servers.

What will you learn?

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

• Describe how the Arm Performix tool in the Arm MCP Server enables AI-driven profiling workflows
• Configure a GitHub Copilot prompt file to run the Code Hotspots recipe on a remote Arm target
• Use an AI agent to interpret flame graph results and identify the hottest functions in a C++ application
• Apply AI-suggested optimizations to reduce application runtime on Arm Neoverse

Prerequisites

Before starting, you will need the following:

• Completion of the Automate x86-to-Arm application migration using Arm MCP Server Learning Path, or equivalent familiarity with configuring the Arm MCP Server in an AI coding assistant
• Access to an Arm-based cloud instance running Linux, such as an AWS Graviton3 instance
• Access to Arm Performix configured with the remote Arm target. See the Arm Performix install guide for setup instructions
• Basic understanding of C++