Profile GPT-2 inference with the Arm Performix Instruction Mix recipe
Introduction
Understand profiling with Arm Performix Instruction Mix
Set up and run a GPT-2 baseline
Find GPT-2 hotspots and profile with the Arm Performix Instruction Mix recipe
(Optional) Optimize matmul with vector intrinsics
Compare Neon and SVE with the Arm Performix Instruction Mix recipe
Accelerate execution with KleidiAI
Next Steps

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Complete the challenge

src/kernels/matmul_user.cpp is your editable implementation file. The baseline behavior in this file is scalar, and the build uses -O2 -g, so compiler optimization is enabled but vector hardware is still underused in the hot loop.

Use the profiling evidence from Performix to implement your own Neon or SVE intrinsics in src/kernels/matmul_user.cpp, then rebuild and profile gpt2_user.

Note

Focus on the accumulation loop in matmul_user (acc += row[j] * x[j];). Think about lane utilization, loop unrolling, and handling the tail when the input width is not an exact multiple of the vector width.

Rebuild after your edits:

    

        
        
cmake -S . -B build -DBUILD_USER_MATMUL=ON
cmake --build build --parallel

Then, profile the build/gpt2_user binary with the same runtime arguments and compare the Instruction Mix and throughput against baseline.

Example solutions are available in:

  • src/kernels/matmul_neon.cpp
  • src/kernels/matmul_sve.cpp

You can use AGENTS.md in the GPT-2 example repository for guided learning support.

Use the Arm MCP Server with Performix

You can also use an MCP-compatible coding assistant, such as GitHub Copilot or Codex, with the Arm MCP Server. This gives the assistant direct tool access to run Performix recipes on your remote Arm target and create a faster feedback loop while you iterate on matmul_user.

For setup details, see Automate x86-to-Arm application migration using Arm MCP Server .

Install Docker if needed, then pull the MCP server image:

    

        
        
docker pull armlimited/arm-mcp:latest

To allow Performix access to remote targets from inside the container, mount your workspace plus SSH key and known hosts in your Codex MCP configuration (example ~/.codex/config.toml):

    

        
        [mcp_servers.arm-mcp]
command = "docker"
args = [
	"run",
	"--rm",
	"-i",
	"-v", "/path/to/your/workspace:/workspace",
	"-v", "/path/to/your/ssh/private_key:/run/keys/ssh-key.pem:ro",
	"-v", "/path/to/your/ssh/known_hosts:/run/keys/known_hosts:ro",
	"armlimited/arm-mcp"
]

Restart your coding assistant, then prompt it to run Performix Instruction Mix and Code Hotspots on your gpt2_user binary and suggest Arm intrinsics improvements.

Image Alt Text:Screenshot of a coding assistant prompt configured to use Arm MCP Server tools for running Performix recipes and analyzing <code>matmul_user</code> optimization opportunities in the GPT-2 workload.Coding assistant prompt for Performix analysis through Arm MCP Server

What you’ve accomplished and what’s next

You’ve now optionally implemented and profiled a custom matmul_user kernel using the same workflow you used for baseline analysis.

Next, you’ll compare Instruction Mix and throughput across scalar, Neon, SVE, and KleidiAI variants.

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