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content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-1.md

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title: Understand profiling with Arm Performix Instruction Mix
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description: See how the Arm Performix Instruction Mix recipe combines static disassembly and runtime sampling to reveal scalar and vector execution patterns in GPT-2 inference.
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content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-2.md

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title: Set up and run a GPT-2 baseline
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description: Prepare an Arm Linux target, export GPT-2 Medium model assets, review the baseline matrix multiplication path, and run the scalar GPT-2 inference binary.
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content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-3.md

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title: Find GPT-2 hotspots and profile with the Arm Performix Instruction Mix recipe
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description: Use Arm Performix Code Hotspots and Instruction Mix recipes to confirm that scalar matrix multiplication dominates GPT-2 baseline runtime.
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content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-4.md

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title: (Optional) Optimize matmul with vector intrinsics
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description: Optionally implement a custom Neon or SVE matrix multiplication kernel and profile it with Arm Performix or the Arm MCP Server.
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content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-5.md

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title: Compare Neon and SVE with the Arm Performix Instruction Mix recipe
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description: Compare Neon and SVE GPT-2 kernels with Arm Performix Instruction Mix data and throughput results to verify vectorized execution.
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content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-6.md

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title: Accelerate execution with KleidiAI
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description: Review the KleidiAI SVE matrix multiplication path, compare it with scalar and intrinsic kernels, and measure GPT-2 throughput gains.
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