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refactor: update titles for clarity in CPU hotspots guide
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content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/_index.md

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minutes_to_complete: 30
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who_is_this_for: Software developers and performance engineers who want to identify code hotspots in applications running on Arm Linux systems.
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who_is_this_for: This is an introductory topic for software developers and performance engineers who want to identify code hotspots in applications running on Arm Linux systems.
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learning_objectives:
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- Run the Code Hotspots recipe in Arm Performix

content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/how-to-1.md

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title: Background Information
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title: Understand flame graphs and profiling tools
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content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/how-to-2.md

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title: Setup
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title: Build the example application
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content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/how-to-3.md

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title: Assess Baseline Performance
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title: Profile baseline performance
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content/learning-paths/servers-and-cloud-computing/cpu_hotspot_performix/how-to-4.md

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title: Optimize
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title: Optimize application performance
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![Side-by-side comparison of Mandelbrot fractal output at MAX_ITERATIONS 1024 (left) and 512 (right). Both images show the same level of detail and structure, confirming that the reduced iteration count doesn't compromise visual quality.#center](./comparison.jpg "Image quality comparison: 1024 vs 512 iterations")
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### Optimization 2 - Parallelising the hot function
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### Optimization 2 - Parallelizing the hot function
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The loop in `Mandelbrot::getIterations` has no loop-carried dependencies — each iteration's result is independent of any other. This means you can parallelize the hot function across multiple threads if your CPU has multiple cores.
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