madeline-underwood/arm-learning-paths
Oct 2025 – Oct 2025
1 Month active
Languages Used
CMakefileMarkdown
Technical Skills
Arm StreamlineEmbedded SystemsLinux Kernel DevelopmentPerformance AnalysisProfiling
Yahya Abouelseoud
PROFILE
Yahya Abouelseoud
Developed a comprehensive Linux kernel profiling learning path for the madeline-underwood/arm-learning-paths repository, focusing on performance analysis and optimization using Arm Streamline. The work guided developers through building and profiling both out-of-tree and in-tree kernel modules, enabling identification of performance bottlenecks in embedded systems. Advanced techniques were introduced by leveraging the Statistical Profiling Extension (SPE) for deeper kernel analysis. The learning path was documented in Markdown and supported with C and Makefile examples, providing clear workflows for onboarding and performance engineering. This contribution enhanced the platform’s ability to support developers in profiling and optimizing Linux kernel modules for embedded environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
633
Activity Months1
Your Network
672 peopleSame Organization
@arm.com538
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Delivered a comprehensive Linux kernel profiling learning path using Arm Streamline, enabling developers to identify performance bottlenecks in both out-of-tree and in-tree kernel modules, and to leverage the Statistical Profiling Extension (SPE) for deeper analysis. This accelerates performance optimization, improves onboarding, and supports our platform's performance engineering goals.
1 Commits • 1 Features
Oct 1, 2025Month: 2025-10 — Delivered a comprehensive Linux kernel profiling learning path using Arm Streamline, enabling developers to identify performance bottlenecks in both out-of-tree and in-tree kernel modules, and to leverage the Statistical Profiling Extension (SPE) for deeper analysis. This accelerates performance optimization, improves onboarding, and supports our platform's performance engineering goals.
October 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CMakefileMarkdown
Technical Skills
Arm StreamlineEmbedded SystemsLinux Kernel DevelopmentPerformance AnalysisProfiling
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