CodeLinaro/onnxruntime
Jan 2026 – Jan 2026
1 Month active
Languages Used
C++
Technical Skills
C++ programmingGEMM operationsmultithreadingperformance optimizationquantization
Melike Kaptan
PROFILE
Melike Kaptan
Melike Kaptan developed a performance-focused feature for the CodeLinaro/onnxruntime repository, implementing multithreading in dynamic quantized GEMM operations using C++ and KleidiAI. Her work targeted the qgemm_kleidi path, aiming to increase inference throughput and optimize memory usage by enabling parallel processing across multiple cores. By integrating dynamic quantization techniques and leveraging advanced multithreading, she addressed scalability challenges and laid the groundwork for future enhancements. The implementation included clear traceability through commit and issue linkage, supporting maintainability and review. This contribution demonstrated depth in performance optimization and effective use of C++ and quantization within a collaborative, Git-based workflow.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
950
Activity Months1
Your Network
526 people
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for CodeLinaro/onnxruntime. Focused on delivering a performance-oriented feature: enabling multithreading for dynamic quantized GEMM via KleidiAI, aimed at boosting inference throughput and optimizing memory usage in the qgemm_kleidi path. No major bug fixes recorded this month; the work centers on performance enhancement with clear traceability to issue #26301. This work lays groundwork for further dynamic quantization optimizations and scalability across cores. Technologies: C++, multithreading, dynamic quantization, memory optimization, KleidiAI integration, Git-based workflow.
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for CodeLinaro/onnxruntime. Focused on delivering a performance-oriented feature: enabling multithreading for dynamic quantized GEMM via KleidiAI, aimed at boosting inference throughput and optimizing memory usage in the qgemm_kleidi path. No major bug fixes recorded this month; the work centers on performance enhancement with clear traceability to issue #26301. This work lays groundwork for further dynamic quantization optimizations and scalability across cores. Technologies: C++, multithreading, dynamic quantization, memory optimization, KleidiAI integration, Git-based workflow.
January 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++ programmingGEMM operationsmultithreadingperformance optimizationquantization
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