CodeLinaro/onnxruntime
Jan 2026 – Feb 2026
2 Months active
Languages Used
C++
Technical Skills
C++ programmingGEMM operationsmultithreadingperformance optimizationquantizationC++ development
Melike Kaptan
PROFILE
Melike Kaptan
Worked on performance engineering for the CodeLinaro/onnxruntime repository, delivering two features over two months focused on optimizing inference throughput and efficiency. Developed multithreading support for dynamic quantized GEMM operations using C++ and KleidiAI, enabling scalable parallel processing and improved memory usage in the qgemm_kleidi path. Subsequently, optimized the GridSample bilinear interpolation operator by precomputing neighbor indices and weights, reusing them across channels, and introducing ARM NEON vectorization for enhanced throughput on ARM CPUs. Emphasized backward compatibility and measurable runtime improvements, applying skills in C++ development, algorithm design, parallel programming, and vectorization to address CPU-bound inference bottlenecks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
1,300
Activity Months2
Your Network
611 peopleSame Organization
@arm.com539
Work History
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for CodeLinaro/onnxruntime. Primary focus: performance optimization of the GridSample bilinear interpolation operator. Delivered a plan-based optimization that precomputes neighbor indices and interpolation weights and reused them across channels, reducing per-output-pixel work. Introduced optional ARM NEON vectorization to boost throughput on ARM CPUs without impacting API. Demonstrated measurable runtime improvements in common workloads, with both single-threaded and multi-threaded configurations, contributing to lower latency in CPU-bound inference such as computer vision pipelines. No separate bug fixes were logged this month; changes are backward-compatible with existing ONNX Runtime workflows and emphasize efficiency, throughput, and scalability.
1 Commits • 1 Features
Feb 1, 2026February 2026 monthly summary for CodeLinaro/onnxruntime. Primary focus: performance optimization of the GridSample bilinear interpolation operator. Delivered a plan-based optimization that precomputes neighbor indices and interpolation weights and reused them across channels, reducing per-output-pixel work. Introduced optional ARM NEON vectorization to boost throughput on ARM CPUs without impacting API. Demonstrated measurable runtime improvements in common workloads, with both single-threaded and multi-threaded configurations, contributing to lower latency in CPU-bound inference such as computer vision pipelines. No separate bug fixes were logged this month; changes are backward-compatible with existing ONNX Runtime workflows and emphasize efficiency, throughput, and scalability.
February 2026
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.
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.
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++ developmentC++ programmingGEMM operationsalgorithm designmultithreadingparallel programmingperformance optimizationquantizationvectorization
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