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Qiujiao Wu

PROFILE

Qiujiao Wu

Qiujiao Wu developed high-performance features across aobolensk/openvino and intel/onnxruntime, focusing on optimizing deep learning model execution and profiling. She refactored reshape_2d in openvino to use block-based parallelization with Intel TBB, improving memory locality and reducing transpose time for large models. In onnxruntime, she enhanced the WebNN backend by introducing session-scoped caching for opSupportLimits and adding detailed tracing for data transfer, which improved performance monitoring and reduced redundant API calls. Her work, primarily in C++ and JavaScript, demonstrated strong skills in algorithm design, parallel computing, and backend development, delivering measurable improvements in inference speed and observability.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

4Total
Bugs
0
Commits
4
Features
4
Lines of code
379
Activity Months3

Work History

July 2025

2 Commits • 2 Features

Jul 1, 2025

July 2025 monthly performance-focused delivery across two repositories. Delivered two major features aimed at reducing hot-path overhead and improving backend observability. No explicit major bugs fixed were documented in the provided data for this period. The work emphasizes business value through lower inference latency, improved throughput on hot paths, and better performance monitoring.

May 2025

1 Commits • 1 Features

May 1, 2025

May 2025: Delivered Performance Profiling Enhancements for ORT Web in ROCm/onnxruntime. Implemented trace event control to enable finer-grained profiling and faster identification of performance bottlenecks in ORT Web workloads, supporting targeted optimizations and improved user experience. No major bug fixes recorded this month. Overall impact: improved observability and faster iteration cycles for performance improvements; demonstrated proficiency with tracing instrumentation and profiling workflows.

April 2025

1 Commits • 1 Features

Apr 1, 2025

Summary for 2025-04: Delivered a high-performance 2D transpose feature for large data in aobolensk/openvino. Refactored reshape_2d to use block-based parallelization with tbb::parallel_for2d_dynamic to improve memory locality and reduce transpose time on large models. This work was implemented via commit b0c7c1b7cb28145fb29ebdc510e177a2aaa6655a: Update transpose reshape_2d algorithm to block structure (#29830). No major bugs reported this period. Technologies/skills demonstrated include C++, Intel TBB, and memory-locality optimization. Business impact: faster model loading and inference for large-scale deployments, enabling higher throughput and better user experience.

Activity

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Quality Metrics

Correctness95.0%
Maintainability80.0%
Architecture85.0%
Performance95.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

C++JavaScriptTypeScript

Technical Skills

Algorithm DesignC++Deep Learning FrameworksJavaScriptNumerical ComputingParallel ComputingPerformance OptimizationTensor managementWebNNbackend developmentperformance optimizationperformance profilingweb development

Repositories Contributed To

3 repos

Overview of all repositories you've contributed to across your timeline

aobolensk/openvino

Apr 2025 Jul 2025
2 Months active

Languages Used

C++

Technical Skills

Algorithm DesignParallel ComputingPerformance OptimizationC++Deep Learning FrameworksNumerical Computing

ROCm/onnxruntime

May 2025 May 2025
1 Month active

Languages Used

C++JavaScriptTypeScript

Technical Skills

C++JavaScriptperformance profilingweb development

intel/onnxruntime

Jul 2025 Jul 2025
1 Month active

Languages Used

C++TypeScript

Technical Skills

Tensor managementWebNNbackend developmentperformance optimization

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