
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.

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.
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: 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.
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.
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.
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.
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