ROCm/onnxruntime
Jun 2025 – Jun 2025
1 Month active
Languages Used
C#C++JavaJavaScriptPython
Technical Skills
C# developmentC++ developmentJava developmentJavaScript developmentPython developmentgraph optimization
Sunny Shukla
PROFILE
Sunny Shukla
Worked on the ROCm/onnxruntime repository to deliver a targeted optimization for mixed-precision workloads. Developed a graph transform that fuses FP16 initializers with FP32 nodes when FP16 compute is unavailable, reducing unnecessary casting operations and improving throughput. This feature was implemented using C++ and Python, focusing on graph optimization and machine learning techniques. The approach enhanced runtime efficiency by minimizing casting overhead and positioned the framework to better utilize FP16-capable hardware without compromising accuracy. The work strengthened the graph optimization infrastructure, enabling future performance improvements in mixed-precision scenarios while maintaining stability and minimizing risk in production environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,296
Activity Months1
Your Network
2255 peopleSame Organization
@intel.com2109
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered a targeted optimization in ROCm/onnxruntime by introducing a FP16 initializer fusion in the graph transform. This feature fuses FP16 initializers with FP32 nodes when FP16 compute is unavailable, reducing unnecessary casting operations and enabling better throughput on mixed-precision workloads. The change enhances runtime efficiency and positions ROCm/onnxruntime to better leverage FP16-capable hardware without sacrificing accuracy or stability.
1 Commits • 1 Features
Jun 1, 2025June 2025: Delivered a targeted optimization in ROCm/onnxruntime by introducing a FP16 initializer fusion in the graph transform. This feature fuses FP16 initializers with FP32 nodes when FP16 compute is unavailable, reducing unnecessary casting operations and enabling better throughput on mixed-precision workloads. The change enhances runtime efficiency and positions ROCm/onnxruntime to better leverage FP16-capable hardware without sacrificing accuracy or stability.
June 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C#C++JavaJavaScriptPython
Technical Skills
C# developmentC++ developmentJava developmentJavaScript developmentPython developmentgraph optimizationmachine learning
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