ROCm/aiter
Jul 2026 – Jul 2026
1 Month active
Languages Used
No languages
Technical Skills
CUDAFlyDSLGPU ProgrammingGPU programmingPerformance EngineeringPyTorch
amd-weisun
PROFILE
Amd-weisun
Developed a high-performance Mixture-of-Experts (MoE) token sorting backend for the ROCm/aiter repository, leveraging FlyDSL and CUDA to optimize expert routing and execution reliability. The work included implementing a new dispatch path in the fused MoE, moving dynamic token count logic on-device to stabilize CUDA graph capture, and introducing mechanisms for precise per-call token counting. Expanded benchmarking and test infrastructure enabled robust validation across diverse workloads and token counts, ensuring end-to-end performance verification. Demonstrated depth in GPU programming, backend development, and performance engineering, with collaborative contributions and a focus on automation, regression testing, and infrastructure improvements for production readiness.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
2,970
Activity Months1
Your Network
1985 peopleSame Organization
@amd.com1733
7b30f3f5e26d48061f873d04cc7e1d1f_amdengMember
GunaShekar, AjayMember
aasbodduMember
Abdul Lateef AttarMember
Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
July 2026
2 Commits • 1 Features
Jul 1, 2026July 2026: Delivered a high-performance MoE token sorting backend using FlyDSL for ROCm/aiter, improved graph capture stability for the sorting kernel, and strengthened benchmarking/test infrastructure. These changes enable faster expert routing, more reliable MoE execution, and robust validation across varying token counts and graph replay scenarios.
2 Commits • 1 Features
Jul 1, 2026July 2026: Delivered a high-performance MoE token sorting backend using FlyDSL for ROCm/aiter, improved graph capture stability for the sorting kernel, and strengthened benchmarking/test infrastructure. These changes enable faster expert routing, more reliable MoE execution, and robust validation across varying token counts and graph replay scenarios.
July 2026
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture100.0%
Performance90.0%
AI Usage80.0%
Skills & Technologies
Programming Languages
No languages yet
Technical Skills
CUDAFlyDSLGPU ProgrammingGPU programmingPerformance EngineeringPyTorchPythonbackend developmentperformance optimization
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