ROCm/aiter
Jul 2026 – Jul 2026
1 Month active
Languages Used
No languages
Technical Skills
GEMMGEMM tuningGPU optimizationKernel TuningMachine Learning InfrastructureROCm
zhangxinyuanliuhengyu
PROFILE
Zhangxinyuanliuhengyu
Worked on the ROCm/aiter repository to optimize GEMM configurations for the Qwen3.5 model on the gfx942 GPU, focusing on enhancing throughput and deployment stability. Applied advanced GPU optimization and kernel tuning techniques to refine the GEMM path, which reduced kernel dispatch errors and improved inference performance on ROCm hardware. Addressed reliability by removing references to missing kernels and enabling valid dispatch paths for large model shapes. Collaborated closely with team members to ensure code quality and maintainability. Leveraged expertise in machine learning infrastructure, ROCm, and GPU compute to deliver targeted improvements that support robust and efficient Qwen3.5 deployments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
0
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 ROCm/aiter monthly summary focusing on performance tuning and reliability improvements for Qwen3.5 on gfx942. Key outcomes include a tuned GEMM configuration for gfx942 that enhances throughput and deployment readiness, together with a bug fix that removes references to missing kernels and enables valid dispatch paths for 397B shapes. These efforts reduce dispatch errors, improve inference performance on ROCm hardware, and strengthen the stability of Qwen3.5 deployments.
2 Commits • 1 Features
Jul 1, 2026July 2026 ROCm/aiter monthly summary focusing on performance tuning and reliability improvements for Qwen3.5 on gfx942. Key outcomes include a tuned GEMM configuration for gfx942 that enhances throughput and deployment readiness, together with a bug fix that removes references to missing kernels and enables valid dispatch paths for 397B shapes. These efforts reduce dispatch errors, improve inference performance on ROCm hardware, and strengthen the stability of Qwen3.5 deployments.
July 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage70.0%
Skills & Technologies
Programming Languages
No languages yet
Technical Skills
GEMMGEMM tuningGPU optimizationKernel TuningMachine Learning InfrastructureROCm
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