ROCm/aiter
Dec 2025 – Jul 2026
5 Months active
Languages Used
CCUDAC++
Technical Skills
Assembly LanguageGPU ProgrammingPerformance Optimizationdata synchronizationlow-level programmingperformance optimization
liyjiang
PROFILE
Liyjiang
Over five months, contributed to the ROCm/aiter repository by developing and optimizing GPU kernels for machine learning workloads, focusing on matrix multiplication and multi-head latent attention decoding. Leveraged C++, CUDA, and Python to deliver new kernel versions, implement performance optimizations, and expand hardware support for MI350 devices. Addressed data synchronization bugs to improve reliability and reproducibility, while introducing features such as kernel-native 4D layouts, C-ABI dispatchers, and comprehensive test suites. Emphasized low-level programming, numerical stability, and cross-architecture compatibility, ensuring robust validation and continuous integration. The work enabled higher throughput, lower latency, and scalable support for advanced ML inference pipelines.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
10Total
Bugs
1
Commits
10
Features
4
Lines of code
3,417
Activity Months5
Your Network
1985 peopleSame Organization
@amd.com1733
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Acim MaravicMember
Adam DickinMember
Pryor, AdamMember
Pryor, AdamMember
Adel JoharMember
Work History
July 2026
6 Commits • 1 Features
Jul 1, 2026July 2026 performance and feature summary for ROCm/aiter MLA v4 nm on gfx950/MI350. Delivered feature-rich kernel improvements, addressed stability and correctness issues, and significantly boosted production-ready performance and GPU utilization. Business impact includes higher inference throughput, lower latency, and more robust deployment on AMD hardware.
6 Commits • 1 Features
Jul 1, 2026July 2026 performance and feature summary for ROCm/aiter MLA v4 nm on gfx950/MI350. Delivered feature-rich kernel improvements, addressed stability and correctness issues, and significantly boosted production-ready performance and GPU utilization. Business impact includes higher inference throughput, lower latency, and more robust deployment on AMD hardware.
July 2026
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter: Delivered MLA v4 Decode Kernel for MI350 (gfx950) with an 18-slot kernarg ABI, including a C-ABI dispatcher, Python wrappers, and a comprehensive test suite. This unlocks high-throughput MLA decoding for MI350 hardware with gqa ratios of 16, 64, and 128, supports attention sink, and enables multi-split KV processing. Also introduced kernel-native 4D layout, sink-aware co, and a streamlined CSV/dispatch schema to improve cross-arch compatibility and future v4 scalability. The update includes robust validation ensuring stability and reproducibility across multiple launches and configurations. Overall, the work expands hardware coverage, improves ML throughput potential, and strengthens the foundation for next-generation MLA workloads.
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter: Delivered MLA v4 Decode Kernel for MI350 (gfx950) with an 18-slot kernarg ABI, including a C-ABI dispatcher, Python wrappers, and a comprehensive test suite. This unlocks high-throughput MLA decoding for MI350 hardware with gqa ratios of 16, 64, and 128, supports attention sink, and enables multi-split KV processing. Also introduced kernel-native 4D layout, sink-aware co, and a streamlined CSV/dispatch schema to improve cross-arch compatibility and future v4 scalability. The update includes robust validation ensuring stability and reproducibility across multiple launches and configurations. Overall, the work expands hardware coverage, improves ML throughput potential, and strengthens the foundation for next-generation MLA workloads.
February 2026
1 Commits • 1 Features
Feb 1, 2026February 2026 ROCm/aiter: Focused on MLA kernel performance optimization for ROCm f8 v3. Implemented minor updates to boost matrix operation throughput. This month included a targeted commit (0a699a77523391eebf8c27dc0b36ae4b807e2e59) titled 'minor update for mla f8 v3 kernel (#1962)'. No major bugs fixed; maintenance and validation performed to ensure stability on f8 v3. Impact: faster ML model workloads on ROCm hardware and better resource utilization. Technologies/skills demonstrated: low-level kernel optimization, performance tuning, ROCm ecosystem, versioned releases for f8 v3.
1 Commits • 1 Features
Feb 1, 2026February 2026 ROCm/aiter: Focused on MLA kernel performance optimization for ROCm f8 v3. Implemented minor updates to boost matrix operation throughput. This month included a targeted commit (0a699a77523391eebf8c27dc0b36ae4b807e2e59) titled 'minor update for mla f8 v3 kernel (#1962)'. No major bugs fixed; maintenance and validation performed to ensure stability on f8 v3. Impact: faster ML model workloads on ROCm hardware and better resource utilization. Technologies/skills demonstrated: low-level kernel optimization, performance tuning, ROCm ecosystem, versioned releases for f8 v3.
February 2026
January 2026
1 Commits
Jan 1, 2026January 2026 monthly summary for ROCm/aiter: Stabilized the MLA F8 V3 data path by fixing a data synchronization bug, leading to improved data integrity and processing reliability across the MLA pipeline. The fix reduces downstream data misalignment and enhances reproducibility for ML workloads.
January 2026
1 Commits
Jan 1, 2026January 2026 monthly summary for ROCm/aiter: Stabilized the MLA F8 V3 data path by fixing a data synchronization bug, leading to improved data integrity and processing reliability across the MLA pipeline. The fix reduces downstream data misalignment and enhances reproducibility for ML workloads.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ROCm/aiter focused on delivering a high-impact kernel optimization and validating collaboration practices.
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for ROCm/aiter focused on delivering a high-impact kernel optimization and validating collaboration practices.
December 2025
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Quality Metrics
Correctness96.0%
Maintainability80.0%
Architecture90.0%
Performance94.0%
AI Usage66.0%
Skills & Technologies
Programming Languages
CC++CUDA
Technical Skills
AssemblyAssembly LanguageBackend DevelopmentC++C/C++ programmingCUDACUDA/HIPGPU ProgrammingHigh Performance ComputingKernel DevelopmentKernel OptimizationPerformance OptimizationPyTorchPythonTesting
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