ROCm/aiter
Aug 2025 – Dec 2025
3 Months active
Languages Used
AssemblyCSVPythonC++
Technical Skills
GPU programmingLow-level programmingPerformance optimizationGPU ComputingKernel ConfigurationLow-level Programming
junxiaguo
PROFILE
Junxiaguo
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
599
Activity Months3
Your Network
1526 people
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 delivered a FP8 Blockscale assembly kernel for Matrix Multiplication in ROCm/aiter, including testing and validation scaffolds, configuration updates, and new tests to validate kernel functionality. The core change is captured in the commit 716e111a9710bb1fd3bca26063fd49c0e3871e19. The work enhances performance for FP8-based DL workloads by enabling faster GEMM operations and providing end-to-end validation tooling. No major bugs were fixed this month; the focus was on feature delivery, correctness validation, and integration with the build/test pipeline.
1 Commits • 1 Features
Dec 1, 2025December 2025 delivered a FP8 Blockscale assembly kernel for Matrix Multiplication in ROCm/aiter, including testing and validation scaffolds, configuration updates, and new tests to validate kernel functionality. The core change is captured in the commit 716e111a9710bb1fd3bca26063fd49c0e3871e19. The work enhances performance for FP8-based DL workloads by enabling faster GEMM operations and providing end-to-end validation tooling. No major bugs were fixed this month; the focus was on feature delivery, correctness validation, and integration with the build/test pipeline.
December 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ROCm/aiter focusing on performance-oriented kernel optimizations and related configuration/test updates. Key achievements and deliverables: - Implemented and delivered an enabling splitK0 assembly optimization for the f4gemm_bf16_per1x32Fp4 kernel, including removal of the KSplit variant for 128x512 tiles and updating corresponding kernel configuration references. This is aimed at improving matrix multiply performance for BF16 workloads on supported GPUs. - Expanded tile-shape support for the splitK0 optimization to include 256x256 and 128x512 configurations, enabling broader kernel tuning and performance opportunities. - Updated the kernel configuration data to reflect splitK differences, including updating f4gemm_bf16_per1x32Fp4.csv with kernels differing in splitk, ensuring consistency between code, tests, and metrics. - Updated tests and validation artifacts to align with the new kernel configurations and splitK values, maintaining test coverage for the new optimization paths. Top 3-5 achievements: 1) Enable splitK0 asm for 256x256 and 128x512 tile shapes (#928) via commit ad4922e1ee21498a17ac8b9575a1a543731b8e98. 2) Synchronize kernel configuration data with splitK variants in f4gemm_bf16_per1x32Fp4.csv (#965) via commit 62d9ddb7ba8219f483ec3739f9c4a0fb4cb95562. 3) Update tests to reflect new kernel configurations and splitK values, improving validation of performance-sensitive paths. 4) Documentation and configuration hygiene improvements through consistent CSV updates and test alignment. Overall impact and accomplishments: - Strengthened performance capability for BF16 f4gemm workloads on ROCm/aiter by enabling and validating splitK0 assembly optimizations across multiple tile shapes. - Improved maintainability and traceability of kernel configuration changes via CSV updates and associated tests, supporting future tuning efforts. - Demonstrated cross-team collaboration readiness through coherent commit messages and alignment between code, tests, and data. Technologies/skills demonstrated: - Assembly-level optimization and kernel tuning (splitK0 path) - Kernel configuration management and data alignment (CSV updates) - Test validation and data-driven verification for performance paths - Version control discipline and documentation of optimization work
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 monthly summary for ROCm/aiter focusing on performance-oriented kernel optimizations and related configuration/test updates. Key achievements and deliverables: - Implemented and delivered an enabling splitK0 assembly optimization for the f4gemm_bf16_per1x32Fp4 kernel, including removal of the KSplit variant for 128x512 tiles and updating corresponding kernel configuration references. This is aimed at improving matrix multiply performance for BF16 workloads on supported GPUs. - Expanded tile-shape support for the splitK0 optimization to include 256x256 and 128x512 configurations, enabling broader kernel tuning and performance opportunities. - Updated the kernel configuration data to reflect splitK differences, including updating f4gemm_bf16_per1x32Fp4.csv with kernels differing in splitk, ensuring consistency between code, tests, and metrics. - Updated tests and validation artifacts to align with the new kernel configurations and splitK values, maintaining test coverage for the new optimization paths. Top 3-5 achievements: 1) Enable splitK0 asm for 256x256 and 128x512 tile shapes (#928) via commit ad4922e1ee21498a17ac8b9575a1a543731b8e98. 2) Synchronize kernel configuration data with splitK variants in f4gemm_bf16_per1x32Fp4.csv (#965) via commit 62d9ddb7ba8219f483ec3739f9c4a0fb4cb95562. 3) Update tests to reflect new kernel configurations and splitK values, improving validation of performance-sensitive paths. 4) Documentation and configuration hygiene improvements through consistent CSV updates and test alignment. Overall impact and accomplishments: - Strengthened performance capability for BF16 f4gemm workloads on ROCm/aiter by enabling and validating splitK0 assembly optimizations across multiple tile shapes. - Improved maintainability and traceability of kernel configuration changes via CSV updates and associated tests, supporting future tuning efforts. - Demonstrated cross-team collaboration readiness through coherent commit messages and alignment between code, tests, and data. Technologies/skills demonstrated: - Assembly-level optimization and kernel tuning (splitK0 path) - Kernel configuration management and data alignment (CSV updates) - Test validation and data-driven verification for performance paths - Version control discipline and documentation of optimization work
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 ROCm/aiter monthly summary: Delivered feature work to extend the f4gemm kernel with additional tile-size support, broadening matrix dimension coverage and enabling potential performance improvements across workloads. Implemented via a data-driven configuration workflow: updated a tile-size CSV and generated corresponding .co files to reflect new configurations. The change was implemented and verified in a single commit (85749d37e268cfb1f2f321352ba2b77564ff81da), co-authored by Lingpeng Jin, demonstrating end-to-end execution from configuration to code generation and collaboration across teams. This lays the groundwork for wider applicability of f4gemm in ROCm/aiter and improves maintainability through automated configuration management.
1 Commits • 1 Features
Aug 1, 2025August 2025 ROCm/aiter monthly summary: Delivered feature work to extend the f4gemm kernel with additional tile-size support, broadening matrix dimension coverage and enabling potential performance improvements across workloads. Implemented via a data-driven configuration workflow: updated a tile-size CSV and generated corresponding .co files to reflect new configurations. The change was implemented and verified in a single commit (85749d37e268cfb1f2f321352ba2b77564ff81da), co-authored by Lingpeng Jin, demonstrating end-to-end execution from configuration to code generation and collaboration across teams. This lays the groundwork for wider applicability of f4gemm in ROCm/aiter and improves maintainability through automated configuration management.
August 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture85.0%
Performance90.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
AssemblyC++CSVPython
Technical Skills
Assembly LanguageCUDADeep LearningGPU ComputingGPU programmingKernel ConfigurationLow-level ProgrammingLow-level programmingPerformance OptimizationPerformance optimizationTesting
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