ROCm/aiter
Jun 2026 – Jul 2026
2 Months active
Languages Used
Python
Technical Skills
Deep LearningGPU ProgrammingMachine LearningMatrix MultiplicationPerformance OptimizationPerformance Tuning
Ao Li
PROFILE
Ao Li
Over a two-month period, contributed to the ROCm/aiter repository by developing and optimizing FP8 GEMM kernels for the gfx1250 GPU, focusing on dense matrix multiplication and robust out-of-bounds handling. Leveraged Python and GPU programming techniques to integrate performance-focused optimizations, including split-k accumulation fixes and preshuffle refactoring, into existing tuning and testing frameworks. Modernized the GEMM kernel stack in FlyDSL with a layout-based API, runtime dispatch, and pointer-based tensor interfaces to improve quantized GEMM throughput and maintainability. Addressed a MoE AOT precompiler activation mapping bug, enhancing cache reliability and aligning runtime and AOT artifacts for production workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
10,011
Activity Months2
Your Network
1985 peopleSame Organization
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Work History
July 2026
2 Commits • 1 Features
Jul 1, 2026Summary for 2026-07: Focused on performance-critical GEMM kernel modernization for gfx1250 in FlyDSL, delivering a robust, layout-based GEMM stack with runtime dispatch, tuning utilities, and pointer-based tensor interfaces to improve quantized GEMM throughput and maintainability. Resolved a MoE AOT precompiler activation mapping bug to prevent cache mismatches, improving reliability of AOT caches and test stability. This work lays foundation for accelerated, scalable GEMM workloads on gfx1250 and aligns runtime and AOT artifacts for reliable performance in production.
2 Commits • 1 Features
Jul 1, 2026Summary for 2026-07: Focused on performance-critical GEMM kernel modernization for gfx1250 in FlyDSL, delivering a robust, layout-based GEMM stack with runtime dispatch, tuning utilities, and pointer-based tensor interfaces to improve quantized GEMM throughput and maintainability. Resolved a MoE AOT precompiler activation mapping bug to prevent cache mismatches, improving reliability of AOT caches and test stability. This work lays foundation for accelerated, scalable GEMM workloads on gfx1250 and aligns runtime and AOT artifacts for reliable performance in production.
July 2026
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter: Delivered a robust FP8 GEMM for gfx1250 with OOB handling and performance-focused optimizations, integrated into the existing tuning and testing frameworks, and hardened the MXScale/bpreshuffle path for production-like workflows. Implemented Ragged-M OOB support for PT-PC FP8 GEMM, improved split-k accumulation semantics, and introduced preshuffle optimizations that reduce per-call latency. Expanded automated tests and CI resilience by gating gfx1250-specific tests and broadening shape coverage. Overall, these efforts deliver higher throughput, improved numerical correctness, and more reliable FP8 GEMM performance on gfx1250, enabling scalable performance improvements for matrix-multiplication workloads. Key achievements include delivering FP8 GEMM for gfx1250 with FP8-based dense GEMM, OOB handling, and integration into FlyDSL MXScale; fixing split-k accumulation with device-scoped atomicfadd and fp32 scratch accumulation to preserve precision across multiple workgroups; hoisting MXScale B-weight preshuffle to reduce latency and adding path-wide improvements to weight and activation reshuffling; integrating MXScale GEMM into the bpreshuffle path for consistent kernel dispatch and performance tuning; enabling Ragged-M OOB support for PTPC FP8 GEMM with tuned M/N warp parameters and runtime shape handling; expanding tests for correctness and performance across shapes and ensuring CI reliability by skipping non-applicable gfx1250 cases. The work demonstrates proficiency in ROCm/aiter, FlyDSL, WMMA, GPU kernel tuning, atomic operations, and performance-focused software engineering.
June 2026
2 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter: Delivered a robust FP8 GEMM for gfx1250 with OOB handling and performance-focused optimizations, integrated into the existing tuning and testing frameworks, and hardened the MXScale/bpreshuffle path for production-like workflows. Implemented Ragged-M OOB support for PT-PC FP8 GEMM, improved split-k accumulation semantics, and introduced preshuffle optimizations that reduce per-call latency. Expanded automated tests and CI resilience by gating gfx1250-specific tests and broadening shape coverage. Overall, these efforts deliver higher throughput, improved numerical correctness, and more reliable FP8 GEMM performance on gfx1250, enabling scalable performance improvements for matrix-multiplication workloads. Key achievements include delivering FP8 GEMM for gfx1250 with FP8-based dense GEMM, OOB handling, and integration into FlyDSL MXScale; fixing split-k accumulation with device-scoped atomicfadd and fp32 scratch accumulation to preserve precision across multiple workgroups; hoisting MXScale B-weight preshuffle to reduce latency and adding path-wide improvements to weight and activation reshuffling; integrating MXScale GEMM into the bpreshuffle path for consistent kernel dispatch and performance tuning; enabling Ragged-M OOB support for PTPC FP8 GEMM with tuned M/N warp parameters and runtime shape handling; expanding tests for correctness and performance across shapes and ensuring CI reliability by skipping non-applicable gfx1250 cases. The work demonstrates proficiency in ROCm/aiter, FlyDSL, WMMA, GPU kernel tuning, atomic operations, and performance-focused software engineering.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture85.0%
Performance80.0%
AI Usage70.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Compiler optimizationDeep LearningGPU ProgrammingGPU programmingLLVMMachine LearningMatrix MultiplicationPerformance OptimizationPerformance TuningPyTorchPythonQuantizationROCmTensor Operationsbackend development
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