ROCm/aiter
Jul 2025 – Jan 2026
7 Months active
Languages Used
C++CUDAPythonCSV
Technical Skills
Assembly Kernel TuningC++CUDAGPU ComputingMachine LearningMatrix Multiplication
yzhou103
PROFILE
Yzhou103
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
45Total
Bugs
5
Commits
45
Features
17
Lines of code
40,968
Activity Months7
Your Network
1526 people
Work History
January 2026
8 Commits • 5 Features
Jan 1, 2026January 2026 ROCm/aiter monthly delivery focused on performance, robustness, and maintainability. The team delivered targeted optimizations and architecture refinements, improving runtime efficiency, scalability, and observability while preparing ground for future MoE work and larger N workloads. Highlights include kernel and memory-management improvements, enhanced tuning tooling, and configurable diagnostics that jointly increase business value through faster runtimes and more reliable deployments.
8 Commits • 5 Features
Jan 1, 2026January 2026 ROCm/aiter monthly delivery focused on performance, robustness, and maintainability. The team delivered targeted optimizations and architecture refinements, improving runtime efficiency, scalability, and observability while preparing ground for future MoE work and larger N workloads. Highlights include kernel and memory-management improvements, enhanced tuning tooling, and configurable diagnostics that jointly increase business value through faster runtimes and more reliable deployments.
January 2026
December 2025
7 Commits • 4 Features
Dec 1, 2025Month: 2025-12 — ROCm/aiter monthly summary emphasizing business value and technical achievements. Key accomplishments include delivering GEMM Tuning Enhancements for bf16 with bias handling and multi-library backends; fixes to prebuild tuning dictionary generation; fused qk rope cat and cache for multi-layer attention; refined FMoE tuner profile logging and tuning result management; and MP tuner memory access fault handling with timeouts and iteration controls. These work items improved performance, stability, and scalability of tuning and model execution across backends, enabling faster, more reliable experimentation and deployment.
December 2025
7 Commits • 4 Features
Dec 1, 2025Month: 2025-12 — ROCm/aiter monthly summary emphasizing business value and technical achievements. Key accomplishments include delivering GEMM Tuning Enhancements for bf16 with bias handling and multi-library backends; fixes to prebuild tuning dictionary generation; fused qk rope cat and cache for multi-layer attention; refined FMoE tuner profile logging and tuning result management; and MP tuner memory access fault handling with timeouts and iteration controls. These work items improved performance, stability, and scalability of tuning and model execution across backends, enabling faster, more reliable experimentation and deployment.
November 2025
9 Commits • 1 Features
Nov 1, 2025Month: 2025-11. ROCm/aiter focused on stabilizing and modernizing the GEMM tuner for bf16 workloads, with targeted improvements to configuration management and tuner reliability. Key outcomes include removal of ROCBLAS to simplify solution mapping, updates to bf16 tuning documentation and data files, and refactored configuration handling to reduce misconfigurations. The work enhances stability, reliability, and performance for model training workloads, delivering more predictable GEMM behavior and easier long-term maintenance.
9 Commits • 1 Features
Nov 1, 2025Month: 2025-11. ROCm/aiter focused on stabilizing and modernizing the GEMM tuner for bf16 workloads, with targeted improvements to configuration management and tuner reliability. Key outcomes include removal of ROCBLAS to simplify solution mapping, updates to bf16 tuning documentation and data files, and refactored configuration handling to reduce misconfigurations. The work enhances stability, reliability, and performance for model training workloads, delivering more predictable GEMM behavior and easier long-term maintenance.
November 2025
October 2025
8 Commits • 2 Features
Oct 1, 2025Concise monthly summary for ROCm/aiter (2025-10). Delivered end-to-end kernel and CI improvements across KV data path and GEMM workloads, enhancing throughput, stability, and PyTorch compatibility while strengthening release readiness across the AMD ROCm stack.
October 2025
8 Commits • 2 Features
Oct 1, 2025Concise monthly summary for ROCm/aiter (2025-10). Delivered end-to-end kernel and CI improvements across KV data path and GEMM workloads, enhancing throughput, stability, and PyTorch compatibility while strengthening release readiness across the AMD ROCm stack.
September 2025
6 Commits • 2 Features
Sep 1, 2025Month 2025-09 — ROCm/aiter: Focused on delivering a more capable and reliable tuning pipeline with clear performance visibility, while advancing kernel tuning for GEMM and FMOE workloads. Key features and fixes below, aligned to business value and technical quality. Key features delivered: - Tuner enhancements and results reporting: added errRatio parameter, profile saving (--profile_file), and in-result display of tflops/bandwidth; refined tuning configurations; improved reliability in profiling/test execution and result reporting; introduced base_tuner file; addressed tuner/interface refinements (e.g., a4w4_gemm tuning adjustments). - Tuning results visibility: implemented a tuning summary for shapes that are tuned successfully or failed, improving traceability and decision-making. Major bugs fixed: - Corrected profiling time calculation when tuning with splitK enabled, improving accuracy of performance metrics. - Fixed tune result handling for a8w8_blockscale_bpreshuffle and related shapes; ensured updated tflops, bandwidth, and errRatio metrics are consistently reported. GEMM and FMOE kernel tuning improvements: - GEMM: improved assembly split-K tuning and error ratio calculations for batched GEMM; addressed splitK tuning in gemm_a4w4_blockscale. - FMOE: refactored tuner for FMOE, added new parameters, and extended gfx950 tuning support. Overall impact and accomplishments: - Enhanced tuning reliability, faster feedback loops, and richer performance reporting, enabling data-driven optimization and faster rollout of tuned configurations. - Improved maintainability and code quality through refactors and lint/compliance fixes; clearer separation of tuning concerns, with a documented base_tuner and streamlined interfaces. Technologies/skills demonstrated: - Python/C++-based tuning framework development, parameterization, and configuration management. - Performance profiling, benchmarking, and results reporting (tflops, bandwidth, errRatio). - Code refactoring, cleanups, lint fixes, and collaboration (co-authored commits). - Cross-workload tuning support (GEMM and FMOE) with gfx950 tuning context.
6 Commits • 2 Features
Sep 1, 2025Month 2025-09 — ROCm/aiter: Focused on delivering a more capable and reliable tuning pipeline with clear performance visibility, while advancing kernel tuning for GEMM and FMOE workloads. Key features and fixes below, aligned to business value and technical quality. Key features delivered: - Tuner enhancements and results reporting: added errRatio parameter, profile saving (--profile_file), and in-result display of tflops/bandwidth; refined tuning configurations; improved reliability in profiling/test execution and result reporting; introduced base_tuner file; addressed tuner/interface refinements (e.g., a4w4_gemm tuning adjustments). - Tuning results visibility: implemented a tuning summary for shapes that are tuned successfully or failed, improving traceability and decision-making. Major bugs fixed: - Corrected profiling time calculation when tuning with splitK enabled, improving accuracy of performance metrics. - Fixed tune result handling for a8w8_blockscale_bpreshuffle and related shapes; ensured updated tflops, bandwidth, and errRatio metrics are consistently reported. GEMM and FMOE kernel tuning improvements: - GEMM: improved assembly split-K tuning and error ratio calculations for batched GEMM; addressed splitK tuning in gemm_a4w4_blockscale. - FMOE: refactored tuner for FMOE, added new parameters, and extended gfx950 tuning support. Overall impact and accomplishments: - Enhanced tuning reliability, faster feedback loops, and richer performance reporting, enabling data-driven optimization and faster rollout of tuned configurations. - Improved maintainability and code quality through refactors and lint/compliance fixes; clearer separation of tuning concerns, with a documented base_tuner and streamlined interfaces. Technologies/skills demonstrated: - Python/C++-based tuning framework development, parameterization, and configuration management. - Performance profiling, benchmarking, and results reporting (tflops, bandwidth, errRatio). - Code refactoring, cleanups, lint fixes, and collaboration (co-authored commits). - Cross-workload tuning support (GEMM and FMOE) with gfx950 tuning context.
September 2025
August 2025
5 Commits • 2 Features
Aug 1, 2025August 2025 ROCm/aiter monthly summary focusing on robust performance tuning enhancements and autotuning improvements for large-scale workloads.
August 2025
5 Commits • 2 Features
Aug 1, 2025August 2025 ROCm/aiter monthly summary focusing on robust performance tuning enhancements and autotuning improvements for large-scale workloads.
July 2025
2 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Delivered substantial GEMM kernel performance and maintainability improvements in ROCm/aiter. Implemented parallel tuning for CK GEMM kernels, added logging for tuned shapes to improve observability, and completed lint fixes across GEMM operations to improve reliability. Enabled and integrated the gemm_a4w4 assembly kernel to tune splitK, and tested compatibility with existing block-scale kernels to enable dynamic parallelism and a broader optimization search space.
2 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Delivered substantial GEMM kernel performance and maintainability improvements in ROCm/aiter. Implemented parallel tuning for CK GEMM kernels, added logging for tuned shapes to improve observability, and completed lint fixes across GEMM operations to improve reliability. Enabled and integrated the gemm_a4w4 assembly kernel to tune splitK, and tested compatibility with existing block-scale kernels to enable dynamic parallelism and a broader optimization search space.
July 2025
Activity
Loading activity data...
Quality Metrics
Correctness83.4%
Maintainability80.8%
Architecture80.2%
Performance81.6%
AI Usage35.2%
Skills & Technologies
Programming Languages
C++CSVCUDAPython
Technical Skills
Algorithm OptimizationAssembly Kernel TuningAssembly ProgrammingBuild SystemsC++CUDACUDA Kernel DevelopmentCUDA ProgrammingCode CleanupCode GenerationCode RefactoringData AnalysisData ProcessingDebuggingDeep Learning
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline