ROCm/aiter
Nov 2025 – Jul 2026
4 Months active
Languages Used
Python
Technical Skills
Deep LearningGPU ProgrammingMachine LearningPerformance OptimizationPyTorchTriton
Lukasz Burzawa
PROFILE
Lukasz Burzawa
Worked on the ROCm/aiter repository to deliver advanced performance optimizations for Mixture of Experts (MoE) workloads, focusing on GPU programming and deep learning frameworks. Over four months, developed and refactored Triton kernels to support mixed precision and new matrix operation layouts, targeting architectures like GFX1250. Enhanced memory management and decoding efficiency, introduced persistent kernels, and improved throughput and latency for matrix-heavy tasks. Integrated robust unit testing and CI improvements to ensure reliability. Leveraged Python and Triton to implement architecture-aware enhancements, enabling scalable, production-ready MoE performance with better resource utilization and maintainability across both Triton and Gluon frameworks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
5
Lines of code
6,340
Activity Months4
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
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly work summary for ROCm/aiter: Focused on performance optimization of the Persistent MoE A8W4 decode kernel on GFX1250, deployment of a persistent kernel, and TP4 tuning with robust testing. Updated kernel to the new TDM API, added extensive unit tests, and improved CI reliability. The work delivered tangible performance and stability improvements for MoE workloads on GFX1250, enabling higher throughput with lower latency and better resource utilization.
1 Commits • 1 Features
Jul 1, 2026July 2026 monthly work summary for ROCm/aiter: Focused on performance optimization of the Persistent MoE A8W4 decode kernel on GFX1250, deployment of a persistent kernel, and TP4 tuning with robust testing. Updated kernel to the new TDM API, added extensive unit tests, and improved CI reliability. The work delivered tangible performance and stability improvements for MoE workloads on GFX1250, enabling higher throughput with lower latency and better resource utilization.
July 2026
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter. Key feature delivered: Moe a8w4 Kernel Performance and Memory Management Enhancements, including decoding optimization, improved memory management for shared memory, and a reorganized matrix operation layout to boost compute efficiency. The implementation was committed as 6aeba412fa057a3d1bf9e1811ddecc9e9cb2af7a with message 'Moe a8w4 optimization for decode (#3504)'. No major bugs fixed this month. Overall impact: faster decoding, better memory utilization, and improved throughput for matrix-heavy workloads, contributing to lower latency and higher compute density. Technologies demonstrated: kernel-level optimization, memory management, shared memory usage, matrix operation optimization, and rigorous version control.
June 2026
1 Commits • 1 Features
Jun 1, 2026June 2026 monthly summary for ROCm/aiter. Key feature delivered: Moe a8w4 Kernel Performance and Memory Management Enhancements, including decoding optimization, improved memory management for shared memory, and a reorganized matrix operation layout to boost compute efficiency. The implementation was committed as 6aeba412fa057a3d1bf9e1811ddecc9e9cb2af7a with message 'Moe a8w4 optimization for decode (#3504)'. No major bugs fixed this month. Overall impact: faster decoding, better memory utilization, and improved throughput for matrix-heavy workloads, contributing to lower latency and higher compute density. Technologies demonstrated: kernel-level optimization, memory management, shared memory usage, matrix operation optimization, and rigorous version control.
May 2026
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary for ROCm/aiter focused on targeted MoE performance optimizations for gfx1250 and Gluon integration. Delivered architecture-aware enhancements that improve matrix multiplication scaling and memory management, enabling faster MoE workloads on next-gen GPUs while reducing memory pressure. The work strengthens cross-framework MoE paths (Triton and Gluon) for scalable, production-ready performance.
3 Commits • 2 Features
May 1, 2026May 2026 monthly summary for ROCm/aiter focused on targeted MoE performance optimizations for gfx1250 and Gluon integration. Delivered architecture-aware enhancements that improve matrix multiplication scaling and memory management, enabling faster MoE workloads on next-gen GPUs while reducing memory pressure. The work strengthens cross-framework MoE paths (Triton and Gluon) for scalable, production-ready performance.
May 2026
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025 ROCm/aiter performance month focused on MoE routing and Triton kernel optimizations to support GPTOSS shapes with mixed precision. Delivered significant throughput gains via new kernel definitions, routing improvements, and fused kernels, with a refactor to consolidate Triton MoE code inside the aiter repo. Fused routing kernels for small batches and batch-size tuning to 1024 reduced routing latency and improved end-to-end performance on MoE workloads.
November 2025
2 Commits • 1 Features
Nov 1, 2025November 2025 ROCm/aiter performance month focused on MoE routing and Triton kernel optimizations to support GPTOSS shapes with mixed precision. Delivered significant throughput gains via new kernel definitions, routing improvements, and fused kernels, with a refactor to consolidate Triton MoE code inside the aiter repo. Fused routing kernels for small batches and batch-size tuning to 1024 reduced routing latency and improved end-to-end performance on MoE workloads.
Activity
Loading activity data...
Quality Metrics
Correctness82.8%
Maintainability80.0%
Architecture85.8%
Performance94.2%
AI Usage51.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
Deep LearningDeep learning frameworksGPU ProgrammingGPU programmingMachine LearningMatrix OperationsMatrix operationsMemory managementMoEParallel ComputingPerformance OptimizationPerformance optimizationPyTorchPythonTriton
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline