ROCm/aiter
Dec 2025 – Mar 2026
3 Months active
Languages Used
COPythonAssemblyC++
Technical Skills
GPU ProgrammingKernel DevelopmentPerformance OptimizationAPI DevelopmentDeep LearningMachine Learning
Sergey Solovyev
PROFILE
Sergey Solovyev
Sergey Solovyev contributed to the ROCm/aiter repository by developing advanced GPU kernels and APIs for large language model inference. Over three months, he engineered kernel tiling optimizations and dynamic paged attention APIs, leveraging C++, Assembly, and Python to improve throughput and scalability for large-token and long-sequence workloads. His work included implementing workload-aware kernel selection, integrating quantization support, and optimizing for specific hardware such as MI300 and gfx950. Sergey also addressed reliability by fixing out-of-bounds access in GPU kernels. The depth of his contributions reflects strong expertise in low-level programming, performance optimization, and hardware-accelerated deep learning systems.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
5
Lines of code
312
Activity Months3
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Work History
March 2026
4 Commits • 3 Features
Mar 1, 2026March 2026 ROCm/aiter performance month: Deliveries centered on large-sequence kernel support, assembly kernel expansions, and reliability improvements across gfx950 and MI300 hardware. The work enhances throughput for long-context MoE workloads, strengthens quantization reliability, and lays groundwork for robust hardware-specific optimizations.
4 Commits • 3 Features
Mar 1, 2026March 2026 ROCm/aiter performance month: Deliveries centered on large-sequence kernel support, assembly kernel expansions, and reliability improvements across gfx950 and MI300 hardware. The work enhances throughput for long-context MoE workloads, strengthens quantization reliability, and lays groundwork for robust hardware-specific optimizations.
March 2026
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a dynamic paged attention API switching between ASM and HIP to optimize kernel selection based on workload characteristics. Implemented integration through paged_attention_common with shuffled KV cache layout considerations and quantization support, plus code quality and formatting improvements to bolster maintainability. HIP demonstrated better performance for low-concurrency workloads (<128), contributing to improved inference throughput in typical low-traffic scenarios. Updated unit tests and cleaned up test scaffolding, removing outdated tests and redundant parameters to reduce maintenance burden.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026: Delivered a dynamic paged attention API switching between ASM and HIP to optimize kernel selection based on workload characteristics. Implemented integration through paged_attention_common with shuffled KV cache layout considerations and quantization support, plus code quality and formatting improvements to bolster maintainability. HIP demonstrated better performance for low-concurrency workloads (<128), contributing to improved inference throughput in typical low-traffic scenarios. Updated unit tests and cleaned up test scaffolding, removing outdated tests and redundant parameters to reduce maintenance burden.
December 2025
1 Commits • 1 Features
Dec 1, 20252025-12 monthly performance summary for ROCm/aiter: delivered a kernel tiling optimization for large-token inputs (32x384 tiling) and introduced a 32x384 blockscale FP8 FMoE kernel. Validated on Qwen3 235B with CONC=256, showing a 2.5% uplift in the larger case and an expected ~20% uplift vs 32x256 tiling for large-token inputs. No critical bugs reported; the work lays groundwork for improved throughput and scalability on large LLMs.
1 Commits • 1 Features
Dec 1, 20252025-12 monthly performance summary for ROCm/aiter: delivered a kernel tiling optimization for large-token inputs (32x384 tiling) and introduced a 32x384 blockscale FP8 FMoE kernel. Validated on Qwen3 235B with CONC=256, showing a 2.5% uplift in the larger case and an expected ~20% uplift vs 32x256 tiling for large-token inputs. No critical bugs reported; the work lays groundwork for improved throughput and scalability on large LLMs.
December 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture86.6%
Performance83.4%
AI Usage30.0%
Skills & Technologies
Programming Languages
AssemblyC++COPython
Technical Skills
API DevelopmentAssembly LanguageC++Deep LearningGPU ProgrammingGPU programmingKernel DevelopmentMachine LearningPerformance OptimizationPyTorchPythonhardware accelerationkernel developmentlow-level programmingmachine learning
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