ROCm/aiter
Dec 2025 – Jul 2026
7 Months active
Languages Used
COPythonAssemblyC++CUDAC
Technical Skills
GPU ProgrammingKernel DevelopmentPerformance OptimizationAPI DevelopmentDeep LearningMachine Learning
Sergey Solovyev
PROFILE
Sergey Solovyev
Over seven months, this developer contributed to ROCm/aiter by building and optimizing GPU kernels for large-scale machine learning workloads. Their work focused on kernel development and performance tuning, delivering fused MoE and multi-head attention kernels with support for FP8 and BF16 precision, as well as dynamic API switching for workload-aware kernel selection. Using C++, CUDA, and Python, they addressed hardware-specific challenges on platforms like GFX950 and MI300, improved quantization reliability, and enhanced test infrastructure. Their engineering approach emphasized low-level programming, error handling, and maintainability, resulting in improved throughput, reliability, and scalability for production deep learning systems.
Overall Statistics
Feature vs Bugs
82%Features
Repository Contributions
22Total
Bugs
3
Commits
22
Features
14
Lines of code
1,757
Activity Months7
Your Network
1985 peopleSame Organization
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Work History
July 2026
2 Commits • 2 Features
Jul 1, 2026July 2026 ROCm/aiter monthly summary: Delivered key kernel and attention improvements on GFX950, introduced MXFP4 flat fused MoE kernels with 16x128 and 16x256 configurations (bf16/fp16) and updated tests for precision-specific references; enabled 256-dimension FMHA with FP8 by updating Python/C++ interfaces and adding required kernels. Fixed a critical i_dim=512 precision bug affecting MoE kernel scaling and improved test infrastructure to validate kernel_bench/ref_dtype runs. These changes increase throughput and accuracy for large-scale MoE/attention workloads and expand FP8 readiness.
2 Commits • 2 Features
Jul 1, 2026July 2026 ROCm/aiter monthly summary: Delivered key kernel and attention improvements on GFX950, introduced MXFP4 flat fused MoE kernels with 16x128 and 16x256 configurations (bf16/fp16) and updated tests for precision-specific references; enabled 256-dimension FMHA with FP8 by updating Python/C++ interfaces and adding required kernels. Fixed a critical i_dim=512 precision bug affecting MoE kernel scaling and improved test infrastructure to validate kernel_bench/ref_dtype runs. These changes increase throughput and accuracy for large-scale MoE/attention workloads and expand FP8 readiness.
July 2026
June 2026
4 Commits • 2 Features
Jun 1, 2026June 2026: Focused on performance, reliability, and compute efficiency in ROCm/aiter. Delivered optimized MHA kernels for gfx950 and 32x384, FP8-precision activation kernels, and paged attention input validation with CPU-GPU sync removal. These changes improve model throughput, reduce training/inference time, and enhance correctness in attention pathways.
June 2026
4 Commits • 2 Features
Jun 1, 2026June 2026: Focused on performance, reliability, and compute efficiency in ROCm/aiter. Delivered optimized MHA kernels for gfx950 and 32x384, FP8-precision activation kernels, and paged attention input validation with CPU-GPU sync removal. These changes improve model throughput, reduce training/inference time, and enhance correctness in attention pathways.
May 2026
6 Commits • 4 Features
May 1, 2026May 2026 ROCm/aiter monthly summary focusing on delivering high-value features, stabilizing memory-sensitive paths, and expanding hardware support. Key work centered on FP8 MHA reliability and performance on gfx950, introduction of a scalable decoding kernel with sub_kv=64 and sub_qh=8, BF16 memory-fault remediation, and a high-throughput FMOE assembly kernel. The work included routing FP8 varlen MHA calls through the CK path to preserve functionality while ASM support is reintroduced, and expanding kernel coverage to include MI300. Collectively these efforts improved hardware compatibility, reliability, and throughput, while driving towards broader enterprise adoption and reduced fault rates.
6 Commits • 4 Features
May 1, 2026May 2026 ROCm/aiter monthly summary focusing on delivering high-value features, stabilizing memory-sensitive paths, and expanding hardware support. Key work centered on FP8 MHA reliability and performance on gfx950, introduction of a scalable decoding kernel with sub_kv=64 and sub_qh=8, BF16 memory-fault remediation, and a high-throughput FMOE assembly kernel. The work included routing FP8 varlen MHA calls through the CK path to preserve functionality while ASM support is reintroduced, and expanding kernel coverage to include MI300. Collectively these efforts improved hardware compatibility, reliability, and throughput, while driving towards broader enterprise adoption and reduced fault rates.
May 2026
April 2026
4 Commits • 1 Features
Apr 1, 2026Month: 2026-04 — ROCm/aiter delivered key kernel-level features and precision improvements for MOE workloads. Features include fused MOE kernels for GELU/SILU with weight prefetching and new 16x128/16x256 implementations, plus asm-based MOE kernels that avoid bf16->fp8 quantization. Also updated the kernel tuner and interfaces and performed legacy cleanup to reduce maintenance burden. A precision fix was applied for SILU/GELU across 32x256, 64x128, and 64x256 configurations to improve accuracy and stability. These efforts improve MOE throughput, reduce quantization overhead, and strengthen maintainability of kernel code, enabling more robust production workloads.
April 2026
4 Commits • 1 Features
Apr 1, 2026Month: 2026-04 — ROCm/aiter delivered key kernel-level features and precision improvements for MOE workloads. Features include fused MOE kernels for GELU/SILU with weight prefetching and new 16x128/16x256 implementations, plus asm-based MOE kernels that avoid bf16->fp8 quantization. Also updated the kernel tuner and interfaces and performed legacy cleanup to reduce maintenance burden. A precision fix was applied for SILU/GELU across 32x256, 64x128, and 64x256 configurations to improve accuracy and stability. These efforts improve MOE throughput, reduce quantization overhead, and strengthen maintainability of kernel code, enabling more robust production workloads.
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
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Quality Metrics
Correctness91.8%
Maintainability83.6%
Architecture86.4%
Performance86.4%
AI Usage30.0%
Skills & Technologies
Programming Languages
AssemblyCC++COCUDAPython
Technical Skills
API DevelopmentAssembly LanguageC++CUDACUDA programmingDeep LearningDeep learningError handlingGCN AssemblyGPU ProgrammingGPU programmingKernel DevelopmentKernel OptimizationKernel developmentMachine Learning
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