oneapi-src/oneDNN
Jan 2025 – Oct 2025
8 Months active
Languages Used
AssemblyCC++Shell
Technical Skills
API DesignBenchmarkingC++CPU OptimizationDeep Learning FrameworksDeep Learning Optimization
Denis Samoilov
PROFILE
Denis Samoilov
Denis Samoylov contributed to the oneapi-src/oneDNN repository by engineering high-performance CPU kernels and optimizing deep learning primitives for x64 architectures. He developed and refined matrix multiplication and convolution paths, focusing on AVX2, AVX512, and AMX instruction sets to improve throughput and numerical stability. Using C++ and assembly, Denis implemented robust memory management, enhanced JIT compilation routines, and expanded support for mixed-precision data types such as FP16 and INT8. His work included rigorous benchmarking, test coverage, and code refactoring, resulting in more reliable, maintainable, and scalable backend components for deep learning frameworks running on modern CPU hardware.
Overall Statistics
Feature vs Bugs
57%Features
Repository Contributions
54Total
Bugs
10
Commits
54
Features
13
Lines of code
3,962
Activity Months8
Your Network
1970 people
Work History
October 2025
7 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for oneapi-src/oneDNN focused on x64 Brgemm matmul performance and reliability, plus stabilization of the matmul primitive lifecycle. Key outcomes include higher FP32 throughput on AVX2, broader non-GEMV coverage, and improved GEMM fallback logic, with a dedicated fix for a memory leak in the x64 matmul descriptor lifecycle.
7 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for oneapi-src/oneDNN focused on x64 Brgemm matmul performance and reliability, plus stabilization of the matmul primitive lifecycle. Key outcomes include higher FP32 throughput on AVX2, broader non-GEMV coverage, and improved GEMM fallback logic, with a dedicated fix for a memory leak in the x64 matmul descriptor lifecycle.
October 2025
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 (oneDNN): Delivered performance-oriented enhancements for the x64 path and improved maintainability in the oneDNN repository. Implemented a dedicated Brgemm GEMV path for batched workloads on x64, including initialization and kernel logic, with an AVX2-aware fallback to GEMM when GEMV is not applicable and stability safeguards by disabling source reduction for GEMV. Conducted JIT kernel maintenance and refactor to improve clarity and consistency: introduced a regops module to encapsulate common x64 JIT register operations, refined horizontal additions for FP values, and aligned parameter naming across kernels. These changes deliver tangible business value by boosting batched GEMV throughput, ensuring correctness across edge cases, and establishing a maintainable foundation for future kernel optimizations.
September 2025
5 Commits • 2 Features
Sep 1, 2025September 2025 (oneDNN): Delivered performance-oriented enhancements for the x64 path and improved maintainability in the oneDNN repository. Implemented a dedicated Brgemm GEMV path for batched workloads on x64, including initialization and kernel logic, with an AVX2-aware fallback to GEMM when GEMV is not applicable and stability safeguards by disabling source reduction for GEMV. Conducted JIT kernel maintenance and refactor to improve clarity and consistency: introduced a regops module to encapsulate common x64 JIT register operations, refined horizontal additions for FP values, and aligned parameter naming across kernels. These changes deliver tangible business value by boosting batched GEMV throughput, ensuring correctness across edge cases, and establishing a maintainable foundation for future kernel optimizations.
August 2025
5 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: Delivered correctness and performance improvements in oneAPI-oneDNN with notable items: fixed JIT Reorder Kernel to avoid incorrect fast returns when compensation is required; extended matmul paths to support FP16 destinations for s8/u8 inputs across BRGEMM, BRGEMM_MATMUL, and inner-product paths; updated benchdnn benchmarks to exercise optimized matmul paths by swapping data types, ensuring robust validation of optimizations. Impact: improved accuracy and correctness in data-path handling, broader precision options for INT8 workflows, and enhanced benchmarking coverage that reduces risk in production deployments. Skills/tech: CPU x64 optimizations, JIT, BRGEMM paths, FP16 and INT8 data types, benchdnn instrumentation.
5 Commits • 2 Features
Aug 1, 2025Summary for 2025-08: Delivered correctness and performance improvements in oneAPI-oneDNN with notable items: fixed JIT Reorder Kernel to avoid incorrect fast returns when compensation is required; extended matmul paths to support FP16 destinations for s8/u8 inputs across BRGEMM, BRGEMM_MATMUL, and inner-product paths; updated benchdnn benchmarks to exercise optimized matmul paths by swapping data types, ensuring robust validation of optimizations. Impact: improved accuracy and correctness in data-path handling, broader precision options for INT8 workflows, and enhanced benchmarking coverage that reduces risk in production deployments. Skills/tech: CPU x64 optimizations, JIT, BRGEMM paths, FP16 and INT8 data types, benchdnn instrumentation.
August 2025
July 2025
2 Commits • 1 Features
Jul 1, 2025Monthly summary for 2025-07: Delivered a new Per-OC-D broadcasting strategy for AMX in the brgemm kernel on x64, along with regression test coverage for bf16 under the benchdnn matmul harness. This work increases flexibility and potential performance for AMX-enabled oneDNN workloads and improves correctness through regression validation. No major bug fixes were observed this month in oneDNN; the focus was on feature delivery and test coverage.
July 2025
2 Commits • 1 Features
Jul 1, 2025Monthly summary for 2025-07: Delivered a new Per-OC-D broadcasting strategy for AMX in the brgemm kernel on x64, along with regression test coverage for bf16 under the benchdnn matmul harness. This work increases flexibility and potential performance for AMX-enabled oneDNN workloads and improves correctness through regression validation. No major bug fixes were observed this month in oneDNN; the focus was on feature delivery and test coverage.
May 2025
2 Commits
May 1, 2025Monthly work summary for 2025-05 focusing on stability and correctness of performance-critical kernels in oneDNN, with emphasis on a critical bug fix in the brgemm path.
2 Commits
May 1, 2025Monthly work summary for 2025-05 focusing on stability and correctness of performance-critical kernels in oneDNN, with emphasis on a critical bug fix in the brgemm path.
May 2025
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 (oneapi-src/oneDNN): Focused on x64 AVX512 kernel robustness, code cleanliness, and error handling to boost numerical robustness and maintainability. Key outcomes include hardening x64 convolution arithmetic to prevent overflows, refactoring the scale mask interface for main and depthwise convolutions, removing dead code from the FP32 x64 GEMM path to streamline maintenance and potentially speed up builds, and relaxing noexcept on apply_zero_padding to better align with error handling patterns. These changes improve numerical stability, reduce maintenance risk, and better position the project for performance on AVX512-enabled hardware.
April 2025
4 Commits • 2 Features
Apr 1, 2025April 2025 (oneapi-src/oneDNN): Focused on x64 AVX512 kernel robustness, code cleanliness, and error handling to boost numerical robustness and maintainability. Key outcomes include hardening x64 convolution arithmetic to prevent overflows, refactoring the scale mask interface for main and depthwise convolutions, removing dead code from the FP32 x64 GEMM path to streamline maintenance and potentially speed up builds, and relaxing noexcept on apply_zero_padding to better align with error handling patterns. These changes improve numerical stability, reduce maintenance risk, and better position the project for performance on AVX512-enabled hardware.
February 2025
12 Commits • 1 Features
Feb 1, 2025February 2025 performance summary for oneapi-src/oneDNN. Key kernel and backend improvements delivered for SYCL CPU and x64 backends, with stronger safety, diagnostics, and benchmarking reliability. The work focuses on enabling SYCL-based matmul in the RNN path, hardening memory handling, improving sparse reordering, and strengthening kernel configuration and ISA guard logic to support stable, scalable workloads.
12 Commits • 1 Features
Feb 1, 2025February 2025 performance summary for oneapi-src/oneDNN. Key kernel and backend improvements delivered for SYCL CPU and x64 backends, with stronger safety, diagnostics, and benchmarking reliability. The work focuses on enabling SYCL-based matmul in the RNN path, hardening memory handling, improving sparse reordering, and strengthening kernel configuration and ISA guard logic to support stable, scalable workloads.
February 2025
January 2025
17 Commits • 4 Features
Jan 1, 2025January 2025: Delivered CPU-side performance optimizations and reliability improvements for oneDNN on x64, with emphasis on matmul-based inner product paths, AVX2/INT8 support, and RNN workloads. Implemented a matmul-based IP across forward inference, backward passes (weights and data), and training-time layouts; introduced gating to ensure stable inference paths and avoid unsupported ISA regressions. Enhanced AVX2/INT8 BRGEMM capabilities and safety checks, enabling 8-bit operations for BRGEMM, RNN INT8 on AVX2, and corrected K=1 stride handling with ISA-based gating. Strengthened RNN benchmarking and test reliability (ndims support, robust skip/unimplemented handling, M=1 test coverage, and harness fixes). Added RNN wei_proj format support for correct weights handling and efficiency. Note: brgemm-based IP path for inference was disabled to ensure stability. These contributions collectively improve performance, FP16/INT8 capabilities, and test/correctness coverage across common workloads.
January 2025
17 Commits • 4 Features
Jan 1, 2025January 2025: Delivered CPU-side performance optimizations and reliability improvements for oneDNN on x64, with emphasis on matmul-based inner product paths, AVX2/INT8 support, and RNN workloads. Implemented a matmul-based IP across forward inference, backward passes (weights and data), and training-time layouts; introduced gating to ensure stable inference paths and avoid unsupported ISA regressions. Enhanced AVX2/INT8 BRGEMM capabilities and safety checks, enabling 8-bit operations for BRGEMM, RNN INT8 on AVX2, and corrected K=1 stride handling with ISA-based gating. Strengthened RNN benchmarking and test reliability (ndims support, robust skip/unimplemented handling, M=1 test coverage, and harness fixes). Added RNN wei_proj format support for correct weights handling and efficiency. Note: brgemm-based IP path for inference was disabled to ensure stability. These contributions collectively improve performance, FP16/INT8 capabilities, and test/correctness coverage across common workloads.
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Quality Metrics
Correctness90.0%
Maintainability88.6%
Architecture86.8%
Performance83.2%
AI Usage20.4%
Skills & Technologies
Programming Languages
AssemblyCC++Shell
Technical Skills
API DesignAVX512AssemblyAssembly (implied)Assembly LanguageBenchmarkingC++CPU ArchitectureCPU Engine DevelopmentCPU OptimizationCPU ReorderingCPU optimizationCode RefactoringCode refactoringConvolution Kernels
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