oneapi-src/oneDNN
Oct 2024 – Oct 2025
10 Months active
Languages Used
C++AssemblyC
Technical Skills
CPU OptimizationMatrix MultiplicationPerformance TuningBug FixingCPU ArchitectureCode Refactoring
Xuxin, Zeng
PROFILE
Xuxin, Zeng
Xuxin Zeng contributed to the oneapi-src/oneDNN repository by engineering and optimizing low-level CPU kernels for deep learning workloads, focusing on matrix multiplication and convolution operations. Leveraging C++ and assembly, Xuxin expanded data-type support—including FP8, HF8, and INT4—across AVX and AMX instruction sets, and implemented performance enhancements such as memory prefetching and ISA-aware optimizations. He addressed correctness and stability by refining boundary checks, type safety, and error handling, while upgrading dependencies like Xbyak for improved hardware compatibility. Xuxin’s work demonstrated deep expertise in CPU architecture, numerical computation, and performance engineering, delivering robust, production-ready code for high-throughput inference.
Overall Statistics
Feature vs Bugs
46%Features
Repository Contributions
41Total
Bugs
15
Commits
41
Features
13
Lines of code
4,887
Activity Months10
Your Network
1970 people
Work History
October 2025
5 Commits • 1 Features
Oct 1, 2025October 2025: OneDNN CPU back-end (x64) Brgemm convolution enhancements and robustness fixes. Delivered performance optimizations and correctness improvements that target large-buffer zero-point handling, memory/compute efficiency, and f32 path prefetching, contributing to higher throughput and reliability for real-world workloads.
5 Commits • 1 Features
Oct 1, 2025October 2025: OneDNN CPU back-end (x64) Brgemm convolution enhancements and robustness fixes. Delivered performance optimizations and correctness improvements that target large-buffer zero-point handling, memory/compute efficiency, and f32 path prefetching, contributing to higher throughput and reliability for real-world workloads.
October 2025
September 2025
1 Commits
Sep 1, 2025September 2025 monthly summary for oneapi-src/oneDNN focusing on stability and reliability improvements in the brgemm convolution path. Key accomplishment: fixed a segmentation fault in the brgemm convolution utility by correcting the calculation of ker_ranges_size in the exec_trans path. This targeted change preserves all other execution paths and avoids introducing regressions, significantly improving runtime stability for high-performance convolution workloads on x64 CPU. Context: The fix was implemented in the brgemm convolution code flow and is committed under 6494344445cc0421b365bf6430934905b894a29a, addressing critical crash scenarios observed in production deployments while maintaining performance characteristics elsewhere. Impact: Increased reliability for users relying on brgemm-based convolutions, reduced crash-related incidents, and stronger confidence in oneDNN for performance-critical workloads. Tech skills demonstrated: low-level kernel debugging, C++/CPU-path development, precision in kernel parameter calculations (ker_ranges_size), and safe, targeted changes within the exec_trans path to avoid broader impact.
September 2025
1 Commits
Sep 1, 2025September 2025 monthly summary for oneapi-src/oneDNN focusing on stability and reliability improvements in the brgemm convolution path. Key accomplishment: fixed a segmentation fault in the brgemm convolution utility by correcting the calculation of ker_ranges_size in the exec_trans path. This targeted change preserves all other execution paths and avoids introducing regressions, significantly improving runtime stability for high-performance convolution workloads on x64 CPU. Context: The fix was implemented in the brgemm convolution code flow and is committed under 6494344445cc0421b365bf6430934905b894a29a, addressing critical crash scenarios observed in production deployments while maintaining performance characteristics elsewhere. Impact: Increased reliability for users relying on brgemm-based convolutions, reduced crash-related incidents, and stronger confidence in oneDNN for performance-critical workloads. Tech skills demonstrated: low-level kernel debugging, C++/CPU-path development, precision in kernel parameter calculations (ker_ranges_size), and safe, targeted changes within the exec_trans path to avoid broader impact.
July 2025
6 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for oneDNN focusing on performance, robustness, and hardware readiness. Key features were delivered in FP8-BF16 data path enhancements for x64 convolution and BRGEMM memory-advice optimizations for NVL, complemented by continued improvements in low-precision handling and matrix operations. Notable reliability fixes include 8-bit saturation conversion robustness in BRGEMM and BF16 conversion correctness in matmul, alongside an Xbyak 7.28 upgrade to improve AVX/AMX compatibility. These changes collectively expand data-type support, optimize data flow for modern hardware, and strengthen numerical correctness, delivering tangible business value through potential performance gains and reduced maintenance risk.
6 Commits • 3 Features
Jul 1, 2025July 2025 monthly summary for oneDNN focusing on performance, robustness, and hardware readiness. Key features were delivered in FP8-BF16 data path enhancements for x64 convolution and BRGEMM memory-advice optimizations for NVL, complemented by continued improvements in low-precision handling and matrix operations. Notable reliability fixes include 8-bit saturation conversion robustness in BRGEMM and BF16 conversion correctness in matmul, alongside an Xbyak 7.28 upgrade to improve AVX/AMX compatibility. These changes collectively expand data-type support, optimize data flow for modern hardware, and strengthen numerical correctness, delivering tangible business value through potential performance gains and reduced maintenance risk.
July 2025
June 2025
5 Commits • 1 Features
Jun 1, 2025June 2025: FP8 data type support across critical kernels in oneDNN, with stability improvements across ISA. Implemented FP8 across eltwise, conv scales, and reorder paths (NVL and SPR), plus a segmentation fault safeguard for f16 on x64 when ISA is unsupported. These changes unlock FP8 workload throughput and broaden hardware compatibility, delivering tangible performance and reliability benefits for FP8-enabled inference workloads.
June 2025
5 Commits • 1 Features
Jun 1, 2025June 2025: FP8 data type support across critical kernels in oneDNN, with stability improvements across ISA. Implemented FP8 across eltwise, conv scales, and reorder paths (NVL and SPR), plus a segmentation fault safeguard for f16 on x64 when ISA is unsupported. These changes unlock FP8 workload throughput and broaden hardware compatibility, delivering tangible performance and reliability benefits for FP8-enabled inference workloads.
May 2025
4 Commits • 2 Features
May 1, 20252025-05 Monthly summary for oneDNN (oneapi-src/oneDNN): Key features delivered include Brgemm FP8 support on AVX10.2 and HF8 support in convolution/deconvolution on AVX10.2. Major bugs fixed include FP8 handling stability fix to prevent segfaults and a LDD correctness fix for M=1 in brgemm_matmul_utils, with an accompanying regression test. Overall impact includes expanded FP8/HF8 data-type support with improved stability and correctness, enabling higher-performance CPU-side workloads. Technologies demonstrated include low-level CPU kernel work on AVX10.2, FP8/HF8 data types, brgemm utilities, conv/deconv paths, and regression testing that safeguards edge cases.
4 Commits • 2 Features
May 1, 20252025-05 Monthly summary for oneDNN (oneapi-src/oneDNN): Key features delivered include Brgemm FP8 support on AVX10.2 and HF8 support in convolution/deconvolution on AVX10.2. Major bugs fixed include FP8 handling stability fix to prevent segfaults and a LDD correctness fix for M=1 in brgemm_matmul_utils, with an accompanying regression test. Overall impact includes expanded FP8/HF8 data-type support with improved stability and correctness, enabling higher-performance CPU-side workloads. Technologies demonstrated include low-level CPU kernel work on AVX10.2, FP8/HF8 data types, brgemm utilities, conv/deconv paths, and regression testing that safeguards edge cases.
May 2025
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for oneDNN (oneapi-src/oneDNN). Focused on expanding datatype support, hardware-specific optimizations, and robustness of core kernels for x64, delivering measurable business value in performance, portability, and reliability.
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for oneDNN (oneapi-src/oneDNN). Focused on expanding datatype support, hardware-specific optimizations, and robustness of core kernels for x64, delivering measurable business value in performance, portability, and reliability.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for oneapi-src/oneDNN: Focused on robustness improvements and hardware optimization readiness. Implemented a guard to disable convolution for excessively large input shapes to prevent integer overflows, and upgraded the Xbyak library to enhance CPU topology detection and ISA support. These changes strengthen production stability and enable better hardware-specific optimizations moving forward.
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for oneapi-src/oneDNN: Focused on robustness improvements and hardware optimization readiness. Implemented a guard to disable convolution for excessively large input shapes to prevent integer overflows, and upgraded the Xbyak library to enhance CPU topology detection and ISA support. These changes strengthen production stability and enable better hardware-specific optimizations moving forward.
March 2025
February 2025
7 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for oneapi-src/oneDNN. Focused on expanding data-type compatibility, stabilizing x64 kernels, and sharpening performance for convolution paths on CPU, with emphasis on business value and technical rigor.
February 2025
7 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for oneapi-src/oneDNN. Focused on expanding data-type compatibility, stabilizing x64 kernels, and sharpening performance for convolution paths on CPU, with emphasis on business value and technical rigor.
January 2025
5 Commits • 1 Features
Jan 1, 2025January 2025 Monthly Summary for oneDNN (oneapi-src/oneDNN): Focused on correctness, performance, and robustness of x64 CPU paths. Delivered critical matmul correctness fixes, tuned brgemm paths for small shapes, and hardened CPU modules against static analysis issues, with concrete test coverage to validate changes. The work directly improves accuracy of neural network matmul, reduces memory overhead in buffering, and strengthens code quality and maintainability across CPU components.
5 Commits • 1 Features
Jan 1, 2025January 2025 Monthly Summary for oneDNN (oneapi-src/oneDNN): Focused on correctness, performance, and robustness of x64 CPU paths. Delivered critical matmul correctness fixes, tuned brgemm paths for small shapes, and hardened CPU modules against static analysis issues, with concrete test coverage to validate changes. The work directly improves accuracy of neural network matmul, reduces memory overhead in buffering, and strengthens code quality and maintainability across CPU components.
January 2025
October 2024
1 Commits
Oct 1, 2024October 2024: Correctness stabilization for int8 matmul on x64 in oneDNN. Implemented a targeted bug fix by disabling the parallel_k_reduction optimization for int8, addressing potential correctness issues when parallelizing across the K dimension. The change updates the bwd_w_par_k_blk logic to exclude int8 computations during K-parallelization. This work is tracked in commit 4896980c03c0a0eca7d8d458aaddf93d53ddf85f, and reduces production risk for int8 inference workloads.
October 2024
1 Commits
Oct 1, 2024October 2024: Correctness stabilization for int8 matmul on x64 in oneDNN. Implemented a targeted bug fix by disabling the parallel_k_reduction optimization for int8, addressing potential correctness issues when parallelizing across the K dimension. The change updates the bwd_w_par_k_blk logic to exclude int8 computations during K-parallelization. This work is tracked in commit 4896980c03c0a0eca7d8d458aaddf93d53ddf85f, and reduces production risk for int8 inference workloads.
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Quality Metrics
Correctness88.8%
Maintainability86.4%
Architecture84.4%
Performance80.0%
AI Usage20.4%
Skills & Technologies
Programming Languages
AssemblyCC++
Technical Skills
AVX InstructionsAssemblyAssembly LanguageBug FixingC++CPU ArchitectureCPU Instruction Set ArchitectureCPU OptimizationCode RefactoringCompiler DevelopmentConvolutional Neural Networks (CNNs)Deep LearningDeep Learning FrameworksDeep Learning OptimizationEmbedded Systems
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