oneapi-src/oneDNN
Oct 2024 – Oct 2025
12 Months active
Languages Used
C++OpenCL CCOpenCLCLShellCUDA
Technical Skills
C++Code OrganizationCompiler DevelopmentDebuggingDeep Learning KernelsDeep Learning Primitives
Umar Arshad
PROFILE
Umar Arshad
Umar Arshad developed and optimized the Scaled Dot Product Attention (SDPA) stack in the oneapi-src/oneDNN repository, focusing on kernel performance, hardware compatibility, and robust testing. He engineered low-level C++ and OpenCL kernels for GPU architectures, enabling quantization, advanced masking, and support for diverse data types. His work included dynamic configuration management, memory and error handling improvements, and integration of Mixture of Experts (MoE) support. By refactoring test infrastructure and enhancing logging, Umar improved reliability and maintainability across SYCL and CUDA backends. His contributions addressed cross-platform stability, accelerated inference, and established a foundation for future deep learning optimizations.
Overall Statistics
Feature vs Bugs
74%Features
Repository Contributions
117Total
Bugs
10
Commits
117
Features
29
Lines of code
10,852
Activity Months12
Your Network
1970 people
Work History
October 2025
3 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for oneapi-src/oneDNN. This period focused on hardware-specific kernel refinement, backend feature expansion, and stability work to sustain performance across Xe generations. Key accomplishments include delivering kernel configuration improvements for f16 accumulation on Xe_sdpa, expanding the xe backend with Mixture of Experts (MoE) support via new microkernel entries and provider updates, and implementing a temporary Xe3 performance workaround that reuses Xe2 configurations to mitigate regressions until Xe3 configurations are in place. These efforts enhance kernel selection accuracy, broaden MoE workload support, and maintain performance stability during platform transitions.
3 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for oneapi-src/oneDNN. This period focused on hardware-specific kernel refinement, backend feature expansion, and stability work to sustain performance across Xe generations. Key accomplishments include delivering kernel configuration improvements for f16 accumulation on Xe_sdpa, expanding the xe backend with Mixture of Experts (MoE) support via new microkernel entries and provider updates, and implementing a temporary Xe3 performance workaround that reuses Xe2 configurations to mitigate regressions until Xe3 configurations are in place. These efforts enhance kernel selection accuracy, broaden MoE workload support, and maintain performance stability during platform transitions.
October 2025
August 2025
7 Commits • 2 Features
Aug 1, 2025August 2025 (2025-08) — For oneDNN, focused on Sdpa improvements to boost single-query GQA performance, strengthen configuration robustness, and enhance test coverage and logging. These changes deliver measurable throughput and accuracy gains, reduce configuration noise, and improve maintainability and debuggability across Xe family architectures.
August 2025
7 Commits • 2 Features
Aug 1, 2025August 2025 (2025-08) — For oneDNN, focused on Sdpa improvements to boost single-query GQA performance, strengthen configuration robustness, and enhance test coverage and logging. These changes deliver measurable throughput and accuracy gains, reduce configuration noise, and improve maintainability and debuggability across Xe family architectures.
July 2025
14 Commits • 2 Features
Jul 1, 2025July 2025: Delivered reliability improvements and performance enhancements to the SDPA test suite in oneDNN, stabilizing cross-architecture behavior across Xe/Windows, enhancing test maintainability, and improving measurement precision. Business impact includes reduced flaky tests, faster iteration cycles, and more predictable performance benchmarks.
14 Commits • 2 Features
Jul 1, 2025July 2025: Delivered reliability improvements and performance enhancements to the SDPA test suite in oneDNN, stabilizing cross-architecture behavior across Xe/Windows, enhancing test maintainability, and improving measurement precision. Business impact includes reduced flaky tests, faster iteration cycles, and more predictable performance benchmarks.
July 2025
June 2025
3 Commits
Jun 1, 2025June 2025 focused on stabilizing SDPA-related components in oneDNN, delivering reliability and correctness improvements with cross-architecture considerations. Business value includes reduced test flakiness, safer performance optimizations, and correct masking logic under edge conditions, enabling robust model evaluation and future optimization work.
June 2025
3 Commits
Jun 1, 2025June 2025 focused on stabilizing SDPA-related components in oneDNN, delivering reliability and correctness improvements with cross-architecture considerations. Business value includes reduced test flakiness, safer performance optimizations, and correct masking logic under edge conditions, enabling robust model evaluation and future optimization work.
May 2025
7 Commits • 1 Features
May 1, 2025May 2025 performance-focused iteration for oneDNN's SDPA integration, with emphasis on reliability, performance, and maintainability. Delivered a set of kernel and test enhancements that improve throughput and correctness, plus a configuration bug fix for LNL with head_size 512. These efforts reduce test fragility, enable better benchmarking, and provide a stronger foundation for future optimizations across SYCL/USM paths.
7 Commits • 1 Features
May 1, 2025May 2025 performance-focused iteration for oneDNN's SDPA integration, with emphasis on reliability, performance, and maintainability. Delivered a set of kernel and test enhancements that improve throughput and correctness, plus a configuration bug fix for LNL with head_size 512. These efforts reduce test fragility, enable better benchmarking, and provide a stronger foundation for future optimizations across SYCL/USM paths.
May 2025
April 2025
14 Commits • 4 Features
Apr 1, 2025April 2025: OneDNN (oneapi-src/oneDNN) SDPA stack enhancements delivered broader hardware support, improved stability, and expanded validation, driving better performance and reliability in production deployments. Major changes include: 1) SDPA Core Kernel and Configuration Improvements for xe2 with improved OpenCL argument handling and prefetch bug fix; 2) Bottom-right Causal Mask Support in SDPA; 3) Safe Softmax and Data Type Validation Enhancements enabling bf16/f16/f32 and stricter tensor shapes; 4) SDPA Testing Suite Enhancements and Robustness with expanded Group Query Attention tests and quantization scenarios. These efforts reduce production risk, speed up inference, and improve QA coverage across data types and configurations.
April 2025
14 Commits • 4 Features
Apr 1, 2025April 2025: OneDNN (oneapi-src/oneDNN) SDPA stack enhancements delivered broader hardware support, improved stability, and expanded validation, driving better performance and reliability in production deployments. Major changes include: 1) SDPA Core Kernel and Configuration Improvements for xe2 with improved OpenCL argument handling and prefetch bug fix; 2) Bottom-right Causal Mask Support in SDPA; 3) Safe Softmax and Data Type Validation Enhancements enabling bf16/f16/f32 and stricter tensor shapes; 4) SDPA Testing Suite Enhancements and Robustness with expanded Group Query Attention tests and quantization scenarios. These efforts reduce production risk, speed up inference, and improve QA coverage across data types and configurations.
March 2025
11 Commits • 5 Features
Mar 1, 2025March 2025 monthly summary for oneapi-src/oneDNN focusing on SDPA integration work across multiple silicon platforms and Windows stability improvements.
11 Commits • 5 Features
Mar 1, 2025March 2025 monthly summary for oneapi-src/oneDNN focusing on SDPA integration work across multiple silicon platforms and Windows stability improvements.
March 2025
February 2025
11 Commits • 2 Features
Feb 1, 2025February 2025: Delivered decisive SDPA core stability and hardware compatibility improvements in oneDNN, along with hardened test suite reliability across CUDA/HIP backends. Implementations included attribute validation, mask handling improvements, robust memory transfers, and Xe-specific configuration tuning, complemented by streamlined test coverage and smarter skip logic. The changes reduced runtime variability, improved cross-SKU stability on Xe GPUs, and accelerated CI feedback. Demonstrated strong capabilities in C++, SYCL, DNNL integration, and automated testing.
February 2025
11 Commits • 2 Features
Feb 1, 2025February 2025: Delivered decisive SDPA core stability and hardware compatibility improvements in oneDNN, along with hardened test suite reliability across CUDA/HIP backends. Implementations included attribute validation, mask handling improvements, robust memory transfers, and Xe-specific configuration tuning, complemented by streamlined test coverage and smarter skip logic. The changes reduced runtime variability, improved cross-SKU stability on Xe GPUs, and accelerated CI feedback. Demonstrated strong capabilities in C++, SYCL, DNNL integration, and automated testing.
January 2025
18 Commits • 4 Features
Jan 1, 2025January 2025 monthly summary for oneDNN (Xe backend). Focused on delivering performance improvements, robustness, and expanded configuration for the SDPA kernel. Key outcomes include prefetch optimization improving SDPA throughput and correctness, causal masking support enabling conditional execution, non-power-of-2 head size support with quantization and work-group validation, boundary handling and quantization robustness fixes, and expanded test coverage for reliability and maintainability. Technologies demonstrated include Xe micro-kernel tuning, tile operations, and work-group configuration; strong emphasis on business value through performance gains, correctness, and test improvements.
18 Commits • 4 Features
Jan 1, 2025January 2025 monthly summary for oneDNN (Xe backend). Focused on delivering performance improvements, robustness, and expanded configuration for the SDPA kernel. Key outcomes include prefetch optimization improving SDPA throughput and correctness, causal masking support enabling conditional execution, non-power-of-2 head size support with quantization and work-group validation, boundary handling and quantization robustness fixes, and expanded test coverage for reliability and maintainability. Technologies demonstrated include Xe micro-kernel tuning, tile operations, and work-group configuration; strong emphasis on business value through performance gains, correctness, and test improvements.
January 2025
December 2024
11 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for oneDNN: Implemented the Scaled Dot Product Attention (SDPA) primitive and strengthened its integration lifecycle, improved the SDPA microkernel for performance and correctness, and refactored SDPA hashing/serialization and pattern matching to enhance maintainability and runtime flexibility. The changes collectively enable efficient SDPA workloads, improve reliability, and establish a solid foundation for future optimizations and feature expansion.
December 2024
11 Commits • 3 Features
Dec 1, 2024December 2024 monthly summary for oneDNN: Implemented the Scaled Dot Product Attention (SDPA) primitive and strengthened its integration lifecycle, improved the SDPA microkernel for performance and correctness, and refactored SDPA hashing/serialization and pattern matching to enhance maintainability and runtime flexibility. The changes collectively enable efficient SDPA workloads, improve reliability, and establish a solid foundation for future optimizations and feature expansion.
November 2024
12 Commits • 3 Features
Nov 1, 2024During 2024-11, oneDNN development delivered a focused set of features and reliability improvements across SDPA quantization, DG2 hardware microkernel optimization, and code quality. The SDPA kernel gained support for u4/s4 data types, per-element quantization (per-tensor and per-channel), and validation checks, improving precision and flexibility for scaled dot-product attention. DG2 microkernel usage was optimized with a newDP flag and a revised SLM allocation strategy to prevent overallocation and ensure compatibility with the DG2 data path. Extensive code hygiene and safety improvements were applied, including const-correctness fixes, improved error reporting, and interface cleanups for microSDPA/SDPA components. These changes enhance hardware support, robustness, and maintainability, enabling faster iteration and more reliable, higher-precision inference for performance-critical workloads.
12 Commits • 3 Features
Nov 1, 2024During 2024-11, oneDNN development delivered a focused set of features and reliability improvements across SDPA quantization, DG2 hardware microkernel optimization, and code quality. The SDPA kernel gained support for u4/s4 data types, per-element quantization (per-tensor and per-channel), and validation checks, improving precision and flexibility for scaled dot-product attention. DG2 microkernel usage was optimized with a newDP flag and a revised SLM allocation strategy to prevent overallocation and ensure compatibility with the DG2 data path. Extensive code hygiene and safety improvements were applied, including const-correctness fixes, improved error reporting, and interface cleanups for microSDPA/SDPA components. These changes enhance hardware support, robustness, and maintainability, enabling faster iteration and more reliable, higher-precision inference for performance-critical workloads.
November 2024
October 2024
6 Commits • 2 Features
Oct 1, 2024October 2024: Focused on quantization and datatype expansion for the oneDNN SDPA path and related microkernels, delivering improved performance, flexibility, and reliability. Key work included enabling quantization for K and V in SDPA, fixing a critical GEMM transposition bug, expanding data-type support and preparing for micro_sdpa, and enhancing initialization/logging for better observability.
October 2024
6 Commits • 2 Features
Oct 1, 2024October 2024: Focused on quantization and datatype expansion for the oneDNN SDPA path and related microkernels, delivering improved performance, flexibility, and reliability. Key work included enabling quantization for K and V in SDPA, fixing a critical GEMM transposition bug, expanding data-type support and preparing for micro_sdpa, and enhancing initialization/logging for better observability.
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Quality Metrics
Correctness88.0%
Maintainability86.0%
Architecture82.6%
Performance78.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CC++CLCUDAOpenCLOpenCL CShell
Technical Skills
API DesignAPI designBenchmarkingBuild SystemsC++C++ DevelopmentCUDACode OrganizationCode RefactoringCompiler DevelopmentCompiler warningsCompute Kernel DevelopmentCompute Kernel OptimizationCompute KernelsConfiguration Management
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