oneapi-src/oneDNN
Apr 2025 – Jun 2025
3 Months active
Languages Used
C++Shell
Technical Skills
CPU OptimizationJIT CompilationPerformance EngineeringTestingARM AssemblyFloating-Point Arithmetic
karmeh01
PROFILE
Karmeh01
Kareem Hisham Mehanna contributed to the oneapi-src/oneDNN repository by developing and optimizing features for ARM aarch64 platforms over a three-month period. He implemented JIT-based FP16-to-FP16 data reordering and expanded FP16 support for element-wise operations by introducing a conversion path through FP32, enhancing inference throughput for deep learning workloads. Kareem also extended the broadcasted grouped matrix multiplication (brdgmm) path to support SVE_128, improving compatibility and performance on ARM hardware. His work involved C++ and ARM Assembly, focusing on low-level optimization, SIMD, and performance engineering, and demonstrated a deep understanding of CPU architecture and embedded systems.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
96
Activity Months3
Your Network
490 people
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key deliverables, impact, and skills demonstrated for oneapi-src/oneDNN.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary focusing on key deliverables, impact, and skills demonstrated for oneapi-src/oneDNN.
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for oneDNN focusing on FP16 enhancements in the aarch64 JIT path. Delivered FP16 support for aarch64 element-wise operations by adding a FP16->FP32->compute->FP16 data path in the Just-In-Time compiler, expanding FP16 compute usability on ARM and enabling higher-performance inference for half-precision workloads.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for oneDNN focusing on FP16 enhancements in the aarch64 JIT path. Delivered FP16 support for aarch64 element-wise operations by adding a FP16->FP32->compute->FP16 data path in the Just-In-Time compiler, expanding FP16 compute usability on ARM and enabling higher-performance inference for half-precision workloads.
April 2025
1 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04: Delivered JIT FP16-to-FP16 reorder on aarch64 for oneDNN by updating the jit_uni_reorder_kernel_f32_t to support FP16->FP16 reordering and adding FP16 reorder test coverage. No major bugs fixed this month; feature-focused work with immediate impact on ARM64 FP16 performance. Business value: faster FP16 data path improves inference throughput for DNN workloads on ARM64 and strengthens reliability with dedicated tests. Technologies/skills demonstrated: JIT compilation, aarch64 optimization, FP16 data path, kernel-level C++ development, and test automation.
1 Commits • 1 Features
Apr 1, 2025Concise monthly summary for 2025-04: Delivered JIT FP16-to-FP16 reorder on aarch64 for oneDNN by updating the jit_uni_reorder_kernel_f32_t to support FP16->FP16 reordering and adding FP16 reorder test coverage. No major bugs fixed this month; feature-focused work with immediate impact on ARM64 FP16 performance. Business value: faster FP16 data path improves inference throughput for DNN workloads on ARM64 and strengthens reliability with dedicated tests. Technologies/skills demonstrated: JIT compilation, aarch64 optimization, FP16 data path, kernel-level C++ development, and test automation.
April 2025
Activity
Loading activity data...
Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture93.4%
Performance86.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Shell
Technical Skills
ARMARM AssemblyCPU ArchitectureCPU OptimizationEmbedded SystemsFloating-Point ArithmeticJIT CompilationLow-Level OptimizationOptimizationPerformance EngineeringSIMDTesting
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline