
Marek Michalowski engineered performance-critical features across oneDNN and PyTorch, focusing on ARM and AArch64 architectures. He delivered optimized matrix multiplication and convolution kernels, including bf16-accelerated paths and JIT SVE enhancements, by leveraging C++ and low-level CPU architecture knowledge. In the oneDNN repository, Marek refactored BRGEMM descriptors, implemented microkernel APIs, and expanded CI coverage for AArch64, improving maintainability and validation. He also developed a global MKL-based random number generator for PyTorch, ensuring reproducibility and eliminating repeated variates. Marek’s work demonstrated depth in benchmarking, embedded systems, and performance engineering, consistently addressing architecture-specific challenges with robust, production-ready solutions.
January 2026: Delivered a new MKL-based Random Number Generator with a global vslStream for PyTorch, significantly improving RNG reproducibility and user experience. Replaced the previous reseeding approach that caused repeating variates with a single seeded MKLGenerator path tied to CPUGenerator state. Implemented MKLGeneratorImpl, ensured a full RNG period, and linked state save/restore to CPUGenerator changes. All relevant tests confirm zero repetitions in sampled draws and stable behavior across runs. The change reduces RNG-related surprises for users and simplifies reproducibility in production workloads.
January 2026: Delivered a new MKL-based Random Number Generator with a global vslStream for PyTorch, significantly improving RNG reproducibility and user experience. Replaced the previous reseeding approach that caused repeating variates with a single seeded MKLGenerator path tied to CPUGenerator state. Implemented MKLGeneratorImpl, ensured a full RNG period, and linked state save/restore to CPUGenerator changes. All relevant tests confirm zero repetitions in sampled draws and stable behavior across runs. The change reduces RNG-related surprises for users and simplifies reproducibility in production workloads.
December 2025: Delivered CI testing enhancements for the brgemm microkernel on AArch64 in oneDNN. Implemented experimental feature enablement in build scripts and updated benchmarks to gracefully handle unimplemented cases, enabling CI to accurately report brgemm functionality status. This work improves test coverage, reduces validation cycles, and provides clearer signals for architecture-specific optimizations, strengthening release readiness and performance validation across AArch64.
December 2025: Delivered CI testing enhancements for the brgemm microkernel on AArch64 in oneDNN. Implemented experimental feature enablement in build scripts and updated benchmarks to gracefully handle unimplemented cases, enabling CI to accurately report brgemm functionality status. This work improves test coverage, reduces validation cycles, and provides clearer signals for architecture-specific optimizations, strengthening release readiness and performance validation across AArch64.
October 2025 monthly summary for oneDNN (repo oneapi-src/oneDNN): BRGEMM subsystem enhancements including descriptor naming refactor and AArch64 microkernel API. These changes improve clarity, maintainability, and enable performance-oriented BRGEMM on ARM. No major bug fixes were required this month. Overall impact: codebase readiness for ARM-optimized BRGEMM and clearer interfaces, facilitating faster delivery of high-performance compute kernels.
October 2025 monthly summary for oneDNN (repo oneapi-src/oneDNN): BRGEMM subsystem enhancements including descriptor naming refactor and AArch64 microkernel API. These changes improve clarity, maintainability, and enable performance-oriented BRGEMM on ARM. No major bug fixes were required this month. Overall impact: codebase readiness for ARM-optimized BRGEMM and clearer interfaces, facilitating faster delivery of high-performance compute kernels.
March 2025 performance-focused update for uxlfoundation/oneDNN. Delivered bf16-accelerated convolution on aarch64 by dispatching bf16 math mode operations to Arm Compute Library (ACL) when available, enabling hardware-optimized bf16 paths and improving performance for relevant workloads. No major bugs fixed this month; focus was on feature delivery, code-path stability, and preparing for broader ACL-based acceleration. Demonstrates cross-architecture optimization, low-level dispatch mechanics, and collaboration with ACL to unlock performance gains.
March 2025 performance-focused update for uxlfoundation/oneDNN. Delivered bf16-accelerated convolution on aarch64 by dispatching bf16 math mode operations to Arm Compute Library (ACL) when available, enabling hardware-optimized bf16 paths and improving performance for relevant workloads. No major bugs fixed this month; focus was on feature delivery, code-path stability, and preparing for broader ACL-based acceleration. Demonstrates cross-architecture optimization, low-level dispatch mechanics, and collaboration with ACL to unlock performance gains.
January 2025 monthly summary for uxlfoundation/oneDNN focused on AArch64 JIT SVE 1x1 convolution improvements delivering correctness fixes, performance gains, and path optimization.
January 2025 monthly summary for uxlfoundation/oneDNN focused on AArch64 JIT SVE 1x1 convolution improvements delivering correctness fixes, performance gains, and path optimization.
Month: 2024-11. Focused work on ensuring correct ACL-layernorm behavior for inference mode on aarch64 and aligning tests with ACL outputs. Implemented non-global statistics mode for ACL LayerNorm and removed mean/variance benchdnn checks to reflect ACL results, preparing the codebase for deployment in inference scenarios.
Month: 2024-11. Focused work on ensuring correct ACL-layernorm behavior for inference mode on aarch64 and aligning tests with ACL outputs. Implemented non-global statistics mode for ACL LayerNorm and removed mean/variance benchdnn checks to reflect ACL results, preparing the codebase for deployment in inference scenarios.

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