Concise monthly performance summary for April 2026 focusing on key business value and technical achievements across oneapi-src/oneDNN. Highlights improved correctness and performance in GEMM JIT, enhanced robustness for numeric configurations, expanded testing coverage for mixed-precision workloads, and fixes ensuring proper compilation and hardware-safe emulation paths.

March 2026 highlights for oneDNN: Delivered stability-focused enhancements and performance improvements to the GEMM JIT across PVC and NVL-P+ architectures. Key work includes robustness fixes with validation checks and regression safeguards (e.g., disabling mx dst for pre-PVC, rejecting NVL-P+ SLM strategies, and correcting kernel selection), expansion of capabilities (force k workgroup alignment, atomic load handling, 2D scaling, and advanced data-type support), and Emulation/Swizzle fixes with expanded benchdnn test coverage for 2D matmul attributes. These changes reduce regression risk, unlock higher-throughput GEMM paths on newer hardware, and improve reliability of DNN workloads on oneDNN.

February 2026: oneDNN enhancements focusing on performance and reliability for GEMM/JIT on XE3P, NGEN regioning/INT4 optimizations, and benchdnn matmul test coverage. Delivered concrete improvements to hardware-specific paths, data handling, and expanded test coverage, resulting in higher throughput, better correctness guarantees, and reduced regression risk.

January 2026 performance summary for oneDNN (oneapi-src/oneDNN). Focused on delivering performance and correctness improvements in GEMM JIT quantization, reinforcing matrix multiplication accuracy, expanding testing and benchmarking, and addressing edge-case handling. The work delivered strengthens production readiness for high-throughput workloads and ensures reliable numeric results across common inferencing scenarios.

December 2025 monthly summary for oneDNN: Delivered key features that enhance performance, scalability, and developer experience. Major work includes GEMM multi-group scaling enhancements with JIT support across multiple group dimensions (including 2D blocked scale tests), a bug fix for API configuration validation to prevent incompatible configurations when group_ndims > 0, and a documentation update for benchdnn data types. These contributions improve multi-group GEMM throughput and correctness, enforce safer API usage, and clarify testing capabilities for benchdnn. The month also strengthened tests and documentation, setting the stage for faster onboarding and more reliable benchmarks.

Monthly summary for 2025-11 focusing on performance, correctness, and stability across oneDNN GEMM/JIT pathways. Delivered core numerical enhancements, robust scaling/rounding, and reliability improvements in quantized formats, enabling broader hardware support and improved business value for high-performance workloads.

Month: 2025-10 — Focused on extending Windows performance testing coverage for benchdnn in oneDNN, delivering cross-platform parity and faster performance validation. Removed a blocking condition in CMakeLists.txt to enable mode p modifier on Windows, enabling performance testing and broader test coverage. This change, tracked in commit d5e144c9432aaeae4f77f214c355c5f580f2fb7a, improves benchmarking reliability on Windows and supports faster feedback in CI. No major bug fixes were required this month; primary value came from enabling and stabilizing Windows benchdnn testing, which expands business value through more robust performance data and platform reach.

September 2025 (oneapi-src/oneDNN): Delivered robust GEMM JIT kernel fixes and enhancements on Xe to improve correctness, reliability, and performance; expanded debugging support and hardware exposure; and strengthened thread configuration robustness for Xe architecture. These changes enhance kernel reliability, accelerate debugging and optimization cycles, and deliver more predictable performance on Xe-based workloads.

Performance summary for 2025-08: Delivered pivotal Xe3 kernel tag mapping fix, expanded GEMM/CPU quantization robustness, and Xe-specific GEMM/JIT enhancements with kernel-DB tuning. These efforts improved hardware kernel selection accuracy on Xe3, increased configurability and correctness of quantization across CPU and GEMM paths, and yielded performance/maintainability gains through JIT and backend optimizations.

Monthly summary for 2025-07 focusing on delivering high-impact features and stability improvements in oneDNN. Highlights include advanced GEMM kernel correctness, scaling, and quantization improvements for Xe architectures, paired with expanded benchdnn matmul test coverage and CI-friendly test configurations. A broad set of bug fixes in quantization and initialization pathways significantly improved accuracy and reliability across Xe and pre-XeHPC devices.

June 2025 performance summary for oneapi-src/oneDNN: Delivered FP4/FP8 dequantization enhancements across GEMM/matmul paths, enabling dequantization of FP4 weights and expanding test coverage; added per-tensor dequant with batched GEMM support. Implemented robust mask handling improvements for per-tensor masks, refined GEMM/JIT attribute mask logic, and removed references to default masks, improving correctness and maintainability. Applied Xe architecture-specific fixes to align GEMM/JIT behavior with Xe capabilities, including FP4 arch restrictions and masking adjustments. Expanded benchdnn batched matmul testing with broader data types and mask scenarios, and updated documentation for FP4/FP8 decomp support. All changes were accompanied by targeted tests, performance considerations, and clear code/documentation updates. Business value: enabled broader quantized inference workloads with lower precision formats, improved reliability, and hardware-aligned performance.

May 2025 monthly summary: Strengthened core matmul paths (OpenCL and XE) with correctness, shape/post-op support, and quantization capabilities; improved attribute management with a query-based model; expanded test coverage and documentation; enabling bf8 emulation in third-party paths and per-tensor source scales in common matmul. These changes increase reliability across large-scale workloads and set the stage for higher performance via reshape-friendly post-ops and batched workflows.

April 2025 (oneDNN) monthly summary: Focused on increasing benchmarking reliability, numerical correctness for low-precision paths, and developer experience through enhanced test coverage, clearer documentation, and robust defaults.

March 2025 monthly summary for oneDNN focused on expanding low-precision data-path support, GPU backend readiness, and stability improvements. Delivered FP8/FP4 data type support across GEMM and convolution paths, improved hardware-specific tuning for e3m0, and enhanced validation and documentation to drive faster, more efficient GPU inference.

February 2025: Delivered FP4 (f4_e3m0) data type support and FP4 matmul enhancements in GEMM for Intel Xe, along with internal GEMM kernel infrastructure cleanup and enhanced testing tooling in oneDNN. The work provides an FP4 compute path for Xe GPUs, strengthens GEMM stability, and expands validation coverage, enabling broader adoption of FP4 for efficient inference and training workloads.

January 2025 (Month: 2025-01) performance snapshot for oneDNN focusing on Xe GPU optimizations and low-precision compute. Delivered FP4 support in GEMM/OpenCL matmul, expanded GPU test coverage, refined GEMM kernel configuration, and reinforced safety checks to improve reliability and future performance work. The work strengthens ML compute efficiency, broadens device support, and reduces risk for production workloads on Xe architectures.

December 2024 (2024-12) Monthly Summary for oneapi-src/oneDNN Key focus: FP8 mixed-precision improvements and stability across JIT, GEMM, and OpenCL backends on Xe, plus test/benchmark workflow refinements to accelerate development and validation. Top achievements (key deliverables): - FP8 mixed-precision support and scaling delivered across JIT, GEMM, and OpenCL backends for Xe architectures. Implemented FP8 typing/retention logic, enabled mixed FP8 compute for convolution and matmul, refined scale handling, and tuned kernel/benchmark paths for FP8 workloads. - Notable commits include: fdd86bcae82a80d684d7a368db76d31f7b1f4f9a (xe: jit: reorder: fixup, align hf8 emulation with gemm), 1cdeed40253bdcbf01d6192ab4293577520e0a60 (xe: jit: conv: fix mad fp8 retyping), 80b61e36646c9d1ab64d439a5bf1ea0966c6f0d9 (xe: jit: backport mixed fp8 compute), 13eddc82e7bdff621ae14063dee3b84ac98ede1b (xe: jit: backport src, dst compute scales), 96785904dcc9b0503b06a17299a04ac4c87d9161 (xe: jit: conv: fix typed scaling), 7f8ce54cd8c5db9067b8a8d713c1f92d572d7768 (xe: jit: gemm: handle quantization offsets), f3ea4941dd68b2b67fe6be70abbeffdda2214b23 (xe: jit: gemm: adjust strategies for fp8 weights decomp), 870e1b72aef3fd1abbe97cbd5bf944cbfae094ff (tests: benchdnn: matmul: reduce int4 weights range), 129e991a3d31e939ed6957c6d60c27b6a6ba1221 (tests: benchdnn: add mixed fp8 conv, matmul inputs), fc17debfb5047385d7333993556c2e3c53335f5c (tests: benchdnn: restrict dst scales to common for cpu), 1b0eb482a839f8bd3cd5dc8570bcf12a41553b12 (xe: ocl: enable typed scales, fp8 for matmul, conv), 1ccda148804f2b9064f5945c013bfd33fdafb29b (xe: ocl: enable per_oc dst scale for ref_matmul) - GEMM kernel debugging and test-suite adjustments delivered: reworked debug strategy to run earlier in finalization flow and simplified test configs by removing outdated DST-scale checks, improving development velocity and relevance of tests. - Key commits: 3d833ff06c3542eef87d699fc1552148cc6d4190 (xe: jit: gemm: fix debug strategy submission), f4159423395fca19f4288170eb8dd24744765e92 (tests: gtests: remove dst scale checks). Major bugs fixed: - Aligned HF8 emulation with GEMM paths to reduce divergence and improve correctness of FP8 computations. - Fixed FP8 MAD retyping and scaling paths to ensure accurate quantization and results in conv and matmul workloads. - Restored and stabilized mixed FP8 compute through careful backports and scale propagation across src/dst paths. - UI/test stability improvements by tightening CPU DST-scale behavior and adjusting benchdnn data ranges to avoid edge-case skew. Overall impact and business value: - Substantial uplift in FP8 throughput and accuracy across JIT, GEMM, and OpenCL on Xe, enabling mixed-precision networks with reduced memory bandwidth and better latency/throughput balance for inference workloads. - Accelerated development and validation cycles through earlier GEMM debug execution and streamlined test configurations, leading to faster iteration and more robust performance guarantees for customers. - Strengthened confidence in production-grade FP8 paths via end-to-end benching and targeted fixes, supporting broader AI/ML workloads on oneDNN-based platforms. Technologies/skills demonstrated: - FP8 mixed-precision engineering, typing/retention logic, and scale handling across JIT, GEMM, and OpenCL backends. - Kernel-level tuning and backporting of FP8 compute paths for conv and matmul on Xe. - Test infrastructure improvements: benchdnn integration, mixed-FP8 input scenarios, and removal of obsolete DST-scale checks. - OpenCL backend enablement for typed scales and per-OC scaling in ref_matmul. Repository: oneapi-src/oneDNN Prepared for review and performance appraisal.

November 2024 monthly performance summary for oneDNN (oneapi-src/oneDNN). This period focused on delivering performance- and reliability-oriented enhancements for FP8/HF8 on Intel Xe, plus optimization of the GEMM kernel database and fixes to RNG correctness. The work aligns with business goals of accelerating FP8-based inference, improving GPU throughput, and ensuring numerical correctness across JIT, GEMM, and convolution paths.

October 2024 performance highlights for uxlfoundation/oneDNN. Delivered stochastic rounding integration across GEMM, JIT, and convolution paths with seed handling and modular RNG design, enabling more robust numeric behavior and reproducibility across CPU, GPU, and OpenCL backends. Implemented support for stochastic rounding as part of eltwise, GEMM, and conv paths, and extended test coverage to exercise these paths.

Monthly summary for 2024-09 focusing on uxlfoundation/oneDNN: Implemented DNNL/NGEN data type conversion support for f8_e5m2 and f8_e4m3, enabling broader FP8 workflow interoperability across the framework, with JIT/NGEN integration to DNNL type conversions. This work lays groundwork for expanded data type coverage and cross-framework collaboration, aligning with roadmap for FP8 support.