oneapi-src/oneDNN
Nov 2024 – Oct 2025
12 Months active
Languages Used
C++OpenCLOpenCL CCShellCMakeMarkdownPython
Technical Skills
Code GenerationCompiler DevelopmentDeep Learning OptimizationGPU ComputingGPU ProgrammingIntel Xe Architecture
Kealan Barbieri
PROFILE
Kealan Barbieri
Kealan Barbieri developed advanced low-precision compute and quantization features for the oneapi-src/oneDNN repository, focusing on GEMM and convolution kernels for Intel Xe architectures. He engineered support for FP4 and FP8 data types, implemented robust dequantization and scaling logic, and expanded test coverage to ensure correctness across batched and mixed-precision workloads. Using C++ and OpenCL, Kealan refactored kernel attribute handling, improved mask logic, and enhanced performance validation on both Linux and Windows platforms. His work addressed hardware-specific constraints, streamlined debugging, and improved reliability, demonstrating deep expertise in GPU programming, code optimization, and performance engineering for production AI workloads.
Overall Statistics
Feature vs Bugs
73%Features
Repository Contributions
163Total
Bugs
12
Commits
163
Features
32
Lines of code
181,998
Activity Months12
Your Network
1970 people
Work History
October 2025
1 Commits • 1 Features
Oct 1, 2025Month: 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.
1 Commits • 1 Features
Oct 1, 2025Month: 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.
October 2025
September 2025
10 Commits • 2 Features
Sep 1, 2025September 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.
September 2025
10 Commits • 2 Features
Sep 1, 2025September 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.
August 2025
11 Commits • 2 Features
Aug 1, 2025Performance 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.
11 Commits • 2 Features
Aug 1, 2025Performance 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.
August 2025
July 2025
17 Commits • 2 Features
Jul 1, 2025Monthly 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.
July 2025
17 Commits • 2 Features
Jul 1, 2025Monthly 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
20 Commits • 3 Features
Jun 1, 2025June 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.
20 Commits • 3 Features
Jun 1, 2025June 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.
June 2025
May 2025
24 Commits • 8 Features
May 1, 2025May 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.
May 2025
24 Commits • 8 Features
May 1, 2025May 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
8 Commits • 3 Features
Apr 1, 2025April 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.
8 Commits • 3 Features
Apr 1, 2025April 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.
April 2025
March 2025
13 Commits • 3 Features
Mar 1, 2025March 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.
March 2025
13 Commits • 3 Features
Mar 1, 2025March 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
19 Commits • 1 Features
Feb 1, 2025February 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.
19 Commits • 1 Features
Feb 1, 2025February 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.
February 2025
January 2025
15 Commits • 3 Features
Jan 1, 2025January 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.
January 2025
15 Commits • 3 Features
Jan 1, 2025January 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
14 Commits • 2 Features
Dec 1, 2024December 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.
14 Commits • 2 Features
Dec 1, 2024December 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.
December 2024
November 2024
11 Commits • 2 Features
Nov 1, 2024November 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.
November 2024
11 Commits • 2 Features
Nov 1, 2024November 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.
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Quality Metrics
Correctness87.6%
Maintainability87.0%
Architecture84.2%
Performance79.8%
AI Usage20.2%
Skills & Technologies
Programming Languages
CC++CMakeMarkdownOpenCLOpenCL CPythonShell
Technical Skills
API DesignAssembly LanguageBenchmarkingBuild SystemsC++C++ DevelopmentCI/CDCMakeCPU OptimizationCode GenerationCode RefactoringCode optimizationCode refactoringCode safetyCompiler Development
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