google/XNNPACK
Dec 2025 – May 2026
6 Months active
Languages Used
C++Python
Technical Skills
C++ developmentC++ programmingalgorithm optimizationperformance tuningC++Kernel Development
Marie White
PROFILE
Marie White
Over six months, contributed to google/XNNPACK by developing and optimizing core features for neural network inference on CPUs. Work included building high-performance kernels for BF16 and INT8, implementing AVX and AMX intrinsics, and modernizing reduction and dot-product paths to improve throughput and numerical stability. Refactored parallel execution logic in C++ to address cache coherency and false-sharing, enhancing multi-threaded scalability. Introduced subgraph optimizations such as Common Subexpression Elimination and improved quantization handling for more reliable model fusion. Enhanced test coverage and tooling using Python and gtest, ensuring robust validation and maintainability across evolving tensor operations and system-level code.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
33Total
Bugs
6
Commits
33
Features
12
Lines of code
5,851
Activity Months6
Your Network
5216 peopleSame Organization
@google.com5154
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
May 2026
7 Commits • 1 Features
May 1, 2026May 2026 monthly summary for google/XNNPACK. Delivered IR-level dot-product optimization, significantly reducing serial loops and improving parallelism, with a new reduction allocation strategy and loop re-scheduling. Fixed critical correctness issues: get_dot_kernel integer type handling, max concurrency traversal across the full AST, and shape correctness for channelwise quantized scales, plus accurate logic for sub-byte constant folding. These changes improve runtime performance and reliability of dot-product computations, reduce risk of shape/mis-broadcast errors, and strengthen the codebase for future hardware targets. Demonstrated skills: C++ IR optimization, AST traversal and analysis, quantization handling, constant folding, and robust test improvements.
7 Commits • 1 Features
May 1, 2026May 2026 monthly summary for google/XNNPACK. Delivered IR-level dot-product optimization, significantly reducing serial loops and improving parallelism, with a new reduction allocation strategy and loop re-scheduling. Fixed critical correctness issues: get_dot_kernel integer type handling, max concurrency traversal across the full AST, and shape correctness for channelwise quantized scales, plus accurate logic for sub-byte constant folding. These changes improve runtime performance and reliability of dot-product computations, reduce risk of shape/mis-broadcast errors, and strengthen the codebase for future hardware targets. Demonstrated skills: C++ IR optimization, AST traversal and analysis, quantization handling, constant folding, and robust test improvements.
May 2026
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for google/XNNPACK focused on performance and scalability improvements in parallel execution. Delivered a Parallel Processing Performance Optimization by refactoring parallel for loops to capture variables by value, addressing false-sharing and L1 cache coherency thrashing observed in workloads with many tiny iterations. This optimization improves multi-threaded throughput and core utilization, reducing contention across threads and boosting throughput on multi-core CPUs. The change lays groundwork for more scalable parallel execution in future releases and demonstrates strong proficiency in C++ concurrency, memory locality, and performance profiling.
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for google/XNNPACK focused on performance and scalability improvements in parallel execution. Delivered a Parallel Processing Performance Optimization by refactoring parallel for loops to capture variables by value, addressing false-sharing and L1 cache coherency thrashing observed in workloads with many tiny iterations. This optimization improves multi-threaded throughput and core utilization, reducing contention across threads and boosting throughput on multi-core CPUs. The change lays groundwork for more scalable parallel execution in future releases and demonstrates strong proficiency in C++ concurrency, memory locality, and performance profiling.
March 2026
3 Commits • 2 Features
Mar 1, 2026In March 2026, delivered performance-focused AMX-2x2 kernels for BF16 and INT8 in google/XNNPACK with optimized tile configurations to maximize throughput on AMX-enabled hardware. Implemented architecture checks and robust error handling in the schedule_bench tool to prevent unsupported kernel execution and provide clearer feedback. Refactored internal constants for clarity by renaming kAmxTileRowBytes to tile_row_bytes, improving maintainability. Overall, these changes delivered measurable performance gains, enhanced stability, and a cleaner codebase, supporting faster inference workloads and easier future development.
3 Commits • 2 Features
Mar 1, 2026In March 2026, delivered performance-focused AMX-2x2 kernels for BF16 and INT8 in google/XNNPACK with optimized tile configurations to maximize throughput on AMX-enabled hardware. Implemented architecture checks and robust error handling in the schedule_bench tool to prevent unsupported kernel execution and provide clearer feedback. Refactored internal constants for clarity by renaming kAmxTileRowBytes to tile_row_bytes, improving maintainability. Overall, these changes delivered measurable performance gains, enhanced stability, and a cleaner codebase, supporting faster inference workloads and easier future development.
March 2026
February 2026
8 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for google/XNNPACK: Strengthened the bf16 data path with AVX2/AVX512BF support and added a stability-focused set of kernels. Implemented f32<->bf16 conversions, bf16-to-fp32 kernels, bf16-based dot product rewrites, and a subtract_fp32_bf16 kernel, plus a temporary accuracy workaround for bf16 dot products. Introduced a Common Subexpression Elimination (CSE) optimization pass to reduce redundant subgraphs and boost throughput. Revamped the dot_bench tooling and test suite with robust CLI parsing and enhanced test reporting using gmock matchers. These changes deliver faster bf16 inference, improved numerical stability, and stronger test coverage, enabling more reliable CPU-based deployment and better resource utilization.
February 2026
8 Commits • 3 Features
Feb 1, 2026February 2026 monthly summary for google/XNNPACK: Strengthened the bf16 data path with AVX2/AVX512BF support and added a stability-focused set of kernels. Implemented f32<->bf16 conversions, bf16-to-fp32 kernels, bf16-based dot product rewrites, and a subtract_fp32_bf16 kernel, plus a temporary accuracy workaround for bf16 dot products. Introduced a Common Subexpression Elimination (CSE) optimization pass to reduce redundant subgraphs and boost throughput. Revamped the dot_bench tooling and test suite with robust CLI parsing and enhanced test reporting using gmock matchers. These changes deliver faster bf16 inference, improved numerical stability, and stronger test coverage, enabling more reliable CPU-based deployment and better resource utilization.
January 2026
12 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for google/XNNPACK focusing on delivering high-value features, stability improvements, and architectural modernization that enable faster, more reliable inference across CPU backends.
12 Commits • 3 Features
Jan 1, 2026January 2026 performance summary for google/XNNPACK focusing on delivering high-value features, stability improvements, and architectural modernization that enable faster, more reliable inference across CPU backends.
January 2026
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025 (2025-12) monthly summary for google/XNNPACK: Delivered two major capabilities and fixed a critical buffer-size bug impacting stencil and dot-product paths. Features include Tile-k > 1 support in stencil_copy with updated output buffer sizing to accommodate the larger element size after transpose, expanding stencil operation flexibility and throughput. Also implemented bias-aware dot product initialization for FP32 and separate handling for quantized types to preserve fusions and correct scaling. A concurrent bug fix corrected an output_buffer sizing issue in stencil_copy when tile_k > 1, preventing mis-sized buffers and memory errors. Overall, these changes enhance performance, correctness, and maintainability across stencil operations and dot-product compute paths.
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025 (2025-12) monthly summary for google/XNNPACK: Delivered two major capabilities and fixed a critical buffer-size bug impacting stencil and dot-product paths. Features include Tile-k > 1 support in stencil_copy with updated output buffer sizing to accommodate the larger element size after transpose, expanding stencil operation flexibility and throughput. Also implemented bias-aware dot product initialization for FP32 and separate handling for quantized types to preserve fusions and correct scaling. A concurrent bug fix corrected an output_buffer sizing issue in stencil_copy when tile_k > 1, preventing mis-sized buffers and memory errors. Overall, these changes enhance performance, correctness, and maintainability across stencil operations and dot-product compute paths.
Activity
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Quality Metrics
Correctness95.2%
Maintainability84.2%
Architecture91.6%
Performance91.0%
AI Usage21.8%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
AVX intrinsicsC++C++ developmentC++ programmingKernel DevelopmentSubgraph ManagementTensor Operationsalgorithm designalgorithm optimizationbenchmarking toolsbug fixingcode refactoringcommand-line argument parsingconcurrent programminggmock
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