google/XNNPACK
Dec 2025 – Apr 2026
5 Months active
Languages Used
C++Python
Technical Skills
C++ developmentC++ programmingalgorithm optimizationperformance tuningC++Kernel Development
Marie White
PROFILE
Marie White
Marie White contributed to google/XNNPACK by engineering high-performance kernel and tensor operation features over five months, focusing on CPU inference optimization. She developed AMX and AVX-optimized kernels for BF16 and INT8, introduced LUT-based tensor operations, and modernized reduction APIs to improve throughput and reliability. Her work included implementing parallel processing optimizations using C++ multi-threading, refactoring code for maintainability, and enhancing test coverage with gtest and gmock. By addressing cache coherency, memory layout, and subgraph redundancy, Marie improved both performance and correctness. Her technical depth in C++, low-level optimization, and numerical computing enabled scalable, robust solutions for neural network workloads.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
26Total
Bugs
1
Commits
26
Features
11
Lines of code
5,741
Activity Months5
Your Network
4710 peopleSame Organization
@google.com4654
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
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.
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
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.
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.
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.
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
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.
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.
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.
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
Activity
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Quality Metrics
Correctness95.4%
Maintainability83.8%
Architecture92.4%
Performance91.6%
AI Usage22.4%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
AVX intrinsicsC++C++ developmentC++ programmingKernel DevelopmentSubgraph ManagementTensor Operationsalgorithm designalgorithm optimizationbenchmarking toolsbug fixingcode refactoringcommand-line argument parsinggmockgtest
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