Alexander Heinecke
Intel Fellow at Intel Corporation
California, United States
Summary
🤩
Rockstar🎓
Top SchoolAlexander Heinecke is an Intel Fellow with 11 years of experience specializing in hardware-aware multi/many-core computing for scientific computing and deep learning, focusing on parallelizing adaptive numerical methods and optimizing DL primitives like CNNs, RNNs/LSTMs, MLPs and models from ResNets to BERT. He rose through research and engineering roles at Intel after a PhD from TUM, blending deep academic rigor with practical, production-grade performance engineering. A frequent contributor to high-performance open-source projects such as libxsmm and PlaidML, he has implemented vectorized 8-bit transforms (VNNI2/VNNI4) and tuned CPU paths to squeeze real-world speedups. Based in California, he combines systems-level insight with hands-on backend development, often surfacing non-obvious microarchitectural optimizations that materially improve dense and sparse matrix operations.
11 years of coding experience
11 years of employment as a software developer
Dr. rer. nat., Informatik, Dr. rer. nat., Informatik at Technical University of MunichMaster of Science with honors (M.Sc.), Finance and Information Management, Master of Science with honors (M.Sc.), Finance and Information Management at University of Augsburg
Github Skills (25)
assembly10
c-language10
matrix10
avx10
c1110
vectorization10
x8610
c1710
mat10
performance-optimization10
assembler10
performance-tuning10
cprogramming-language10
optimization10
mlr9
Programming languages (6)
Github contributions (5)
libxsmm/libxsmm
Mar 2015 - Jan 2023
Library for specialized dense and sparse matrix operations, and deep learning primitives.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 releases, 598 reviews, 3259 commits in 7 years 11 months
Contributions summary:Alexander focused on optimizing dense and sparse matrix operations for deep learning primitives. Their contributions include fixing conditions, optimizing existing code, and adding support for vectorized instructions. These changes suggest a focus on performance enhancement within the library. They also implemented and tested the VNNI2 and VNNI4 transforms for 8bit.
bfloat16avxsimdlapackavx512
plaidml/plaidml
Apr 2020 - Oct 2022
PlaidML is a framework for making deep learning work everywhere.
Role in this project:Backend Developer
Backend DeveloperContributions:28 reviews, 11 commits, 17 PRs in 2 years 6 months
Contributions summary:Alexander primarily focused on enhancing the PlaidML framework, contributing to its core functionality and performance. The contributions include updating dependencies like libxsmm, which involved bug fixes and improved compatibility. Further work involved refactoring the stencil pass, optimizing the code, and integrating best-performing options for environment variable configurations. The user also updated documentation and addressed issues related to CPU support.
pytorchtvmdeep-learningmachine-learningcompiler
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