Nathan Luehr
Principal Systems Software Engineer at NVIDIA
Minneapolis, Minnesota, United States
Summary
🤩
Rockstar🎓
Top SchoolNathan Luehr is a Principal Systems Software Engineer in Minneapolis with a decade of experience optimizing GPU-accelerated ML frameworks and low-level multi-GPU communication libraries. At NVIDIA he has driven TensorFlow and XLA performance improvements, integrated reduced-precision support, and fixed subtle concurrency and deadlock bugs in NCCL to scale beyond eight GPUs. His work spans production-grade build and dependency fixes, CUDA kernel optimizations, and MLIR/TF32 numerical tuning—contributions that directly improve training throughput on modern NVIDIA hardware. A PhD-trained computational chemist from Stanford, he brings uncommon domain depth in scientific computing and cluster design that informs practical performance engineering. Active in prominent open-source projects like TensorFlow, XLA, and NVIDIA’s NCCL/CNTK, he blends research rigor with production engineering to squeeze real-world speedups from complex systems.
10 years of coding experience
10 years of employment as a software developer
Ph.D. Theoretical Chemistry, Ph.D. Theoretical Chemistry at Stanford UniversityBachelor of Arts Chemistry and Mathematics, Bachelor of Arts Chemistry and Mathematics at Trinity Christian CollegeIgnite Program for Innovation and Entrepreneurship, Ignite Program for Innovation and Entrepreneurship at Stanford University Graduate School of BusinessPh.D. Theoretical Chemistry Chemistry, Ph.D. Theoretical Chemistry Chemistry at University of Illinois Urbana-ChampaignMaster of Theological Studies, Master of Theological Studies at Calvin Theological Seminary
English
Github Skills (33)
dependency-management10
c-language10
cublas10
python10
gpu-programming10
machine-learning10
build-system10
cntk10
deep-learning10
tensorflow10
parallel-computing10
gpu10
performance-optimization10
optimisation10
ccl10
Programming languages (4)
Github contributions (5)
NVIDIA/tensorflow
May 2019 - Dec 2022
An Open Source Machine Learning Framework for Everyone
Role in this project:
Backend & DevOps Engineer
Backend & DevOps EngineerContributions:12 releases, 233 commits, 11 PRs in 3 years 7 months
Contributions summary:Nathan's contributions primarily focused on improving the TensorFlow project's build and dependency management and fixing several bugs. They addressed compilation errors, fixed an overflow issue, and made improvements to the CUDA header version checks. Furthermore, the user bumped zlib version, and configured LLVM to use TF's zlib archive to resolve build breaks. In addition, the user also optimized some L0 resnet tests and XLA auto tuning.
machine-learning
NVIDIA/nccl
Nov 2015 - Mar 2017
Optimized primitives for collective multi-GPU communication
Role in this project:Back-end Developer
Back-end DeveloperContributions:21 commits, 8 PRs, 15 pushes in 1 year 3 months
Contributions summary:Nathan primarily focused on improving the NCCL library, a crucial component for multi-GPU communication. Their contributions included fixing race conditions in core collective operations like reduce and broadcast, addressing bugs in MPI initialization, and resolving deadlocks in reduce_scatter functionalities. Furthermore, the user added support for more than 8 GPUs and implemented memory access optimizations to enhance overall performance.
multi-gpucommunicationscppcudadeep-learning
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