Tim Moon
Senior Performance Engineer at NVIDIA
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTim Moon is a Senior Performance Engineer at NVIDIA with 11 years of experience optimizing GPU-accelerated systems at the confluence of distributed algorithms, high-performance computing, and deep learning. He builds and tunes low-level libraries and training stacks—contributing performance wins to OpenBLAS multi-threaded GEMM, Spack build configurations for HPC ML stacks, and NVIDIA Apex optimizations for mixed-precision, distributed optimizers. At NVIDIA he develops Transformer Engine and drives MLPerf LLM benchmark performance, pairing C++, CUDA, and Python expertise to push FP8 and model-parallel training forward. His background at Lawrence Livermore and early GPU research work reflect a consistent focus on scaling neural network training on supercomputers. Colleagues rely on him for pragmatic, benchmark-driven improvements that span CUDA kernels to build systems. He combines a Physics BS from Rice and an MSE in Computational and Mathematical Engineering from Stanford with a taste for squeezing performance from both algorithms and infrastructure.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Science (BS), Physics, Bachelor of Science (BS), Physics at Rice UniversityMaster’s Degree, Computational and Mathematical Engineering, Master’s Degree, Computational and Mathematical Engineering at Stanford University
Github Skills (26)
pytorch10
distributed-training10
package-management10
multithreading10
cmake10
c1110
blas10
c1710
kernel10
gemfire10
performance-optimization10
optimisation10
build-automation10
cuda10
scientific-computing10
Programming languages (6)
Github contributions (5)
NVIDIA/apex
Jun 2022 - Jan 2023
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:29 reviews, 18 commits, 37 PRs in 7 months
Contributions summary:Tim primarily contributes to the `DistributedFusedAdam` optimizer, a core component of the repository, by introducing and optimizing features for mixed precision and distributed training. Their work focuses on enhancing the optimizer's capabilities to support features such as ZeRO-2, Megatron integration, and improved gradient clipping. The user also refactored and added tests for the optimizer, demonstrating a focus on performance and reliability. This involved modifying CUDA kernels and optimizing the underlying communication strategies.
pytorchraymixed-precisiondeep-learningtemporal-data
spack/spack
Nov 2020 - Jan 2022
A flexible package manager that supports multiple versions, configurations, platforms, and compilers.
Role in this project:Automation Engineer / Build & Release Engineer
Automation Engineer / Build & Release EngineerContributions:5 commits, 5 PRs, 2 comments in 1 year 2 months
Contributions summary:Tim's contributions center around modifying build configurations and dependencies within the Spack package manager. Their work includes setting environment variables for NVSHMEM, updating CMake versions for LBANN-related projects, and ensuring packages link to necessary libraries like py-protobuf and nvshmem. These changes primarily focus on ensuring correct build processes and dependency management for scientific computing packages. This suggests a focus on build system configuration.
compilerspythonradiussplatformslinux
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