Changhui Lin
Software Engineer at Google DeepMind
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolChanghui Lin is a software engineer with 11 years of experience specializing in parallel systems and the intersection of computing, networking, and storage to squeeze performance and reliability from the full stack. He has driven ML infrastructure and GPU runtime work at Google, contributing key refactors to TensorFlow, TFRT and XLA GPU backends to improve memory management and kernel launches for widely used open-source ML frameworks. Prior roles at Samsung focused on high-performance memory, FPGA proof-of-concepts, and large-scale streaming and semantic data platforms, combining architecture design, RTL/system debug, and software-hardware co-design. Based in Sunnyvale, he blends deep academic training (PhD in CS) with hands-on engineering across CPUs, GPUs, FPGAs, NVM and high-speed NICs, and has a track record of turning low-level systems research into production-grade improvements. A less obvious strength is his fluency moving between compiler/IR changes and runtime allocator fixes, enabling end-to-end performance wins for ML workloads.
11 years of coding experience
12 years of employment as a software developer
Master's degree Applied Mathematics, Master's degree Applied Mathematics at Peking UniversityDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at University of California, RiversideBachelor's degree Applied Mathematics, Bachelor's degree Applied Mathematics at Xiamen University
Chinese, English
Github Skills (21)
c-language10
back-end-development10
python10
llvm10
memory-management10
gpu-programming10
machine-learning10
mlr10
deep-learning10
tensorflow10
gpu10
cuda10
xla10
compiler10
cprogramming-language10
Programming languages (4)
Github contributions (5)
tensorflow/runtime
May 2021 - Dec 2022
A performant and modular runtime for TensorFlow
Role in this project:
Back-end Developer
Back-end DeveloperContributions:16 commits in 1 year 7 months
Contributions summary:Changhui primarily focused on refactoring and improving the GPU backend of the TensorFlow runtime. Their work involved removing unused GPU allocators, introducing and replacing methods for buffer allocation, and cleaning up the GPU buffer/allocator implementations. They updated the code to use the new buffer allocation methods throughout various GPU operations and device conversion functions, optimizing memory management. The user also addressed issues in GpuOneShotAllocator, including allocating zero-sized buffers.
runtimeperformantmodulartensorflow
openxla/xla
Jul 2021 - Dec 2022
A machine learning compiler for GPUs, CPUs, and ML accelerators
Role in this project:Back-end Developer
Back-end DeveloperContributions:26 commits in 1 year 5 months
Contributions summary:Changhui primarily focused on refactoring the XLA compiler's GPU code generation. They moved the launch dimension setting for kernel thunks, refactoring related code and modifying the `ir_emitter_unnested.cc` and `.h` files. The commits show a strong understanding of the compilation process and specifically the GPU-related code, and they also appear to optimize kernel launch parameters. These contributions reflect a focus on improving the efficiency and structure of the XLA compiler's GPU backend.
compilercommunity-drivenmachine-learningmodular
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.