Qianfeng Zhang
Software Developer at 中软国际
Beijing, Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolQianfeng Zhang is a software developer with eight years’ experience specializing in data warehouse design, ETL development, and performance engineering for machine learning kernels. Based in Beijing and currently at 中软国际, he blends backend system craftsmanship with a strong interest in data mining and practical deployment of analytics pipelines. His open-source contributions to high-profile projects like AMD ROCm, MIOpen, and Facebook Research’s xformers demonstrate deep expertise optimizing tensor operators, attention mechanisms, and reduction kernels for ROCm hardware. He routinely tackles low-level kernel performance, precision handling (bfloat16), and memory-access issues, translating research-grade ML primitives into production-ready code. With a telecommunications engineering background, he brings a systems-thinking approach that connects database architecture to machine-learning performance tuning. Colleagues would note his knack for simplifying complex kernels and improving readability while squeezing out measurable speedups.
8 years of coding experience
Bachelor's degree, Telecommunications Engineering, Bachelor's degree, Telecommunications Engineering at Shaanxi University of Technology
Chinese, English
Github Skills (16)
attention-mechanism10
cuda10
tensorrt10
pytorch10
machine-learning10
roc10
c-language10
tensor10
tensorflow10
cprogramming-language10
performance-optimization10
operation10
linear-algebra10
numerical-computing9
opencl9
Programming languages (3)
Github contributions (5)
ROCm/composable_kernel
Sep 2021 - Jan 2023
Composable Kernel: Performance Portable Programming Model for Machine Learning Tensor Operators
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:608 reviews, 159 commits, 84 PRs in 1 year 5 months
Contributions summary:Qianfeng primarily contributed to the optimization of machine learning tensor operators within the composable_kernel repository. Their work focused on refining reduction kernels, including fixes related to data type handling and zero-value retrieval. They made improvements to the code for better readability and simplification, and they introduced enhancements to tensor dimension reordering on the host, as well as splitting files. This indicates a focus on improving performance.
kerneldeep-learningprogramming-modelmachine-learningtensor-learning
ROCm/MIOpen
Jul 2020 - Feb 2022
AMD's Machine Intelligence Library
Role in this project:Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:96 reviews, 29 commits, 39 PRs in 1 year 7 months
Contributions summary:Qianfeng primarily contributed to the implementation and optimization of generic tensor reduction operations within the MIOPEN library. Their work involved adding support for new reduction operations (AVG, AMAX, NORM1, NORM2) and enhancing existing ones. They focused on improving performance through kernel-level optimizations and addressing memory access issues. Additionally, the user integrated new kernels and made adjustments to the build process for the specific hardware architecture (gfx908).
amdcluster-computingmachine-learningblasmachine-intelligence
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