Jongsoo Park
Member Of Technical Staff at OpenAI
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJongsoo Park is a Member of Technical Staff at OpenAI with a decade-long track record of software and processor-architecture co-design focused on accelerating machine learning, HPC, and graph analytics. He led co-design efforts at Meta—driving Llama3 pre-train scalability, training/inference performance, and the Meta Training and Inference Accelerator—and previously spearheaded FBGEMM and quantization work that improved real-world recommendation model performance. His contributions span low-level SIMD and AVX512 optimizations, compiler backends, and memory-aware kernel tuning, evidenced by impactful open-source work in PyTorch, libxsmm, and FBGEMM (including performance fixes to the inductor compiler and Flash Attention). A Stanford PhD and former Intel researcher with prize-winning HPC publications, he blends deep academic rigor with production-grade systems engineering and a knack for squeezing latency and throughput from both hardware and software.
10 years of coding experience
17 years of employment as a software developer
BS Electrical Engineering, BS Electrical Engineering at Seoul National UniversityHigh school, High school at Seoul Science High SchoolPhD Electrical Engineering, PhD Electrical Engineering at Stanford University
Stackoverflow
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0reached
0answers
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Github Skills (28)
pytorch10
c-language10
caffe10
back-end-development10
avx10
matrix-multiplication10
machine-learning10
deeplearning-ai10
deep-learning10
gpu10
performance-optimization10
ai10
convolution10
simd10
cprogramming-language10
Programming languages (5)
Github contributions (5)
pytorch/FBGEMM
Nov 2018 - Jan 2023
FB (Facebook) + GEMM (General Matrix-Matrix Multiplication) - https://code.fb.com/ml-applications/fbgemm/
Role in this project:
Back-end Developer & Performance Engineer
Back-end Developer & Performance EngineerContributions:8 reviews, 316 commits, 344 PRs in 4 years 2 months
Contributions summary:Jongsoo focused on the implementation and optimization of core functionality within the fbgemm library, specifically contributing to matrix-matrix multiplication (GEMM) operations. Their work involved the addition of new methods, such as `equals` and `metaEquals`, to the `PackBMatrix` class, as well as significant refactoring of transpose code, optimizing it with SIMD instructions. Additionally, the user addressed rounding consistency issues and adapted the code to support group convolutions, highlighting their dedication to performance improvements and feature enhancements.
matrix-multiplicationfacebookmultiplicationmatrixml-applications
pytorch/pytorch
Apr 2018 - Dec 2022
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Role in this project:ML Engineer
ML EngineerContributions:11 reviews, 217 commits, 299 PRs in 4 years 8 months
Contributions summary:Jongsoo primarily focused on improving and optimizing the performance of machine learning models and related infrastructure within the PyTorch ecosystem. Their contributions included fixing issues in the inductor compiler, a component used for optimizing model performance, and addressing problems in the transformer benchmark, particularly with scaled dot-product attention. They also added bfloat16 support in erfinv and made changes to the flash attention implementation.
pythongpu-accelerationdeep-learninggpunumpy
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