Dian-lun Lin - Research Engineer at IBM

Dian-lun Lin

Research Engineer at IBM

Bellevue, Washington, United States

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Summary

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Rockstar

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Top School

Dian-lun Lin is a research engineer with a decade of experience specializing in high-performance computing and GPU-accelerated systems, currently working on IBM Watsonx Data in Bellevue. He holds a PhD in Computer Engineering from UW–Madison and advanced degrees from National Taiwan University and NCKU, combining deep academic rigor with practical research experience at NVIDIA and Intel Labs. Dian-lun’s open-source contributions to the widely used taskflow project show hands-on expertise in modern C++ and CUDA, including algorithms, cudaGraph integration, and expanded cublas-focused tests. He excels at bridging research and production, turning novel GPU algorithms into robust backend components for data- and compute-intensive workloads.

10 years of coding experience

1 year of employment as a software developer

Bachelor's degree Electrical and Electronics Engineering, Bachelor's degree Electrical and Electronics Engineering at National Cheng Kung University

Doctor of Philosophy - PhD Computer Engineering, Doctor of Philosophy - PhD Computer Engineering at University of Wisconsin-Madison

Master's degree Computer Science, Master's degree Computer Science at National Taiwan University

Github Skills (14)

cuda10

gpu-programming10

concurrent-programming10

parallel-processing10

c-language10

parallel10

parallel-computing10

cprogramming-language10

parallel-execution10

scientific-computing9

multithreading9

cluster-computing9

cublas9

unit-testing8

Programming languages (2)

C++Verilog

Github contributions (5)

taskflow/taskflow

Aug 2020 - Feb 2022

A General-purpose Task-parallel Programming System using Modern C++

Role in this project:

Back-end Developer

Contributions:17 commits, 12 pushes, 1 branch in 1 year 6 months

Contributions summary:Dian-lun implemented and updated CUDA-based algorithms within the taskflow library. Their work included adding a clear_native_graph function, a transpose algorithm, and supporting updates to the cudaGraph in cudaFlow. The commits demonstrate a focus on GPU programming and the integration of CUDA functionalities for high-performance computing tasks. The user also made several updates to unit tests including the addition of multiple tests related to cublas and pipeline functionalities.

multithreadingcuda-programmingtask-programmingmulti-threadingconcurrent-programming

dian-lun-lin/RTLflow-benchmarks

Sep 2021 - Aug 2022

Contributions:59 commits, 32 pushes, 2 branches in 10 months

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