Daniel Margala
Research Software Engineer at National Energy Research Scientific Computing Center (NERSC)
San Francisco Bay Area United States
Summary
👤
Senior🎓
Top SchoolDaniel Margala is a research software engineer with 14 years of experience applying physics-grade quantitative rigor to data science and high-performance computing. Based in the San Francisco Bay Area, he combines a PhD in cosmology with hands-on expertise in Python, SQL, web applications, and GPU-accelerated numerical libraries. At NERSC he builds scalable scientific software, and his open-source contributions to the prominent CuPy project include implementing GPU linear-algebra routines (cuSOLVER syevj/posv) and batched operations that improve performance for large-scale numerical workloads. Previously he delivered data-driven insights at Fitbit and Apple, bridging research and product analytics. Colleagues rely on him for designing robust solutions that translate complex simulations into production-ready tools.
14 years of coding experience
4 years of employment as a software developer
University of California, Los AngelesDoctor of Philosophy (PhD), Physics, Doctor of Philosophy (PhD), Physics at University of California, Irvine
Github Skills (12)
decompose10
cuda10
gpu-programming10
deco10
matrix-decomposition10
linear-algebra10
eigenvalue10
decomposition10
cusolver10
numpy9
python9
cublas9
Programming languages (5)
Github contributions (5)
cupy/cupy
Jun 2020 - Nov 2020
NumPy & SciPy for GPU
Role in this project:
Back-end Developer / Library Developer
Back-end Developer / Library DeveloperContributions:17 commits, 2 PRs, 10 comments in 4 months
Contributions summary:Daniel contributed to the cuPy library by adding and modifying functions related to the cuSOLVER library, specifically focusing on symmetric eigenvalue solvers (syevj) and Cholesky solvers (posv). Their work included implementing buffer size calculations, core function calls, and supporting batched operations. They also addressed issues related to code formatting and documentation, ensuring code quality and usability. The contributions involved working with CUDA and cuSOLVER functions for GPU-accelerated linear algebra operations.
cudapythoncusolvergpunumpy
desihub/gpu_specter
Apr 2020 - Jan 2023
Scratch work for porting spectroperfectionism extractions to GPUs
Contributions:260 commits, 57 PRs, 167 pushes in 2 years 9 months
cudapythonextractionsgpugpus
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