Arturo Vargas
Computer Scientist at Lawrence Livermore National Laboratory
Livermore, California, United States
Summary
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Rockstar🎓
Top SchoolArturo Vargas is a computer scientist with 11 years of experience at the intersection of mathematics, high-performance computing, and computational engineering, currently based at Lawrence Livermore National Laboratory. He specializes in performance-portable HPC development for finite element applications, including contributing RAJA support and CUDA device setup improvements to the widely used MFEM C++ library. His work blends deep numerical methods (PhD in Computational and Applied Mathematics) with practical GPU-accelerated implementations—bringing MPI, OCCA, and RAJA expertise to production-grade code. Arturo’s background includes developing GPU solvers for wave propagation and image registration, and a lesser-known thread of his career is leading teaching and communication efforts, regularly presenting performance results and mentoring students.
11 years of coding experience
PhD, Computational and Applied Mathematics, PhD, Computational and Applied Mathematics at Rice UniversityB.S, Math; Mathematics; computational and applied math, B.S, Math; Mathematics; computational and applied math at University of California, Irvine
Spanish
Github Skills (7)
cuda10
el10
hpc10
c-language10
f10
cprogramming-language10
performance-optimization9
Programming languages (6)
Github contributions (5)
mfem/mfem
Jan 2019 - Jan 2023
Lightweight, general, scalable C++ library for finite element methods
Role in this project:
Back-end Developer
Back-end DeveloperContributions:227 reviews, 469 commits, 94 PRs in 4 years
Contributions summary:Arturo implemented RAJA support, a performance portable programming model, within the C++ finite element library, MFEM. They added new CUDA device setup functions in the general/config.cpp and included the RAJA header in general/okina.hpp. Furthermore, the user addressed compilation issues by refactoring dependencies and adjusting the build process with modifications to the setupMFEM.sh build script. The work improved code maintainability for future development.
c-plus-plusfinitefemamrc-library
LLNL/raja-suite-tutorial
Apr 2023 - Sep 2024
Tutorial materials for the RAJA Portability Suite
Contributions:9 reviews, 16 PRs, 84 pushes in 1 year 4 months
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