Matthew Larsen
Developer at Luminary Cloud
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolMatthew Larsen is a scientific software developer with 12 years of experience, currently building advanced scientific visualization tooling at Luminary Cloud from the San Francisco Bay Area. He combines a strong research background — including graduate work on distributed ray tracing and GPU/CPU parallelism at the University of Oregon and years at Lawrence Livermore National Laboratory — with practical production coding. Matthew contributes high-performance back-end enhancements to open-source projects like LLNL/zfp, adding CUDA encode/decode support to accelerate compressed numerical array workflows. He excels at bridging visualization research and HPC implementation, optimizing performance-critical paths for GPU architectures. Known for tackling low-level performance and build-system challenges, he brings both academic rigor and pragmatic engineering to complex data visualization problems.
12 years of coding experience
10 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at University of OregonJesuit High SchoolDoctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at Univerity of OregonBachelor's of Science, Computer Science, 3.83, Bachelor's of Science, Computer Science, 3.83 at CSUS
English
Github Skills (11)
c1710
cuda10
compression10
floating-point10
dynamic-array10
arrayobject10
parallel-computing10
c1110
parallel-arrays10
typed-array10
multidimensional-arrays10
Programming languages (6)
Github contributions (5)
LLNL/zfp
Jun 2018 - Apr 2019
Compressed numerical arrays that support high-speed random access
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 commits, 2 PRs, 16 pushes in 9 months
Contributions summary:Matthew's contributions focus on adding and enabling CUDA support for the ZFP library. They implemented initial CUDA versions for encoding and decoding, including modifications to core C and CUDA files, and developed supporting structures. The user also addressed compilation issues and optimized encode/decode launch processes, indicating their focus on performance enhancements within a high-performance computing context. These changes included the addition of CUDA-specific functions and integration within existing code structures.
radiussspeedcompressiondata-vizeigen3
Alpine-DAV/ascent
Feb 2017 - Sep 2021
A flyweight in situ visualization and analysis runtime for multi-physics HPC simulations
Contributions:3 releases, 22 reviews, 527 commits in 4 years 8 months
mpipythonin-situruntimesimulation
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