John Camier is a computational scientist and GPU software engineer with nearly three decades of experience bridging hardware, embedded systems and high-performance scientific computing. He has led HPC and runtime research at CEA and LLNL, founded Gamis to develop GPU-accelerated high-order finite element solvers, and was a 2025 Gordon Bell Prize finalist for a tsunami digital twin. Skilled in C/C++, CUDA, LLVM toolchains and numerical analysis, he has a track record of doubling multi‑physics kernel performance and porting complex unstructured overset methods entirely to GPUs. He led the nabla-lang DSL project and contributes performance-critical changes to the widely used MFEM finite element library, reflecting both compiler and application-level expertise. Early experience designing safety-critical RTOS and numerical hardware for avionics gives him a rare combination of rigorous V&V, embedded FPGA work and large-scale simulation optimization. Based in the United States with an MS from ENSIMAG, he blends research leadership, vendor engagement and hands-on kernel tuning to turn cutting-edge algorithms into production-grade, GPU-first implementations.
9 years of coding experience
15 years of employment as a software developer
Master of Science - MS Computational and Applied Mathematics, Master of Science - MS Computational and Applied Mathematics at National School of Computer Science and Applied Mathematics of Grenoble
Lightweight, general, scalable C++ library for finite element methods
Role in this project:
Backend Developer
Contributions:442 reviews, 2766 commits, 178 PRs in 5 years 4 months
Contributions summary:John's commits primarily involve modifications to the core numerical methods library MFEM, focusing on implementing and optimizing high-order kernels for the finite element method. Their work includes code changes related to the convection and diffusion bilinear forms, indicating a focus on numerical simulations. The commits show proficiency in applying various methods and code changes to adapt the library.
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