Morgan & Niemeyer - Performant and Portable High-Performance Computing at Scale via Python and Numba

MCDC (Monte Carlo Dynamic Code) is a Python/Numba-based framework for nuclear transport simulations that can run on both CPUs and GPUs at scale

The project achieves high performance using MPI for parallelization rather than shared memory, with each thread handling independent particle histories

Numba JIT compilation enables performant code execution but comes with limitations like lack of ahead-of-time compilation and restricted GPU operations

The framework can model complex time-dependent nuclear reactions, including the movement of fissile materials and neutron transport through materials

Performance is within 2-10x of traditional compiled codes, with 21-24x speedup when using GPUs compared to CPUs

Key challenges include handling AMD GPU support, debugging complexity compared to C++, and managing statistical error that converges slowly

The codebase is open-source, pip-installable, and designed for collaboration through GitHub

Monte Carlo methods allow handling 7 independent variables without discretization issues common in deterministic approaches

The project demonstrates that Python + Numba can enable rapid methods development while maintaining performance portability across architectures

Statistical error decreases by 1/√N, requiring massive particle counts to achieve acceptable error levels for simulations