Scalable Domain-decomposed Monte Carlo Neutral Transport for Nuclear Fusion

TL;DR

This paper introduces a domain-decomposed Monte Carlo algorithm in the new open source code Eiron, enabling scalable neutral transport simulations for nuclear fusion on large supercomputers.

Contribution

A novel domain decomposition algorithm implemented in Eiron improves Monte Carlo neutral transport simulation scalability and performance.

Findings

DDMC outperforms existing algorithms in strong scaling tests.

Superlinear scaling observed on Mahti supercomputer for certain grid sizes.

Weak scaling efficiency reaches 45% with 16384 cores in high-collisional cases.

Abstract

EIRENE [1] is a Monte Carlo neutral transport solver heavily used in the fusion community. EIRENE does not implement domain decomposition, making it impossible to use for simulations where the grid data does not fit on one compute node (see e.g. [2]). This paper presents a domain-decomposed Monte Carlo (DDMC) algorithm implemented in a new open source Monte Carlo code, Eiron. Two parallel algorithms currently used in EIRENE are also implemented in Eiron, and the three algorithms are compared by running strong scaling tests, with DDMC performing better than the other two algorithms in nearly all cases. On the supercomputer Mahti [3], DDMC strong scaling is superlinear for grids that do not fit into an L3 cache slice (4 MiB). The DDMC algorithm is also scaled up to 16384 cores in weak scaling tests, with a weak scaling efficiency of 45% in a high-collisional (heavier compute load) case, and 26% in a low-collisional (lighter compute load) case. We conclude that implementing this domain decomposition algorithm in EIRENE would improve performance and enable simulations that are currently impossible due to memory constraints.