Scalable Domain-decomposed Monte Carlo Neutral Transport for Nuclear Fusion

2025-11-06

AI Summary

The EIRENE Monte Carlo neutral transport solver lacks domain decomposition capability, suffering from severe single-node memory bottlenecks when handling ultra-large-scale meshes. Method: This work proposes and implements a Domain-Decomposed Monte Carlo (DDMC) algorithm—integrating scalable domain decomposition with neutral particle transport modeling for the first time—and embeds it within the open-source Eiron framework. Contribution/Results: The DDMC algorithm achieves strong scalability beyond linear scaling and large-scale weak scalability: on the Mahti supercomputer, it demonstrates weak scaling up to 16,384 cores, attaining 45% and 26% parallel efficiency for high- and low-collision regimes, respectively. It overcomes memory limitations, enabling previously infeasible large-scale fusion simulations. Compared to the native EIRENE algorithm, DDMC significantly improves parallel efficiency and extends the tractable problem size, establishing a scalable, high-performance computing paradigm for fusion boundary physics simulation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Enables large-scale simulations overcoming single-node memory limitations
• Develops domain-decomposed Monte Carlo algorithm for neutral transport
• Compares parallel algorithms through strong scaling performance tests

Innovation

• Domain-decomposed Monte Carlo algorithm for neutral transport
• Open source implementation enabling large-scale simulations
• Superlinear strong scaling performance on supercomputers