Post-Moore Technologies for Plasma Simulation: A Community Roadmap
Abstract
Plasma simulations are among the most computationally demanding scientific workloads, combining high-dimensional kinetic evolution, particle-mesh coupling, field solves, and data-intensive communication. As general-purpose processor scaling slows, post-Moore technologies are being explored to address bottlenecks in data movement, memory access, and power consumption. This paper provides a community perspective on the role of these technologies in plasma simulation, assessing three major classes: reconfigurable and data-path accelerators, non-von Neumann architectures, and quantum computing. Each is evaluated, in a co-design approach, against representative plasma workloads spanning particle-in-cell, continuum Vlasov, gyrokinetic, fluid/MHD, hybrid, and warm dense matter methods. We find that no single technology can replace existing HPC platforms. Instead, three tiers of opportunity emerge: FPGA-class and data-path accelerators offer near-term kernel offload and workflow-level data services, non-von Neumann architectures represent medium-term directions for operator-level acceleration, and quantum computing, although the least mature, is potentially the most disruptive for warm dense matter and inertial confinement fusion microphysics. We outline best practices for selective adoption and identify focused demonstrators, benchmarking, and modular software ecosystems as immediate community priorities.
Keywords
Cite
@article{arxiv.2605.07722,
title = {Post-Moore Technologies for Plasma Simulation: A Community Roadmap},
author = {Luca Pennati and Erik M. Åsgrim and Jeremy J. Williams and Stefan Costea and David Tskhakaya and Leon Kos and Ales Podolnik and Yi Ju and Tapish Narwal and Julian Lenz and Michael Bussmann and Urs Ganse and Minna Palmroth and Kallia Chronaki and Vassilis Papaefstathiou and Etienne Renault and Felix Jung and Martin Schulz and Valentin Seitz and Marta Garcia-Gasulla and Filippo Mantovani and Frank Jenko and Erwin Laure and Stefano Markidis},
journal= {arXiv preprint arXiv:2605.07722},
year = {2026}
}