most citedApple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC2026

Enabling AI Deep Potentials for Ab Initio-quality Molecular Dynamics Simulations in GROMACS

Andong Hu, Luca Pennati, Stefano Markidis +1

State-of-the-art AI deep potentials provide ab initio-quality results, but at a fraction of the computational cost of first-principles quantum mechanical calculations, such as dens…

cs.CE20251 cited

Exascale Implicit Kinetic Plasma Simulations on El~Capitan for Solving the Micro-Macro Coupling in Magnetospheric Physics

Stefano Markidis, Andong Hu, Ivy Peng +9

Our fully kinetic, implicit Particle-in-Cell (PIC) simulations of global magnetospheres on up to 32,768 of El Capitan's AMD Instinct MI300A Accelerated Processing Units (APUs) repr…

cs.CE2025

Discovering Governing Equations of Geomagnetic Storm Dynamics with Symbolic Regression

Stefano Markidis, Jonah Ekelund, Luca Pennati +2

Geomagnetic storms are large-scale disturbances of the Earth's magnetosphere driven by solar wind interactions, posing significant risks to space-based and ground-based infrastruct…

cs.CE2025

Physics-Aware Compression of Plasma Distribution Functions with GPU-Accelerated Gaussian Mixture Models

Andong Hu, Luca Pennati, Ivy Peng +1

Data compression is a critical technology for large-scale plasma simulations. Storing complete particle information requires Terabyte-scale data storage, and analysis requires ad-h…

cs.AR20251 cited

Apple vs. Oranges: Evaluating the Apple Silicon M-Series SoCs for HPC Performance and Efficiency

Paul Hübner, Andong Hu, Ivy Peng +1

This paper investigates the architectural features and performance potential of the Apple Silicon M-Series SoCs (M1, M2, M3, and M4) for HPC. We provide a detailed review of the CP…