The Disturbance Storm Time (Dst) index quantifies geomagnetic storm intensity by measuring global magnetic field variations. In this study, we apply interpretable machine-learning (ML) techniques to derive data-driven models describing the temporal evolution of the Dst index. We use historical data from the NASA OMNIWeb database, including solar wind density, bulk velocity, convective electric field, dynamic pressure, and magnetic pressure. We employ KAN networks and the symbolic regression framework PyOperon, based on an evolutionary algorithm, to identify closed-form expressions linking (Formula presented) to key solar wind parameters. The equations obtained via symbolic regression form a hierarchy of complexity levels and capture nonlinear dependencies and threshold effects in Dst evolution. In addition, we use a conventional MLP network as a reference black-box model. We benchmark all ML models against observed Dst data and compare their performance with empirical formulations such as the Burton-McPherron–Russell and O’Brien-McPherron models. The performance evaluation on historical storm events includes the 2003 Halloween storm, the 2015 St. Patrick’s Day storm, a moderate storm in 2017, and the extreme storm of May 2024. The data-driven models, particularly the MLP, demonstrate superior accuracy in most cases. While the symbolic regression expressions provide insight into the underlying physics, the results highlight an intrinsic trade-off between model interpretability and predictive accuracy. This is an extended version of a previous work presented in Markidis et al. (2025) [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 CPU and GPU designs, the unified memory architecture, and coprocessors such as Advanced Matrix Extensions (AMX). We design and develop benchmarks in the Metal Shading Language and Objective-C++ to assess FP32 computational and memory performance. We also measure power consumption and efficiency using Apple's powermetrics tool. Our results show that the M-Series chips offer up to 100 GB/s memory bandwidth, and significant generational improvements in computational performance, with up to 2.9 FP32 TFLOPS on the M4. Power consumption varies from a few Watts to 10-20 Watts, with more than 200 GFLOPS per Watt efficiency of GPU and accelerator reached by all four chips. Despite limitations in FP64 support on the GPU, the M-Series chips demonstrate strong potential for energy-efficient HPC applications. While existing HPC solutions such as the Nvidia Grace-Hopper superchip outperform Apple Silicon in both memory bandwidth and computational performance, we see that the M-Series provides a competitive power-efficient alternative to traditional HPC architectures and represents a distinct category altogether - forming an apples-to-oranges comparison.

Exascale computing revolutionizes scientific research by facilitating large-scale simulations and data analysis. As applications generate massive, heterogeneous data streams, the I/O subsystem increasingly dominates runtime, limiting scalability and time-to-solution. Emerging storage accelerators such as the Rabbit system offer a promising path forward. Rabbit provides dynamically configurable hybrid storage, combining high-bandwidth, node-local NVMe (e.g., XFS) with shared, distributed file systems (e.g., Lustre), to match diverse I/O patterns more efficiently. In this work, we evaluate Rabbit's capability to mitigate I/O bottlenecks using iPIC3D, a representative implicit Particle-in-Cell (PIC) code for exascale scientific workloads. We perform a detailed characterization of the key phases of I/O (restart, field, and moment) and identify the dominant access patterns in these phases. We then benchmark these patterns using IOR in Rabbit hybrid storage configurations, identifying the ideal performance achievable for each access pattern under node-local and distributed storage modes. We use phase-aware mapping of these patterns to Rabbit storage systems, achieving a 4.85 times improvement in I/O throughput, reducing the I/O share of runtime from 38% to 11% and delivering an end-to-end speedup of 1.45 times. These results outline the importance of hardware-software co-design and highlight Rabbit as a scalable, data-intensive storage solution.

We present performance scaling and optimization of iPIC3D - an exascale-class, GPU-enabled implicit particle-in-cell code for planetary-scale magnetosphere modeling and plasma simulation - on El Capitan. Our strong and weak scaling studies demonstrate near-linear scaling up to 8,000 nodes (32,000 APUs) with a parallel efficiency of nearly 100%. We optimize iPIC3D to leverage AMD’s MI300A APUs, the Merced Lustre filesystem, and Rabbit nodes for high-bandwidth I/O. Optimizations reduce memory usage by 97% and runtime by 74%, enabling simulations that are 1.8 times larger than before. Rabbit further improve checkpointing bandwidth by 2 times, ensuring scalable fault- tolerant simulations on exascale architectures.

Data compression is a critical technology for large-scale plasma simulations. Storing complete particle information requires Terabyte-scale data storage, and analysis requires ad-hoc scalable post-processing tools. We propose a physics-aware in-situ compression method using Gaussian Mixture Models (GMMs) to approximate electron and ion velocity distribution functions with a number of Gaussian components. This GMM-based method allows us to capture plasma features such as mean velocity and temperature, and it enables us to identify heating processes and generate beams. We first construct a histogram to reduce computational overhead and apply GPU-accelerated, in-situ GMM fitting within iPIC3D, a large-scale implicit Particle-in-Cell simulator, ensuring real-time compression. The compressed representation is stored using the ADIOS 2 library, thus optimizing the I/O process. The GPU and histogramming implementation provides a significant speed-up with respect to GMM on particles (both in time and required memory at run-time), enabling real-time compression. Compared to algorithms like SZ, MGARD, and BLOSC2, our GMM-based method has a physics-based approach, retaining the physical interpretation of plasma phenomena such as beam formation, acceleration, and heating mechanisms. Our GMM algorithm achieves a compression ratio of up to 104, requiring a processing time comparable to, or even lower than, standard compression engines.