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

2025-04-21

AI Summary

In large-scale plasma particle-in-cell (PIC) simulations, particle data storage and analysis face critical bottlenecks—terabyte-scale data volumes and I/O-bound workloads. To address this, we propose a physics-aware in situ compression method that embeds domain-specific physical priors into a Gaussian Mixture Model (GMM), directly modeling the velocity distribution function to preserve essential physical features: temperature, bulk velocity, beam structures, and heating signatures. The approach combines histogram-based pre-dimensionality reduction with GPU-accelerated real-time in situ fitting within the iPIC3D framework and integrates ADIOS2 for optimized I/O. Compared to general-purpose compressors (e.g., SZ, MGARD), our method achieves a compression ratio of up to 10⁴ while matching or exceeding their throughput. It significantly reduces memory footprint and I/O overhead, and—uniquely—enables physically interpretable, mechanism-traceable compression and post-processing.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 exttt{iPIC3D}, a large-scale implicit Particle-in-Cell simulator, ensuring real-time compression. The compressed representation is stored using the exttt{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 $10^4$, requiring a processing time comparable to, or even lower than, standard compression engines.

Problem

• Compressing large-scale plasma simulation data efficiently
• Capturing plasma features like velocity and temperature accurately
• Enabling real-time compression with GPU-accelerated GMM fitting

Innovation

• GPU-accelerated GMM for real-time compression
• Physics-aware in-situ compression method
• Histogramming reduces computational overhead significantly