Physics-Aware Compression of Plasma Distribution Functions with GPU-Accelerated Gaussian Mixture Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909588740112384 |
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| author | Hu, Andong Pennati, Luca Peng, Ivy Markidis, Stefano |
| author_facet | Hu, Andong Pennati, Luca Peng, Ivy Markidis, Stefano |
| contents | 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 $10^4$, requiring a processing time comparable to, or even lower than, standard compression engines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Aware Compression of Plasma Distribution Functions with GPU-Accelerated Gaussian Mixture Models Hu, Andong Pennati, Luca Peng, Ivy Markidis, Stefano Computational Engineering, Finance, and Science 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 $10^4$, requiring a processing time comparable to, or even lower than, standard compression engines. |
| title | Physics-Aware Compression of Plasma Distribution Functions with GPU-Accelerated Gaussian Mixture Models |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2504.14897 |