# PyParSVD: A streaming, distributed and randomized singular-value-decomposition library

arXiv: https://arxiv.org/abs/2108.08845 · HTML: https://mlsanity.com/paper/2108.08845/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data](https://mlsanity.com/paper/2204.03216/) (2022)
- [Latent State Models of Training Dynamics](https://mlsanity.com/paper/2308.09543/) (2023)
- [Enhancing Dynamic Mode Decomposition Workflow with In-Situ Visualization and Data Compression](https://mlsanity.com/paper/2208.07767/) (2022)

## Abstract (arXiv)

We introduce PyParSVDfootnotehttps://github.com/Romit-Maulik/PyParSVD, a Python library that implements a streaming, distributed and randomized algorithm for the singular value decomposition. To demonstrate its effectiveness, we extract coherent structures from scientific data. Futhermore, we show weak scaling assessments on up to 256 nodes of the Theta machine at Argonne Leadership Computing Facility, demonstrating potential for large-scale data analyses of practical data sets.