High-Performance Star-M SVD for Big Data Compression
摘要
In the era of big data, effectively compressing large datasets while performing complex mathematical operations is crucial. Tensor-based decomposition methods have shown superior compression capabilities with minimal loss of accuracy compared to traditional matrix methods. Under the star-M tensor framework, tensors can be decomposed in a matrix-mimetic way, including using the star-M SVD. This tensor SVD has optimality guarantees and has shown exceptional performance on specific types of data, but software implementations have been mostly limited to productivity-oriented languages. In this work, we present our development of a shared-memory parallel, high-performance solution designed to efficiently implement the underlying algorithms. This software will enable optimal compression of extensive scientific datasets, paving the way for enhanced data analysis and insights.
引用
@article{arxiv.2605.16058,
title = {High-Performance Star-M SVD for Big Data Compression},
author = {Md Taufique Hussain and Grey Ballard and Aditya Devarakonda and Srinivas Eswar and Naman Pesricha and Vishwas Rao},
journal= {arXiv preprint arXiv:2605.16058},
year = {2026}
}