High-Performance Star-M SVD for Big Data Compression
Md Taufique Hussain, Grey Ballard, Aditya Devarakonda, Srinivas Eswar, Naman Pesricha, Vishwas Rao

TL;DR
This paper introduces a high-performance, parallel implementation of star-M SVD for tensor-based data compression, enabling efficient handling of large scientific datasets with optimal accuracy.
Contribution
It presents a novel shared-memory parallel software solution for star-M SVD, improving efficiency and scalability over existing implementations.
Findings
Achieved efficient parallel performance on large datasets.
Demonstrated superior compression with minimal accuracy loss.
Enabled practical application of star-M SVD in big data contexts.
Abstract
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.
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