@inproceedings{c46fb82f74b34e4cb31a2a304d9546a1,
title = "Spark-based large-scale matrix inversion for big data processing",
abstract = "Matrix inversion is a fundamental operation to solve linear equations for many computational applications. However, it is a challenging task to invert large-scale matrices of extremely high order (several thousands), which are common in most of web-scale systems like social networks and recommendation systems. In this paper, we present a LU decomposition based block-recursive algorithm for large-scale matrix inversion, and its well-designed implementation with optimized data structure, reduction of space complexity and effective matrix multiplication on the Spark parallel computing platform. The experimental evaluation results show that the proposed algorithm is efficient to invert large-scale matrices on a cluster composed of commodity servers and scalable to invert even larger matrices. The proposed algorithm and implementation will be a solid base to build a high-performance linear algebra library on Spark for big data processing.",
keywords = "LU decomposition, Spark, distributed computing, linear algebra, matrix inversion, parallel algorithm",
author = "Yang Liang and Jun Liu and Cheng Fang and Nirwan Ansari",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 35th IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016 ; Conference date: 10-04-2016 Through 14-04-2016",
year = "2016",
month = sep,
day = "6",
doi = "10.1109/INFCOMW.2016.7562171",
language = "English (US)",
series = "Proceedings - IEEE INFOCOM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "718--723",
booktitle = "2016 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2016",
address = "United States",
}