Can I read Scalable Linear Algebra on a Relational Database System on EtoBox?

Scalable Linear Algebra on a Relational Database System by Shangyu Luo; Zekai J. Gao; Michael Gubanov; Luis L. Perez; Christopher Jermaine is a Computer Science article available to read on EtoBox.

What is Scalable Linear Algebra on a Relational Database System about?

Scalable linear algebra is important for analytics and machine learning (including deep learning). In this paper, we argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most relational systems already have support for cost-based optimization-which is vital to scaling linear algebra computations-and it is well-known how to make relational systems scale. We show that by making just a few changes to a parallel/distributed relational database system, such a system can be a competitive platform for scalable linear algebra. Our results suggest that brand new systems supporting scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing relational technology.

Who reads Scalable Linear Algebra on a Relational Database System?

Author

Shangyu Luo; Zekai J. Gao; Michael Gubanov; Luis L. Perez; Christopher Jermaine

Publisher

ACM

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)