Affiliation:
1. Rice University
2. U. of Texas, San Antonio
Abstract
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.
Publisher
Association for Computing Machinery (ACM)
Subject
Information Systems,Software
Reference25 articles.
1. Apache spark mllib: http://spark.apache.org/docs/latest/ mllib-data-types.html. Apache spark mllib: http://spark.apache.org/docs/latest/ mllib-data-types.html.
2. Oracle corporation: https://docs.oracle.com/cd/B19306_01/index.htm. Oracle corporation: https://docs.oracle.com/cd/B19306_01/index.htm.
3. LAPACK Users' Guide
4. Spark SQL
Cited by
13 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems;Proceedings of the VLDB Endowment;2024-07
2. A survey on machine learning in array databases;Applied Intelligence;2022-08-12
3. On matrices and K-relations;Annals of Mathematics and Artificial Intelligence;2021-07-15
4. Expressive Power of Linear Algebra Query Languages;Proceedings of the 40th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems;2021-06-20
5. HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries;Proceedings of the 2021 International Conference on Management of Data;2021-06-09