Relational Learning with GPUs: Accelerating Rule Coverage

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Alberto Martinez Angeles, CA (Author) Other; Wu, HC (Author) Other; Ines Dutra (Author) FCUP; Costa, VS (Author) FCUP; Buenabad Chavez, J (Author) Other

Journal

Title: International Journal of Parallel Programming

Vol. 44

Pages: 663-685

ISSN: 0885-7458

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00K-9ZT

DOI: 10.1007/s10766-015-0364-7

Abstract (EN): Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23