Relational Learning with GPUs: Accelerating Rule Coverage

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 a...

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Bibliographic Details
Published in:International journal of parallel programming 2016-06, Vol.44 (3), p.663-685
Main Authors: Martínez-Angeles, Carlos Alberto, Wu, Haicheng, Dutra, Inês, Costa, Vítor Santos, Buenabad-Chávez, Jorge
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Benchmarking
Central processing units
Computer programming
Computer Science
Consumption
CPUs
Data mining
Design engineering
Graphics processing units
Informatics
Learning
Logic programming
Mining
Parallel processing
Processor Architectures
Relational data bases
Rodents
Sentiment analysis
Social networks
Software Engineering/Programming and Operating Systems
Studies
Theory of Computation
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Summary: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.
ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-015-0364-7