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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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| Published in: | International journal of parallel programming 2016-06, Vol.44 (3), p.663-685 |
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| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c392t-222b601ffea58ad3ff88cf07766c334b65957d4431e377c061a4769f713717d63 |
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| cites | cdi_FETCH-LOGICAL-c392t-222b601ffea58ad3ff88cf07766c334b65957d4431e377c061a4769f713717d63 |
| container_end_page | 685 |
| container_issue | 3 |
| container_start_page | 663 |
| container_title | International journal of parallel programming |
| container_volume | 44 |
| creator | Martínez-Angeles, Carlos Alberto Wu, Haicheng Dutra, Inês Costa, Vítor Santos Buenabad-Chávez, Jorge |
| description | 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. |
| doi_str_mv | 10.1007/s10766-015-0364-7 |
| format | article |
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| ispartof | International journal of parallel programming, 2016-06, Vol.44 (3), p.663-685 |
| issn | 0885-7458 1573-7640 |
| language | eng |
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| source | ABI/INFORM Global; Springer Nature:Jisc Collections:Springer Nature Read and Publish 2023-2025: Springer Reading List |
| 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 |
| title | Relational Learning with GPUs: Accelerating Rule Coverage |
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