Relational Learning with GPUs: Accelerating Rule Coverage Article Swipe
Carlos Alberto Martínez-Angeles
,
Haicheng Wu
,
Inês Dutra
,
Vı́tor Santos Costa
,
Jorge Buenabad-Chávez
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1007/s10766-015-0364-7
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1007/s10766-015-0364-7
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. © 2015 Springer Science+Business Media New York
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10766-015-0364-7
- OA Status
- gold
- Cited By
- 11
- References
- 41
- Related Works
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- OpenAlex ID
- https://openalex.org/W2032368684
All OpenAlex metadata
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https://openalex.org/W2032368684Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s10766-015-0364-7Digital Object Identifier
- Title
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Relational Learning with GPUs: Accelerating Rule CoverageWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
-
2015-03-28Full publication date if available
- Authors
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Carlos Alberto Martínez-Angeles, Haicheng Wu, Inês Dutra, Vı́tor Santos Costa, Jorge Buenabad-ChávezList of authors in order
- Landing page
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https://doi.org/10.1007/s10766-015-0364-7Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://repositorio.inesctec.pt/handle/123456789/6974Direct OA link when available
- Concepts
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Computer science, Benchmark (surveying), Graphics, Statistical relational learning, Multi-core processor, Relational database, Machine learning, Data mining, Parallel computing, Operating system, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 3, 2022: 1, 2020: 1, 2019: 1, 2018: 2Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W102843695, https://openalex.org/W1978108034, https://openalex.org/W7074565695, https://openalex.org/W2085761620, https://openalex.org/W1984187667, https://openalex.org/W4293249558, https://openalex.org/W2108510732, https://openalex.org/W2158220740, https://openalex.org/W102682372, https://openalex.org/W2059966434, https://openalex.org/W1971962972, https://openalex.org/W2119831128, https://openalex.org/W2085248745, https://openalex.org/W2088409331, https://openalex.org/W1970217364, https://openalex.org/W2113644983, https://openalex.org/W1976683819, https://openalex.org/W2106329447, https://openalex.org/W4229957504, https://openalex.org/W1992558790, https://openalex.org/W2003251023, https://openalex.org/W1975761710, https://openalex.org/W48620657, https://openalex.org/W1860880244, https://openalex.org/W1549556210, https://openalex.org/W1736726159, https://openalex.org/W144096106, https://openalex.org/W3022860042, https://openalex.org/W2140141795, https://openalex.org/W2146812825, https://openalex.org/W2068418796, https://openalex.org/W2129479338, https://openalex.org/W2257258878, https://openalex.org/W1509067745, https://openalex.org/W136734405, https://openalex.org/W2162621793, https://openalex.org/W2009537245, https://openalex.org/W1992810975, https://openalex.org/W1585529040, https://openalex.org/W1579914521, https://openalex.org/W2111643426 |
| referenced_works_count | 41 |
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