The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Accelerating the XGBoost algorithm using GPU computing
Open accessAbstract
We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3× and 6× using a Titan X compared to a 4 core i7 CPU, and 1.2× using a Titan X compared to 2× Xeon CPUs (24 cores). We show that it is possible to process the Higgs dataset (10 million instances, 28 features) entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks.
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XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
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The WEKA data mining software: an update
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Self-citationXGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1603.02754 · Cited by 7,104
Hillis, W. D., & Steele, G. L. (1986) · Communications of the ACM · doi:10.1145/7902.7903 · Cited by 899
(2013) · Cited by 651
Parallel Prefix Sum (Scan) with CUDA
Harris, M. (2011) · Cited by 537
Prefix sums and their applications
Blelloch, G. E. (2018) · Figshare · doi:10.1184/r1/6608579 · Cited by 379
The Response of Durum Wheat to the Preceding Crop in a Mediterranean Environment
Ercoli, L., Masoni, A., Pampana, S., Mariotti, M., & Arduini, I. (2014) · The Scientific World Journal · doi:10.1155/2014/717562 · Cited by 314
Cited by
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512
Machine learning-enabled optimization of extrusion-based 3D printing
Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.002 · Cited by 94
Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing
Liu, Y., Ma, Z., Liu, X., Ma, S., Nepal, S., & Deng, R. (2019) · arXiv · doi:10.48550/arxiv.1907.10218 · Cited by 52
An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis
Elouataoui, W., El Mendili, S., & Gahi, Y. (2023) · Data · doi:10.3390/data8120182 · Cited by 27
Bandwidth-Optimal Random Shuffling for GPUs
Mitchell, R., Stokes, D., Frank, E., & Holmes, G. (2022) · ACM Transactions on Parallel Computing · doi:10.1145/3505287 · Cited by 6
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Metadata
- Authors
- Mitchell, R., & Frank, E.
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2017-07-24
- Volume
- 3
- Pages
- e127
- ISSN
- 2376-5992 (ISSN-L)
- OpenAlex
- W2604808181
- Cited by
- 359
- References
- 14
- FWCI
- 22.06
- Type
- journal article
- Language
- en
- Open access
- gold
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