Parallel construction of Random Forest on GPU
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-021-04290-6.pdf
Reference35 articles.
1. Kirk DB, Hwu WW (2010) Programming massive parallel processors. Elsevier Inc., eBook ISBN: 9780123814739
2. Zheng R, Hu Q, Jin H (2018) GPUPerfML: a performance analytical model based on decision tree for GPU architectures. In: The Proceedings of the 20th International Conference on High Performance Computing and Communications, IEEE. https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00110
3. Senagi K, Jouandeau N (2018) Confidence in Random Forest for performance optimization. In: Bramer M, Petridis M (eds) Artificial intelligence. XXXV SGAI 2018. Lecture notes in computer science, vol 11311. Springer, Cham. https://doi.org/10.1007/978-3-030-04191-5_31
4. Vouzis PD, Sahinidis NV (2011) GPU-BLAST: using graphics processors to accelerate protein sequence alignment. J Bioinf (Oxford England) 27(2):182–188. https://doi.org/10.1093/2Fbioinformatics/2Fbtq644
5. Breiman L (2001) Random Forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
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