Fast support vector machine training and classification on graphics processors
Abstract
Recent developments in programmable, highly parallel Graphics Processing Units (GPUs) have enabled high performance implementations of machine learning algorithms. We describe a solver for Support Vector Machine training running on a GPU, using the Sequential Minimal Optimization algorithm and an adaptive first and second order working set selection heuristic, which achieves speedups of 9-35x over LIBSVM running on a traditional processor. We also present a GPU-based system for SVM classification which achieves speedups of 81-138x over LIBSVM (5-24x over our own CPU based SVM classifier).
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Other types of ML models, such as support-vector machines (SVMs), were previously also trained on GPUs.
“Other types of ML models, such as support-vector machines (SVMs) were previously also trained on GPUs (Catanzaro et al., 2008).”
From Compute Trends Across Three Eras of Machine Learning (2022) · cites this paper as Catanzaro et al., 2008
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554 · see citing works
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Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818 · see citing works
A dynamically configurable coprocessor for convolutional neural networks
Chakradhar, S., Sankaradas, M., Jakkula, V., & Cadambi, S. (2010) · Proceedings of the 37th annual international symposium on Computer architecture · doi:10.1145/1815961.1815993 · Cited by 343 · see citing works
Compute Trends Across Three Eras of Machine Learning
Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891914 · Cited by 363 · see citing works
“CNNs were trained on GPUs at least as early as 2006 (Chellapilla et al., 2006), as were some other models that were large at the time (such as the 100M parameter Deep Belief Network by Raina et al. (2009a) and the neural networks of D. C. Cire¸san et al. (2011)).”
MethodInfluentialShirahama, K., & Grzegorzek, M. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2292-8 · Cited by 24 · see citing works
- Authors
- Catanzaro, B., Sundaram, N., & Keutzer, K.
- Affiliations
- University of California, Berkeley (US)
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 104-111
- OpenAlex
- W2159350554
- Cited by
- 372
- References
- 2
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 4 · 2023: 4 · 2022: 5 · 2021: 8 · 2020: 11 · 2019: 10 · 2018: 24 · 2017: 22 · 2016: 32 · 2015: 25 · 2014: 33 · 2013: 28 · 2012: 39
- FWCI
- 23.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning (publishedVersion)
- UC Berkeley (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- support vector machine, GPU computing, sequential minimal optimization, working set selection, SVM training, support vector machine classification, parallel machine learning, LIBSVM, speedup
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