Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!

• URL: http://arxiv.org/abs/2207.01016v1
• Date: Sun, 3 Jul 2022 11:51:41 GMT
• Title: Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!
• Authors: Tobias Glasmachers
• Abstract summary: Support vector machines (SVMs) are a standard method in the machine learning toolbox. Non-linear kernel SVMs often deliver highly accurate predictors, however, at the cost of long training times. In this work, we combine both approaches to design an extremely fast dual SVM solver.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Support vector machines (SVMs) are a standard method in the machine learning toolbox, in particular for tabular data. Non-linear kernel SVMs often deliver highly accurate predictors, however, at the cost of long training times. That problem is aggravated by the exponential growth of data volumes over time. It was tackled in the past mainly by two types of techniques: approximate solvers, and parallel GPU implementations. In this work, we combine both approaches to design an extremely fast dual SVM solver. We fully exploit the capabilities of modern compute servers: many-core architectures, multiple high-end GPUs, and large random access memory. On such a machine, we train a large-margin classifier on the ImageNet data set in 24 minutes.

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