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

arXiv: https://arxiv.org/abs/2207.01016 · HTML: https://mlsanity.com/paper/2207.01016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Diving into the shallows: a computational perspective on large-scale shallow learning](https://mlsanity.com/paper/1703.10622/) (2017)
- [Dual SVM Training on a Budget](https://mlsanity.com/paper/1806.10182/) (2018)

## Abstract (arXiv)

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.