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

Source: [Recipe for Fast Large-scale SVM Training: Polishing, Parallelism, and more RAM!](<https://www.sota2.com/research/paper/68fa6cf71e209131cea36e99>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:22:36.594Z

Authors: Tobias Glasmachers

Published: 2022-07-03T11:51:41.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2207.01016>)

[PDF](<https://arxiv.org/pdf/2207.01016.pdf>)