High-performance Kernel Machines with Implicit Distributed Optimization and Randomization

TL;DR

This paper introduces a scalable, high-performance framework for training large-scale kernel models by combining distributed convex optimization and randomization techniques, enabling efficient big data analysis.

Contribution

It presents a novel algorithmic framework that integrates distributed optimization with randomization to significantly improve the scalability of kernel methods for big data applications.

Findings

Supports various loss functions, kernels, and regularization schemes.

Efficiently handles large random feature matrices on modern multicore clusters.

Outperforms existing sequential and parallel libraries in scalability and speed.

Abstract

In order to fully utilize "big data", it is often required to use "big models". Such models tend to grow with the complexity and size of the training data, and do not make strong parametric assumptions upfront on the nature of the underlying statistical dependencies. Kernel methods fit this need well, as they constitute a versatile and principled statistical methodology for solving a wide range of non-parametric modelling problems. However, their high computational costs (in storage and time) pose a significant barrier to their widespread adoption in big data applications. We propose an algorithmic framework and high-performance implementation for massive-scale training of kernel-based statistical models, based on combining two key technical ingredients: (i) distributed general purpose convex optimization, and (ii) the use of randomization to improve the scalability of kernel methods. Our approach is based on a block-splitting variant of the Alternating Directions Method of Multipliers, carefully reconfigured to handle very large random feature matrices, while exploiting hybrid parallelism typically found in modern clusters of multicore machines. Our implementation supports a variety of statistical learning tasks by enabling several loss functions, regularization schemes, kernels, and layers of randomized approximations for both dense and sparse datasets, in a highly extensible framework. We evaluate the ability of our framework to learn models on data from applications, and provide a comparison against existing sequential and parallel libraries.