# Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models

## TL;DR

This paper introduces a doubly sparse data-parallel sampler for hierarchical Dirichlet process (HDP) topic models, enabling efficient large-scale training on big text corpora using parallel computing.

## Contribution

It presents a novel sparse parallel sampling method for HDP topic models that leverages natural language sparsity to improve scalability and efficiency.

## Key findings

- Successfully trained on 8 million documents in under four days
- Achieved efficient parallelization by exploiting data sparsity
- Demonstrated scalability on large-scale text data

## Abstract

To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems. In this work, we study data-parallel training for the hierarchical Dirichlet process (HDP) topic model. Based upon a representation of certain conditional distributions within an HDP, we propose a doubly sparse data-parallel sampler for the HDP topic model. This sampler utilizes all available sources of sparsity found in natural language - an important way to make computation efficient. We benchmark our method on a well-known corpus (PubMed) with 8m documents and 768m tokens, using a single multi-core machine in under four days.