一句话：提出 SaberLDA，旨在解决现有 GPU LDA 系统因稠密数据结构而无法支持大量主题的问题。
本文关注 Latent Dirichlet Allocation（LDA）在文本、图像等离散计数数据分析中的扩展性问题。实际应用要求 LDA 同时处理大规模数据集和大量主题。尽管已有分布式 CPU 系统，GPU 因其高计算能力和内存带宽成为有吸引力的替代方案，但现有基于 GPU 的 LDA 系统无法支持大量主题，根源在于其使用稠密数据结构，时间和空间复杂度随主题数线性增长。论文提出 SaberLDA，目标是在 GPU 上突破这一扩展性瓶颈，使 LDA 能面向大量主题场景；所给摘要未披露具体算法与实验评估结果。
Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images. Applications require LDA to handle both large datasets and a large number of topics. Though distributed CPU systems have been used, GPU-based systems have emerged as a promising alternative because of the high computational power and memory bandwidth of GPUs. However, existing GPU-based LDA systems cannot support a large number of topics because they use algorithms on dense data structures whose time and space complexity is linear to the number of topics.
DOI 原文 ·
@inproceedings{paperbot4095,
title = {SaberLDA},
author = {Kaiwei Li and Jianfei Chen and Wenguang Chen and Jun Zhu},
booktitle = {ASPLOS},
year = {2017},
doi = {10.1145/3037697.3037740}
}