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CuLDA_CGS: solving large-scale LDA problems on GPUs

Authors:

Xiaolong Xie,

Yun Liang,

Xiuhong Li,

Wei TanAuthors Info & Claims

PPoPP '19: Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

Pages 435 - 436

https://doi.org/10.1145/3293883.3301496

Published: 16 February 2019 Publication History

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Abstract

GPUs have benefited many ML algorithms. However, we observe that the performance of existing Latent Dirichlet Allocation(LDA) solutions on GPUs are not satisfying. We present CuLDA_CGS, an efficient approach to accelerate large-scale LDA problems. We delicately design workload partition and synchronization mechanism to exploit multiple GPUs. We also optimize the algorithm from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA_CGS outperforms them by a large margin (up to 7.3X) on a single GPU.

References

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Jianfei Chen, Kaiwei Li, Jun Zhu, and Wenguang Chen. 2016. WarpLDA: A Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation. Proc. VLDB Endow. (2016).

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James Foulds, Levi Boyles, Christopher DuBois, Padhraic Smyth, and Max Welling. 2013. Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation (KDD '13).

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Kaiwei Li, Jianfei Chen, Wenguang Chen, and Jun Zhu. 2017. SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs (ASPLOS '17).

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Xiaolong Xie, Yun Liang, Guangyu Sun, and Deming Chen. 2013. An efficient compiler framework for cache bypassing on GPUs (ICCAD'13).

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Limin Yao, David Mimno, and Andrew McCallum. 2009. Efficient Methods for Topic Model Inference on Streaming Document Collections (KDD '09).

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Cited By

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AKBULUT MTONTA Y(2022)Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant RetrievalTurk Kutuphaneciligi - Turkish Librarianship10.24146/tk.1062751Online publication date: 10-Apr-2022
https://doi.org/10.24146/tk.1062751
Xie XLiang YLi XTan WWeissman JButt ASmirni E(2019)CuLDAProceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3307681.3325407(195-205)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3307681.3325407

Index Terms

CuLDA_CGS: solving large-scale LDA problems on GPUs
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Single instruction, multiple data
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Topic modeling

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Information & Contributors

Information

Published In

PPoPP '19: Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

February 2019

472 pages

ISBN:9781450362252

DOI:10.1145/3293883

General Chair:
Jeff Hollingsworth
University of Maryland
,
Program Chair:
Idit Keidar
Technion, Israel

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 February 2019

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PPoPP '19

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PPoPP '19: 24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 16 - 20, 2019

District of Columbia, Washington

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PPoPP '19 Paper Acceptance Rate 29 of 152 submissions, 19%;

Overall Acceptance Rate 230 of 1,014 submissions, 23%

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AKBULUT MTONTA Y(2022)Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant RetrievalTurk Kutuphaneciligi - Turkish Librarianship10.24146/tk.1062751Online publication date: 10-Apr-2022
https://doi.org/10.24146/tk.1062751
Xie XLiang YLi XTan WWeissman JButt ASmirni E(2019)CuLDAProceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing10.1145/3307681.3325407(195-205)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3307681.3325407

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CuLDA: Solving Large-scale LDA Problems on GPUs

Heterogeneous-Length Text Topic Modeling for Reader-Aware Multi-Document Summarization

A topic modeled unsupervised approach to single document extractive text summarization